From ab28a62e86585bde76f52839013325827d3f416b Mon Sep 17 00:00:00 2001 From: remsky Date: Mon, 20 Jan 2025 22:42:29 -0700 Subject: [PATCH 01/32] Refactor inference architecture: remove legacy TTS model, add ONNX and PyTorch backends, and introduce model configuration schemas --- .gitignore | 3 +- api/src/core/paths.py | 198 ++++++++++++++++ api/src/inference/__init__.py | 20 ++ api/src/inference/base.py | 97 ++++++++ api/src/inference/model_manager.py | 251 ++++++++++++++++++++ api/src/inference/onnx_cpu.py | 154 ++++++++++++ api/src/inference/onnx_gpu.py | 163 +++++++++++++ api/src/inference/pytorch_cpu.py | 181 ++++++++++++++ api/src/inference/pytorch_gpu.py | 170 ++++++++++++++ api/src/inference/voice_manager.py | 191 +++++++++++++++ api/src/services/tts_base.py | 175 -------------- api/src/services/tts_cpu.py | 167 ------------- api/src/services/tts_gpu.py | 262 --------------------- api/src/services/tts_model.py | 8 - api/src/services/tts_service.py | 353 ++++++++++++---------------- api/src/structures/model_schemas.py | 26 ++ 16 files changed, 1606 insertions(+), 813 deletions(-) create mode 100644 api/src/core/paths.py create mode 100644 api/src/inference/__init__.py create mode 100644 api/src/inference/base.py create mode 100644 api/src/inference/model_manager.py create mode 100644 api/src/inference/onnx_cpu.py create mode 100644 api/src/inference/onnx_gpu.py create mode 100644 api/src/inference/pytorch_cpu.py create mode 100644 api/src/inference/pytorch_gpu.py create mode 100644 api/src/inference/voice_manager.py delete mode 100644 api/src/services/tts_base.py delete mode 100644 api/src/services/tts_cpu.py delete mode 100644 api/src/services/tts_gpu.py delete mode 100644 api/src/services/tts_model.py create mode 100644 api/src/structures/model_schemas.py diff --git a/.gitignore b/.gitignore index 351aee1..03479ec 100644 --- a/.gitignore +++ b/.gitignore @@ -18,7 +18,8 @@ __pycache__/ *.egg dist/ build/ - +*.onnx +*.pth # Environment # .env .venv/ diff --git a/api/src/core/paths.py b/api/src/core/paths.py new file mode 100644 index 0000000..d9096a7 --- /dev/null +++ b/api/src/core/paths.py @@ -0,0 +1,198 @@ +"""Async file and path operations.""" + +import io +import os +from pathlib import Path +from typing import List, Optional, AsyncIterator, Callable, Set + +import aiofiles +import aiofiles.os +import torch +from loguru import logger + +from .config import settings + + +async def _find_file( + filename: str, + search_paths: List[str], + filter_fn: Optional[Callable[[str], bool]] = None +) -> str: + """Find file in search paths. + + Args: + filename: Name of file to find + search_paths: List of paths to search in + filter_fn: Optional function to filter files + + Returns: + Absolute path to file + + Raises: + RuntimeError: If file not found + """ + if os.path.isabs(filename) and await aiofiles.os.path.exists(filename): + return filename + + for path in search_paths: + full_path = os.path.join(path, filename) + if await aiofiles.os.path.exists(full_path): + if filter_fn is None or filter_fn(full_path): + return full_path + + raise RuntimeError(f"File not found: {filename} in paths: {search_paths}") + + +async def _scan_directories( + search_paths: List[str], + filter_fn: Optional[Callable[[str], bool]] = None +) -> Set[str]: + """Scan directories for files. + + Args: + search_paths: List of paths to scan + filter_fn: Optional function to filter files + + Returns: + Set of matching filenames + """ + results = set() + + for path in search_paths: + if not await aiofiles.os.path.exists(path): + continue + + try: + # Get directory entries first + entries = await aiofiles.os.scandir(path) + # Then process entries after await completes + for entry in entries: + if filter_fn is None or filter_fn(entry.name): + results.add(entry.name) + except Exception as e: + logger.warning(f"Error scanning {path}: {e}") + + return results + + +async def get_model_path(model_name: str) -> str: + """Get path to model file. + + Args: + model_name: Name of model file + + Returns: + Absolute path to model file + + Raises: + RuntimeError: If model not found + """ + search_paths = [ + settings.model_dir, + os.path.join(os.path.dirname(__file__), "..", "..", "..", "models") + ] + + return await _find_file(model_name, search_paths) + + +async def get_voice_path(voice_name: str) -> str: + """Get path to voice file. + + Args: + voice_name: Name of voice file (without .pt extension) + + Returns: + Absolute path to voice file + + Raises: + RuntimeError: If voice not found + """ + voice_file = f"{voice_name}.pt" + + search_paths = [ + os.path.join(settings.model_dir, "..", settings.voices_dir), + os.path.join(os.path.dirname(__file__), "..", settings.voices_dir) + ] + + return await _find_file(voice_file, search_paths) + + +async def list_voices() -> List[str]: + """List available voice files. + + Returns: + List of voice names (without .pt extension) + """ + search_paths = [ + os.path.join(settings.model_dir, "..", settings.voices_dir), + os.path.join(os.path.dirname(__file__), "..", settings.voices_dir) + ] + + def filter_voice_files(name: str) -> bool: + return name.endswith('.pt') + + voices = await _scan_directories(search_paths, filter_voice_files) + return sorted([name[:-3] for name in voices]) # Remove .pt extension + + +async def load_voice_tensor(voice_path: str, device: str = "cpu") -> torch.Tensor: + """Load voice tensor from file. + + Args: + voice_path: Path to voice file + device: Device to load tensor to + + Returns: + Voice tensor + + Raises: + RuntimeError: If file cannot be read + """ + try: + async with aiofiles.open(voice_path, 'rb') as f: + data = await f.read() + return torch.load( + io.BytesIO(data), + map_location=device, + weights_only=True + ) + except Exception as e: + raise RuntimeError(f"Failed to load voice tensor from {voice_path}: {e}") + + +async def save_voice_tensor(tensor: torch.Tensor, voice_path: str) -> None: + """Save voice tensor to file. + + Args: + tensor: Voice tensor to save + voice_path: Path to save voice file + + Raises: + RuntimeError: If file cannot be written + """ + try: + buffer = io.BytesIO() + torch.save(tensor, buffer) + async with aiofiles.open(voice_path, 'wb') as f: + await f.write(buffer.getvalue()) + except Exception as e: + raise RuntimeError(f"Failed to save voice tensor to {voice_path}: {e}") + + +async def read_file(path: str) -> str: + """Read text file asynchronously. + + Args: + path: Path to file + + Returns: + File contents as string + + Raises: + RuntimeError: If file cannot be read + """ + try: + async with aiofiles.open(path, 'r', encoding='utf-8') as f: + return await f.read() + except Exception as e: + raise RuntimeError(f"Failed to read file {path}: {e}") \ No newline at end of file diff --git a/api/src/inference/__init__.py b/api/src/inference/__init__.py new file mode 100644 index 0000000..c98b884 --- /dev/null +++ b/api/src/inference/__init__.py @@ -0,0 +1,20 @@ +"""Inference backends and model management.""" + +from .base import BaseModelBackend +from .model_manager import ModelManager, get_manager +from .onnx_cpu import ONNXCPUBackend +from .onnx_gpu import ONNXGPUBackend +from .pytorch_cpu import PyTorchCPUBackend +from .pytorch_gpu import PyTorchGPUBackend +from ..structures.model_schemas import ModelConfig + +__all__ = [ + 'BaseModelBackend', + 'ModelManager', + 'get_manager', + 'ModelConfig', + 'ONNXCPUBackend', + 'ONNXGPUBackend', + 'PyTorchCPUBackend', + 'PyTorchGPUBackend' +] \ No newline at end of file diff --git a/api/src/inference/base.py b/api/src/inference/base.py new file mode 100644 index 0000000..21d859b --- /dev/null +++ b/api/src/inference/base.py @@ -0,0 +1,97 @@ +"""Base interfaces for model inference.""" + +from abc import ABC, abstractmethod +from typing import List, Optional + +import numpy as np +import torch + + +class ModelBackend(ABC): + """Abstract base class for model inference backends.""" + + @abstractmethod + async def load_model(self, path: str) -> None: + """Load model from path. + + Args: + path: Path to model file + + Raises: + RuntimeError: If model loading fails + """ + pass + + @abstractmethod + def generate( + self, + tokens: List[int], + voice: torch.Tensor, + speed: float = 1.0 + ) -> np.ndarray: + """Generate audio from tokens. + + Args: + tokens: Input token IDs + voice: Voice embedding tensor + speed: Speed multiplier + + Returns: + Generated audio samples + + Raises: + RuntimeError: If generation fails + """ + pass + + @abstractmethod + def unload(self) -> None: + """Unload model and free resources.""" + pass + + @property + @abstractmethod + def is_loaded(self) -> bool: + """Check if model is loaded. + + Returns: + True if model is loaded, False otherwise + """ + pass + + @property + @abstractmethod + def device(self) -> str: + """Get device model is running on. + + Returns: + Device string ('cpu' or 'cuda') + """ + pass + + +class BaseModelBackend(ModelBackend): + """Base implementation of model backend.""" + + def __init__(self): + """Initialize base backend.""" + self._model: Optional[torch.nn.Module] = None + self._device: str = "cpu" + + @property + def is_loaded(self) -> bool: + """Check if model is loaded.""" + return self._model is not None + + @property + def device(self) -> str: + """Get device model is running on.""" + return self._device + + def unload(self) -> None: + """Unload model and free resources.""" + if self._model is not None: + del self._model + self._model = None + if torch.cuda.is_available(): + torch.cuda.empty_cache() \ No newline at end of file diff --git a/api/src/inference/model_manager.py b/api/src/inference/model_manager.py new file mode 100644 index 0000000..c3b76c7 --- /dev/null +++ b/api/src/inference/model_manager.py @@ -0,0 +1,251 @@ +"""Model management and caching.""" + +import os +from typing import Dict, List, Optional, Union + +import torch +from loguru import logger +from pydantic import BaseModel + +from .base import BaseModelBackend +from .voice_manager import get_manager as get_voice_manager +from .onnx_cpu import ONNXCPUBackend +from .onnx_gpu import ONNXGPUBackend +from .pytorch_cpu import PyTorchCPUBackend +from .pytorch_gpu import PyTorchGPUBackend +from ..core import paths +from ..core.config import settings +from ..structures.model_schemas import ModelConfig + + +class ModelManager: + """Manages model loading and inference across backends.""" + + def __init__(self, config: Optional[ModelConfig] = None): + """Initialize model manager. + + Args: + config: Optional configuration + """ + self._config = config or ModelConfig() + self._backends: Dict[str, BaseModelBackend] = {} + self._current_backend: Optional[str] = None + self._voice_manager = get_voice_manager() + self._initialize_backends() + + def _initialize_backends(self) -> None: + """Initialize available backends.""" + """Initialize available backends.""" + # Initialize GPU backends if available + if settings.use_gpu and torch.cuda.is_available(): + try: + # PyTorch GPU + self._backends['pytorch_gpu'] = PyTorchGPUBackend() + self._current_backend = 'pytorch_gpu' + logger.info("Initialized PyTorch GPU backend") + + # ONNX GPU + self._backends['onnx_gpu'] = ONNXGPUBackend() + logger.info("Initialized ONNX GPU backend") + except Exception as e: + logger.error(f"Failed to initialize GPU backends: {e}") + # Fallback to CPU if GPU fails + self._initialize_cpu_backends() + else: + self._initialize_cpu_backends() + + def _initialize_cpu_backends(self) -> None: + """Initialize CPU backends.""" + try: + # PyTorch CPU + self._backends['pytorch_cpu'] = PyTorchCPUBackend() + self._current_backend = 'pytorch_cpu' + logger.info("Initialized PyTorch CPU backend") + + # ONNX CPU + self._backends['onnx_cpu'] = ONNXCPUBackend() + logger.info("Initialized ONNX CPU backend") + except Exception as e: + logger.error(f"Failed to initialize CPU backends: {e}") + raise RuntimeError("No backends available") + + def get_backend(self, backend_type: Optional[str] = None) -> BaseModelBackend: + """Get specified backend. + + Args: + backend_type: Backend type ('pytorch_cpu', 'pytorch_gpu', 'onnx_cpu', 'onnx_gpu'), + uses default if None + + Returns: + Model backend instance + + Raises: + ValueError: If backend type is invalid + RuntimeError: If no backends are available + """ + if not self._backends: + raise RuntimeError("No backends available") + + if backend_type is None: + backend_type = self._current_backend + + if backend_type not in self._backends: + raise ValueError( + f"Invalid backend type: {backend_type}. " + f"Available backends: {', '.join(self._backends.keys())}" + ) + + return self._backends[backend_type] + + def _determine_backend(self, model_path: str) -> str: + """Determine appropriate backend based on model file. + + Args: + model_path: Path to model file + + Returns: + Backend type to use + """ + is_onnx = model_path.lower().endswith('.onnx') + has_gpu = settings.use_gpu and torch.cuda.is_available() + + if is_onnx: + return 'onnx_gpu' if has_gpu else 'onnx_cpu' + else: + return 'pytorch_gpu' if has_gpu else 'pytorch_cpu' + + async def load_model( + self, + model_path: str, + backend_type: Optional[str] = None + ) -> None: + """Load model on specified backend. + + Args: + model_path: Path to model file + backend_type: Backend to load on, uses default if None + + Raises: + RuntimeError: If model loading fails + """ + try: + # Get absolute model path + abs_path = await paths.get_model_path(model_path) + + # Auto-determine backend if not specified + if backend_type is None: + backend_type = self._determine_backend(abs_path) + + backend = self.get_backend(backend_type) + + # Load model and run warmup + await backend.load_model(abs_path) + logger.info(f"Loaded model on {backend_type} backend") + await self._warmup_inference(backend) + + except Exception as e: + raise RuntimeError(f"Failed to load model: {e}") + + async def _warmup_inference(self, backend: BaseModelBackend) -> None: + """Run warmup inference to initialize model.""" + try: + # Import here to avoid circular imports + from ..text_processing import process_text + + # Load default voice for warmup + voice = await self._voice_manager.load_voice(settings.default_voice, device=backend.device) + logger.info(f"Loaded voice {settings.default_voice} for warmup") + + # Use real text + text = "Testing text to speech synthesis." + logger.info(f"Running warmup inference with voice: af") + + # Process through pipeline + sequences = process_text(text) + if not sequences: + raise ValueError("Text processing failed") + + # Run inference + backend.generate(sequences[0], voice, speed=1.0) + + except Exception as e: + logger.warning(f"Warmup inference failed: {e}") + raise + + async def generate( + self, + tokens: list[int], + voice_name: str, + speed: float = 1.0, + backend_type: Optional[str] = None + ) -> torch.Tensor: + """Generate audio using specified backend. + + Args: + tokens: Input token IDs + voice_name: Name of voice to use + speed: Speed multiplier + backend_type: Backend to use, uses default if None + + Returns: + Generated audio tensor + + Raises: + RuntimeError: If generation fails + """ + backend = self.get_backend(backend_type) + if not backend.is_loaded: + raise RuntimeError("Model not loaded") + + try: + # Load voice using voice manager + voice = await self._voice_manager.load_voice(voice_name, device=backend.device) + + # Generate audio + return backend.generate(tokens, voice, speed) + + except Exception as e: + raise RuntimeError(f"Generation failed: {e}") + + def unload_all(self) -> None: + """Unload models from all backends.""" + for backend in self._backends.values(): + backend.unload() + logger.info("Unloaded all models") + + @property + def available_backends(self) -> list[str]: + """Get list of available backends. + + Returns: + List of backend names + """ + return list(self._backends.keys()) + + @property + def current_backend(self) -> str: + """Get current default backend. + + Returns: + Backend name + """ + return self._current_backend + + +# Module-level instance +_manager: Optional[ModelManager] = None + + +def get_manager(config: Optional[ModelConfig] = None) -> ModelManager: + """Get or create global model manager instance. + + Args: + config: Optional model configuration + + Returns: + ModelManager instance + """ + global _manager + if _manager is None: + _manager = ModelManager(config) + return _manager \ No newline at end of file diff --git a/api/src/inference/onnx_cpu.py b/api/src/inference/onnx_cpu.py new file mode 100644 index 0000000..600157f --- /dev/null +++ b/api/src/inference/onnx_cpu.py @@ -0,0 +1,154 @@ +"""CPU-based ONNX inference backend.""" + +from typing import Dict, Optional + +import numpy as np +import torch +from loguru import logger +from onnxruntime import ( + ExecutionMode, + GraphOptimizationLevel, + InferenceSession, + SessionOptions +) + +from ..core import paths +from ..core.config import settings +from ..structures.model_schemas import ONNXConfig +from .base import BaseModelBackend + + +class ONNXCPUBackend(BaseModelBackend): + """ONNX-based CPU inference backend.""" + + def __init__(self): + """Initialize CPU backend.""" + super().__init__() + self._device = "cpu" + self._session: Optional[InferenceSession] = None + self._config = ONNXConfig( + optimization_level=settings.onnx_optimization_level, + num_threads=settings.onnx_num_threads, + inter_op_threads=settings.onnx_inter_op_threads, + execution_mode=settings.onnx_execution_mode, + memory_pattern=settings.onnx_memory_pattern, + arena_extend_strategy=settings.onnx_arena_extend_strategy + ) + + async def load_model(self, path: str) -> None: + """Load ONNX model. + + Args: + path: Path to model file + + Raises: + RuntimeError: If model loading fails + """ + try: + # Get verified model path + model_path = await paths.get_model_path(path) + + logger.info(f"Loading ONNX model: {model_path}") + + # Configure session + options = self._create_session_options() + provider_options = self._create_provider_options() + + # Create session + self._session = InferenceSession( + model_path, + sess_options=options, + providers=["CPUExecutionProvider"], + provider_options=[provider_options] + ) + + except Exception as e: + raise RuntimeError(f"Failed to load ONNX model: {e}") + + def generate( + self, + tokens: list[int], + voice: torch.Tensor, + speed: float = 1.0 + ) -> np.ndarray: + """Generate audio using ONNX model. + + Args: + tokens: Input token IDs + voice: Voice embedding tensor + speed: Speed multiplier + + Returns: + Generated audio samples + + Raises: + RuntimeError: If generation fails + """ + if not self.is_loaded: + raise RuntimeError("Model not loaded") + + try: + # Prepare inputs + tokens_input = np.array([tokens], dtype=np.int64) + style_input = voice[len(tokens)].numpy() + speed_input = np.full(1, speed, dtype=np.float32) + + # Run inference + result = self._session.run( + None, + { + "tokens": tokens_input, + "style": style_input, + "speed": speed_input + } + ) + + return result[0] + + except Exception as e: + raise RuntimeError(f"Generation failed: {e}") + + def _create_session_options(self) -> SessionOptions: + """Create ONNX session options. + + Returns: + Configured session options + """ + options = SessionOptions() + + # Set optimization level + if self._config.optimization_level == "all": + options.graph_optimization_level = GraphOptimizationLevel.ORT_ENABLE_ALL + elif self._config.optimization_level == "basic": + options.graph_optimization_level = GraphOptimizationLevel.ORT_ENABLE_BASIC + else: + options.graph_optimization_level = GraphOptimizationLevel.ORT_DISABLE_ALL + + # Configure threading + options.intra_op_num_threads = self._config.num_threads + options.inter_op_num_threads = self._config.inter_op_threads + + # Set execution mode + options.execution_mode = ( + ExecutionMode.ORT_PARALLEL + if self._config.execution_mode == "parallel" + else ExecutionMode.ORT_SEQUENTIAL + ) + + # Configure memory optimization + options.enable_mem_pattern = self._config.memory_pattern + + return options + + def _create_provider_options(self) -> Dict: + """Create CPU provider options. + + Returns: + Provider configuration + """ + return { + "CPUExecutionProvider": { + "arena_extend_strategy": self._config.arena_extend_strategy, + "cpu_memory_arena_cfg": "cpu:0" + } + } \ No newline at end of file diff --git a/api/src/inference/onnx_gpu.py b/api/src/inference/onnx_gpu.py new file mode 100644 index 0000000..d003a86 --- /dev/null +++ b/api/src/inference/onnx_gpu.py @@ -0,0 +1,163 @@ +"""GPU-based ONNX inference backend.""" + +from typing import Dict, Optional + +import numpy as np +import torch +from loguru import logger +from onnxruntime import ( + ExecutionMode, + GraphOptimizationLevel, + InferenceSession, + SessionOptions +) + +from ..core import paths +from ..core.config import settings +from ..structures.model_schemas import ONNXGPUConfig +from .base import BaseModelBackend + + +class ONNXGPUBackend(BaseModelBackend): + """ONNX-based GPU inference backend.""" + + def __init__(self): + """Initialize GPU backend.""" + super().__init__() + if not torch.cuda.is_available(): + raise RuntimeError("CUDA not available") + self._device = "cuda" + self._session: Optional[InferenceSession] = None + self._config = ONNXGPUConfig( + optimization_level=settings.onnx_optimization_level, + num_threads=settings.onnx_num_threads, + inter_op_threads=settings.onnx_inter_op_threads, + execution_mode=settings.onnx_execution_mode, + memory_pattern=settings.onnx_memory_pattern, + arena_extend_strategy=settings.onnx_arena_extend_strategy, + device_id=0, + gpu_mem_limit=0.7, + cudnn_conv_algo_search="EXHAUSTIVE", + do_copy_in_default_stream=True + ) + + async def load_model(self, path: str) -> None: + """Load ONNX model. + + Args: + path: Path to model file + + Raises: + RuntimeError: If model loading fails + """ + try: + # Get verified model path + model_path = await paths.get_model_path(path) + + logger.info(f"Loading ONNX model on GPU: {model_path}") + + # Configure session + options = self._create_session_options() + provider_options = self._create_provider_options() + + # Create session with CUDA provider + self._session = InferenceSession( + model_path, + sess_options=options, + providers=["CUDAExecutionProvider"], + provider_options=[provider_options] + ) + + except Exception as e: + raise RuntimeError(f"Failed to load ONNX model: {e}") + + def generate( + self, + tokens: list[int], + voice: torch.Tensor, + speed: float = 1.0 + ) -> np.ndarray: + """Generate audio using ONNX model. + + Args: + tokens: Input token IDs + voice: Voice embedding tensor + speed: Speed multiplier + + Returns: + Generated audio samples + + Raises: + RuntimeError: If generation fails + """ + if not self.is_loaded: + raise RuntimeError("Model not loaded") + + try: + # Prepare inputs + tokens_input = np.array([tokens], dtype=np.int64) + style_input = voice[len(tokens)].cpu().numpy() # Move to CPU for ONNX + speed_input = np.full(1, speed, dtype=np.float32) + + # Run inference + result = self._session.run( + None, + { + "tokens": tokens_input, + "style": style_input, + "speed": speed_input + } + ) + + return result[0] + + except Exception as e: + raise RuntimeError(f"Generation failed: {e}") + + def _create_session_options(self) -> SessionOptions: + """Create ONNX session options. + + Returns: + Configured session options + """ + options = SessionOptions() + + # Set optimization level + if self._config.optimization_level == "all": + options.graph_optimization_level = GraphOptimizationLevel.ORT_ENABLE_ALL + elif self._config.optimization_level == "basic": + options.graph_optimization_level = GraphOptimizationLevel.ORT_ENABLE_BASIC + else: + options.graph_optimization_level = GraphOptimizationLevel.ORT_DISABLE_ALL + + # Configure threading + options.intra_op_num_threads = self._config.num_threads + options.inter_op_num_threads = self._config.inter_op_threads + + # Set execution mode + options.execution_mode = ( + ExecutionMode.ORT_PARALLEL + if self._config.execution_mode == "parallel" + else ExecutionMode.ORT_SEQUENTIAL + ) + + # Configure memory optimization + options.enable_mem_pattern = self._config.memory_pattern + + return options + + def _create_provider_options(self) -> Dict: + """Create CUDA provider options. + + Returns: + Provider configuration + """ + return { + "CUDAExecutionProvider": { + "device_id": self._config.device_id, + "arena_extend_strategy": self._config.arena_extend_strategy, + "gpu_mem_limit": int(self._config.gpu_mem_limit * torch.cuda.get_device_properties(0).total_memory), + "cudnn_conv_algo_search": self._config.cudnn_conv_algo_search, + "do_copy_in_default_stream": self._config.do_copy_in_default_stream + } + } \ No newline at end of file diff --git a/api/src/inference/pytorch_cpu.py b/api/src/inference/pytorch_cpu.py new file mode 100644 index 0000000..e3fe5fa --- /dev/null +++ b/api/src/inference/pytorch_cpu.py @@ -0,0 +1,181 @@ +"""CPU-based PyTorch inference backend.""" + +import gc +from typing import Optional + +import numpy as np +import torch +from loguru import logger + +from ..builds.models import build_model +from ..core import paths +from ..structures.model_schemas import PyTorchCPUConfig +from .base import BaseModelBackend + + +@torch.no_grad() +def forward(model: torch.nn.Module, tokens: list[int], ref_s: torch.Tensor, speed: float) -> np.ndarray: + """Forward pass through model with memory management. + + Args: + model: PyTorch model + tokens: Input tokens + ref_s: Reference signal + speed: Speed multiplier + + Returns: + Generated audio + """ + device = ref_s.device + pred_aln_trg = None + asr = None + + try: + # Initial tensor setup + tokens = torch.LongTensor([[0, *tokens, 0]]).to(device) + input_lengths = torch.LongTensor([tokens.shape[-1]]).to(device) + text_mask = length_to_mask(input_lengths).to(device) + + # Split reference signals + s_content = ref_s[:, 128:].clone().to(device) + s_ref = ref_s[:, :128].clone().to(device) + + # BERT and encoder pass + bert_dur = model.bert(tokens, attention_mask=(~text_mask).int()) + d_en = model.bert_encoder(bert_dur).transpose(-1, -2) + + # Predictor forward pass + d = model.predictor.text_encoder(d_en, s_content, input_lengths, text_mask) + x, _ = model.predictor.lstm(d) + + # Duration prediction + duration = model.predictor.duration_proj(x) + duration = torch.sigmoid(duration).sum(axis=-1) / speed + pred_dur = torch.round(duration).clamp(min=1).long() + del duration, x # Free large intermediates + + # Alignment matrix construction + pred_aln_trg = torch.zeros( + input_lengths.item(), + pred_dur.sum().item(), + device=device + ) + c_frame = 0 + for i in range(pred_aln_trg.size(0)): + pred_aln_trg[i, c_frame:c_frame + pred_dur[0, i].item()] = 1 + c_frame += pred_dur[0, i].item() + pred_aln_trg = pred_aln_trg.unsqueeze(0) + + # Matrix multiplications with cleanup + en = d.transpose(-1, -2) @ pred_aln_trg + del d + + F0_pred, N_pred = model.predictor.F0Ntrain(en, s_content) + del en + + # Final text encoding and decoding + t_en = model.text_encoder(tokens, input_lengths, text_mask) + asr = t_en @ pred_aln_trg + del t_en + + # Generate output + output = model.decoder(asr, F0_pred, N_pred, s_ref) + result = output.squeeze().cpu().numpy() + + return result + + finally: + # Clean up largest tensors if they were created + if pred_aln_trg is not None: + del pred_aln_trg + if asr is not None: + del asr + + +def length_to_mask(lengths: torch.Tensor) -> torch.Tensor: + """Create attention mask from lengths. + + Args: + lengths: Sequence lengths + + Returns: + Boolean mask tensor + """ + max_len = lengths.max() + mask = torch.arange(max_len, device=lengths.device)[None, :].expand( + lengths.shape[0], -1 + ) + if lengths.dtype != mask.dtype: + mask = mask.to(dtype=lengths.dtype) + return mask + 1 > lengths[:, None] + + +class PyTorchCPUBackend(BaseModelBackend): + """PyTorch CPU inference backend.""" + + def __init__(self): + """Initialize CPU backend.""" + super().__init__() + self._device = "cpu" + self._model: Optional[torch.nn.Module] = None + self._config = PyTorchCPUConfig() + + # Configure PyTorch CPU settings + if self._config.num_threads > 0: + torch.set_num_threads(self._config.num_threads) + if self._config.pin_memory: + torch.set_default_tensor_type(torch.FloatTensor) + + async def load_model(self, path: str) -> None: + """Load PyTorch model. + + Args: + path: Path to model file + + Raises: + RuntimeError: If model loading fails + """ + try: + # Get verified model path + model_path = await paths.get_model_path(path) + + logger.info(f"Loading PyTorch model on CPU: {model_path}") + self._model = await build_model(model_path, self._device) + + except Exception as e: + raise RuntimeError(f"Failed to load PyTorch model: {e}") + + def generate( + self, + tokens: list[int], + voice: torch.Tensor, + speed: float = 1.0 + ) -> np.ndarray: + """Generate audio using CPU model. + + Args: + tokens: Input token IDs + voice: Voice embedding tensor + speed: Speed multiplier + + Returns: + Generated audio samples + + Raises: + RuntimeError: If generation fails + """ + if not self.is_loaded: + raise RuntimeError("Model not loaded") + + try: + # Prepare input + ref_s = voice[len(tokens)].clone() + + # Generate audio + return forward(self._model, tokens, ref_s, speed) + + except Exception as e: + raise RuntimeError(f"Generation failed: {e}") + finally: + # Clean up memory + gc.collect() \ No newline at end of file diff --git a/api/src/inference/pytorch_gpu.py b/api/src/inference/pytorch_gpu.py new file mode 100644 index 0000000..3b41f9f --- /dev/null +++ b/api/src/inference/pytorch_gpu.py @@ -0,0 +1,170 @@ +"""GPU-based PyTorch inference backend.""" + +import gc +from typing import Optional + +import numpy as np +import torch +from loguru import logger + +from ..builds.models import build_model +from ..core import paths +from ..structures.model_schemas import PyTorchConfig +from .base import BaseModelBackend + + +@torch.no_grad() +def forward(model: torch.nn.Module, tokens: list[int], ref_s: torch.Tensor, speed: float) -> np.ndarray: + """Forward pass through model. + + Args: + model: PyTorch model + tokens: Input tokens + ref_s: Reference signal (shape: [1, n_features]) + speed: Speed multiplier + + Returns: + Generated audio + """ + device = ref_s.device + + # Initial tensor setup + tokens = torch.LongTensor([[0, *tokens, 0]]).to(device) + input_lengths = torch.LongTensor([tokens.shape[-1]]).to(device) + text_mask = length_to_mask(input_lengths).to(device) + + # Split reference signals (style_dim=128 from config) + style_dim = 128 + s_ref = ref_s[:, :style_dim].clone().to(device) + s_content = ref_s[:, style_dim:].clone().to(device) + + # BERT and encoder pass + bert_dur = model.bert(tokens, attention_mask=(~text_mask).int()) + d_en = model.bert_encoder(bert_dur).transpose(-1, -2) + + # Predictor forward pass + d = model.predictor.text_encoder(d_en, s_content, input_lengths, text_mask) + x, _ = model.predictor.lstm(d) + + # Duration prediction + duration = model.predictor.duration_proj(x) + duration = torch.sigmoid(duration).sum(axis=-1) / speed + pred_dur = torch.round(duration).clamp(min=1).long() + del duration, x + + # Alignment matrix construction + pred_aln_trg = torch.zeros(input_lengths.item(), pred_dur.sum().item(), device=device) + c_frame = 0 + for i in range(pred_aln_trg.size(0)): + pred_aln_trg[i, c_frame:c_frame + pred_dur[0, i].item()] = 1 + c_frame += pred_dur[0, i].item() + pred_aln_trg = pred_aln_trg.unsqueeze(0) + + # Matrix multiplications + en = d.transpose(-1, -2) @ pred_aln_trg + del d + + F0_pred, N_pred = model.predictor.F0Ntrain(en, s_content) + del en + + # Final text encoding and decoding + t_en = model.text_encoder(tokens, input_lengths, text_mask) + asr = t_en @ pred_aln_trg + del t_en + + # Generate output + output = model.decoder(asr, F0_pred, N_pred, s_ref) + return output.squeeze().cpu().numpy() + + +def length_to_mask(lengths: torch.Tensor) -> torch.Tensor: + """Create attention mask from lengths.""" + max_len = lengths.max() + mask = torch.arange(max_len, device=lengths.device)[None, :].expand(lengths.shape[0], -1) + if lengths.dtype != mask.dtype: + mask = mask.to(dtype=lengths.dtype) + return mask + 1 > lengths[:, None] + + +class PyTorchGPUBackend(BaseModelBackend): + """PyTorch GPU inference backend.""" + + def __init__(self): + """Initialize GPU backend.""" + super().__init__() + if not torch.cuda.is_available(): + raise RuntimeError("CUDA not available") + self._device = "cuda" + self._model: Optional[torch.nn.Module] = None + self._config = PyTorchConfig() + + async def load_model(self, path: str) -> None: + """Load PyTorch model. + + Args: + path: Path to model file + + Raises: + RuntimeError: If model loading fails + """ + try: + # Get verified model path + model_path = await paths.get_model_path(path) + + logger.info(f"Loading PyTorch model: {model_path}") + self._model = await build_model(model_path, self._device) + + except Exception as e: + raise RuntimeError(f"Failed to load PyTorch model: {e}") + + def generate( + self, + tokens: list[int], + voice: torch.Tensor, + speed: float = 1.0 + ) -> np.ndarray: + """Generate audio using GPU model. + + Args: + tokens: Input token IDs + voice: Voice embedding tensor + speed: Speed multiplier + + Returns: + Generated audio samples + + Raises: + RuntimeError: If generation fails + """ + if not self.is_loaded: + raise RuntimeError("Model not loaded") + + try: + # Check memory and cleanup if needed + if self._check_memory(): + self._clear_memory() + + # Get reference style from voice pack + ref_s = voice[len(tokens)].clone().to(self._device) + if ref_s.dim() == 1: + ref_s = ref_s.unsqueeze(0) # Add batch dimension if needed + + # Generate audio + return forward(self._model, tokens, ref_s, speed) + + except Exception as e: + logger.error(f"Generation failed: {e}") + raise + + def _check_memory(self) -> bool: + """Check if memory usage is above threshold.""" + if torch.cuda.is_available(): + memory_gb = torch.cuda.memory_allocated() / 1e9 + return memory_gb > self._config.memory_threshold + return False + + def _clear_memory(self) -> None: + """Clear GPU memory.""" + if torch.cuda.is_available(): + torch.cuda.empty_cache() + gc.collect() \ No newline at end of file diff --git a/api/src/inference/voice_manager.py b/api/src/inference/voice_manager.py new file mode 100644 index 0000000..295040c --- /dev/null +++ b/api/src/inference/voice_manager.py @@ -0,0 +1,191 @@ +"""Voice pack management and caching.""" + +import os +from typing import Dict, List, Optional, Union + +import torch +from loguru import logger +from pydantic import BaseModel + +from ..core import paths +from ..core.config import settings +from ..structures.model_schemas import VoiceConfig + + +class VoiceManager: + """Manages voice loading, caching, and operations.""" + + def __init__(self, config: Optional[VoiceConfig] = None): + """Initialize voice manager. + + Args: + config: Optional voice configuration + """ + self._config = config or VoiceConfig() + self._voice_cache: Dict[str, torch.Tensor] = {} + + def get_voice_path(self, voice_name: str) -> Optional[str]: + """Get path to voice file. + + Args: + voice_name: Name of voice + + Returns: + Path to voice file if exists, None otherwise + """ + voice_path = os.path.join(settings.voices_dir, f"{voice_name}.pt") + return voice_path if os.path.exists(voice_path) else None + + async def load_voice(self, voice_name: str, device: str = "cpu") -> torch.Tensor: + """Load voice tensor. + + Args: + voice_name: Name of voice to load + device: Device to load voice on + + Returns: + Voice tensor + + Raises: + RuntimeError: If voice loading fails + """ + voice_path = self.get_voice_path(voice_name) + if not voice_path: + raise RuntimeError(f"Voice not found: {voice_name}") + + # Check cache first + cache_key = f"{voice_path}_{device}" + if self._config.use_cache and cache_key in self._voice_cache: + return self._voice_cache[cache_key] + + try: + # Load voice tensor + voice = await paths.load_voice_tensor(voice_path, device=device) + + # Cache if enabled + if self._config.use_cache: + self._manage_cache() + self._voice_cache[cache_key] = voice + logger.debug(f"Cached voice: {voice_name} on {device}") + + return voice + + except Exception as e: + raise RuntimeError(f"Failed to load voice {voice_name}: {e}") + + def _manage_cache(self) -> None: + """Manage voice cache size.""" + if len(self._voice_cache) >= self._config.cache_size: + # Remove oldest voice + oldest = next(iter(self._voice_cache)) + del self._voice_cache[oldest] + logger.debug(f"Removed from voice cache: {oldest}") + + async def combine_voices(self, voices: List[str], device: str = "cpu") -> str: + """Combine multiple voices into a new voice. + + Args: + voices: List of voice names to combine + device: Device to load voices on + + Returns: + Name of combined voice + + Raises: + ValueError: If fewer than 2 voices provided + RuntimeError: If voice combination fails + """ + if len(voices) < 2: + raise ValueError("At least 2 voices are required for combination") + + # Load voices + voice_tensors: List[torch.Tensor] = [] + for voice in voices: + try: + voice_tensor = await self.load_voice(voice, device) + voice_tensors.append(voice_tensor) + except Exception as e: + raise RuntimeError(f"Failed to load voice {voice}: {e}") + + try: + # Combine voices + combined_name = "_".join(voices) + combined_tensor = torch.mean(torch.stack(voice_tensors), dim=0) + + # Save combined voice + combined_path = os.path.join(settings.voices_dir, f"{combined_name}.pt") + try: + torch.save(combined_tensor, combined_path) + except Exception as e: + raise RuntimeError(f"Failed to save combined voice: {e}") + + return combined_name + + except Exception as e: + raise RuntimeError(f"Failed to combine voices: {e}") + + async def list_voices(self) -> List[str]: + """List available voices. + + Returns: + List of voice names + """ + voices = [] + try: + for entry in os.listdir(settings.voices_dir): + if entry.endswith(".pt"): + voices.append(entry[:-3]) # Remove .pt extension + except Exception as e: + logger.error(f"Error listing voices: {e}") + return sorted(voices) + + def validate_voice(self, voice_path: str) -> bool: + """Validate voice file. + + Args: + voice_path: Path to voice file + + Returns: + True if valid, False otherwise + """ + try: + if not os.path.exists(voice_path): + return False + + # Try loading voice + voice = torch.load(voice_path, map_location="cpu") + return isinstance(voice, torch.Tensor) + + except Exception: + return False + + @property + def cache_info(self) -> Dict[str, int]: + """Get cache statistics. + + Returns: + Dictionary with cache info + """ + return { + 'size': len(self._voice_cache), + 'max_size': self._config.cache_size + } + + +# Module-level instance +_manager: Optional[VoiceManager] = None + + +def get_manager(config: Optional[VoiceConfig] = None) -> VoiceManager: + """Get or create global voice manager instance. + + Args: + config: Optional voice configuration + + Returns: + VoiceManager instance + """ + global _manager + if _manager is None: + _manager = VoiceManager(config) + return _manager \ No newline at end of file diff --git a/api/src/services/tts_base.py b/api/src/services/tts_base.py deleted file mode 100644 index 3c87cfd..0000000 --- a/api/src/services/tts_base.py +++ /dev/null @@ -1,175 +0,0 @@ -import os -import threading -from abc import ABC, abstractmethod -from typing import List, Tuple - -import numpy as np -import torch -from loguru import logger - -from ..core.config import settings - - -class TTSBaseModel(ABC): - _instance = None - _lock = threading.Lock() - _device = None - VOICES_DIR = os.path.join(os.path.dirname(os.path.dirname(__file__)), "voices") - - @classmethod - async def setup(cls): - """Initialize model and setup voices""" - with cls._lock: - # Set device - cuda_available = torch.cuda.is_available() - logger.info(f"CUDA available: {cuda_available}") - if cuda_available: - try: - # Test CUDA device - test_tensor = torch.zeros(1).cuda() - logger.info("CUDA test successful") - model_path = os.path.join( - settings.model_dir, settings.pytorch_model_path - ) - cls._device = "cuda" - except Exception as e: - logger.error(f"CUDA test failed: {e}") - cls._device = "cpu" - else: - cls._device = "cpu" - model_path = os.path.join(settings.model_dir, settings.onnx_model_path) - logger.info(f"Initializing model on {cls._device}") - logger.info(f"Model dir: {settings.model_dir}") - logger.info(f"Model path: {model_path}") - logger.info(f"Files in model dir: {os.listdir(settings.model_dir)}") - - # Initialize model first - model = cls.initialize(settings.model_dir, model_path=model_path) - if model is None: - raise RuntimeError(f"Failed to initialize {cls._device.upper()} model") - cls._instance = model - - # Setup voices directory - os.makedirs(cls.VOICES_DIR, exist_ok=True) - - # Copy base voices to local directory - base_voices_dir = os.path.join(settings.model_dir, settings.voices_dir) - if os.path.exists(base_voices_dir): - for file in os.listdir(base_voices_dir): - if file.endswith(".pt"): - voice_name = file[:-3] - voice_path = os.path.join(cls.VOICES_DIR, file) - if not os.path.exists(voice_path): - try: - logger.info( - f"Copying base voice {voice_name} to voices directory" - ) - base_path = os.path.join(base_voices_dir, file) - voicepack = torch.load( - base_path, - map_location=cls._device, - weights_only=True, - ) - torch.save(voicepack, voice_path) - except Exception as e: - logger.error( - f"Error copying voice {voice_name}: {str(e)}" - ) - - # Count voices in directory - voice_count = len( - [f for f in os.listdir(cls.VOICES_DIR) if f.endswith(".pt")] - ) - - # Now that model and voices are ready, do warmup - try: - with open( - os.path.join( - os.path.dirname(os.path.dirname(__file__)), - "core", - "don_quixote.txt", - ) - ) as f: - warmup_text = f.read() - except Exception as e: - logger.warning(f"Failed to load warmup text: {e}") - warmup_text = "This is a warmup text that will be split into chunks for processing." - - # Use warmup service after model is fully initialized - from .warmup import WarmupService - - warmup = WarmupService() - - # Load and warm up voices - loaded_voices = warmup.load_voices() - await warmup.warmup_voices(warmup_text, loaded_voices) - - logger.info("Model warm-up complete") - - # Count voices in directory - voice_count = len( - [f for f in os.listdir(cls.VOICES_DIR) if f.endswith(".pt")] - ) - return voice_count - - @classmethod - @abstractmethod - def initialize(cls, model_dir: str, model_path: str = None): - """Initialize the model""" - pass - - @classmethod - @abstractmethod - def process_text(cls, text: str, language: str) -> Tuple[str, List[int]]: - """Process text into phonemes and tokens - - Args: - text: Input text - language: Language code - - Returns: - tuple[str, list[int]]: Phonemes and token IDs - """ - pass - - @classmethod - @abstractmethod - def generate_from_text( - cls, text: str, voicepack: torch.Tensor, language: str, speed: float - ) -> Tuple[np.ndarray, str]: - """Generate audio from text - - Args: - text: Input text - voicepack: Voice tensor - language: Language code - speed: Speed factor - - Returns: - tuple[np.ndarray, str]: Generated audio samples and phonemes - """ - pass - - @classmethod - @abstractmethod - def generate_from_tokens( - cls, tokens: List[int], voicepack: torch.Tensor, speed: float - ) -> np.ndarray: - """Generate audio from tokens - - Args: - tokens: Token IDs - voicepack: Voice tensor - speed: Speed factor - - Returns: - np.ndarray: Generated audio samples - """ - pass - - @classmethod - def get_device(cls): - """Get the current device""" - if cls._device is None: - raise RuntimeError("Model not initialized. Call setup() first.") - return cls._device diff --git a/api/src/services/tts_cpu.py b/api/src/services/tts_cpu.py deleted file mode 100644 index b8e6236..0000000 --- a/api/src/services/tts_cpu.py +++ /dev/null @@ -1,167 +0,0 @@ -import os - -import numpy as np -import torch -from loguru import logger -from onnxruntime import ( - ExecutionMode, - GraphOptimizationLevel, - InferenceSession, - SessionOptions, -) - -from ..core.config import settings -from .text_processing import phonemize, tokenize -from .tts_base import TTSBaseModel - - -class TTSCPUModel(TTSBaseModel): - _instance = None - _onnx_session = None - _device = "cpu" - - @classmethod - def get_instance(cls): - """Get the model instance""" - if cls._onnx_session is None: - raise RuntimeError("ONNX model not initialized. Call initialize() first.") - return cls._onnx_session - - @classmethod - def initialize(cls, model_dir: str, model_path: str = None): - """Initialize ONNX model for CPU inference""" - if cls._onnx_session is None: - try: - # Try loading ONNX model - onnx_path = os.path.join(model_dir, settings.onnx_model_path) - if not os.path.exists(onnx_path): - logger.error(f"ONNX model not found at {onnx_path}") - return None - - logger.info(f"Loading ONNX model from {onnx_path}") - - # Configure ONNX session for optimal performance - session_options = SessionOptions() - - # Set optimization level - if settings.onnx_optimization_level == "all": - session_options.graph_optimization_level = ( - GraphOptimizationLevel.ORT_ENABLE_ALL - ) - elif settings.onnx_optimization_level == "basic": - session_options.graph_optimization_level = ( - GraphOptimizationLevel.ORT_ENABLE_BASIC - ) - else: - session_options.graph_optimization_level = ( - GraphOptimizationLevel.ORT_DISABLE_ALL - ) - - # Configure threading - session_options.intra_op_num_threads = settings.onnx_num_threads - session_options.inter_op_num_threads = settings.onnx_inter_op_threads - - # Set execution mode - session_options.execution_mode = ( - ExecutionMode.ORT_PARALLEL - if settings.onnx_execution_mode == "parallel" - else ExecutionMode.ORT_SEQUENTIAL - ) - - # Enable/disable memory pattern optimization - session_options.enable_mem_pattern = settings.onnx_memory_pattern - - # Configure CPU provider options - provider_options = { - "CPUExecutionProvider": { - "arena_extend_strategy": settings.onnx_arena_extend_strategy, - "cpu_memory_arena_cfg": "cpu:0", - } - } - - session = InferenceSession( - onnx_path, - sess_options=session_options, - providers=["CPUExecutionProvider"], - provider_options=[provider_options], - ) - cls._onnx_session = session - return session - except Exception as e: - logger.error(f"Failed to initialize ONNX model: {e}") - return None - return cls._onnx_session - - @classmethod - def process_text(cls, text: str, language: str) -> tuple[str, list[int]]: - """Process text into phonemes and tokens - - Args: - text: Input text - language: Language code - - Returns: - tuple[str, list[int]]: Phonemes and token IDs - """ - phonemes = phonemize(text, language) - tokens = tokenize(phonemes) - tokens = [0] + tokens + [0] # Add start/end tokens - return phonemes, tokens - - @classmethod - def generate_from_text( - cls, text: str, voicepack: torch.Tensor, language: str, speed: float - ) -> tuple[np.ndarray, str]: - """Generate audio from text - - Args: - text: Input text - voicepack: Voice tensor - language: Language code - speed: Speed factor - - Returns: - tuple[np.ndarray, str]: Generated audio samples and phonemes - """ - if cls._onnx_session is None: - raise RuntimeError("ONNX model not initialized") - - # Process text - phonemes, tokens = cls.process_text(text, language) - - # Generate audio - audio = cls.generate_from_tokens(tokens, voicepack, speed) - - return audio, phonemes - - @classmethod - def generate_from_tokens( - cls, tokens: list[int], voicepack: torch.Tensor, speed: float - ) -> np.ndarray: - """Generate audio from tokens - - Args: - tokens: Token IDs - voicepack: Voice tensor - speed: Speed factor - - Returns: - np.ndarray: Generated audio samples - """ - if cls._onnx_session is None: - raise RuntimeError("ONNX model not initialized") - - # Pre-allocate and prepare inputs - tokens_input = np.array([tokens], dtype=np.int64) - style_input = voicepack[ - len(tokens) - 2 - ].numpy() # Already has correct dimensions - speed_input = np.full( - 1, speed, dtype=np.float32 - ) # More efficient than ones * speed - - # Run inference with optimized inputs - result = cls._onnx_session.run( - None, {"tokens": tokens_input, "style": style_input, "speed": speed_input} - ) - return result[0] diff --git a/api/src/services/tts_gpu.py b/api/src/services/tts_gpu.py deleted file mode 100644 index d2b8a44..0000000 --- a/api/src/services/tts_gpu.py +++ /dev/null @@ -1,262 +0,0 @@ -import os -import time - -import numpy as np -import torch -from ..builds.models import build_model -from loguru import logger - -from ..core.config import settings -from .text_processing import phonemize, tokenize -from .tts_base import TTSBaseModel - - -# @torch.no_grad() -# def forward(model, tokens, ref_s, speed): -# """Forward pass through the model""" -# device = ref_s.device -# tokens = torch.LongTensor([[0, *tokens, 0]]).to(device) -# input_lengths = torch.LongTensor([tokens.shape[-1]]).to(device) -# text_mask = length_to_mask(input_lengths).to(device) -# bert_dur = model.bert(tokens, attention_mask=(~text_mask).int()) -# d_en = model.bert_encoder(bert_dur).transpose(-1, -2) -# s = ref_s[:, 128:] -# d = model.predictor.text_encoder(d_en, s, input_lengths, text_mask) -# x, _ = model.predictor.lstm(d) -# duration = model.predictor.duration_proj(x) -# duration = torch.sigmoid(duration).sum(axis=-1) / speed -# pred_dur = torch.round(duration).clamp(min=1).long() -# pred_aln_trg = torch.zeros(input_lengths, pred_dur.sum().item()) -# c_frame = 0 -# for i in range(pred_aln_trg.size(0)): -# pred_aln_trg[i, c_frame : c_frame + pred_dur[0, i].item()] = 1 -# c_frame += pred_dur[0, i].item() -# en = d.transpose(-1, -2) @ pred_aln_trg.unsqueeze(0).to(device) -# F0_pred, N_pred = model.predictor.F0Ntrain(en, s) -# t_en = model.text_encoder(tokens, input_lengths, text_mask) -# asr = t_en @ pred_aln_trg.unsqueeze(0).to(device) -# return model.decoder(asr, F0_pred, N_pred, ref_s[:, :128]).squeeze().cpu().numpy() -@torch.no_grad() -def forward(model, tokens, ref_s, speed): - """Forward pass through the model with moderate memory management""" - device = ref_s.device - - try: - # Initial tensor setup with proper device placement - tokens = torch.LongTensor([[0, *tokens, 0]]).to(device) - input_lengths = torch.LongTensor([tokens.shape[-1]]).to(device) - text_mask = length_to_mask(input_lengths).to(device) - - # Split and clone reference signals with explicit device placement - s_content = ref_s[:, 128:].clone().to(device) - s_ref = ref_s[:, :128].clone().to(device) - - # BERT and encoder pass - bert_dur = model.bert(tokens, attention_mask=(~text_mask).int()) - d_en = model.bert_encoder(bert_dur).transpose(-1, -2) - - # Predictor forward pass - d = model.predictor.text_encoder(d_en, s_content, input_lengths, text_mask) - x, _ = model.predictor.lstm(d) - - # Duration prediction - duration = model.predictor.duration_proj(x) - duration = torch.sigmoid(duration).sum(axis=-1) / speed - pred_dur = torch.round(duration).clamp(min=1).long() - # Only cleanup large intermediates - del duration, x - - # Alignment matrix construction - pred_aln_trg = torch.zeros(input_lengths.item(), pred_dur.sum().item(), device=device) - c_frame = 0 - for i in range(pred_aln_trg.size(0)): - pred_aln_trg[i, c_frame : c_frame + pred_dur[0, i].item()] = 1 - c_frame += pred_dur[0, i].item() - pred_aln_trg = pred_aln_trg.unsqueeze(0) - - # Matrix multiplications with selective cleanup - en = d.transpose(-1, -2) @ pred_aln_trg - del d # Free large intermediate tensor - - F0_pred, N_pred = model.predictor.F0Ntrain(en, s_content) - del en # Free large intermediate tensor - - # Final text encoding and decoding - t_en = model.text_encoder(tokens, input_lengths, text_mask) - asr = t_en @ pred_aln_trg - del t_en # Free large intermediate tensor - - # Final decoding and transfer to CPU - output = model.decoder(asr, F0_pred, N_pred, s_ref) - result = output.squeeze().cpu().numpy() - - return result - - finally: - # Let PyTorch handle most cleanup automatically - # Only explicitly free the largest tensors - del pred_aln_trg, asr - - -# def length_to_mask(lengths): -# """Create attention mask from lengths""" -# mask = ( -# torch.arange(lengths.max()) -# .unsqueeze(0) -# .expand(lengths.shape[0], -1) -# .type_as(lengths) -# ) -# mask = torch.gt(mask + 1, lengths.unsqueeze(1)) -# return mask - - -def length_to_mask(lengths): - """Create attention mask from lengths - possibly optimized version""" - max_len = lengths.max() - # Create mask directly on the same device as lengths - mask = torch.arange(max_len, device=lengths.device)[None, :].expand( - lengths.shape[0], -1 - ) - # Avoid type_as by using the correct dtype from the start - if lengths.dtype != mask.dtype: - mask = mask.to(dtype=lengths.dtype) - # Fuse operations using broadcasting - return mask + 1 > lengths[:, None] - - -class TTSGPUModel(TTSBaseModel): - _instance = None - _device = "cuda" - - @classmethod - def get_instance(cls): - """Get the model instance""" - if cls._instance is None: - raise RuntimeError("GPU model not initialized. Call initialize() first.") - return cls._instance - - @classmethod - def initialize(cls, model_dir: str, model_path: str): - """Initialize PyTorch model for GPU inference""" - if cls._instance is None and torch.cuda.is_available(): - try: - logger.info("Initializing GPU model") - model_path = os.path.join(model_dir, settings.pytorch_model_path) - model = build_model(model_path, cls._device) - cls._instance = model - return model - except Exception as e: - logger.error(f"Failed to initialize GPU model: {e}") - return None - return cls._instance - - @classmethod - def process_text(cls, text: str, language: str) -> tuple[str, list[int]]: - """Process text into phonemes and tokens - - Args: - text: Input text - language: Language code - - Returns: - tuple[str, list[int]]: Phonemes and token IDs - """ - phonemes = phonemize(text, language) - tokens = tokenize(phonemes) - return phonemes, tokens - - @classmethod - def generate_from_text( - cls, text: str, voicepack: torch.Tensor, language: str, speed: float - ) -> tuple[np.ndarray, str]: - """Generate audio from text - - Args: - text: Input text - voicepack: Voice tensor - language: Language code - speed: Speed factor - - Returns: - tuple[np.ndarray, str]: Generated audio samples and phonemes - """ - if cls._instance is None: - raise RuntimeError("GPU model not initialized") - - # Process text - phonemes, tokens = cls.process_text(text, language) - - # Generate audio - audio = cls.generate_from_tokens(tokens, voicepack, speed) - - return audio, phonemes - - @classmethod - def generate_from_tokens( - cls, tokens: list[int], voicepack: torch.Tensor, speed: float - ) -> np.ndarray: - """Generate audio from tokens with moderate memory management - - Args: - tokens: Token IDs - voicepack: Voice tensor - speed: Speed factor - - Returns: - np.ndarray: Generated audio samples - """ - if cls._instance is None: - raise RuntimeError("GPU model not initialized") - - try: - device = cls._device - - # Check memory pressure - if torch.cuda.is_available(): - memory_allocated = torch.cuda.memory_allocated(device) / 1e9 # Convert to GB - if memory_allocated > 2.0: # 2GB limit - logger.info( - f"Memory usage above 2GB threshold:{memory_allocated:.2f}GB " - f"Clearing cache" - ) - torch.cuda.empty_cache() - import gc - gc.collect() - - # Get reference style with proper device placement - ref_s = voicepack[len(tokens)].clone().to(device) - - # Generate audio - audio = forward(cls._instance, tokens, ref_s, speed) - - return audio - - except RuntimeError as e: - if "out of memory" in str(e): - # On OOM, do a full cleanup and retry - if torch.cuda.is_available(): - logger.warning("Out of memory detected, performing full cleanup") - torch.cuda.synchronize() - torch.cuda.empty_cache() - import gc - gc.collect() - - # Log memory stats after cleanup - memory_allocated = torch.cuda.memory_allocated(device) - memory_reserved = torch.cuda.memory_reserved(device) - logger.info( - f"Memory after OOM cleanup: " - f"Allocated: {memory_allocated / 1e9:.2f}GB, " - f"Reserved: {memory_reserved / 1e9:.2f}GB" - ) - - # Retry generation - ref_s = voicepack[len(tokens)].clone().to(device) - audio = forward(cls._instance, tokens, ref_s, speed) - return audio - raise - - finally: - # Only synchronize at the top level, no empty_cache - if torch.cuda.is_available(): - torch.cuda.synchronize() diff --git a/api/src/services/tts_model.py b/api/src/services/tts_model.py deleted file mode 100644 index 1e04939..0000000 --- a/api/src/services/tts_model.py +++ /dev/null @@ -1,8 +0,0 @@ -import torch - -if torch.cuda.is_available(): - from .tts_gpu import TTSGPUModel as TTSModel -else: - from .tts_cpu import TTSCPUModel as TTSModel - -__all__ = ["TTSModel"] diff --git a/api/src/services/tts_service.py b/api/src/services/tts_service.py index d0f93b9..20e9b69 100644 --- a/api/src/services/tts_service.py +++ b/api/src/services/tts_service.py @@ -1,120 +1,114 @@ +"""TTS service using model and voice managers.""" + import io import os -import re import time -from functools import lru_cache -from typing import List, Optional, Tuple +from typing import List, Tuple + +import torch -import aiofiles.os import numpy as np import scipy.io.wavfile as wavfile -import torch from loguru import logger from ..core.config import settings +from ..inference.model_manager import get_manager as get_model_manager +from ..inference.voice_manager import get_manager as get_voice_manager from .audio import AudioNormalizer, AudioService from .text_processing import chunker, normalize_text -from .tts_model import TTSModel class TTSService: + """Text-to-speech service.""" + def __init__(self, output_dir: str = None): + """Initialize service. + + Args: + output_dir: Optional output directory for saving audio + """ self.output_dir = output_dir - self.model = TTSModel.get_instance() + self.model_manager = get_model_manager() + self.voice_manager = get_voice_manager() + self._initialized = False + self._initialization_error = None - @staticmethod - @lru_cache(maxsize=3) # Cache up to 3 most recently used voices - def _load_voice(voice_path: str) -> torch.Tensor: - """Load and cache a voice model""" - return torch.load( - voice_path, map_location=TTSModel.get_device(), weights_only=True - ) + async def ensure_initialized(self): + """Ensure model is initialized.""" + if self._initialized: + return + if self._initialization_error: + raise self._initialization_error - def _get_voice_path(self, voice_name: str) -> Optional[str]: - """Get the path to a voice file""" - voice_path = os.path.join(TTSModel.VOICES_DIR, f"{voice_name}.pt") - return voice_path if os.path.exists(voice_path) else None + try: + # Determine model path based on hardware + if settings.use_gpu and torch.cuda.is_available(): + model_path = os.path.join(settings.model_dir, settings.pytorch_model_path) + backend_type = 'pytorch_gpu' + else: + model_path = os.path.join(settings.model_dir, settings.onnx_model_path) + backend_type = 'onnx_cpu' + + # Initialize model + await self.model_manager.load_model(model_path, backend_type) + logger.info(f"Initialized model on {backend_type} backend") + self._initialized = True + + except Exception as e: + logger.error(f"Failed to initialize model: {e}") + self._initialization_error = RuntimeError(f"Model initialization failed: {e}") + raise self._initialization_error - def _generate_audio( - self, text: str, voice: str, speed: float, stitch_long_output: bool = True - ) -> Tuple[torch.Tensor, float]: - """Generate complete audio and return with processing time""" - audio, processing_time = self._generate_audio_internal( - text, voice, speed, stitch_long_output - ) - return audio, processing_time - - def _generate_audio_internal( - self, text: str, voice: str, speed: float, stitch_long_output: bool = True - ) -> Tuple[torch.Tensor, float]: - """Generate audio and measure processing time""" + async def generate_audio( + self, text: str, voice: str, speed: float = 1.0 + ) -> Tuple[np.ndarray, float]: + """Generate audio for text. + + Args: + text: Input text + voice: Voice name + speed: Speed multiplier + + Returns: + Audio samples and processing time + """ + await self.ensure_initialized() start_time = time.time() try: - # Normalize text once at the start - if not text: - raise ValueError("Text is empty after preprocessing") + # Normalize text normalized = normalize_text(text) if not normalized: raise ValueError("Text is empty after preprocessing") text = str(normalized) - # Check voice exists - voice_path = self._get_voice_path(voice) - if not voice_path: - raise ValueError(f"Voice not found: {voice}") - - # Load voice using cached loader - voicepack = self._load_voice(voice_path) - - # For non-streaming, preprocess all chunks first - if stitch_long_output: - # Preprocess all chunks to phonemes/tokens - chunks_data = [] - for chunk in chunker.split_text(text): - try: - phonemes, tokens = TTSModel.process_text(chunk, voice[0]) - chunks_data.append((chunk, tokens)) - except Exception as e: - logger.error( - f"Failed to process chunk: '{chunk}'. Error: {str(e)}" - ) + # Process text into chunks + audio_chunks = [] + for chunk in chunker.split_text(text): + try: + # Process text + + sequences = process_text(chunk) + if not sequences: continue - if not chunks_data: - raise ValueError("No chunks were processed successfully") + # Generate audio + chunk_audio = await self.model_manager.generate( + sequences[0], + voice, + speed=speed + ) + if chunk_audio is not None: + audio_chunks.append(chunk_audio) + except Exception as e: + logger.error(f"Failed to process chunk: '{chunk}'. Error: {str(e)}") + continue - # Generate audio for all chunks - audio_chunks = [] - for chunk, tokens in chunks_data: - try: - chunk_audio = TTSModel.generate_from_tokens( - tokens, voicepack, speed - ) - if chunk_audio is not None: - audio_chunks.append(chunk_audio) - else: - logger.error(f"No audio generated for chunk: '{chunk}'") - except Exception as e: - logger.error( - f"Failed to generate audio for chunk: '{chunk}'. Error: {str(e)}" - ) - continue - - if not audio_chunks: - raise ValueError("No audio chunks were generated successfully") - - # Concatenate all chunks - audio = ( - np.concatenate(audio_chunks) - if len(audio_chunks) > 1 - else audio_chunks[0] - ) - else: - # Process single chunk - phonemes, tokens = TTSModel.process_text(text, voice[0]) - audio = TTSModel.generate_from_tokens(tokens, voicepack, speed) + if not audio_chunks: + raise ValueError("No audio chunks were generated successfully") + # Combine chunks + audio = np.concatenate(audio_chunks) if len(audio_chunks) > 1 else audio_chunks[0] processing_time = time.time() - start_time return audio, processing_time @@ -126,144 +120,103 @@ class TTSService: self, text: str, voice: str, - speed: float, + speed: float = 1.0, output_format: str = "wav", - silent=False, ): - """Generate and yield audio chunks as they're generated for real-time streaming""" - try: - stream_start = time.time() - # Create normalizer for consistent audio levels - stream_normalizer = AudioNormalizer() + """Generate and stream audio chunks. + + Args: + text: Input text + voice: Voice name + speed: Speed multiplier + output_format: Output audio format + + Yields: + Audio chunks as bytes + """ + await self.ensure_initialized() - # Input validation and preprocessing - if not text: - raise ValueError("Text is empty") - preprocess_start = time.time() + try: + # Setup audio processing + stream_normalizer = AudioNormalizer() + + # Normalize text normalized = normalize_text(text) if not normalized: raise ValueError("Text is empty after preprocessing") text = str(normalized) - logger.debug( - f"Text preprocessing took: {(time.time() - preprocess_start)*1000:.1f}ms" - ) - # Voice validation and loading - voice_start = time.time() - voice_path = self._get_voice_path(voice) - if not voice_path: - raise ValueError(f"Voice not found: {voice}") - voicepack = self._load_voice(voice_path) - logger.debug( - f"Voice loading took: {(time.time() - voice_start)*1000:.1f}ms" - ) - - # Process chunks as they're generated + # Process chunks is_first = True - chunks_processed = 0 - - # Process chunks as they come from generator chunk_gen = chunker.split_text(text) current_chunk = next(chunk_gen, None) while current_chunk is not None: - next_chunk = next(chunk_gen, None) # Peek at next chunk - chunks_processed += 1 + next_chunk = next(chunk_gen, None) try: - # Process text and generate audio - phonemes, tokens = TTSModel.process_text(current_chunk, voice[0]) - chunk_audio = TTSModel.generate_from_tokens( - tokens, voicepack, speed - ) - - if chunk_audio is not None: - # Convert chunk with proper streaming header handling - chunk_bytes = AudioService.convert_audio( - chunk_audio, - 24000, - output_format, - is_first_chunk=is_first, - normalizer=stream_normalizer, - is_last_chunk=(next_chunk is None), # Last if no next chunk - stream=True # Ensure proper streaming format handling + # Process text + from ..text_processing import process_text + sequences = process_text(current_chunk) + if sequences: + # Generate audio + chunk_audio = await self.model_manager.generate( + sequences[0], + voice, + speed=speed ) - yield chunk_bytes - is_first = False - else: - logger.error(f"No audio generated for chunk: '{current_chunk}'") + if chunk_audio is not None: + # Convert to bytes + chunk_bytes = AudioService.convert_audio( + chunk_audio, + 24000, + output_format, + is_first_chunk=is_first, + normalizer=stream_normalizer, + is_last_chunk=(next_chunk is None), + stream=True + ) + yield chunk_bytes + is_first = False except Exception as e: - logger.error( - f"Failed to generate audio for chunk: '{current_chunk}'. Error: {str(e)}" - ) + logger.error(f"Failed to generate audio for chunk: '{current_chunk}'. Error: {str(e)}") - current_chunk = next_chunk # Move to next chunk + current_chunk = next_chunk except Exception as e: logger.error(f"Error in audio generation stream: {str(e)}") raise - def _save_audio(self, audio: torch.Tensor, filepath: str): - """Save audio to file""" - os.makedirs(os.path.dirname(filepath), exist_ok=True) - wavfile.write(filepath, 24000, audio) - - def _audio_to_bytes(self, audio: torch.Tensor) -> bytes: - """Convert audio tensor to WAV bytes""" - buffer = io.BytesIO() - wavfile.write(buffer, 24000, audio) - return buffer.getvalue() - async def combine_voices(self, voices: List[str]) -> str: - """Combine multiple voices into a new voice""" - if len(voices) < 2: - raise ValueError("At least 2 voices are required for combination") - - # Load voices - t_voices: List[torch.Tensor] = [] - v_name: List[str] = [] - - for voice in voices: - try: - voice_path = os.path.join(TTSModel.VOICES_DIR, f"{voice}.pt") - voicepack = torch.load( - voice_path, map_location=TTSModel.get_device(), weights_only=True - ) - t_voices.append(voicepack) - v_name.append(voice) - except Exception as e: - raise ValueError(f"Failed to load voice {voice}: {str(e)}") - - # Combine voices - try: - f: str = "_".join(v_name) - v = torch.mean(torch.stack(t_voices), dim=0) - combined_path = os.path.join(TTSModel.VOICES_DIR, f"{f}.pt") - - # Save combined voice - try: - torch.save(v, combined_path) - except Exception as e: - raise RuntimeError( - f"Failed to save combined voice to {combined_path}: {str(e)}" - ) - - return f - - except Exception as e: - if not isinstance(e, (ValueError, RuntimeError)): - raise RuntimeError(f"Error combining voices: {str(e)}") - raise + """Combine multiple voices. + + Args: + voices: List of voice names + + Returns: + Name of combined voice + """ + await self.ensure_initialized() + return await self.voice_manager.combine_voices(voices) async def list_voices(self) -> List[str]: - """List all available voices""" - voices = [] - try: - it = await aiofiles.os.scandir(TTSModel.VOICES_DIR) - for entry in it: - if entry.name.endswith(".pt"): - voices.append(entry.name[:-3]) # Remove .pt extension - except Exception as e: - logger.error(f"Error listing voices: {str(e)}") - return sorted(voices) + """List available voices. + + Returns: + List of voice names + """ + return await self.voice_manager.list_voices() + + def _audio_to_bytes(self, audio: np.ndarray) -> bytes: + """Convert audio to WAV bytes. + + Args: + audio: Audio samples + + Returns: + WAV bytes + """ + buffer = io.BytesIO() + wavfile.write(buffer, 24000, audio) + return buffer.getvalue() \ No newline at end of file diff --git a/api/src/structures/model_schemas.py b/api/src/structures/model_schemas.py new file mode 100644 index 0000000..51ef910 --- /dev/null +++ b/api/src/structures/model_schemas.py @@ -0,0 +1,26 @@ +"""Model and voice configuration schemas.""" + +from pydantic import BaseModel + + +class ModelConfig(BaseModel): + """Model configuration.""" + optimization_level: str = "all" # all, basic, none + num_threads: int = 4 + inter_op_threads: int = 4 + execution_mode: str = "parallel" # parallel, sequential + memory_pattern: bool = True + arena_extend_strategy: str = "kNextPowerOfTwo" + + class Config: + frozen = True # Make config immutable + + +class VoiceConfig(BaseModel): + """Voice configuration.""" + use_cache: bool = True + cache_size: int = 3 # Number of voices to cache + validate_on_load: bool = True # Whether to validate voices when loading + + class Config: + frozen = True # Make config immutable \ No newline at end of file From 21bf810f97e7bb119d6a8f6ba4d8c6c7d5a8dc82 Mon Sep 17 00:00:00 2001 From: remsky Date: Tue, 21 Jan 2025 21:44:21 -0700 Subject: [PATCH 02/32] Enhance model inference: update documentation, add model download scripts for PyTorch and ONNX, and refactor configuration handling --- api/src/builds/models.py | 11 +- api/src/core/config.py | 19 ++- api/src/core/model_config.py | 109 +++++++++++++++ api/src/core/paths.py | 96 +++++++++++-- api/src/inference/__init__.py | 8 +- api/src/inference/model_manager.py | 133 +++++++++++-------- api/src/inference/onnx_cpu.py | 41 +++--- api/src/inference/onnx_gpu.py | 54 ++++---- api/src/inference/pytorch_cpu.py | 10 +- api/src/inference/pytorch_gpu.py | 17 ++- api/src/inference/voice_manager.py | 29 +++- api/src/main.py | 22 +-- api/src/routers/development.py | 36 ++--- api/src/services/text_processing/__init__.py | 39 ++++-- api/src/services/tts_service.py | 62 ++++++--- api/src/structures/model_schemas.py | 23 +--- docker/cpu/Dockerfile | 24 +--- docker/cpu/docker-compose.yml | 39 +++--- docker/cpu/download_onnx.py | 53 ++++++++ docker/cpu/download_onnx.sh | 32 +++++ docker/gpu/Dockerfile | 29 +--- docker/gpu/docker-compose.yml | 42 +++--- docker/gpu/download_pth.py | 57 ++++++++ docker/gpu/download_pth.sh | 31 +++++ uv.lock | 67 +++++++--- 25 files changed, 774 insertions(+), 309 deletions(-) create mode 100644 api/src/core/model_config.py create mode 100755 docker/cpu/download_onnx.py create mode 100755 docker/cpu/download_onnx.sh create mode 100755 docker/gpu/download_pth.py create mode 100755 docker/gpu/download_pth.sh diff --git a/api/src/builds/models.py b/api/src/builds/models.py index 929dc47..bfd52ad 100644 --- a/api/src/builds/models.py +++ b/api/src/builds/models.py @@ -337,11 +337,13 @@ def recursive_munch(d): else: return d -def build_model(path, device): +async def build_model(path, device): + from ..core.paths import load_json, load_model_weights + config = Path(__file__).parent / 'config.json' assert config.exists(), f'Config path incorrect: config.json not found at {config}' - with open(config, 'r') as r: - args = recursive_munch(json.load(r)) + + args = recursive_munch(await load_json(config)) assert args.decoder.type == 'istftnet', f'Unknown decoder type: {args.decoder.type}' decoder = Decoder(dim_in=args.hidden_dim, style_dim=args.style_dim, dim_out=args.n_mels, resblock_kernel_sizes = args.decoder.resblock_kernel_sizes, @@ -365,7 +367,8 @@ def build_model(path, device): decoder=decoder.to(device).eval(), text_encoder=text_encoder.to(device).eval(), ) - for key, state_dict in torch.load(path, map_location='cpu', weights_only=True)['net'].items(): + weights = await load_model_weights(path, device='cpu') + for key, state_dict in weights['net'].items(): assert key in model, key try: model[key].load_state_dict(state_dict) diff --git a/api/src/core/config.py b/api/src/core/config.py index 3f44350..91aec63 100644 --- a/api/src/core/config.py +++ b/api/src/core/config.py @@ -13,10 +13,15 @@ class Settings(BaseSettings): output_dir: str = "output" output_dir_size_limit_mb: float = 500.0 # Maximum size of output directory in MB default_voice: str = "af" - model_dir: str = "/app/models" # Base directory for model files - pytorch_model_path: str = "kokoro-v0_19.pth" - onnx_model_path: str = "kokoro-v0_19.onnx" - voices_dir: str = "voices" + use_gpu: bool = False # Whether to use GPU acceleration if available + use_onnx: bool = True # Whether to use ONNX runtime + # Paths relative to api directory + model_dir: str = "src/models" # Model directory relative to api/ + voices_dir: str = "src/voices" # Voices directory relative to api/ + + # Model filenames + pytorch_model_file: str = "kokoro-v0_19.pth" + onnx_model_file: str = "kokoro-v0_19.onnx" sample_rate: int = 24000 max_chunk_size: int = 300 # Maximum size of text chunks for processing gap_trim_ms: int = 250 # Amount to trim from streaming chunk ends in milliseconds @@ -28,6 +33,12 @@ class Settings(BaseSettings): onnx_optimization_level: str = "all" # all, basic, or disabled onnx_memory_pattern: bool = True # Enable memory pattern optimization onnx_arena_extend_strategy: str = "kNextPowerOfTwo" # Memory allocation strategy + + # ONNX GPU Settings + onnx_device_id: int = 0 # GPU device ID to use + onnx_gpu_mem_limit: float = 0.7 # Limit GPU memory usage to 70% + onnx_cudnn_conv_algo_search: str = "EXHAUSTIVE" # CUDNN convolution algorithm search + onnx_do_copy_in_default_stream: bool = True # Copy in default CUDA stream class Config: env_file = ".env" diff --git a/api/src/core/model_config.py b/api/src/core/model_config.py new file mode 100644 index 0000000..dc82aee --- /dev/null +++ b/api/src/core/model_config.py @@ -0,0 +1,109 @@ +"""Model configuration schemas.""" + +from pydantic import BaseModel, Field + + +class ONNXCPUConfig(BaseModel): + """ONNX CPU runtime configuration.""" + + num_threads: int = Field(8, description="Number of threads for parallel operations") + inter_op_threads: int = Field(4, description="Number of threads for operator parallelism") + execution_mode: str = Field("parallel", description="ONNX execution mode") + optimization_level: str = Field("all", description="ONNX optimization level") + memory_pattern: bool = Field(True, description="Enable memory pattern optimization") + arena_extend_strategy: str = Field("kNextPowerOfTwo", description="Memory arena strategy") + + class Config: + frozen = True + + +class ONNXGPUConfig(ONNXCPUConfig): + """ONNX GPU-specific configuration.""" + + device_id: int = Field(0, description="CUDA device ID") + gpu_mem_limit: float = Field(0.7, description="Fraction of GPU memory to use") + cudnn_conv_algo_search: str = Field("EXHAUSTIVE", description="CuDNN convolution algorithm search") + do_copy_in_default_stream: bool = Field(True, description="Copy in default CUDA stream") + + class Config: + frozen = True + + +class PyTorchCPUConfig(BaseModel): + """PyTorch CPU backend configuration.""" + + max_batch_size: int = Field(32, description="Maximum batch size for batched inference") + stream_buffer_size: int = Field(8, description="Size of stream buffer") + memory_threshold: float = Field(0.8, description="Memory threshold for cleanup") + retry_on_oom: bool = Field(True, description="Whether to retry on OOM errors") + num_threads: int = Field(8, description="Number of threads for parallel operations") + pin_memory: bool = Field(True, description="Whether to pin memory for faster CPU-GPU transfer") + + class Config: + frozen = True + + +class PyTorchGPUConfig(BaseModel): + """PyTorch GPU backend configuration.""" + + device_id: int = Field(0, description="CUDA device ID") + use_fp16: bool = Field(True, description="Whether to use FP16 precision") + use_triton: bool = Field(True, description="Whether to use Triton for CUDA kernels") + max_batch_size: int = Field(32, description="Maximum batch size for batched inference") + stream_buffer_size: int = Field(8, description="Size of CUDA stream buffer") + memory_threshold: float = Field(0.8, description="Memory threshold for cleanup") + retry_on_oom: bool = Field(True, description="Whether to retry on OOM errors") + sync_cuda: bool = Field(True, description="Whether to synchronize CUDA operations") + + class Config: + frozen = True + """PyTorch CPU-specific configuration.""" + + num_threads: int = Field(8, description="Number of threads for parallel operations") + pin_memory: bool = Field(True, description="Whether to pin memory for faster CPU-GPU transfer") + + class Config: + frozen = True + + +class ModelConfig(BaseModel): + """Model configuration.""" + + # General settings + model_type: str = Field("pytorch", description="Model type ('pytorch' or 'onnx')") + device_type: str = Field("auto", description="Device type ('cpu', 'gpu', or 'auto')") + cache_models: bool = Field(True, description="Whether to cache loaded models") + cache_voices: bool = Field(True, description="Whether to cache voice tensors") + voice_cache_size: int = Field(10, description="Maximum number of cached voices") + + # Backend-specific configs + onnx_cpu: ONNXCPUConfig = Field(default_factory=ONNXCPUConfig) + onnx_gpu: ONNXGPUConfig = Field(default_factory=ONNXGPUConfig) + pytorch_cpu: PyTorchCPUConfig = Field(default_factory=PyTorchCPUConfig) + pytorch_gpu: PyTorchGPUConfig = Field(default_factory=PyTorchGPUConfig) + + class Config: + frozen = True + + def get_backend_config(self, backend_type: str): + """Get configuration for specific backend. + + Args: + backend_type: Backend type ('pytorch_cpu', 'pytorch_gpu', 'onnx_cpu', 'onnx_gpu') + + Returns: + Backend-specific configuration + + Raises: + ValueError: If backend type is invalid + """ + if backend_type not in { + 'pytorch_cpu', 'pytorch_gpu', 'onnx_cpu', 'onnx_gpu' + }: + raise ValueError(f"Invalid backend type: {backend_type}") + + return getattr(self, backend_type) + + +# Global instance +model_config = ModelConfig() \ No newline at end of file diff --git a/api/src/core/paths.py b/api/src/core/paths.py index d9096a7..3124ece 100644 --- a/api/src/core/paths.py +++ b/api/src/core/paths.py @@ -1,9 +1,10 @@ """Async file and path operations.""" import io +import json import os from pathlib import Path -from typing import List, Optional, AsyncIterator, Callable, Set +from typing import List, Optional, AsyncIterator, Callable, Set, Dict, Any import aiofiles import aiofiles.os @@ -87,10 +88,18 @@ async def get_model_path(model_name: str) -> str: Raises: RuntimeError: If model not found """ - search_paths = [ - settings.model_dir, - os.path.join(os.path.dirname(__file__), "..", "..", "..", "models") - ] + # Get api directory path (two levels up from core) + api_dir = os.path.dirname(os.path.dirname(os.path.dirname(__file__))) + + # Construct model directory path relative to api directory + model_dir = os.path.join(api_dir, settings.model_dir) + + # Ensure model directory exists + os.makedirs(model_dir, exist_ok=True) + + # Search in model directory + search_paths = [model_dir] + logger.debug(f"Searching for model in path: {model_dir}") return await _find_file(model_name, search_paths) @@ -107,12 +116,20 @@ async def get_voice_path(voice_name: str) -> str: Raises: RuntimeError: If voice not found """ + # Get api directory path + api_dir = os.path.dirname(os.path.dirname(os.path.dirname(__file__))) + + # Construct voice directory path relative to api directory + voice_dir = os.path.join(api_dir, settings.voices_dir) + + # Ensure voice directory exists + os.makedirs(voice_dir, exist_ok=True) + voice_file = f"{voice_name}.pt" - search_paths = [ - os.path.join(settings.model_dir, "..", settings.voices_dir), - os.path.join(os.path.dirname(__file__), "..", settings.voices_dir) - ] + # Search in voice directory + search_paths = [voice_dir] + logger.debug(f"Searching for voice in path: {voice_dir}") return await _find_file(voice_file, search_paths) @@ -123,10 +140,18 @@ async def list_voices() -> List[str]: Returns: List of voice names (without .pt extension) """ - search_paths = [ - os.path.join(settings.model_dir, "..", settings.voices_dir), - os.path.join(os.path.dirname(__file__), "..", settings.voices_dir) - ] + # Get api directory path + api_dir = os.path.dirname(os.path.dirname(os.path.dirname(__file__))) + + # Construct voice directory path relative to api directory + voice_dir = os.path.join(api_dir, settings.voices_dir) + + # Ensure voice directory exists + os.makedirs(voice_dir, exist_ok=True) + + # Search in voice directory + search_paths = [voice_dir] + logger.debug(f"Scanning for voices in path: {voice_dir}") def filter_voice_files(name: str) -> bool: return name.endswith('.pt') @@ -179,6 +204,51 @@ async def save_voice_tensor(tensor: torch.Tensor, voice_path: str) -> None: raise RuntimeError(f"Failed to save voice tensor to {voice_path}: {e}") +async def load_json(path: str) -> dict: + """Load JSON file asynchronously. + + Args: + path: Path to JSON file + + Returns: + Parsed JSON data + + Raises: + RuntimeError: If file cannot be read or parsed + """ + try: + async with aiofiles.open(path, 'r', encoding='utf-8') as f: + content = await f.read() + return json.loads(content) + except Exception as e: + raise RuntimeError(f"Failed to load JSON file {path}: {e}") + + +async def load_model_weights(path: str, device: str = "cpu") -> dict: + """Load model weights asynchronously. + + Args: + path: Path to model file (.pth or .onnx) + device: Device to load model to + + Returns: + Model weights + + Raises: + RuntimeError: If file cannot be read + """ + try: + async with aiofiles.open(path, 'rb') as f: + data = await f.read() + return torch.load( + io.BytesIO(data), + map_location=device, + weights_only=True + ) + except Exception as e: + raise RuntimeError(f"Failed to load model weights from {path}: {e}") + + async def read_file(path: str) -> str: """Read text file asynchronously. diff --git a/api/src/inference/__init__.py b/api/src/inference/__init__.py index c98b884..98dc42c 100644 --- a/api/src/inference/__init__.py +++ b/api/src/inference/__init__.py @@ -1,4 +1,4 @@ -"""Inference backends and model management.""" +"""Model inference package.""" from .base import BaseModelBackend from .model_manager import ModelManager, get_manager @@ -6,15 +6,13 @@ from .onnx_cpu import ONNXCPUBackend from .onnx_gpu import ONNXGPUBackend from .pytorch_cpu import PyTorchCPUBackend from .pytorch_gpu import PyTorchGPUBackend -from ..structures.model_schemas import ModelConfig __all__ = [ 'BaseModelBackend', 'ModelManager', 'get_manager', - 'ModelConfig', 'ONNXCPUBackend', - 'ONNXGPUBackend', + 'ONNXGPUBackend', 'PyTorchCPUBackend', - 'PyTorchGPUBackend' + 'PyTorchGPUBackend', ] \ No newline at end of file diff --git a/api/src/inference/model_manager.py b/api/src/inference/model_manager.py index c3b76c7..26621c2 100644 --- a/api/src/inference/model_manager.py +++ b/api/src/inference/model_manager.py @@ -1,21 +1,18 @@ """Model management and caching.""" -import os -from typing import Dict, List, Optional, Union +from typing import Dict, Optional import torch from loguru import logger -from pydantic import BaseModel +from ..core import paths +from ..core.config import settings +from ..core.model_config import ModelConfig, model_config from .base import BaseModelBackend -from .voice_manager import get_manager as get_voice_manager from .onnx_cpu import ONNXCPUBackend from .onnx_gpu import ONNXGPUBackend from .pytorch_cpu import PyTorchCPUBackend from .pytorch_gpu import PyTorchGPUBackend -from ..core import paths -from ..core.config import settings -from ..structures.model_schemas import ModelConfig class ModelManager: @@ -27,44 +24,63 @@ class ModelManager: Args: config: Optional configuration """ - self._config = config or ModelConfig() + self._config = config or model_config self._backends: Dict[str, BaseModelBackend] = {} self._current_backend: Optional[str] = None - self._voice_manager = get_voice_manager() self._initialize_backends() def _initialize_backends(self) -> None: - """Initialize available backends.""" - """Initialize available backends.""" - # Initialize GPU backends if available - if settings.use_gpu and torch.cuda.is_available(): - try: - # PyTorch GPU - self._backends['pytorch_gpu'] = PyTorchGPUBackend() - self._current_backend = 'pytorch_gpu' - logger.info("Initialized PyTorch GPU backend") - - # ONNX GPU - self._backends['onnx_gpu'] = ONNXGPUBackend() - logger.info("Initialized ONNX GPU backend") - except Exception as e: - logger.error(f"Failed to initialize GPU backends: {e}") - # Fallback to CPU if GPU fails + """Initialize available backends based on settings.""" + has_gpu = settings.use_gpu and torch.cuda.is_available() + + try: + if has_gpu: + if settings.use_onnx: + # ONNX GPU primary + self._backends['onnx_gpu'] = ONNXGPUBackend() + self._current_backend = 'onnx_gpu' + logger.info("Initialized ONNX GPU backend") + + # PyTorch GPU fallback + self._backends['pytorch_gpu'] = PyTorchGPUBackend() + logger.info("Initialized PyTorch GPU backend") + else: + # PyTorch GPU primary + self._backends['pytorch_gpu'] = PyTorchGPUBackend() + self._current_backend = 'pytorch_gpu' + logger.info("Initialized PyTorch GPU backend") + + # ONNX GPU fallback + self._backends['onnx_gpu'] = ONNXGPUBackend() + logger.info("Initialized ONNX GPU backend") + else: self._initialize_cpu_backends() - else: + except Exception as e: + logger.error(f"Failed to initialize GPU backends: {e}") + # Fallback to CPU if GPU fails self._initialize_cpu_backends() def _initialize_cpu_backends(self) -> None: - """Initialize CPU backends.""" + """Initialize CPU backends based on settings.""" try: - # PyTorch CPU - self._backends['pytorch_cpu'] = PyTorchCPUBackend() - self._current_backend = 'pytorch_cpu' - logger.info("Initialized PyTorch CPU backend") - - # ONNX CPU - self._backends['onnx_cpu'] = ONNXCPUBackend() - logger.info("Initialized ONNX CPU backend") + if settings.use_onnx: + # ONNX CPU primary + self._backends['onnx_cpu'] = ONNXCPUBackend() + self._current_backend = 'onnx_cpu' + logger.info("Initialized ONNX CPU backend") + + # PyTorch CPU fallback + self._backends['pytorch_cpu'] = PyTorchCPUBackend() + logger.info("Initialized PyTorch CPU backend") + else: + # PyTorch CPU primary + self._backends['pytorch_cpu'] = PyTorchCPUBackend() + self._current_backend = 'pytorch_cpu' + logger.info("Initialized PyTorch CPU backend") + + # ONNX CPU fallback + self._backends['onnx_cpu'] = ONNXCPUBackend() + logger.info("Initialized ONNX CPU backend") except Exception as e: logger.error(f"Failed to initialize CPU backends: {e}") raise RuntimeError("No backends available") @@ -98,7 +114,7 @@ class ModelManager: return self._backends[backend_type] def _determine_backend(self, model_path: str) -> str: - """Determine appropriate backend based on model file. + """Determine appropriate backend based on model file and settings. Args: model_path: Path to model file @@ -106,10 +122,10 @@ class ModelManager: Returns: Backend type to use """ - is_onnx = model_path.lower().endswith('.onnx') has_gpu = settings.use_gpu and torch.cuda.is_available() - if is_onnx: + # If ONNX is preferred or model is ONNX format + if settings.use_onnx or model_path.lower().endswith('.onnx'): return 'onnx_gpu' if has_gpu else 'onnx_cpu' else: return 'pytorch_gpu' if has_gpu else 'pytorch_cpu' @@ -117,12 +133,14 @@ class ModelManager: async def load_model( self, model_path: str, + warmup_voice: Optional[torch.Tensor] = None, backend_type: Optional[str] = None ) -> None: """Load model on specified backend. Args: model_path: Path to model file + warmup_voice: Optional voice tensor for warmup, skips warmup if None backend_type: Backend to load on, uses default if None Raises: @@ -138,35 +156,39 @@ class ModelManager: backend = self.get_backend(backend_type) - # Load model and run warmup + # Load model await backend.load_model(abs_path) logger.info(f"Loaded model on {backend_type} backend") - await self._warmup_inference(backend) + + # Run warmup if voice provided + if warmup_voice is not None: + await self._warmup_inference(backend, warmup_voice) except Exception as e: raise RuntimeError(f"Failed to load model: {e}") - async def _warmup_inference(self, backend: BaseModelBackend) -> None: - """Run warmup inference to initialize model.""" + async def _warmup_inference(self, backend: BaseModelBackend, voice: torch.Tensor) -> None: + """Run warmup inference to initialize model. + + Args: + backend: Model backend to warm up + voice: Voice tensor already loaded on correct device + """ try: # Import here to avoid circular imports - from ..text_processing import process_text - - # Load default voice for warmup - voice = await self._voice_manager.load_voice(settings.default_voice, device=backend.device) - logger.info(f"Loaded voice {settings.default_voice} for warmup") + from ..services.text_processing import process_text # Use real text text = "Testing text to speech synthesis." - logger.info(f"Running warmup inference with voice: af") # Process through pipeline - sequences = process_text(text) - if not sequences: + tokens = process_text(text) + if not tokens: raise ValueError("Text processing failed") # Run inference - backend.generate(sequences[0], voice, speed=1.0) + backend.generate(tokens, voice, speed=1.0) + logger.info("Completed warmup inference") except Exception as e: logger.warning(f"Warmup inference failed: {e}") @@ -175,7 +197,7 @@ class ModelManager: async def generate( self, tokens: list[int], - voice_name: str, + voice: torch.Tensor, speed: float = 1.0, backend_type: Optional[str] = None ) -> torch.Tensor: @@ -183,7 +205,7 @@ class ModelManager: Args: tokens: Input token IDs - voice_name: Name of voice to use + voice: Voice tensor already loaded on correct device speed: Speed multiplier backend_type: Backend to use, uses default if None @@ -198,10 +220,7 @@ class ModelManager: raise RuntimeError("Model not loaded") try: - # Load voice using voice manager - voice = await self._voice_manager.load_voice(voice_name, device=backend.device) - - # Generate audio + # Generate audio using provided voice tensor return backend.generate(tokens, voice, speed) except Exception as e: diff --git a/api/src/inference/onnx_cpu.py b/api/src/inference/onnx_cpu.py index 600157f..34d68ac 100644 --- a/api/src/inference/onnx_cpu.py +++ b/api/src/inference/onnx_cpu.py @@ -13,8 +13,7 @@ from onnxruntime import ( ) from ..core import paths -from ..core.config import settings -from ..structures.model_schemas import ONNXConfig +from ..core.model_config import model_config from .base import BaseModelBackend @@ -26,14 +25,11 @@ class ONNXCPUBackend(BaseModelBackend): super().__init__() self._device = "cpu" self._session: Optional[InferenceSession] = None - self._config = ONNXConfig( - optimization_level=settings.onnx_optimization_level, - num_threads=settings.onnx_num_threads, - inter_op_threads=settings.onnx_inter_op_threads, - execution_mode=settings.onnx_execution_mode, - memory_pattern=settings.onnx_memory_pattern, - arena_extend_strategy=settings.onnx_arena_extend_strategy - ) + + @property + def is_loaded(self) -> bool: + """Check if model is loaded.""" + return self._session is not None async def load_model(self, path: str) -> None: """Load ONNX model. @@ -115,28 +111,29 @@ class ONNXCPUBackend(BaseModelBackend): Configured session options """ options = SessionOptions() + config = model_config.onnx_cpu # Set optimization level - if self._config.optimization_level == "all": + if config.optimization_level == "all": options.graph_optimization_level = GraphOptimizationLevel.ORT_ENABLE_ALL - elif self._config.optimization_level == "basic": + elif config.optimization_level == "basic": options.graph_optimization_level = GraphOptimizationLevel.ORT_ENABLE_BASIC else: options.graph_optimization_level = GraphOptimizationLevel.ORT_DISABLE_ALL # Configure threading - options.intra_op_num_threads = self._config.num_threads - options.inter_op_num_threads = self._config.inter_op_threads + options.intra_op_num_threads = config.num_threads + options.inter_op_num_threads = config.inter_op_threads # Set execution mode options.execution_mode = ( ExecutionMode.ORT_PARALLEL - if self._config.execution_mode == "parallel" + if config.execution_mode == "parallel" else ExecutionMode.ORT_SEQUENTIAL ) # Configure memory optimization - options.enable_mem_pattern = self._config.memory_pattern + options.enable_mem_pattern = config.memory_pattern return options @@ -148,7 +145,15 @@ class ONNXCPUBackend(BaseModelBackend): """ return { "CPUExecutionProvider": { - "arena_extend_strategy": self._config.arena_extend_strategy, + "arena_extend_strategy": model_config.onnx_cpu.arena_extend_strategy, "cpu_memory_arena_cfg": "cpu:0" } - } \ No newline at end of file + } + + def unload(self) -> None: + """Unload model and free resources.""" + if self._session is not None: + del self._session + self._session = None + if torch.cuda.is_available(): + torch.cuda.empty_cache() \ No newline at end of file diff --git a/api/src/inference/onnx_gpu.py b/api/src/inference/onnx_gpu.py index d003a86..2df32e2 100644 --- a/api/src/inference/onnx_gpu.py +++ b/api/src/inference/onnx_gpu.py @@ -13,8 +13,7 @@ from onnxruntime import ( ) from ..core import paths -from ..core.config import settings -from ..structures.model_schemas import ONNXGPUConfig +from ..core.model_config import model_config from .base import BaseModelBackend @@ -28,18 +27,11 @@ class ONNXGPUBackend(BaseModelBackend): raise RuntimeError("CUDA not available") self._device = "cuda" self._session: Optional[InferenceSession] = None - self._config = ONNXGPUConfig( - optimization_level=settings.onnx_optimization_level, - num_threads=settings.onnx_num_threads, - inter_op_threads=settings.onnx_inter_op_threads, - execution_mode=settings.onnx_execution_mode, - memory_pattern=settings.onnx_memory_pattern, - arena_extend_strategy=settings.onnx_arena_extend_strategy, - device_id=0, - gpu_mem_limit=0.7, - cudnn_conv_algo_search="EXHAUSTIVE", - do_copy_in_default_stream=True - ) + + @property + def is_loaded(self) -> bool: + """Check if model is loaded.""" + return self._session is not None async def load_model(self, path: str) -> None: """Load ONNX model. @@ -121,28 +113,29 @@ class ONNXGPUBackend(BaseModelBackend): Configured session options """ options = SessionOptions() + config = model_config.onnx_gpu # Set optimization level - if self._config.optimization_level == "all": + if config.optimization_level == "all": options.graph_optimization_level = GraphOptimizationLevel.ORT_ENABLE_ALL - elif self._config.optimization_level == "basic": + elif config.optimization_level == "basic": options.graph_optimization_level = GraphOptimizationLevel.ORT_ENABLE_BASIC else: options.graph_optimization_level = GraphOptimizationLevel.ORT_DISABLE_ALL # Configure threading - options.intra_op_num_threads = self._config.num_threads - options.inter_op_num_threads = self._config.inter_op_threads + options.intra_op_num_threads = config.num_threads + options.inter_op_num_threads = config.inter_op_threads # Set execution mode options.execution_mode = ( ExecutionMode.ORT_PARALLEL - if self._config.execution_mode == "parallel" + if config.execution_mode == "parallel" else ExecutionMode.ORT_SEQUENTIAL ) # Configure memory optimization - options.enable_mem_pattern = self._config.memory_pattern + options.enable_mem_pattern = config.memory_pattern return options @@ -152,12 +145,21 @@ class ONNXGPUBackend(BaseModelBackend): Returns: Provider configuration """ + config = model_config.onnx_gpu return { "CUDAExecutionProvider": { - "device_id": self._config.device_id, - "arena_extend_strategy": self._config.arena_extend_strategy, - "gpu_mem_limit": int(self._config.gpu_mem_limit * torch.cuda.get_device_properties(0).total_memory), - "cudnn_conv_algo_search": self._config.cudnn_conv_algo_search, - "do_copy_in_default_stream": self._config.do_copy_in_default_stream + "device_id": config.device_id, + "arena_extend_strategy": config.arena_extend_strategy, + "gpu_mem_limit": int(config.gpu_mem_limit * torch.cuda.get_device_properties(0).total_memory), + "cudnn_conv_algo_search": config.cudnn_conv_algo_search, + "do_copy_in_default_stream": config.do_copy_in_default_stream } - } \ No newline at end of file + } + + def unload(self) -> None: + """Unload model and free resources.""" + if self._session is not None: + del self._session + self._session = None + if torch.cuda.is_available(): + torch.cuda.empty_cache() \ No newline at end of file diff --git a/api/src/inference/pytorch_cpu.py b/api/src/inference/pytorch_cpu.py index e3fe5fa..b1d4328 100644 --- a/api/src/inference/pytorch_cpu.py +++ b/api/src/inference/pytorch_cpu.py @@ -9,7 +9,7 @@ from loguru import logger from ..builds.models import build_model from ..core import paths -from ..structures.model_schemas import PyTorchCPUConfig +from ..core.model_config import model_config from .base import BaseModelBackend @@ -118,12 +118,12 @@ class PyTorchCPUBackend(BaseModelBackend): super().__init__() self._device = "cpu" self._model: Optional[torch.nn.Module] = None - self._config = PyTorchCPUConfig() # Configure PyTorch CPU settings - if self._config.num_threads > 0: - torch.set_num_threads(self._config.num_threads) - if self._config.pin_memory: + config = model_config.pytorch_cpu + if config.num_threads > 0: + torch.set_num_threads(config.num_threads) + if config.pin_memory: torch.set_default_tensor_type(torch.FloatTensor) async def load_model(self, path: str) -> None: diff --git a/api/src/inference/pytorch_gpu.py b/api/src/inference/pytorch_gpu.py index 3b41f9f..2995818 100644 --- a/api/src/inference/pytorch_gpu.py +++ b/api/src/inference/pytorch_gpu.py @@ -9,7 +9,7 @@ from loguru import logger from ..builds.models import build_model from ..core import paths -from ..structures.model_schemas import PyTorchConfig +from ..core.model_config import model_config from .base import BaseModelBackend @@ -96,7 +96,12 @@ class PyTorchGPUBackend(BaseModelBackend): raise RuntimeError("CUDA not available") self._device = "cuda" self._model: Optional[torch.nn.Module] = None - self._config = PyTorchConfig() + + # Configure GPU settings + config = model_config.pytorch_gpu + if config.sync_cuda: + torch.cuda.synchronize() + torch.cuda.set_device(config.device_id) async def load_model(self, path: str) -> None: """Load PyTorch model. @@ -154,13 +159,19 @@ class PyTorchGPUBackend(BaseModelBackend): except Exception as e: logger.error(f"Generation failed: {e}") + if model_config.pytorch_gpu.retry_on_oom and "out of memory" in str(e).lower(): + self._clear_memory() + return self.generate(tokens, voice, speed) # Retry once raise + finally: + if model_config.pytorch_gpu.sync_cuda: + torch.cuda.synchronize() def _check_memory(self) -> bool: """Check if memory usage is above threshold.""" if torch.cuda.is_available(): memory_gb = torch.cuda.memory_allocated() / 1e9 - return memory_gb > self._config.memory_threshold + return memory_gb > model_config.pytorch_gpu.memory_threshold return False def _clear_memory(self) -> None: diff --git a/api/src/inference/voice_manager.py b/api/src/inference/voice_manager.py index 295040c..15932c7 100644 --- a/api/src/inference/voice_manager.py +++ b/api/src/inference/voice_manager.py @@ -33,7 +33,15 @@ class VoiceManager: Returns: Path to voice file if exists, None otherwise """ - voice_path = os.path.join(settings.voices_dir, f"{voice_name}.pt") + # Get api directory path (two levels up from inference) + api_dir = os.path.dirname(os.path.dirname(os.path.dirname(__file__))) + + # Construct voice path relative to api directory + voice_path = os.path.join(api_dir, settings.voices_dir, f"{voice_name}.pt") + + # Ensure voices directory exists + os.makedirs(os.path.dirname(voice_path), exist_ok=True) + return voice_path if os.path.exists(voice_path) else None async def load_voice(self, voice_name: str, device: str = "cpu") -> torch.Tensor: @@ -112,8 +120,15 @@ class VoiceManager: combined_name = "_".join(voices) combined_tensor = torch.mean(torch.stack(voice_tensors), dim=0) + # Get api directory path + api_dir = os.path.dirname(os.path.dirname(os.path.dirname(__file__))) + voices_dir = os.path.join(api_dir, settings.voices_dir) + + # Ensure voices directory exists + os.makedirs(voices_dir, exist_ok=True) + # Save combined voice - combined_path = os.path.join(settings.voices_dir, f"{combined_name}.pt") + combined_path = os.path.join(voices_dir, f"{combined_name}.pt") try: torch.save(combined_tensor, combined_path) except Exception as e: @@ -132,7 +147,15 @@ class VoiceManager: """ voices = [] try: - for entry in os.listdir(settings.voices_dir): + # Get api directory path + api_dir = os.path.dirname(os.path.dirname(os.path.dirname(__file__))) + voices_dir = os.path.join(api_dir, settings.voices_dir) + + # Ensure voices directory exists + os.makedirs(voices_dir, exist_ok=True) + + # List voice files + for entry in os.listdir(voices_dir): if entry.endswith(".pt"): voices.append(entry[:-3]) # Remove .pt extension except Exception as e: diff --git a/api/src/main.py b/api/src/main.py index 4effda2..ec1b6c8 100644 --- a/api/src/main.py +++ b/api/src/main.py @@ -13,7 +13,6 @@ from loguru import logger from .core.config import settings from .routers.development import router as dev_router from .routers.openai_compatible import router as openai_router -from .services.tts_model import TTSModel from .services.tts_service import TTSService @@ -44,25 +43,32 @@ async def lifespan(app: FastAPI): """Lifespan context manager for model initialization""" logger.info("Loading TTS model and voice packs...") - # Initialize the main model with warm-up - voicepack_count = await TTSModel.setup() - # boundary = "█████╗"*9 + # Initialize service + service = TTSService() + await service.ensure_initialized() + + # Get available voices + voices = await service.list_voices() + voicepack_count = len(voices) + + # Get device info from model manager + device = "GPU" if settings.use_gpu else "CPU" + model = "ONNX" if settings.use_onnx else "PyTorch" boundary = "░" * 2*12 startup_msg = f""" {boundary} ╔═╗┌─┐┌─┐┌┬┐ - ╠╣ ├─┤└─┐ │ - ╚ ┴ ┴└─┘ ┴ + ╠╣ ├─┤└─┐ │ + ╚ ┴ ┴└─┘ ┴ ╦╔═┌─┐┬┌─┌─┐ ╠╩╗│ │├┴┐│ │ ╩ ╩└─┘┴ ┴└─┘ {boundary} """ - # TODO: Improve CPU warmup, threads, memory, etc - startup_msg += f"\nModel warmed up on {TTSModel.get_device()}" + startup_msg += f"\nModel warmed up on {device}: {model}" startup_msg += f"\n{voicepack_count} voice packs loaded\n" startup_msg += f"\n{boundary}\n" logger.info(startup_msg) diff --git a/api/src/routers/development.py b/api/src/routers/development.py index bc43eb0..4713440 100644 --- a/api/src/routers/development.py +++ b/api/src/routers/development.py @@ -6,7 +6,6 @@ from loguru import logger from ..services.audio import AudioService from ..services.text_processing import phonemize, tokenize -from ..services.tts_model import TTSModel from ..services.tts_service import TTSService from ..structures.text_schemas import ( GenerateFromPhonemesRequest, @@ -82,27 +81,34 @@ async def generate_from_phonemes( detail={"error": "Invalid request", "message": "Phonemes cannot be empty"}, ) - # Validate voice exists - voice_path = tts_service._get_voice_path(request.voice) - if not voice_path: - raise HTTPException( - status_code=400, - detail={ - "error": "Invalid request", - "message": f"Voice not found: {request.voice}", - }, - ) - try: - # Load voice - voicepack = tts_service._load_voice(voice_path) + # Ensure service is initialized + await tts_service.ensure_initialized() + + # Validate voice exists + available_voices = await tts_service.list_voices() + if request.voice not in available_voices: + raise HTTPException( + status_code=400, + detail={ + "error": "Invalid request", + "message": f"Voice not found: {request.voice}", + }, + ) # Convert phonemes to tokens tokens = tokenize(request.phonemes) tokens = [0] + tokens + [0] # Add start/end tokens # Generate audio directly from tokens - audio = TTSModel.generate_from_tokens(tokens, voicepack, request.speed) + audio = await tts_service.model_manager.generate( + tokens, + request.voice, + speed=request.speed + ) + + if audio is None: + raise ValueError("Failed to generate audio") # Convert to WAV bytes wav_bytes = AudioService.convert_audio( diff --git a/api/src/services/text_processing/__init__.py b/api/src/services/text_processing/__init__.py index 8427232..c9b3eb4 100644 --- a/api/src/services/text_processing/__init__.py +++ b/api/src/services/text_processing/__init__.py @@ -1,13 +1,28 @@ -from .normalizer import normalize_text -from .phonemizer import EspeakBackend, PhonemizerBackend, phonemize -from .vocabulary import VOCAB, decode_tokens, tokenize +"""Text processing pipeline.""" -__all__ = [ - "normalize_text", - "phonemize", - "tokenize", - "decode_tokens", - "VOCAB", - "PhonemizerBackend", - "EspeakBackend", -] +from .chunker import split_text +from .normalizer import normalize_text +from .phonemizer import phonemize +from .vocabulary import tokenize + + +def process_text(text: str, language: str = "a") -> list[int]: + """Process text through the full pipeline. + + Args: + text: Input text + language: Language code ('a' for US English, 'b' for British English) + + Returns: + List of token IDs + + Note: + The pipeline: + 1. Converts text to phonemes using phonemizer + 2. Converts phonemes to token IDs using vocabulary + """ + # Convert text to phonemes + phonemes = phonemize(text, language=language) + + # Convert phonemes to token IDs + return tokenize(phonemes) diff --git a/api/src/services/tts_service.py b/api/src/services/tts_service.py index 20e9b69..9c51b19 100644 --- a/api/src/services/tts_service.py +++ b/api/src/services/tts_service.py @@ -5,17 +5,16 @@ import os import time from typing import List, Tuple -import torch - import numpy as np import scipy.io.wavfile as wavfile +import torch from loguru import logger from ..core.config import settings from ..inference.model_manager import get_manager as get_model_manager from ..inference.voice_manager import get_manager as get_voice_manager from .audio import AudioNormalizer, AudioService -from .text_processing import chunker, normalize_text +from .text_processing import chunker, normalize_text, process_text class TTSService: @@ -41,16 +40,33 @@ class TTSService: raise self._initialization_error try: - # Determine model path based on hardware + # Get api directory path (one level up from src) + api_dir = os.path.dirname(os.path.dirname(os.path.dirname(__file__))) + + # Determine model file and backend based on hardware if settings.use_gpu and torch.cuda.is_available(): - model_path = os.path.join(settings.model_dir, settings.pytorch_model_path) + model_file = settings.pytorch_model_file backend_type = 'pytorch_gpu' else: - model_path = os.path.join(settings.model_dir, settings.onnx_model_path) + model_file = settings.onnx_model_file backend_type = 'onnx_cpu' - # Initialize model - await self.model_manager.load_model(model_path, backend_type) + # Construct model path relative to api directory + model_path = os.path.join(api_dir, settings.model_dir, model_file) + + # Ensure model directory exists + os.makedirs(os.path.dirname(model_path), exist_ok=True) + + if not os.path.exists(model_path): + raise RuntimeError(f"Model file not found: {model_path}") + + # Load default voice for warmup + backend = self.model_manager.get_backend(backend_type) + warmup_voice = await self.voice_manager.load_voice(settings.default_voice, device=backend.device) + logger.info(f"Loaded voice {settings.default_voice} for warmup") + + # Initialize model with warmup voice + await self.model_manager.load_model(model_path, warmup_voice, backend_type) logger.info(f"Initialized model on {backend_type} backend") self._initialized = True @@ -86,16 +102,19 @@ class TTSService: audio_chunks = [] for chunk in chunker.split_text(text): try: - # Process text - - sequences = process_text(chunk) - if not sequences: + # Convert chunk to token IDs + tokens = process_text(chunk) + if not tokens: continue + # Get backend and load voice + backend = self.model_manager.get_backend() + voice_tensor = await self.voice_manager.load_voice(voice, device=backend.device) + # Generate audio chunk_audio = await self.model_manager.generate( - sequences[0], - voice, + tokens, + voice_tensor, speed=speed ) if chunk_audio is not None: @@ -154,14 +173,17 @@ class TTSService: while current_chunk is not None: next_chunk = next(chunk_gen, None) try: - # Process text - from ..text_processing import process_text - sequences = process_text(current_chunk) - if sequences: + # Convert chunk to token IDs + tokens = process_text(current_chunk) + if tokens: + # Get backend and load voice + backend = self.model_manager.get_backend() + voice_tensor = await self.voice_manager.load_voice(voice, device=backend.device) + # Generate audio chunk_audio = await self.model_manager.generate( - sequences[0], - voice, + tokens, + voice_tensor, speed=speed ) diff --git a/api/src/structures/model_schemas.py b/api/src/structures/model_schemas.py index 51ef910..b6ed61d 100644 --- a/api/src/structures/model_schemas.py +++ b/api/src/structures/model_schemas.py @@ -1,26 +1,13 @@ -"""Model and voice configuration schemas.""" +"""Voice configuration schemas.""" -from pydantic import BaseModel - - -class ModelConfig(BaseModel): - """Model configuration.""" - optimization_level: str = "all" # all, basic, none - num_threads: int = 4 - inter_op_threads: int = 4 - execution_mode: str = "parallel" # parallel, sequential - memory_pattern: bool = True - arena_extend_strategy: str = "kNextPowerOfTwo" - - class Config: - frozen = True # Make config immutable +from pydantic import BaseModel, Field class VoiceConfig(BaseModel): """Voice configuration.""" - use_cache: bool = True - cache_size: int = 3 # Number of voices to cache - validate_on_load: bool = True # Whether to validate voices when loading + use_cache: bool = Field(True, description="Whether to cache loaded voices") + cache_size: int = Field(3, description="Number of voices to cache") + validate_on_load: bool = Field(True, description="Whether to validate voices when loading") class Config: frozen = True # Make config immutable \ No newline at end of file diff --git a/docker/cpu/Dockerfile b/docker/cpu/Dockerfile index e4bd32c..0613022 100644 --- a/docker/cpu/Dockerfile +++ b/docker/cpu/Dockerfile @@ -17,29 +17,10 @@ COPY --from=ghcr.io/astral-sh/uv:latest /uv /uvx /bin/ RUN useradd -m -u 1000 appuser # Create directories and set ownership -RUN mkdir -p /app/models && \ - mkdir -p /app/api/src/voices && \ +RUN mkdir -p /app/api/src/voices && \ chown -R appuser:appuser /app USER appuser - -# Download and extract models -WORKDIR /app/models -RUN set -x && \ - curl -L -o model.tar.gz https://github.com/remsky/Kokoro-FastAPI/releases/download/v0.0.1/kokoro-82m-onnx.tar.gz && \ - echo "Downloaded model.tar.gz:" && ls -lh model.tar.gz && \ - tar xzf model.tar.gz && \ - echo "Contents after extraction:" && ls -lhR && \ - rm model.tar.gz && \ - echo "Final contents:" && ls -lhR - -# Download and extract voice models -WORKDIR /app/api/src/voices -RUN curl -L -o voices.tar.gz https://github.com/remsky/Kokoro-FastAPI/releases/download/v0.0.1/voice-models.tar.gz && \ - tar xzf voices.tar.gz && \ - rm voices.tar.gz - -# Switch back to app directory WORKDIR /app # Copy dependency files @@ -59,9 +40,10 @@ RUN --mount=type=cache,target=/root/.cache/uv \ # Set environment variables ENV PYTHONUNBUFFERED=1 -ENV PYTHONPATH=/app:/app/models +ENV PYTHONPATH=/app ENV PATH="/app/.venv/bin:$PATH" ENV UV_LINK_MODE=copy +ENV USE_GPU=false # Run FastAPI server CMD ["uv", "run", "python", "-m", "uvicorn", "api.src.main:app", "--host", "0.0.0.0", "--port", "8880", "--log-level", "debug"] diff --git a/docker/cpu/docker-compose.yml b/docker/cpu/docker-compose.yml index 0f9003a..2138594 100644 --- a/docker/cpu/docker-compose.yml +++ b/docker/cpu/docker-compose.yml @@ -6,12 +6,11 @@ services: context: ../.. dockerfile: docker/cpu/Dockerfile volumes: - - ../../api/src:/app/api/src - - ../../api/src/voices:/app/api/src/voices + - ../../api:/app/api ports: - "8880:8880" environment: - - PYTHONPATH=/app:/app/models + - PYTHONPATH=/app:/app/api # ONNX Optimization Settings for vectorized operations - ONNX_NUM_THREADS=8 # Maximize core usage for vectorized ops - ONNX_INTER_OP_THREADS=4 # Higher inter-op for parallel matrix operations @@ -20,20 +19,20 @@ services: - ONNX_MEMORY_PATTERN=true - ONNX_ARENA_EXTEND_STRATEGY=kNextPowerOfTwo - # Gradio UI service [Comment out everything below if you don't need it] - gradio-ui: - image: ghcr.io/remsky/kokoro-fastapi-ui:v0.1.0 - # Uncomment below (and comment out above) to build from source instead of using the released image - build: - context: ../../ui - ports: - - "7860:7860" - volumes: - - ../../ui/data:/app/ui/data - - ../../ui/app.py:/app/app.py # Mount app.py for hot reload - environment: - - GRADIO_WATCH=True # Enable hot reloading - - PYTHONUNBUFFERED=1 # Ensure Python output is not buffered - - DISABLE_LOCAL_SAVING=false # Set to 'true' to disable local saving and hide file view - - API_HOST=kokoro-tts # Set TTS service URL - - API_PORT=8880 # Set TTS service PORT + # # Gradio UI service [Comment out everything below if you don't need it] + # gradio-ui: + # image: ghcr.io/remsky/kokoro-fastapi-ui:v0.1.0 + # # Uncomment below (and comment out above) to build from source instead of using the released image + # build: + # context: ../../ui + # ports: + # - "7860:7860" + # volumes: + # - ../../ui/data:/app/ui/data + # - ../../ui/app.py:/app/app.py # Mount app.py for hot reload + # environment: + # - GRADIO_WATCH=True # Enable hot reloading + # - PYTHONUNBUFFERED=1 # Ensure Python output is not buffered + # - DISABLE_LOCAL_SAVING=false # Set to 'true' to disable local saving and hide file view + # - API_HOST=kokoro-tts # Set TTS service URL + # - API_PORT=8880 # Set TTS service PORT diff --git a/docker/cpu/download_onnx.py b/docker/cpu/download_onnx.py new file mode 100755 index 0000000..a04718c --- /dev/null +++ b/docker/cpu/download_onnx.py @@ -0,0 +1,53 @@ +#!/usr/bin/env python3 +import os +import sys +import requests +from pathlib import Path +from typing import List + +def download_file(url: str, output_dir: Path) -> None: + """Download a file from URL to the specified directory.""" + filename = os.path.basename(url) + output_path = output_dir / filename + + print(f"Downloading {filename}...") + response = requests.get(url, stream=True) + response.raise_for_status() + + with open(output_path, 'wb') as f: + for chunk in response.iter_content(chunk_size=8192): + f.write(chunk) + +def find_project_root() -> Path: + """Find project root by looking for api directory.""" + max_steps = 5 + current = Path(__file__).resolve() + for _ in range(max_steps): + if (current / 'api').is_dir(): + return current + current = current.parent + raise RuntimeError("Could not find project root (no api directory found)") + +def main(custom_models: List[str] = None): + # Always use top-level models directory relative to project root + project_root = find_project_root() + models_dir = project_root / 'api' / 'src' / 'models' + models_dir.mkdir(exist_ok=True, parents=True) + + # Default ONNX model if no arguments provided + default_models = [ + "https://github.com/remsky/Kokoro-FastAPI/releases/download/v0.1.0/kokoro-v0_19.onnx", + "https://github.com/remsky/Kokoro-FastAPI/releases/download/v0.1.0/kokoro-v0_19_fp16.onnx" + ] + + # Use provided models or default + models_to_download = custom_models if custom_models else default_models + + for model_url in models_to_download: + try: + download_file(model_url, models_dir) + except Exception as e: + print(f"Error downloading {model_url}: {e}") + +if __name__ == "__main__": + main(sys.argv[1:] if len(sys.argv) > 1 else None) \ No newline at end of file diff --git a/docker/cpu/download_onnx.sh b/docker/cpu/download_onnx.sh new file mode 100755 index 0000000..c0a250b --- /dev/null +++ b/docker/cpu/download_onnx.sh @@ -0,0 +1,32 @@ +#!/bin/bash + +# Ensure models directory exists +mkdir -p api/src/models + +# Function to download a file +download_file() { + local url="$1" + local filename=$(basename "$url") + echo "Downloading $filename..." + curl -L "$url" -o "api/src/models/$filename" +} + +# Default ONNX model if no arguments provided +DEFAULT_MODELS=( + "https://github.com/remsky/Kokoro-FastAPI/releases/download/v0.1.0/kokoro-v0_19.onnx" + "https://github.com/remsky/Kokoro-FastAPI/releases/download/v0.1.0/kokoro-v0_19_fp16.onnx" +) + +# Use provided models or default +if [ $# -gt 0 ]; then + MODELS=("$@") +else + MODELS=("${DEFAULT_MODELS[@]}") +fi + +# Download all models +for model in "${MODELS[@]}"; do + download_file "$model" +done + +echo "ONNX model download complete!" \ No newline at end of file diff --git a/docker/gpu/Dockerfile b/docker/gpu/Dockerfile index ed4676d..e6bf9ff 100644 --- a/docker/gpu/Dockerfile +++ b/docker/gpu/Dockerfile @@ -1,9 +1,8 @@ -FROM nvidia/cuda:12.1.0-base-ubuntu22.04 - -# Install Python and other dependencies +FROM pytorch/pytorch:2.1.0-cuda11.8-cudnn8-runtime +# Set non-interactive frontend +ENV DEBIAN_FRONTEND=noninteractive +# Install dependencies RUN apt-get update && apt-get install -y --no-install-recommends \ - python3.10 \ - python3.10-venv \ espeak-ng \ git \ libsndfile1 \ @@ -19,25 +18,10 @@ COPY --from=ghcr.io/astral-sh/uv:latest /uv /uvx /bin/ RUN useradd -m -u 1000 appuser # Create directories and set ownership -RUN mkdir -p /app/models && \ - mkdir -p /app/api/src/voices && \ +RUN mkdir -p /app/api/src/voices && \ chown -R appuser:appuser /app USER appuser - -# Download and extract models -WORKDIR /app/models -RUN curl -L -o model.tar.gz https://github.com/remsky/Kokoro-FastAPI/releases/download/v0.0.1/kokoro-82m-pytorch.tar.gz && \ - tar xzf model.tar.gz && \ - rm model.tar.gz - -# Download and extract voice models -WORKDIR /app/api/src/voices -RUN curl -L -o voices.tar.gz https://github.com/remsky/Kokoro-FastAPI/releases/download/v0.0.1/voice-models.tar.gz && \ - tar xzf voices.tar.gz && \ - rm voices.tar.gz - -# Switch back to app directory WORKDIR /app # Copy dependency files @@ -57,9 +41,10 @@ RUN --mount=type=cache,target=/root/.cache/uv \ # Set environment variables ENV PYTHONUNBUFFERED=1 -ENV PYTHONPATH=/app:/app/models +ENV PYTHONPATH=/app ENV PATH="/app/.venv/bin:$PATH" ENV UV_LINK_MODE=copy +ENV USE_GPU=true # Run FastAPI server CMD ["uv", "run", "python", "-m", "uvicorn", "api.src.main:app", "--host", "0.0.0.0", "--port", "8880", "--log-level", "debug"] diff --git a/docker/gpu/docker-compose.yml b/docker/gpu/docker-compose.yml index 9433dc6..458eff9 100644 --- a/docker/gpu/docker-compose.yml +++ b/docker/gpu/docker-compose.yml @@ -6,12 +6,14 @@ services: context: ../.. dockerfile: docker/gpu/Dockerfile volumes: - - ../../api/src:/app/api/src # Mount src for development - - ../../api/src/voices:/app/api/src/voices # Mount voices for persistence + - ../../api:/app/api ports: - "8880:8880" environment: - - PYTHONPATH=/app:/app/models + - PYTHONPATH=/app + - USE_GPU=true + - USE_ONNX=false + - PYTHONUNBUFFERED=1 deploy: resources: reservations: @@ -20,20 +22,20 @@ services: count: 1 capabilities: [gpu] - # Gradio UI service - gradio-ui: - image: ghcr.io/remsky/kokoro-fastapi-ui:v0.1.0 - # Uncomment below to build from source instead of using the released image - # build: - # context: ../../ui - ports: - - "7860:7860" - volumes: - - ../../ui/data:/app/ui/data - - ../../ui/app.py:/app/app.py # Mount app.py for hot reload - environment: - - GRADIO_WATCH=1 # Enable hot reloading - - PYTHONUNBUFFERED=1 # Ensure Python output is not buffered - - DISABLE_LOCAL_SAVING=false # Set to 'true' to disable local saving and hide file view - - API_HOST=kokoro-tts # Set TTS service URL - - API_PORT=8880 # Set TTS service PORT + # # Gradio UI service + # gradio-ui: + # image: ghcr.io/remsky/kokoro-fastapi-ui:v0.1.0 + # # Uncomment below to build from source instead of using the released image + # # build: + # # context: ../../ui + # ports: + # - "7860:7860" + # volumes: + # - ../../ui/data:/app/ui/data + # - ../../ui/app.py:/app/app.py # Mount app.py for hot reload + # environment: + # - GRADIO_WATCH=1 # Enable hot reloading + # - PYTHONUNBUFFERED=1 # Ensure Python output is not buffered + # - DISABLE_LOCAL_SAVING=false # Set to 'true' to disable local saving and hide file view + # - API_HOST=kokoro-tts # Set TTS service URL + # - API_PORT=8880 # Set TTS service PORT diff --git a/docker/gpu/download_pth.py b/docker/gpu/download_pth.py new file mode 100755 index 0000000..f58f29d --- /dev/null +++ b/docker/gpu/download_pth.py @@ -0,0 +1,57 @@ +#!/usr/bin/env python3 +import os +import sys +import requests +from pathlib import Path +from typing import List + +def download_file(url: str, output_dir: Path) -> None: + """Download a file from URL to the specified directory.""" + filename = os.path.basename(url) + if not filename.endswith('.pth'): + print(f"Warning: {filename} is not a .pth file") + return + + output_path = output_dir / filename + + print(f"Downloading {filename}...") + response = requests.get(url, stream=True) + response.raise_for_status() + + with open(output_path, 'wb') as f: + for chunk in response.iter_content(chunk_size=8192): + f.write(chunk) + +def find_project_root() -> Path: + """Find project root by looking for api directory.""" + max_steps = 5 + current = Path(__file__).resolve() + for _ in range(max_steps): + if (current / 'api').is_dir(): + return current + current = current.parent + raise RuntimeError("Could not find project root (no api directory found)") + +def main(custom_models: List[str] = None): + # Find project root and ensure models directory exists + project_root = find_project_root() + models_dir = project_root / 'api' / 'src' / 'models' + print(f"Downloading models to {models_dir}") + models_dir.mkdir(exist_ok=True) + + # Default PTH model if no arguments provided + default_models = [ + "https://github.com/remsky/Kokoro-FastAPI/releases/download/v0.1.0/kokoro-v0_19.pth" + ] + + # Use provided models or default + models_to_download = custom_models if custom_models else default_models + + for model_url in models_to_download: + try: + download_file(model_url, models_dir) + except Exception as e: + print(f"Error downloading {model_url}: {e}") + +if __name__ == "__main__": + main(sys.argv[1:] if len(sys.argv) > 1 else None) \ No newline at end of file diff --git a/docker/gpu/download_pth.sh b/docker/gpu/download_pth.sh new file mode 100755 index 0000000..c8bda83 --- /dev/null +++ b/docker/gpu/download_pth.sh @@ -0,0 +1,31 @@ +#!/bin/bash + +# Ensure models directory exists +mkdir -p api/src/models + +# Function to download a file +download_file() { + local url="$1" + local filename=$(basename "$url") + echo "Downloading $filename..." + curl -L "$url" -o "api/src/models/$filename" +} + +# Default PTH model if no arguments provided +DEFAULT_MODELS=( + "https://github.com/remsky/Kokoro-FastAPI/releases/download/v0.1.0/kokoro-v0_19.pth" +) + +# Use provided models or default +if [ $# -gt 0 ]; then + MODELS=("$@") +else + MODELS=("${DEFAULT_MODELS[@]}") +fi + +# Download all models +for model in "${MODELS[@]}"; do + download_file "$model" +done + +echo "PyTorch model download complete!" \ No newline at end of file diff --git a/uv.lock b/uv.lock index 52e4de2..ab36884 100644 --- a/uv.lock +++ b/uv.lock @@ -2,9 +2,17 @@ version = 1 requires-python = ">=3.10" resolution-markers = [ "python_full_version == '3.11.*'", + "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux')", + "(python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.11.*' and sys_platform == 'darwin')", "python_full_version < '3.11'", + "(python_full_version < '3.11' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux')", + "(python_full_version < '3.11' and platform_machine == 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform == 'darwin')", "python_full_version >= '3.13'", + "(python_full_version >= '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.13' and sys_platform != 'darwin' and sys_platform != 'linux')", + "(python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.13' and sys_platform == 'darwin')", "python_full_version == '3.12.*'", + "(python_full_version == '3.12.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'linux')", + "(python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.12.*' and sys_platform == 'darwin')", ] conflicts = [[ { package = "kokoro-fastapi", extra = "cpu" }, @@ -798,6 +806,7 @@ dependencies = [ { name = "phonemizer" }, { name = "pydantic" }, { name = "pydantic-settings" }, + { name = "pydub" }, { name = "python-dotenv" }, { name = "regex" }, { name = "requests" }, @@ -812,7 +821,8 @@ dependencies = [ [package.optional-dependencies] cpu = [ - { name = "torch", version = "2.5.1+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" } }, + { name = "torch", version = "2.5.1", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or sys_platform == 'darwin'" }, + { name = "torch", version = "2.5.1+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" }, ] gpu = [ { name = "torch", version = "2.5.1+cu121", source = { registry = "https://download.pytorch.org/whl/cu121" } }, @@ -824,7 +834,6 @@ test = [ { name = "pytest" }, { name = "pytest-asyncio" }, { name = "pytest-cov" }, - { name = "ruff" }, ] [package.metadata] @@ -845,18 +854,18 @@ requires-dist = [ { name = "phonemizer", specifier = "==3.3.0" }, { name = "pydantic", specifier = "==2.10.4" }, { name = "pydantic-settings", specifier = "==2.7.0" }, + { name = "pydub", specifier = ">=0.25.1" }, { name = "pytest", marker = "extra == 'test'", specifier = "==8.0.0" }, { name = "pytest-asyncio", marker = "extra == 'test'", specifier = "==0.23.5" }, { name = "pytest-cov", marker = "extra == 'test'", specifier = "==4.1.0" }, { name = "python-dotenv", specifier = "==1.0.1" }, { name = "regex", specifier = "==2024.11.6" }, { name = "requests", specifier = "==2.32.3" }, - { name = "ruff", marker = "extra == 'test'", specifier = ">=0.2.2" }, { name = "scipy", specifier = "==1.14.1" }, { name = "soundfile", specifier = "==0.13.0" }, { name = "sqlalchemy", specifier = "==2.0.27" }, { name = "tiktoken", specifier = "==0.8.0" }, - { name = "torch", marker = "extra == 'cpu'", specifier = "==2.5.1+cpu", index = "https://download.pytorch.org/whl/cpu", conflict = { package = "kokoro-fastapi", extra = "cpu" } }, + { name = "torch", marker = "extra == 'cpu'", specifier = "==2.5.1", index = "https://download.pytorch.org/whl/cpu", conflict = { package = "kokoro-fastapi", extra = "cpu" } }, { name = "torch", marker = "extra == 'gpu'", specifier = "==2.5.1+cu121", index = "https://download.pytorch.org/whl/cu121", conflict = { package = "kokoro-fastapi", extra = "gpu" } }, { name = "tqdm", specifier = "==4.67.1" }, { name = "transformers", specifier = "==4.47.1" }, @@ -2334,24 +2343,52 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/68/4f/12207897848a653d03ebbf6775a29d949408ded5f99b2d87198bc5c93508/tomlkit-0.12.0-py3-none-any.whl", hash = "sha256:926f1f37a1587c7a4f6c7484dae538f1345d96d793d9adab5d3675957b1d0766", size = 37334 }, ] +[[package]] +name = "torch" +version = "2.5.1" +source = { registry = "https://download.pytorch.org/whl/cpu" } +resolution-markers = [ + "(python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.11.*' and sys_platform == 'darwin')", + "(python_full_version < '3.11' and platform_machine == 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform == 'darwin')", + "(python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.13' and sys_platform == 'darwin')", + "(python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.12.*' and sys_platform == 'darwin')", +] +dependencies = [ + { name = "filelock", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or sys_platform == 'darwin'" }, + { name = "fsspec", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or sys_platform == 'darwin'" }, + { name = "jinja2", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or sys_platform == 'darwin'" }, + { name = "networkx", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or sys_platform == 'darwin'" }, + { name = "setuptools", marker = "(python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.12' and sys_platform == 'darwin')" }, + { name = "sympy", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or sys_platform == 'darwin'" }, + { name = "typing-extensions", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or sys_platform == 'darwin'" }, +] +wheels = [ + { url = "https://download.pytorch.org/whl/cpu/torch-2.5.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:269b10c34430aa8e9643dbe035dc525c4a9b1d671cd3dbc8ecbcaed280ae322d" }, + { url = "https://download.pytorch.org/whl/cpu/torch-2.5.1-cp310-none-macosx_11_0_arm64.whl", hash = "sha256:23d062bf70776a3d04dbe74db950db2a5245e1ba4f27208a87f0d743b0d06e86" }, + { url = "https://download.pytorch.org/whl/cpu/torch-2.5.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d5b3203f191bc40783c99488d2e776dcf93ac431a59491d627a1ca5b3ae20b22" }, + { url = "https://download.pytorch.org/whl/cpu/torch-2.5.1-cp311-none-macosx_11_0_arm64.whl", hash = "sha256:31f8c39660962f9ae4eeec995e3049b5492eb7360dd4f07377658ef4d728fa4c" }, + { url = "https://download.pytorch.org/whl/cpu/torch-2.5.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:36d1be99281b6f602d9639bd0af3ee0006e7aab16f6718d86f709d395b6f262c" }, + { url = "https://download.pytorch.org/whl/cpu/torch-2.5.1-cp312-none-macosx_11_0_arm64.whl", hash = "sha256:8c712df61101964eb11910a846514011f0b6f5920c55dbf567bff8a34163d5b1" }, +] + [[package]] name = "torch" version = "2.5.1+cpu" source = { registry = "https://download.pytorch.org/whl/cpu" } resolution-markers = [ - "python_full_version == '3.11.*'", - "python_full_version < '3.11'", - "python_full_version >= '3.13'", - "python_full_version == '3.12.*'", + "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux')", + "(python_full_version < '3.11' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux')", + "(python_full_version >= '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.13' and sys_platform != 'darwin' and sys_platform != 'linux')", + "(python_full_version == '3.12.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'linux')", ] dependencies = [ - { name = "filelock" }, - { name = "fsspec" }, - { name = "jinja2" }, - { name = "networkx" }, - { name = "setuptools", marker = "python_full_version >= '3.12'" }, - { name = "sympy" }, - { name = "typing-extensions" }, + { name = "filelock", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" }, + { name = "fsspec", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" }, + { name = "jinja2", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" }, + { name = "networkx", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" }, + { name = "setuptools", marker = "(python_full_version >= '3.12' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.12' and sys_platform != 'darwin' and sys_platform != 'linux')" }, + { name = "sympy", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" }, + { name = "typing-extensions", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" }, ] wheels = [ { url = "https://download.pytorch.org/whl/cpu/torch-2.5.1%2Bcpu-cp310-cp310-linux_x86_64.whl", hash = "sha256:7f91a2200e352745d70e22396bd501448e28350fbdbd8d8b1c83037e25451150" }, From 4a24be1605ff9ad4a305ea7d41f1cebd674658d3 Mon Sep 17 00:00:00 2001 From: remsky Date: Wed, 22 Jan 2025 02:33:29 -0700 Subject: [PATCH 03/32] Refactor model loading and configuration: update, adjust model loading device,. add async streaming examples and remove unused warmup service. --- api/src/builds/models.py | 2 +- api/src/core/config.py | 6 +- api/src/core/model_config.py | 25 +- api/src/inference/model_manager.py | 175 ++++++----- api/src/inference/onnx_cpu.py | 66 +---- api/src/inference/onnx_gpu.py | 78 +---- api/src/inference/pytorch_gpu.py | 134 ++++++--- api/src/inference/session_pool.py | 271 ++++++++++++++++++ api/src/inference/voice_manager.py | 60 ++-- api/src/main.py | 63 +++- api/src/routers/development.py | 9 +- api/src/routers/openai_compatible.py | 38 ++- .../services/text_processing/phonemizer.py | 9 +- api/src/services/tts_service.py | 201 ++++++------- api/src/services/warmup.py | 60 ---- docker/gpu/docker-compose.yml | 2 +- examples/openai_streaming_audio.py | 4 +- examples/simul_file_test.py | 53 ++++ examples/simul_openai_streaming_audio.py | 91 ++++++ examples/simul_speaker_test.py | 66 +++++ examples/speech.mp3 | Bin 0 -> 73440 bytes 21 files changed, 929 insertions(+), 484 deletions(-) create mode 100644 api/src/inference/session_pool.py delete mode 100644 api/src/services/warmup.py create mode 100644 examples/simul_file_test.py create mode 100644 examples/simul_openai_streaming_audio.py create mode 100644 examples/simul_speaker_test.py diff --git a/api/src/builds/models.py b/api/src/builds/models.py index bfd52ad..5495465 100644 --- a/api/src/builds/models.py +++ b/api/src/builds/models.py @@ -367,7 +367,7 @@ async def build_model(path, device): decoder=decoder.to(device).eval(), text_encoder=text_encoder.to(device).eval(), ) - weights = await load_model_weights(path, device='cpu') + weights = await load_model_weights(path, device=device) for key, state_dict in weights['net'].items(): assert key in model, key try: diff --git a/api/src/core/config.py b/api/src/core/config.py index 91aec63..03ce30e 100644 --- a/api/src/core/config.py +++ b/api/src/core/config.py @@ -15,9 +15,9 @@ class Settings(BaseSettings): default_voice: str = "af" use_gpu: bool = False # Whether to use GPU acceleration if available use_onnx: bool = True # Whether to use ONNX runtime - # Paths relative to api directory - model_dir: str = "src/models" # Model directory relative to api/ - voices_dir: str = "src/voices" # Voices directory relative to api/ + # Container absolute paths + model_dir: str = "/app/api/src/models" # Absolute path in container + voices_dir: str = "/app/api/src/voices" # Absolute path in container # Model filenames pytorch_model_file: str = "kokoro-v0_19.pth" diff --git a/api/src/core/model_config.py b/api/src/core/model_config.py index dc82aee..f9ae254 100644 --- a/api/src/core/model_config.py +++ b/api/src/core/model_config.py @@ -6,6 +6,11 @@ from pydantic import BaseModel, Field class ONNXCPUConfig(BaseModel): """ONNX CPU runtime configuration.""" + # Session pooling + max_instances: int = Field(4, description="Maximum concurrent model instances") + instance_timeout: int = Field(300, description="Session timeout in seconds") + + # Runtime settings num_threads: int = Field(8, description="Number of threads for parallel operations") inter_op_threads: int = Field(4, description="Number of threads for operator parallelism") execution_mode: str = Field("parallel", description="ONNX execution mode") @@ -20,9 +25,14 @@ class ONNXCPUConfig(BaseModel): class ONNXGPUConfig(ONNXCPUConfig): """ONNX GPU-specific configuration.""" + # CUDA settings device_id: int = Field(0, description="CUDA device ID") gpu_mem_limit: float = Field(0.7, description="Fraction of GPU memory to use") cudnn_conv_algo_search: str = Field("EXHAUSTIVE", description="CuDNN convolution algorithm search") + + # Stream management + cuda_streams: int = Field(2, description="Number of CUDA streams for inference") + stream_timeout: int = Field(60, description="Stream timeout in seconds") do_copy_in_default_stream: bool = Field(True, description="Copy in default CUDA stream") class Config: @@ -32,8 +42,6 @@ class ONNXGPUConfig(ONNXCPUConfig): class PyTorchCPUConfig(BaseModel): """PyTorch CPU backend configuration.""" - max_batch_size: int = Field(32, description="Maximum batch size for batched inference") - stream_buffer_size: int = Field(8, description="Size of stream buffer") memory_threshold: float = Field(0.8, description="Memory threshold for cleanup") retry_on_oom: bool = Field(True, description="Whether to retry on OOM errors") num_threads: int = Field(8, description="Number of threads for parallel operations") @@ -49,18 +57,11 @@ class PyTorchGPUConfig(BaseModel): device_id: int = Field(0, description="CUDA device ID") use_fp16: bool = Field(True, description="Whether to use FP16 precision") use_triton: bool = Field(True, description="Whether to use Triton for CUDA kernels") - max_batch_size: int = Field(32, description="Maximum batch size for batched inference") - stream_buffer_size: int = Field(8, description="Size of CUDA stream buffer") memory_threshold: float = Field(0.8, description="Memory threshold for cleanup") retry_on_oom: bool = Field(True, description="Whether to retry on OOM errors") sync_cuda: bool = Field(True, description="Whether to synchronize CUDA operations") - - class Config: - frozen = True - """PyTorch CPU-specific configuration.""" - - num_threads: int = Field(8, description="Number of threads for parallel operations") - pin_memory: bool = Field(True, description="Whether to pin memory for faster CPU-GPU transfer") + cuda_streams: int = Field(2, description="Number of CUDA streams for inference") + stream_timeout: int = Field(60, description="Stream timeout in seconds") class Config: frozen = True @@ -74,7 +75,7 @@ class ModelConfig(BaseModel): device_type: str = Field("auto", description="Device type ('cpu', 'gpu', or 'auto')") cache_models: bool = Field(True, description="Whether to cache loaded models") cache_voices: bool = Field(True, description="Whether to cache voice tensors") - voice_cache_size: int = Field(10, description="Maximum number of cached voices") + voice_cache_size: int = Field(2, description="Maximum number of cached voices") # Backend-specific configs onnx_cpu: ONNXCPUConfig = Field(default_factory=ONNXCPUConfig) diff --git a/api/src/inference/model_manager.py b/api/src/inference/model_manager.py index 26621c2..ed67ce8 100644 --- a/api/src/inference/model_manager.py +++ b/api/src/inference/model_manager.py @@ -1,5 +1,6 @@ """Model management and caching.""" +import asyncio from typing import Dict, Optional import torch @@ -13,11 +14,19 @@ from .onnx_cpu import ONNXCPUBackend from .onnx_gpu import ONNXGPUBackend from .pytorch_cpu import PyTorchCPUBackend from .pytorch_gpu import PyTorchGPUBackend +from .session_pool import CPUSessionPool, StreamingSessionPool + + +# Global singleton instance and state +_manager_instance = None +_manager_lock = asyncio.Lock() +_loaded_models = {} +_backends = {} class ModelManager: """Manages model loading and inference across backends.""" - + def __init__(self, config: Optional[ModelConfig] = None): """Initialize model manager. @@ -25,65 +34,60 @@ class ModelManager: config: Optional configuration """ self._config = config or model_config - self._backends: Dict[str, BaseModelBackend] = {} - self._current_backend: Optional[str] = None - self._initialize_backends() + global _loaded_models, _backends + self._loaded_models = _loaded_models + self._backends = _backends + + # Initialize session pools + self._session_pools = { + 'onnx_cpu': CPUSessionPool(), + 'onnx_gpu': StreamingSessionPool() + } + + # Initialize locks + self._backend_locks: Dict[str, asyncio.Lock] = {} - def _initialize_backends(self) -> None: - """Initialize available backends based on settings.""" - has_gpu = settings.use_gpu and torch.cuda.is_available() + def _determine_device(self) -> str: + """Determine device based on settings.""" + if settings.use_gpu and torch.cuda.is_available(): + return "cuda" + return "cpu" + + async def initialize(self) -> None: + """Initialize backends.""" + if self._backends: + logger.debug("Using existing backend instances") + return + + device = self._determine_device() try: - if has_gpu: + if device == "cuda": if settings.use_onnx: - # ONNX GPU primary self._backends['onnx_gpu'] = ONNXGPUBackend() self._current_backend = 'onnx_gpu' - logger.info("Initialized ONNX GPU backend") - - # PyTorch GPU fallback - self._backends['pytorch_gpu'] = PyTorchGPUBackend() - logger.info("Initialized PyTorch GPU backend") + logger.info("Initialized new ONNX GPU backend") else: - # PyTorch GPU primary self._backends['pytorch_gpu'] = PyTorchGPUBackend() self._current_backend = 'pytorch_gpu' - logger.info("Initialized PyTorch GPU backend") + logger.info("Initialized new PyTorch GPU backend") + else: + if settings.use_onnx: + self._backends['onnx_cpu'] = ONNXCPUBackend() + self._current_backend = 'onnx_cpu' + logger.info("Initialized new ONNX CPU backend") + else: + self._backends['pytorch_cpu'] = PyTorchCPUBackend() + self._current_backend = 'pytorch_cpu' + logger.info("Initialized new PyTorch CPU backend") - # ONNX GPU fallback - self._backends['onnx_gpu'] = ONNXGPUBackend() - logger.info("Initialized ONNX GPU backend") - else: - self._initialize_cpu_backends() - except Exception as e: - logger.error(f"Failed to initialize GPU backends: {e}") - # Fallback to CPU if GPU fails - self._initialize_cpu_backends() - - def _initialize_cpu_backends(self) -> None: - """Initialize CPU backends based on settings.""" - try: - if settings.use_onnx: - # ONNX CPU primary - self._backends['onnx_cpu'] = ONNXCPUBackend() - self._current_backend = 'onnx_cpu' - logger.info("Initialized ONNX CPU backend") + # Initialize locks for each backend + for backend in self._backends: + self._backend_locks[backend] = asyncio.Lock() - # PyTorch CPU fallback - self._backends['pytorch_cpu'] = PyTorchCPUBackend() - logger.info("Initialized PyTorch CPU backend") - else: - # PyTorch CPU primary - self._backends['pytorch_cpu'] = PyTorchCPUBackend() - self._current_backend = 'pytorch_cpu' - logger.info("Initialized PyTorch CPU backend") - - # ONNX CPU fallback - self._backends['onnx_cpu'] = ONNXCPUBackend() - logger.info("Initialized ONNX CPU backend") except Exception as e: - logger.error(f"Failed to initialize CPU backends: {e}") - raise RuntimeError("No backends available") + logger.error(f"Failed to initialize backend: {e}") + raise RuntimeError("Failed to initialize backend") def get_backend(self, backend_type: Optional[str] = None) -> BaseModelBackend: """Get specified backend. @@ -154,19 +158,42 @@ class ModelManager: if backend_type is None: backend_type = self._determine_backend(abs_path) - backend = self.get_backend(backend_type) + # Get backend lock + lock = self._backend_locks[backend_type] - # Load model - await backend.load_model(abs_path) - logger.info(f"Loaded model on {backend_type} backend") - - # Run warmup if voice provided - if warmup_voice is not None: - await self._warmup_inference(backend, warmup_voice) + async with lock: + backend = self.get_backend(backend_type) + + # For ONNX backends, use session pool + if backend_type.startswith('onnx'): + pool = self._session_pools[backend_type] + backend._session = await pool.get_session(abs_path) + self._loaded_models[backend_type] = abs_path + logger.info(f"Fetched model instance from {backend_type} pool") + + # For PyTorch backends, load normally + else: + # Check if model is already loaded + if (backend_type in self._loaded_models and + self._loaded_models[backend_type] == abs_path and + backend.is_loaded): + logger.info(f"Fetching existing model instance from {backend_type}") + return + + # Load model + await backend.load_model(abs_path) + self._loaded_models[backend_type] = abs_path + logger.info(f"Initialized new model instance on {backend_type}") + + # Run warmup if voice provided + if warmup_voice is not None: + await self._warmup_inference(backend, warmup_voice) except Exception as e: + # Clear cached path on failure + self._loaded_models.pop(backend_type, None) raise RuntimeError(f"Failed to load model: {e}") - + async def _warmup_inference(self, backend: BaseModelBackend, voice: torch.Tensor) -> None: """Run warmup inference to initialize model. @@ -188,7 +215,7 @@ class ModelManager: # Run inference backend.generate(tokens, voice, speed=1.0) - logger.info("Completed warmup inference") + logger.debug("Completed warmup inference") except Exception as e: logger.warning(f"Warmup inference failed: {e}") @@ -221,16 +248,23 @@ class ModelManager: try: # Generate audio using provided voice tensor + # No lock needed here since inference is thread-safe return backend.generate(tokens, voice, speed) - except Exception as e: raise RuntimeError(f"Generation failed: {e}") def unload_all(self) -> None: - """Unload models from all backends.""" + """Unload models from all backends and clear cache.""" + # Clean up session pools + for pool in self._session_pools.values(): + pool.cleanup() + + # Unload PyTorch backends for backend in self._backends.values(): backend.unload() - logger.info("Unloaded all models") + + self._loaded_models.clear() + logger.info("Unloaded all models and cleared cache") @property def available_backends(self) -> list[str]: @@ -251,12 +285,8 @@ class ModelManager: return self._current_backend -# Module-level instance -_manager: Optional[ModelManager] = None - - -def get_manager(config: Optional[ModelConfig] = None) -> ModelManager: - """Get or create global model manager instance. +async def get_manager(config: Optional[ModelConfig] = None) -> ModelManager: + """Get global model manager instance. Args: config: Optional model configuration @@ -264,7 +294,10 @@ def get_manager(config: Optional[ModelConfig] = None) -> ModelManager: Returns: ModelManager instance """ - global _manager - if _manager is None: - _manager = ModelManager(config) - return _manager \ No newline at end of file + global _manager_instance + + async with _manager_lock: + if _manager_instance is None: + _manager_instance = ModelManager(config) + await _manager_instance.initialize() + return _manager_instance \ No newline at end of file diff --git a/api/src/inference/onnx_cpu.py b/api/src/inference/onnx_cpu.py index 34d68ac..b17227c 100644 --- a/api/src/inference/onnx_cpu.py +++ b/api/src/inference/onnx_cpu.py @@ -1,20 +1,16 @@ """CPU-based ONNX inference backend.""" -from typing import Dict, Optional +from typing import Optional import numpy as np import torch from loguru import logger -from onnxruntime import ( - ExecutionMode, - GraphOptimizationLevel, - InferenceSession, - SessionOptions -) +from onnxruntime import InferenceSession from ..core import paths from ..core.model_config import model_config from .base import BaseModelBackend +from .session_pool import create_session_options, create_provider_options class ONNXCPUBackend(BaseModelBackend): @@ -47,8 +43,8 @@ class ONNXCPUBackend(BaseModelBackend): logger.info(f"Loading ONNX model: {model_path}") # Configure session - options = self._create_session_options() - provider_options = self._create_provider_options() + options = create_session_options(is_gpu=False) + provider_options = create_provider_options(is_gpu=False) # Create session self._session = InferenceSession( @@ -84,9 +80,9 @@ class ONNXCPUBackend(BaseModelBackend): raise RuntimeError("Model not loaded") try: - # Prepare inputs - tokens_input = np.array([tokens], dtype=np.int64) - style_input = voice[len(tokens)].numpy() + # Prepare inputs with start/end tokens + tokens_input = np.array([[0, *tokens, 0]], dtype=np.int64) # Add start/end tokens + style_input = voice[len(tokens) + 2].numpy() # Adjust index for start/end tokens speed_input = np.full(1, speed, dtype=np.float32) # Run inference @@ -104,52 +100,6 @@ class ONNXCPUBackend(BaseModelBackend): except Exception as e: raise RuntimeError(f"Generation failed: {e}") - def _create_session_options(self) -> SessionOptions: - """Create ONNX session options. - - Returns: - Configured session options - """ - options = SessionOptions() - config = model_config.onnx_cpu - - # Set optimization level - if config.optimization_level == "all": - options.graph_optimization_level = GraphOptimizationLevel.ORT_ENABLE_ALL - elif config.optimization_level == "basic": - options.graph_optimization_level = GraphOptimizationLevel.ORT_ENABLE_BASIC - else: - options.graph_optimization_level = GraphOptimizationLevel.ORT_DISABLE_ALL - - # Configure threading - options.intra_op_num_threads = config.num_threads - options.inter_op_num_threads = config.inter_op_threads - - # Set execution mode - options.execution_mode = ( - ExecutionMode.ORT_PARALLEL - if config.execution_mode == "parallel" - else ExecutionMode.ORT_SEQUENTIAL - ) - - # Configure memory optimization - options.enable_mem_pattern = config.memory_pattern - - return options - - def _create_provider_options(self) -> Dict: - """Create CPU provider options. - - Returns: - Provider configuration - """ - return { - "CPUExecutionProvider": { - "arena_extend_strategy": model_config.onnx_cpu.arena_extend_strategy, - "cpu_memory_arena_cfg": "cpu:0" - } - } - def unload(self) -> None: """Unload model and free resources.""" if self._session is not None: diff --git a/api/src/inference/onnx_gpu.py b/api/src/inference/onnx_gpu.py index 2df32e2..f11534e 100644 --- a/api/src/inference/onnx_gpu.py +++ b/api/src/inference/onnx_gpu.py @@ -1,20 +1,16 @@ """GPU-based ONNX inference backend.""" -from typing import Dict, Optional +from typing import Optional import numpy as np import torch from loguru import logger -from onnxruntime import ( - ExecutionMode, - GraphOptimizationLevel, - InferenceSession, - SessionOptions -) +from onnxruntime import InferenceSession from ..core import paths from ..core.model_config import model_config from .base import BaseModelBackend +from .session_pool import create_session_options, create_provider_options class ONNXGPUBackend(BaseModelBackend): @@ -27,6 +23,9 @@ class ONNXGPUBackend(BaseModelBackend): raise RuntimeError("CUDA not available") self._device = "cuda" self._session: Optional[InferenceSession] = None + + # Configure GPU + torch.cuda.set_device(model_config.onnx_gpu.device_id) @property def is_loaded(self) -> bool: @@ -49,8 +48,8 @@ class ONNXGPUBackend(BaseModelBackend): logger.info(f"Loading ONNX model on GPU: {model_path}") # Configure session - options = self._create_session_options() - provider_options = self._create_provider_options() + options = create_session_options(is_gpu=True) + provider_options = create_provider_options(is_gpu=True) # Create session with CUDA provider self._session = InferenceSession( @@ -87,8 +86,8 @@ class ONNXGPUBackend(BaseModelBackend): try: # Prepare inputs - tokens_input = np.array([tokens], dtype=np.int64) - style_input = voice[len(tokens)].cpu().numpy() # Move to CPU for ONNX + tokens_input = np.array([[0, *tokens, 0]], dtype=np.int64) # Add start/end tokens + style_input = voice[len(tokens) + 2].cpu().numpy() # Move to CPU for ONNX speed_input = np.full(1, speed, dtype=np.float32) # Run inference @@ -104,62 +103,15 @@ class ONNXGPUBackend(BaseModelBackend): return result[0] except Exception as e: + if "out of memory" in str(e).lower(): + # Clear CUDA cache and retry + torch.cuda.empty_cache() + return self.generate(tokens, voice, speed) raise RuntimeError(f"Generation failed: {e}") - def _create_session_options(self) -> SessionOptions: - """Create ONNX session options. - - Returns: - Configured session options - """ - options = SessionOptions() - config = model_config.onnx_gpu - - # Set optimization level - if config.optimization_level == "all": - options.graph_optimization_level = GraphOptimizationLevel.ORT_ENABLE_ALL - elif config.optimization_level == "basic": - options.graph_optimization_level = GraphOptimizationLevel.ORT_ENABLE_BASIC - else: - options.graph_optimization_level = GraphOptimizationLevel.ORT_DISABLE_ALL - - # Configure threading - options.intra_op_num_threads = config.num_threads - options.inter_op_num_threads = config.inter_op_threads - - # Set execution mode - options.execution_mode = ( - ExecutionMode.ORT_PARALLEL - if config.execution_mode == "parallel" - else ExecutionMode.ORT_SEQUENTIAL - ) - - # Configure memory optimization - options.enable_mem_pattern = config.memory_pattern - - return options - - def _create_provider_options(self) -> Dict: - """Create CUDA provider options. - - Returns: - Provider configuration - """ - config = model_config.onnx_gpu - return { - "CUDAExecutionProvider": { - "device_id": config.device_id, - "arena_extend_strategy": config.arena_extend_strategy, - "gpu_mem_limit": int(config.gpu_mem_limit * torch.cuda.get_device_properties(0).total_memory), - "cudnn_conv_algo_search": config.cudnn_conv_algo_search, - "do_copy_in_default_stream": config.do_copy_in_default_stream - } - } - def unload(self) -> None: """Unload model and free resources.""" if self._session is not None: del self._session self._session = None - if torch.cuda.is_available(): - torch.cuda.empty_cache() \ No newline at end of file + torch.cuda.empty_cache() \ No newline at end of file diff --git a/api/src/inference/pytorch_gpu.py b/api/src/inference/pytorch_gpu.py index 2995818..fc07a0d 100644 --- a/api/src/inference/pytorch_gpu.py +++ b/api/src/inference/pytorch_gpu.py @@ -13,8 +13,37 @@ from ..core.model_config import model_config from .base import BaseModelBackend +class CUDAStreamManager: + """CUDA stream manager.""" + + def __init__(self, num_streams: int): + """Initialize stream manager. + + Args: + num_streams: Number of CUDA streams + """ + self.streams = [torch.cuda.Stream() for _ in range(num_streams)] + self._current = 0 + + def get_next_stream(self) -> torch.cuda.Stream: + """Get next available stream. + + Returns: + CUDA stream + """ + stream = self.streams[self._current] + self._current = (self._current + 1) % len(self.streams) + return stream + + @torch.no_grad() -def forward(model: torch.nn.Module, tokens: list[int], ref_s: torch.Tensor, speed: float) -> np.ndarray: +def forward( + model: torch.nn.Module, + tokens: list[int], + ref_s: torch.Tensor, + speed: float, + stream: Optional[torch.cuda.Stream] = None +) -> np.ndarray: """Forward pass through model. Args: @@ -22,59 +51,67 @@ def forward(model: torch.nn.Module, tokens: list[int], ref_s: torch.Tensor, spee tokens: Input tokens ref_s: Reference signal (shape: [1, n_features]) speed: Speed multiplier + stream: Optional CUDA stream Returns: Generated audio """ device = ref_s.device - # Initial tensor setup - tokens = torch.LongTensor([[0, *tokens, 0]]).to(device) - input_lengths = torch.LongTensor([tokens.shape[-1]]).to(device) - text_mask = length_to_mask(input_lengths).to(device) + # Use provided stream or default + with torch.cuda.stream(stream) if stream else torch.cuda.default_stream(): + # Initial tensor setup + tokens = torch.LongTensor([[0, *tokens, 0]]).to(device) + input_lengths = torch.LongTensor([tokens.shape[-1]]).to(device) + text_mask = length_to_mask(input_lengths).to(device) - # Split reference signals (style_dim=128 from config) - style_dim = 128 - s_ref = ref_s[:, :style_dim].clone().to(device) - s_content = ref_s[:, style_dim:].clone().to(device) + # Split reference signals (style_dim=128 from config) + style_dim = 128 + s_ref = ref_s[:, :style_dim].clone().to(device) + s_content = ref_s[:, style_dim:].clone().to(device) - # BERT and encoder pass - bert_dur = model.bert(tokens, attention_mask=(~text_mask).int()) - d_en = model.bert_encoder(bert_dur).transpose(-1, -2) + # BERT and encoder pass + bert_dur = model.bert(tokens, attention_mask=(~text_mask).int()) + d_en = model.bert_encoder(bert_dur).transpose(-1, -2) - # Predictor forward pass - d = model.predictor.text_encoder(d_en, s_content, input_lengths, text_mask) - x, _ = model.predictor.lstm(d) + # Predictor forward pass + d = model.predictor.text_encoder(d_en, s_content, input_lengths, text_mask) + x, _ = model.predictor.lstm(d) - # Duration prediction - duration = model.predictor.duration_proj(x) - duration = torch.sigmoid(duration).sum(axis=-1) / speed - pred_dur = torch.round(duration).clamp(min=1).long() - del duration, x + # Duration prediction + duration = model.predictor.duration_proj(x) + duration = torch.sigmoid(duration).sum(axis=-1) / speed + pred_dur = torch.round(duration).clamp(min=1).long() + del duration, x - # Alignment matrix construction - pred_aln_trg = torch.zeros(input_lengths.item(), pred_dur.sum().item(), device=device) - c_frame = 0 - for i in range(pred_aln_trg.size(0)): - pred_aln_trg[i, c_frame:c_frame + pred_dur[0, i].item()] = 1 - c_frame += pred_dur[0, i].item() - pred_aln_trg = pred_aln_trg.unsqueeze(0) + # Alignment matrix construction + pred_aln_trg = torch.zeros(input_lengths.item(), pred_dur.sum().item(), device=device) + c_frame = 0 + for i in range(pred_aln_trg.size(0)): + pred_aln_trg[i, c_frame:c_frame + pred_dur[0, i].item()] = 1 + c_frame += pred_dur[0, i].item() + pred_aln_trg = pred_aln_trg.unsqueeze(0) - # Matrix multiplications - en = d.transpose(-1, -2) @ pred_aln_trg - del d - - F0_pred, N_pred = model.predictor.F0Ntrain(en, s_content) - del en + # Matrix multiplications + en = d.transpose(-1, -2) @ pred_aln_trg + del d + + F0_pred, N_pred = model.predictor.F0Ntrain(en, s_content) + del en - # Final text encoding and decoding - t_en = model.text_encoder(tokens, input_lengths, text_mask) - asr = t_en @ pred_aln_trg - del t_en + # Final text encoding and decoding + t_en = model.text_encoder(tokens, input_lengths, text_mask) + asr = t_en @ pred_aln_trg + del t_en - # Generate output - output = model.decoder(asr, F0_pred, N_pred, s_ref) - return output.squeeze().cpu().numpy() + # Generate output + output = model.decoder(asr, F0_pred, N_pred, s_ref) + + # Ensure operation completion if using custom stream + if stream: + stream.synchronize() + + return output.squeeze().cpu().numpy() def length_to_mask(lengths: torch.Tensor) -> torch.Tensor: @@ -92,9 +129,10 @@ class PyTorchGPUBackend(BaseModelBackend): def __init__(self): """Initialize GPU backend.""" super().__init__() - if not torch.cuda.is_available(): - raise RuntimeError("CUDA not available") - self._device = "cuda" + from ..core.config import settings + if not (settings.use_gpu and torch.cuda.is_available()): + raise RuntimeError("GPU backend requires GPU support and use_gpu=True") + self._device = "cuda" # Device is enforced by backend selection in model_manager self._model: Optional[torch.nn.Module] = None # Configure GPU settings @@ -102,6 +140,9 @@ class PyTorchGPUBackend(BaseModelBackend): if config.sync_cuda: torch.cuda.synchronize() torch.cuda.set_device(config.device_id) + + # Initialize stream manager + self._stream_manager = CUDAStreamManager(config.cuda_streams) async def load_model(self, path: str) -> None: """Load PyTorch model. @@ -154,8 +195,11 @@ class PyTorchGPUBackend(BaseModelBackend): if ref_s.dim() == 1: ref_s = ref_s.unsqueeze(0) # Add batch dimension if needed - # Generate audio - return forward(self._model, tokens, ref_s, speed) + # Get next available stream + stream = self._stream_manager.get_next_stream() + + # Generate audio using stream + return forward(self._model, tokens, ref_s, speed, stream) except Exception as e: logger.error(f"Generation failed: {e}") diff --git a/api/src/inference/session_pool.py b/api/src/inference/session_pool.py new file mode 100644 index 0000000..4674a91 --- /dev/null +++ b/api/src/inference/session_pool.py @@ -0,0 +1,271 @@ +"""Session pooling for model inference.""" + +import asyncio +import time +from dataclasses import dataclass +from typing import Dict, Optional, Set + +import torch +from loguru import logger +from onnxruntime import ( + ExecutionMode, + GraphOptimizationLevel, + InferenceSession, + SessionOptions +) + +from ..core import paths +from ..core.model_config import model_config + + +@dataclass +class SessionInfo: + """Session information.""" + session: InferenceSession + last_used: float + stream_id: Optional[int] = None + + +def create_session_options(is_gpu: bool = False) -> SessionOptions: + """Create ONNX session options. + + Args: + is_gpu: Whether to use GPU configuration + + Returns: + Configured session options + """ + options = SessionOptions() + config = model_config.onnx_gpu if is_gpu else model_config.onnx_cpu + + # Set optimization level + if config.optimization_level == "all": + options.graph_optimization_level = GraphOptimizationLevel.ORT_ENABLE_ALL + elif config.optimization_level == "basic": + options.graph_optimization_level = GraphOptimizationLevel.ORT_ENABLE_BASIC + else: + options.graph_optimization_level = GraphOptimizationLevel.ORT_DISABLE_ALL + + # Configure threading + options.intra_op_num_threads = config.num_threads + options.inter_op_num_threads = config.inter_op_threads + + # Set execution mode + options.execution_mode = ( + ExecutionMode.ORT_PARALLEL + if config.execution_mode == "parallel" + else ExecutionMode.ORT_SEQUENTIAL + ) + + # Configure memory optimization + options.enable_mem_pattern = config.memory_pattern + + return options + + +def create_provider_options(is_gpu: bool = False) -> Dict: + """Create provider options. + + Args: + is_gpu: Whether to use GPU configuration + + Returns: + Provider configuration + """ + if is_gpu: + config = model_config.onnx_gpu + return { + "CUDAExecutionProvider": { + "device_id": config.device_id, + "arena_extend_strategy": config.arena_extend_strategy, + "gpu_mem_limit": int(config.gpu_mem_limit * torch.cuda.get_device_properties(0).total_memory), + "cudnn_conv_algo_search": config.cudnn_conv_algo_search, + "do_copy_in_default_stream": config.do_copy_in_default_stream + } + } + else: + return { + "CPUExecutionProvider": { + "arena_extend_strategy": model_config.onnx_cpu.arena_extend_strategy, + "cpu_memory_arena_cfg": "cpu:0" + } + } + + +class BaseSessionPool: + """Base session pool implementation.""" + + def __init__(self, max_size: int, timeout: int): + """Initialize session pool. + + Args: + max_size: Maximum number of concurrent sessions + timeout: Session timeout in seconds + """ + self._max_size = max_size + self._timeout = timeout + self._sessions: Dict[str, SessionInfo] = {} + self._lock = asyncio.Lock() + + async def get_session(self, model_path: str) -> InferenceSession: + """Get session from pool. + + Args: + model_path: Path to model file + + Returns: + ONNX inference session + + Raises: + RuntimeError: If no sessions available + """ + async with self._lock: + # Clean expired sessions + self._cleanup_expired() + + # Check if session exists and is valid + if model_path in self._sessions: + session_info = self._sessions[model_path] + session_info.last_used = time.time() + return session_info.session + + # Check if we can create new session + if len(self._sessions) >= self._max_size: + raise RuntimeError( + f"Maximum number of sessions reached ({self._max_size})" + ) + + # Create new session + session = await self._create_session(model_path) + self._sessions[model_path] = SessionInfo( + session=session, + last_used=time.time() + ) + return session + + def _cleanup_expired(self) -> None: + """Remove expired sessions.""" + current_time = time.time() + expired = [ + path for path, info in self._sessions.items() + if current_time - info.last_used > self._timeout + ] + for path in expired: + logger.info(f"Removing expired session: {path}") + del self._sessions[path] + + async def _create_session(self, model_path: str) -> InferenceSession: + """Create new session. + + Args: + model_path: Path to model file + + Returns: + ONNX inference session + """ + raise NotImplementedError + + def cleanup(self) -> None: + """Clean up all sessions.""" + self._sessions.clear() + + +class StreamingSessionPool(BaseSessionPool): + """GPU session pool with CUDA streams.""" + + def __init__(self): + """Initialize GPU session pool.""" + config = model_config.onnx_gpu + super().__init__(config.cuda_streams, config.stream_timeout) + self._available_streams: Set[int] = set(range(config.cuda_streams)) + + async def get_session(self, model_path: str) -> InferenceSession: + """Get session with CUDA stream. + + Args: + model_path: Path to model file + + Returns: + ONNX inference session + + Raises: + RuntimeError: If no streams available + """ + async with self._lock: + # Clean expired sessions + self._cleanup_expired() + + # Try to find existing session + if model_path in self._sessions: + session_info = self._sessions[model_path] + session_info.last_used = time.time() + return session_info.session + + # Get available stream + if not self._available_streams: + raise RuntimeError("No CUDA streams available") + stream_id = self._available_streams.pop() + + try: + # Create new session + session = await self._create_session(model_path) + self._sessions[model_path] = SessionInfo( + session=session, + last_used=time.time(), + stream_id=stream_id + ) + return session + + except Exception: + # Return stream to pool on failure + self._available_streams.add(stream_id) + raise + + def _cleanup_expired(self) -> None: + """Remove expired sessions and return streams.""" + current_time = time.time() + expired = [ + path for path, info in self._sessions.items() + if current_time - info.last_used > self._timeout + ] + for path in expired: + info = self._sessions[path] + if info.stream_id is not None: + self._available_streams.add(info.stream_id) + logger.info(f"Removing expired session: {path}") + del self._sessions[path] + + async def _create_session(self, model_path: str) -> InferenceSession: + """Create new session with CUDA provider.""" + abs_path = await paths.get_model_path(model_path) + options = create_session_options(is_gpu=True) + provider_options = create_provider_options(is_gpu=True) + + return InferenceSession( + abs_path, + sess_options=options, + providers=["CUDAExecutionProvider"], + provider_options=[provider_options] + ) + + +class CPUSessionPool(BaseSessionPool): + """CPU session pool.""" + + def __init__(self): + """Initialize CPU session pool.""" + config = model_config.onnx_cpu + super().__init__(config.max_instances, config.instance_timeout) + + async def _create_session(self, model_path: str) -> InferenceSession: + """Create new session with CPU provider.""" + abs_path = await paths.get_model_path(model_path) + options = create_session_options(is_gpu=False) + provider_options = create_provider_options(is_gpu=False) + + return InferenceSession( + abs_path, + sess_options=options, + providers=["CPUExecutionProvider"], + provider_options=[provider_options] + ) \ No newline at end of file diff --git a/api/src/inference/voice_manager.py b/api/src/inference/voice_manager.py index 15932c7..67f34a3 100644 --- a/api/src/inference/voice_manager.py +++ b/api/src/inference/voice_manager.py @@ -1,11 +1,10 @@ """Voice pack management and caching.""" import os -from typing import Dict, List, Optional, Union +from typing import Dict, List, Optional import torch from loguru import logger -from pydantic import BaseModel from ..core import paths from ..core.config import settings @@ -13,7 +12,7 @@ from ..structures.model_schemas import VoiceConfig class VoiceManager: - """Manages voice loading, caching, and operations.""" + """Manages voice loading and operations.""" def __init__(self, config: Optional[VoiceConfig] = None): """Initialize voice manager. @@ -33,15 +32,8 @@ class VoiceManager: Returns: Path to voice file if exists, None otherwise """ - # Get api directory path (two levels up from inference) api_dir = os.path.dirname(os.path.dirname(os.path.dirname(__file__))) - - # Construct voice path relative to api directory voice_path = os.path.join(api_dir, settings.voices_dir, f"{voice_name}.pt") - - # Ensure voices directory exists - os.makedirs(os.path.dirname(voice_path), exist_ok=True) - return voice_path if os.path.exists(voice_path) else None async def load_voice(self, voice_name: str, device: str = "cpu") -> torch.Tensor: @@ -66,21 +58,20 @@ class VoiceManager: if self._config.use_cache and cache_key in self._voice_cache: return self._voice_cache[cache_key] + # Load voice tensor try: - # Load voice tensor voice = await paths.load_voice_tensor(voice_path, device=device) - - # Cache if enabled - if self._config.use_cache: - self._manage_cache() - self._voice_cache[cache_key] = voice - logger.debug(f"Cached voice: {voice_name} on {device}") - - return voice - except Exception as e: raise RuntimeError(f"Failed to load voice {voice_name}: {e}") + # Cache if enabled + if self._config.use_cache: + self._manage_cache() + self._voice_cache[cache_key] = voice + logger.debug(f"Cached voice: {voice_name} on {device}") + + return voice + def _manage_cache(self) -> None: """Manage voice cache size.""" if len(self._voice_cache) >= self._config.cache_size: @@ -123,14 +114,14 @@ class VoiceManager: # Get api directory path api_dir = os.path.dirname(os.path.dirname(os.path.dirname(__file__))) voices_dir = os.path.join(api_dir, settings.voices_dir) - - # Ensure voices directory exists os.makedirs(voices_dir, exist_ok=True) # Save combined voice combined_path = os.path.join(voices_dir, f"{combined_name}.pt") try: torch.save(combined_tensor, combined_path) + # Cache the new combined voice + self._voice_cache[f"{combined_path}_{device}"] = combined_tensor except Exception as e: raise RuntimeError(f"Failed to save combined voice: {e}") @@ -147,17 +138,13 @@ class VoiceManager: """ voices = [] try: - # Get api directory path api_dir = os.path.dirname(os.path.dirname(os.path.dirname(__file__))) voices_dir = os.path.join(api_dir, settings.voices_dir) - - # Ensure voices directory exists os.makedirs(voices_dir, exist_ok=True) - # List voice files for entry in os.listdir(voices_dir): if entry.endswith(".pt"): - voices.append(entry[:-3]) # Remove .pt extension + voices.append(entry[:-3]) except Exception as e: logger.error(f"Error listing voices: {e}") return sorted(voices) @@ -174,11 +161,8 @@ class VoiceManager: try: if not os.path.exists(voice_path): return False - - # Try loading voice voice = torch.load(voice_path, map_location="cpu") return isinstance(voice, torch.Tensor) - except Exception: return False @@ -195,12 +179,12 @@ class VoiceManager: } -# Module-level instance -_manager: Optional[VoiceManager] = None +# Global singleton instance +_manager_instance = None -def get_manager(config: Optional[VoiceConfig] = None) -> VoiceManager: - """Get or create global voice manager instance. +async def get_manager(config: Optional[VoiceConfig] = None) -> VoiceManager: + """Get global voice manager instance. Args: config: Optional voice configuration @@ -208,7 +192,7 @@ def get_manager(config: Optional[VoiceConfig] = None) -> VoiceManager: Returns: VoiceManager instance """ - global _manager - if _manager is None: - _manager = VoiceManager(config) - return _manager \ No newline at end of file + global _manager_instance + if _manager_instance is None: + _manager_instance = VoiceManager(config) + return _manager_instance \ No newline at end of file diff --git a/api/src/main.py b/api/src/main.py index ec1b6c8..8516325 100644 --- a/api/src/main.py +++ b/api/src/main.py @@ -1,10 +1,13 @@ + """ FastAPI OpenAI Compatible API """ +import os import sys from contextlib import asynccontextmanager +import torch import uvicorn from fastapi import FastAPI from fastapi.middleware.cors import CORSMiddleware @@ -41,19 +44,59 @@ setup_logger() @asynccontextmanager async def lifespan(app: FastAPI): """Lifespan context manager for model initialization""" + from .inference.model_manager import get_manager + from .inference.voice_manager import get_manager as get_voice_manager + logger.info("Loading TTS model and voice packs...") - # Initialize service - service = TTSService() - await service.ensure_initialized() - - # Get available voices - voices = await service.list_voices() - voicepack_count = len(voices) + try: + # Initialize managers globally + model_manager = await get_manager() + voice_manager = await get_voice_manager() - # Get device info from model manager - device = "GPU" if settings.use_gpu else "CPU" - model = "ONNX" if settings.use_onnx else "PyTorch" + # Determine backend type based on settings + if settings.use_gpu and torch.cuda.is_available(): + backend_type = 'pytorch_gpu' if not settings.use_onnx else 'onnx_gpu' + else: + backend_type = 'pytorch_cpu' if not settings.use_onnx else 'onnx_cpu' + + # Get backend and initialize model + backend = model_manager.get_backend(backend_type) + + # Use model path directly from settings + model_file = settings.pytorch_model_file if not settings.use_onnx else settings.onnx_model_file + model_path = os.path.join(settings.model_dir, model_file) + + + if not os.path.exists(model_path): + raise RuntimeError(f"Model file not found: {model_path}") + + # Pre-cache default voice and use for warmup + warmup_voice = await voice_manager.load_voice(settings.default_voice, device=backend.device) + logger.info(f"Pre-cached voice {settings.default_voice} for warmup") + + # Initialize model with warmup voice + await model_manager.load_model(model_path, warmup_voice, backend_type) + + # Pre-cache common voices in background + common_voices = ['af', 'af_bella', 'af_sarah', 'af_nicole'] + for voice_name in common_voices: + try: + await voice_manager.load_voice(voice_name, device=backend.device) + logger.debug(f"Pre-cached voice {voice_name}") + except Exception as e: + logger.warning(f"Failed to pre-cache voice {voice_name}: {e}") + + # Get available voices for startup message + voices = await voice_manager.list_voices() + voicepack_count = len(voices) + + # Get device info for startup message + device = "GPU" if settings.use_gpu else "CPU" + model = "ONNX" if settings.use_onnx else "PyTorch" + except Exception as e: + logger.error(f"Failed to initialize model: {e}") + raise boundary = "░" * 2*12 startup_msg = f""" diff --git a/api/src/routers/development.py b/api/src/routers/development.py index 4713440..b8ecf35 100644 --- a/api/src/routers/development.py +++ b/api/src/routers/development.py @@ -1,7 +1,7 @@ from typing import List import numpy as np -from fastapi import APIRouter, Depends, HTTPException, Response +from fastapi import APIRouter, Depends, HTTPException, Request, Response from loguru import logger from ..services.audio import AudioService @@ -16,9 +16,9 @@ from ..structures.text_schemas import ( router = APIRouter(tags=["text processing"]) -def get_tts_service() -> TTSService: +async def get_tts_service() -> TTSService: """Dependency to get TTSService instance""" - return TTSService() + return await TTSService.create() # Create service with properly initialized managers @router.post("/text/phonemize", response_model=PhonemeResponse, tags=["deprecated"]) @@ -82,9 +82,6 @@ async def generate_from_phonemes( ) try: - # Ensure service is initialized - await tts_service.ensure_initialized() - # Validate voice exists available_voices = await tts_service.list_voices() if request.voice not in available_voices: diff --git a/api/src/routers/openai_compatible.py b/api/src/routers/openai_compatible.py index 57d1257..4d1ca7a 100644 --- a/api/src/routers/openai_compatible.py +++ b/api/src/routers/openai_compatible.py @@ -1,6 +1,6 @@ from typing import AsyncGenerator, List, Union -from fastapi import APIRouter, Depends, Header, HTTPException, Response, Request +from fastapi import APIRouter, Depends, Header, HTTPException, Request, Response from fastapi.responses import StreamingResponse from loguru import logger @@ -13,10 +13,28 @@ router = APIRouter( responses={404: {"description": "Not found"}}, ) +# Global TTSService instance with lock +_tts_service = None +_init_lock = None -def get_tts_service() -> TTSService: - """Dependency to get TTSService instance with database session""" - return TTSService() # Initialize TTSService with default settings +async def get_tts_service() -> TTSService: + """Get global TTSService instance""" + global _tts_service, _init_lock + + # Create lock if needed + if _init_lock is None: + import asyncio + _init_lock = asyncio.Lock() + + # Initialize service if needed + if _tts_service is None: + async with _init_lock: + # Double check pattern + if _tts_service is None: + _tts_service = await TTSService.create() + logger.info("Created global TTSService instance") + + return _tts_service async def process_voices( @@ -78,11 +96,13 @@ async def stream_audio_chunks( async def create_speech( request: OpenAISpeechRequest, client_request: Request, - tts_service: TTSService = Depends(get_tts_service), x_raw_response: str = Header(None, alias="x-raw-response"), ): """OpenAI-compatible endpoint for text-to-speech""" try: + # Get global service instance + tts_service = await get_tts_service() + # Process voice combination and validate voice_to_use = await process_voices(request.voice, tts_service) @@ -145,9 +165,10 @@ async def create_speech( @router.get("/audio/voices") -async def list_voices(tts_service: TTSService = Depends(get_tts_service)): +async def list_voices(): """List all available voices for text-to-speech""" try: + tts_service = await get_tts_service() voices = await tts_service.list_voices() return {"voices": voices} except Exception as e: @@ -156,9 +177,7 @@ async def list_voices(tts_service: TTSService = Depends(get_tts_service)): @router.post("/audio/voices/combine") -async def combine_voices( - request: Union[str, List[str]], tts_service: TTSService = Depends(get_tts_service) -): +async def combine_voices(request: Union[str, List[str]]): """Combine multiple voices into a new voice. Args: @@ -174,6 +193,7 @@ async def combine_voices( - 500: Server error (file system issues, combination failed) """ try: + tts_service = await get_tts_service() combined_voice = await process_voices(request, tts_service) voices = await tts_service.list_voices() return {"voices": voices, "voice": combined_voice} diff --git a/api/src/services/text_processing/phonemizer.py b/api/src/services/text_processing/phonemizer.py index a328bb5..b0782f3 100644 --- a/api/src/services/text_processing/phonemizer.py +++ b/api/src/services/text_processing/phonemizer.py @@ -4,7 +4,7 @@ from abc import ABC, abstractmethod import phonemizer from .normalizer import normalize_text - +phonemizers = {} class PhonemizerBackend(ABC): """Abstract base class for phonemization backends""" @@ -91,8 +91,9 @@ def phonemize(text: str, language: str = "a", normalize: bool = True) -> str: Returns: Phonemized text """ + global phonemizers if normalize: text = normalize_text(text) - - phonemizer = create_phonemizer(language) - return phonemizer.phonemize(text) + if language not in phonemizers: + phonemizers[language]=create_phonemizer(language) + return phonemizers[language].phonemize(text) \ No newline at end of file diff --git a/api/src/services/tts_service.py b/api/src/services/tts_service.py index 9c51b19..83bf8e1 100644 --- a/api/src/services/tts_service.py +++ b/api/src/services/tts_service.py @@ -1,9 +1,8 @@ """TTS service using model and voice managers.""" import io -import os import time -from typing import List, Tuple +from typing import List, Tuple, Optional import numpy as np import scipy.io.wavfile as wavfile @@ -17,9 +16,14 @@ from .audio import AudioNormalizer, AudioService from .text_processing import chunker, normalize_text, process_text +import asyncio + class TTSService: """Text-to-speech service.""" + # Limit concurrent chunk processing + _chunk_semaphore = asyncio.Semaphore(4) + def __init__(self, output_dir: str = None): """Initialize service. @@ -27,53 +31,24 @@ class TTSService: output_dir: Optional output directory for saving audio """ self.output_dir = output_dir - self.model_manager = get_model_manager() - self.voice_manager = get_voice_manager() - self._initialized = False - self._initialization_error = None + self.model_manager = None + self._voice_manager = None - async def ensure_initialized(self): - """Ensure model is initialized.""" - if self._initialized: - return - if self._initialization_error: - raise self._initialization_error - - try: - # Get api directory path (one level up from src) - api_dir = os.path.dirname(os.path.dirname(os.path.dirname(__file__))) + @classmethod + async def create(cls, output_dir: str = None) -> 'TTSService': + """Create and initialize TTSService instance. + + Args: + output_dir: Optional output directory for saving audio - # Determine model file and backend based on hardware - if settings.use_gpu and torch.cuda.is_available(): - model_file = settings.pytorch_model_file - backend_type = 'pytorch_gpu' - else: - model_file = settings.onnx_model_file - backend_type = 'onnx_cpu' - - # Construct model path relative to api directory - model_path = os.path.join(api_dir, settings.model_dir, model_file) - - # Ensure model directory exists - os.makedirs(os.path.dirname(model_path), exist_ok=True) - - if not os.path.exists(model_path): - raise RuntimeError(f"Model file not found: {model_path}") - - # Load default voice for warmup - backend = self.model_manager.get_backend(backend_type) - warmup_voice = await self.voice_manager.load_voice(settings.default_voice, device=backend.device) - logger.info(f"Loaded voice {settings.default_voice} for warmup") - - # Initialize model with warmup voice - await self.model_manager.load_model(model_path, warmup_voice, backend_type) - logger.info(f"Initialized model on {backend_type} backend") - self._initialized = True - - except Exception as e: - logger.error(f"Failed to initialize model: {e}") - self._initialization_error = RuntimeError(f"Model initialization failed: {e}") - raise self._initialization_error + Returns: + Initialized TTSService instance + """ + service = cls(output_dir) + # Initialize managers + service.model_manager = await get_model_manager() + service._voice_manager = await get_voice_manager() + return service async def generate_audio( self, text: str, voice: str, speed: float = 1.0 @@ -88,8 +63,8 @@ class TTSService: Returns: Audio samples and processing time """ - await self.ensure_initialized() start_time = time.time() + voice_tensor = None try: # Normalize text @@ -98,31 +73,40 @@ class TTSService: raise ValueError("Text is empty after preprocessing") text = str(normalized) - # Process text into chunks - audio_chunks = [] - for chunk in chunker.split_text(text): - try: - # Convert chunk to token IDs - tokens = process_text(chunk) - if not tokens: - continue + # Get backend and load voice + backend = self.model_manager.get_backend() + voice_tensor = await self._voice_manager.load_voice(voice, device=backend.device) - # Get backend and load voice - backend = self.model_manager.get_backend() - voice_tensor = await self.voice_manager.load_voice(voice, device=backend.device) - - # Generate audio - chunk_audio = await self.model_manager.generate( - tokens, - voice_tensor, - speed=speed - ) - if chunk_audio is not None: - audio_chunks.append(chunk_audio) - except Exception as e: - logger.error(f"Failed to process chunk: '{chunk}'. Error: {str(e)}") - continue + # Get all chunks upfront + chunks = list(chunker.split_text(text)) + if not chunks: + raise ValueError("No text chunks to process") + # Process chunk with concurrency control + async def process_chunk(chunk: str) -> Optional[np.ndarray]: + async with self._chunk_semaphore: + try: + tokens = process_text(chunk) + if not tokens: + return None + + # Generate audio + return await self.model_manager.generate( + tokens, + voice_tensor, + speed=speed + ) + except Exception as e: + logger.error(f"Failed to process chunk: '{chunk}'. Error: {str(e)}") + return None + + # Process all chunks concurrently + chunk_results = await asyncio.gather(*[ + process_chunk(chunk) for chunk in chunks + ]) + + # Filter out None results and combine + audio_chunks = [chunk for chunk in chunk_results if chunk is not None] if not audio_chunks: raise ValueError("No audio chunks were generated successfully") @@ -134,6 +118,11 @@ class TTSService: except Exception as e: logger.error(f"Error in audio generation: {str(e)}") raise + finally: + # Always clean up voice tensor + if voice_tensor is not None: + del voice_tensor + torch.cuda.empty_cache() async def generate_audio_stream( self, @@ -153,33 +142,34 @@ class TTSService: Yields: Audio chunks as bytes """ - await self.ensure_initialized() - + # Setup audio processing + stream_normalizer = AudioNormalizer() + voice_tensor = None + try: - # Setup audio processing - stream_normalizer = AudioNormalizer() - # Normalize text normalized = normalize_text(text) if not normalized: raise ValueError("Text is empty after preprocessing") text = str(normalized) - # Process chunks - is_first = True - chunk_gen = chunker.split_text(text) - current_chunk = next(chunk_gen, None) + # Get backend and load voice + backend = self.model_manager.get_backend() + voice_tensor = await self._voice_manager.load_voice(voice, device=backend.device) + + # Get all chunks upfront + chunks = list(chunker.split_text(text)) + if not chunks: + raise ValueError("No text chunks to process") + + # Process chunk with concurrency control + async def process_chunk(chunk: str, is_first: bool, is_last: bool) -> Optional[bytes]: + async with self._chunk_semaphore: + try: + tokens = process_text(chunk) + if not tokens: + return None - while current_chunk is not None: - next_chunk = next(chunk_gen, None) - try: - # Convert chunk to token IDs - tokens = process_text(current_chunk) - if tokens: - # Get backend and load voice - backend = self.model_manager.get_backend() - voice_tensor = await self.voice_manager.load_voice(voice, device=backend.device) - # Generate audio chunk_audio = await self.model_manager.generate( tokens, @@ -189,26 +179,38 @@ class TTSService: if chunk_audio is not None: # Convert to bytes - chunk_bytes = AudioService.convert_audio( + return AudioService.convert_audio( chunk_audio, 24000, output_format, is_first_chunk=is_first, normalizer=stream_normalizer, - is_last_chunk=(next_chunk is None), + is_last_chunk=is_last, stream=True ) - yield chunk_bytes - is_first = False + except Exception as e: + logger.error(f"Failed to generate audio for chunk: '{chunk}'. Error: {str(e)}") + return None - except Exception as e: - logger.error(f"Failed to generate audio for chunk: '{current_chunk}'. Error: {str(e)}") + # Create tasks for all chunks + tasks = [ + process_chunk(chunk, i==0, i==len(chunks)-1) + for i, chunk in enumerate(chunks) + ] - current_chunk = next_chunk + # Process chunks concurrently and yield results in order + for chunk_bytes in await asyncio.gather(*tasks): + if chunk_bytes is not None: + yield chunk_bytes except Exception as e: logger.error(f"Error in audio generation stream: {str(e)}") raise + finally: + # Always clean up voice tensor + if voice_tensor is not None: + del voice_tensor + torch.cuda.empty_cache() async def combine_voices(self, voices: List[str]) -> str: """Combine multiple voices. @@ -219,8 +221,7 @@ class TTSService: Returns: Name of combined voice """ - await self.ensure_initialized() - return await self.voice_manager.combine_voices(voices) + return await self._voice_manager.combine_voices(voices) async def list_voices(self) -> List[str]: """List available voices. @@ -228,7 +229,7 @@ class TTSService: Returns: List of voice names """ - return await self.voice_manager.list_voices() + return await self._voice_manager.list_voices() def _audio_to_bytes(self, audio: np.ndarray) -> bytes: """Convert audio to WAV bytes. diff --git a/api/src/services/warmup.py b/api/src/services/warmup.py deleted file mode 100644 index 3a50000..0000000 --- a/api/src/services/warmup.py +++ /dev/null @@ -1,60 +0,0 @@ -import os -from typing import List, Tuple - -import torch -from loguru import logger - -from ..core.config import settings -from .tts_model import TTSModel -from .tts_service import TTSService - - -class WarmupService: - """Service for warming up TTS models and voice caches""" - - def __init__(self): - """Initialize warmup service and ensure model is ready""" - # Initialize model if not already initialized - if TTSModel._instance is None: - TTSModel.initialize(settings.model_dir) - self.tts_service = TTSService() - - def load_voices(self) -> List[Tuple[str, torch.Tensor]]: - """Load and cache voices up to LRU limit""" - # Get all voices sorted by filename length (shorter names first, usually base voices) - voice_files = sorted( - [f for f in os.listdir(TTSModel.VOICES_DIR) if f.endswith(".pt")], key=len - ) - - n_voices_cache = 1 - loaded_voices = [] - for voice_file in voice_files[:n_voices_cache]: - try: - voice_path = os.path.join(TTSModel.VOICES_DIR, voice_file) - # load using service, lru cache - voicepack = self.tts_service._load_voice(voice_path) - loaded_voices.append( - (voice_file[:-3], voicepack) - ) # Store name and tensor - # voicepack = torch.load(voice_path, map_location=TTSModel.get_device(), weights_only=True) - # logger.info(f"Loaded voice {voice_file[:-3]} into cache") - except Exception as e: - logger.error(f"Failed to load voice {voice_file}: {e}") - logger.info(f"Pre-loaded {len(loaded_voices)} voices into cache") - return loaded_voices - - async def warmup_voices( - self, warmup_text: str, loaded_voices: List[Tuple[str, torch.Tensor]] - ): - """Warm up voice inference and streaming""" - n_warmups = 1 - for voice_name, _ in loaded_voices[:n_warmups]: - try: - logger.info(f"Running warmup inference on voice {voice_name}") - async for _ in self.tts_service.generate_audio_stream( - warmup_text, voice_name, 1.0, "pcm" - ): - pass # Process all chunks to properly warm up - logger.info(f"Completed warmup for voice {voice_name}") - except Exception as e: - logger.warning(f"Warmup failed for voice {voice_name}: {e}") diff --git a/docker/gpu/docker-compose.yml b/docker/gpu/docker-compose.yml index 458eff9..f27e15b 100644 --- a/docker/gpu/docker-compose.yml +++ b/docker/gpu/docker-compose.yml @@ -10,7 +10,7 @@ services: ports: - "8880:8880" environment: - - PYTHONPATH=/app + - PYTHONPATH=/app:/app/api - USE_GPU=true - USE_ONNX=false - PYTHONUNBUFFERED=1 diff --git a/examples/openai_streaming_audio.py b/examples/openai_streaming_audio.py index 35ef58f..353ee3d 100644 --- a/examples/openai_streaming_audio.py +++ b/examples/openai_streaming_audio.py @@ -25,9 +25,7 @@ def main() -> None: def stream_to_speakers() -> None: import pyaudio - player_stream = pyaudio.PyAudio().open( - format=pyaudio.paInt16, channels=1, rate=24000, output=True - ) + player_stream = pyaudio.PyAudio().open(format=pyaudio.paInt16, channels=1, rate=24000, output=True) start_time = time.time() diff --git a/examples/simul_file_test.py b/examples/simul_file_test.py new file mode 100644 index 0000000..c1f14a0 --- /dev/null +++ b/examples/simul_file_test.py @@ -0,0 +1,53 @@ +#!/usr/bin/env rye run python +import asyncio +import time +from pathlib import Path +from openai import AsyncOpenAI + +# Initialize async client +openai = AsyncOpenAI(base_url="http://localhost:8880/v1", api_key="not-needed-for-local") + +async def save_to_file(text: str, file_id: int) -> None: + """Save TTS output to file asynchronously""" + speech_file_path = Path(__file__).parent / f"speech_{file_id}.mp3" + + start_time = time.time() + print(f"Starting file {file_id}") + + try: + # Use streaming endpoint with mp3 format + async with openai.audio.speech.with_streaming_response.create( + model="kokoro", + voice="af_bella", + input=text, + response_format="mp3" + ) as response: + print(f"File {file_id} - Time to first byte: {int((time.time() - start_time) * 1000)}ms") + + # Open file in binary write mode + with open(speech_file_path, 'wb') as f: + async for chunk in response.iter_bytes(): + f.write(chunk) + + print(f"File {file_id} completed in {int((time.time() - start_time) * 1000)}ms") + except Exception as e: + print(f"Error processing file {file_id}: {e}") + +async def main() -> None: + # Different text samples for variety + texts = [ + "The quick brown fox jumped over the lazy dogs. I see skies of blue and clouds of white", + "I see skies of blue and clouds of white. I see skies of blue and clouds of white", + ] + + # Create tasks for saving to files + file_tasks = [ + save_to_file(text, i) + for i, text in enumerate(texts) + ] + + # Run file tasks concurrently + await asyncio.gather(*file_tasks) + +if __name__ == "__main__": + asyncio.run(main()) \ No newline at end of file diff --git a/examples/simul_openai_streaming_audio.py b/examples/simul_openai_streaming_audio.py new file mode 100644 index 0000000..befae01 --- /dev/null +++ b/examples/simul_openai_streaming_audio.py @@ -0,0 +1,91 @@ +#!/usr/bin/env rye run python +import asyncio +import time +from pathlib import Path +import pyaudio +from openai import AsyncOpenAI + +# Initialize async client +openai = AsyncOpenAI(base_url="http://localhost:8880/v1", api_key="not-needed-for-local") + +# Create a shared PyAudio instance +p = pyaudio.PyAudio() + +async def stream_to_speakers(text: str, stream_id: int) -> None: + """Stream TTS audio to speakers asynchronously""" + player_stream = p.open( + format=pyaudio.paInt16, + channels=1, + rate=24000, + output=True + ) + + start_time = time.time() + print(f"Starting stream {stream_id}") + + try: + async with openai.audio.speech.with_streaming_response.create( + model="kokoro", + voice="af_bella", + response_format="pcm", + input=text + ) as response: + print(f"Stream {stream_id} - Time to first byte: {int((time.time() - start_time) * 1000)}ms") + + async for chunk in response.iter_bytes(chunk_size=1024): + player_stream.write(chunk) + # Small sleep to allow other coroutines to run + await asyncio.sleep(0.001) + + print(f"Stream {stream_id} completed in {int((time.time() - start_time) * 1000)}ms") + + finally: + player_stream.stop_stream() + player_stream.close() + +async def save_to_file(text: str, file_id: int) -> None: + """Save TTS output to file asynchronously""" + speech_file_path = Path(__file__).parent / f"speech_{file_id}.mp3" + + async with openai.audio.speech.with_streaming_response.create( + model="kokoro", + voice="af_bella", + input=text + ) as response: + # Open file in binary write mode + with open(speech_file_path, 'wb') as f: + async for chunk in response.iter_bytes(): + f.write(chunk) + print(f"File {file_id} saved to {speech_file_path}") + +async def main() -> None: + # Different text samples for variety + texts = [ + "The quick brown fox jumped over the lazy dogs. I see skies of blue and clouds of white", + "I see skies of blue and clouds of white. I see skies of blue and clouds of white", + ] + + # Create tasks for streaming to speakers + speaker_tasks = [ + stream_to_speakers(text, i) + for i, text in enumerate(texts) + ] + + # Create tasks for saving to files + file_tasks = [ + save_to_file(text, i) + for i, text in enumerate(texts) + ] + + # Combine all tasks + all_tasks = speaker_tasks + file_tasks + + # Run all tasks concurrently + try: + await asyncio.gather(*all_tasks) + finally: + # Clean up PyAudio + p.terminate() + +if __name__ == "__main__": + asyncio.run(main()) \ No newline at end of file diff --git a/examples/simul_speaker_test.py b/examples/simul_speaker_test.py new file mode 100644 index 0000000..d2e4354 --- /dev/null +++ b/examples/simul_speaker_test.py @@ -0,0 +1,66 @@ +#!/usr/bin/env rye run python +import asyncio +import time +import pyaudio +from openai import AsyncOpenAI + +# Initialize async client +openai = AsyncOpenAI(base_url="http://localhost:8880/v1", api_key="not-needed-for-local") + +# Create a shared PyAudio instance +p = pyaudio.PyAudio() + +async def stream_to_speakers(text: str, stream_id: int) -> None: + """Stream TTS audio to speakers asynchronously""" + player_stream = p.open( + format=pyaudio.paInt16, + channels=1, + rate=24000, + output=True + ) + + start_time = time.time() + print(f"Starting stream {stream_id}") + + try: + async with openai.audio.speech.with_streaming_response.create( + model="kokoro", + voice="af_bella", + response_format="pcm", + input=text + ) as response: + print(f"Stream {stream_id} - Time to first byte: {int((time.time() - start_time) * 1000)}ms") + + async for chunk in response.iter_bytes(chunk_size=1024): + player_stream.write(chunk) + # Small sleep to allow other coroutines to run + await asyncio.sleep(0.001) + + print(f"Stream {stream_id} completed in {int((time.time() - start_time) * 1000)}ms") + + finally: + player_stream.stop_stream() + player_stream.close() + +async def main() -> None: + # Different text samples for variety + texts = [ + "The quick brown fox jumped over the lazy dogs. I see skies of blue and clouds of white", + "I see skies of blue and clouds of white. I see skies of blue and clouds of white", + ] + + # Create tasks for streaming to speakers + speaker_tasks = [ + stream_to_speakers(text, i) + for i, text in enumerate(texts) + ] + + # Run speaker tasks concurrently + try: + await asyncio.gather(*speaker_tasks) + finally: + # Clean up PyAudio + p.terminate() + +if __name__ == "__main__": + asyncio.run(main()) \ No newline at end of file diff --git a/examples/speech.mp3 b/examples/speech.mp3 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..be3431b8562f5065dfb4f5f81ff1eae2a4511047 100644 GIT binary patch literal 73440 zcmeFZc~DbXxA1*3l7tYF5Fmk|Nq`t8JqbYpwL5_j0z?gy0-`2Nf>R@);?Q;`LV$?O zB#0VD0nuh~Kt(%@GN~ZWtu~X0V>`FqZR@Act$V+!@BQa{{=9EJ`&25YDm#^R*00uD zd#|;2zPz331^#;kZA<#$>%HaImmYx3n*d-)l(_|-Xl-jprO}<4u5OD!u9uJRcYJ@L zC}f2sEOJdutW=hmv}yAXsTn(V@7b?VX|#C<3W`e0D=Ljub@ffhe{MN-`rP?TT|NB+ z!&gSH-@JYA{>0>?*{9EbfA#MDpMQP+`dczFe?@@jLN*9OGXGl?lFwn?Y|M2}^2<_hx`Tu&poHTz|M^8e}LOQ2iUx772|ePCf?iQT 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z`SRs(b*4{`TTU;FVM9z8kSwIzm#+q?x5q1q!h|=Wu$Xuf8&<_m+!yvu>Kof P`oDbt8$$axZu$QJ+4lEo literal 0 HcmV?d00001 From d50214d3be9c1f8f799c4c3b7495dc53e1d54bae Mon Sep 17 00:00:00 2001 From: remsky Date: Wed, 22 Jan 2025 05:00:38 -0700 Subject: [PATCH 04/32] Enable ONNX GPU support in Docker configurations and refactor model file handling --- README.md | 3 + api/src/core/config.py | 21 +- api/src/core/model_config.py | 6 +- api/src/inference/model_manager.py | 25 +- api/src/inference/session_pool.py | 18 +- api/src/inference/voice_manager.py | 7 +- api/src/main.py | 6 +- api/src/routers/openai_compatible.py | 75 +- api/src/services/tts_service.py | 7 +- docker/cpu/Dockerfile | 3 +- docker/cpu/pyproject.toml | 22 - docker/cpu/requirements.lock | 229 --- docker/cpu/uv.lock | 1841 ------------------------- docker/gpu/Dockerfile | 13 +- docker/gpu/pyproject.toml | 22 - docker/gpu/requirements.lock | 229 --- docker/gpu/uv.lock | 1914 -------------------------- examples/speech.mp3 | Bin 73440 -> 73440 bytes pyproject.toml | 3 +- 19 files changed, 123 insertions(+), 4321 deletions(-) delete mode 100644 docker/cpu/pyproject.toml delete mode 100644 docker/cpu/requirements.lock delete mode 100644 docker/cpu/uv.lock delete mode 100644 docker/gpu/pyproject.toml delete mode 100644 docker/gpu/requirements.lock delete mode 100644 docker/gpu/uv.lock diff --git a/README.md b/README.md index 156fd97..0414b29 100644 --- a/README.md +++ b/README.md @@ -7,6 +7,9 @@ [![Coverage](https://img.shields.io/badge/coverage-60%25-grey)]() [![Tested at Model Commit](https://img.shields.io/badge/last--tested--model--commit-a67f113-blue)](https://huggingface.co/hexgrad/Kokoro-82M/tree/c3b0d86e2a980e027ef71c28819ea02e351c2667) [![Try on Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Try%20on-Spaces-blue)](https://huggingface.co/spaces/Remsky/Kokoro-TTS-Zero) +> [!INFO] +> Pre-release. Not fully tested + Dockerized FastAPI wrapper for [Kokoro-82M](https://huggingface.co/hexgrad/Kokoro-82M) text-to-speech model - OpenAI-compatible Speech endpoint, with inline voice combination functionality - NVIDIA GPU accelerated or CPU Onnx inference diff --git a/api/src/core/config.py b/api/src/core/config.py index 03ce30e..3269665 100644 --- a/api/src/core/config.py +++ b/api/src/core/config.py @@ -9,37 +9,22 @@ class Settings(BaseSettings): host: str = "0.0.0.0" port: int = 8880 - # TTS Settings + # Application Settings output_dir: str = "output" output_dir_size_limit_mb: float = 500.0 # Maximum size of output directory in MB default_voice: str = "af" use_gpu: bool = False # Whether to use GPU acceleration if available use_onnx: bool = True # Whether to use ONNX runtime + # Container absolute paths model_dir: str = "/app/api/src/models" # Absolute path in container voices_dir: str = "/app/api/src/voices" # Absolute path in container - # Model filenames - pytorch_model_file: str = "kokoro-v0_19.pth" - onnx_model_file: str = "kokoro-v0_19.onnx" + # Audio Settings sample_rate: int = 24000 max_chunk_size: int = 300 # Maximum size of text chunks for processing gap_trim_ms: int = 250 # Amount to trim from streaming chunk ends in milliseconds - # ONNX Optimization Settings - onnx_num_threads: int = 4 # Number of threads for intra-op parallelism - onnx_inter_op_threads: int = 4 # Number of threads for inter-op parallelism - onnx_execution_mode: str = "parallel" # parallel or sequential - onnx_optimization_level: str = "all" # all, basic, or disabled - onnx_memory_pattern: bool = True # Enable memory pattern optimization - onnx_arena_extend_strategy: str = "kNextPowerOfTwo" # Memory allocation strategy - - # ONNX GPU Settings - onnx_device_id: int = 0 # GPU device ID to use - onnx_gpu_mem_limit: float = 0.7 # Limit GPU memory usage to 70% - onnx_cudnn_conv_algo_search: str = "EXHAUSTIVE" # CUDNN convolution algorithm search - onnx_do_copy_in_default_stream: bool = True # Copy in default CUDA stream - class Config: env_file = ".env" diff --git a/api/src/core/model_config.py b/api/src/core/model_config.py index f9ae254..7cb7b58 100644 --- a/api/src/core/model_config.py +++ b/api/src/core/model_config.py @@ -11,7 +11,7 @@ class ONNXCPUConfig(BaseModel): instance_timeout: int = Field(300, description="Session timeout in seconds") # Runtime settings - num_threads: int = Field(8, description="Number of threads for parallel operations") + num_threads: int = Field(4, description="Number of threads for parallel operations") inter_op_threads: int = Field(4, description="Number of threads for operator parallelism") execution_mode: str = Field("parallel", description="ONNX execution mode") optimization_level: str = Field("all", description="ONNX optimization level") @@ -77,6 +77,10 @@ class ModelConfig(BaseModel): cache_voices: bool = Field(True, description="Whether to cache voice tensors") voice_cache_size: int = Field(2, description="Maximum number of cached voices") + # Model filenames + pytorch_model_file: str = Field("kokoro-v0_19.pth", description="PyTorch model filename") + onnx_model_file: str = Field("kokoro-v0_19.onnx", description="ONNX model filename") + # Backend-specific configs onnx_cpu: ONNXCPUConfig = Field(default_factory=ONNXCPUConfig) onnx_gpu: ONNXGPUConfig = Field(default_factory=ONNXGPUConfig) diff --git a/api/src/inference/model_manager.py b/api/src/inference/model_manager.py index ed67ce8..34a7075 100644 --- a/api/src/inference/model_manager.py +++ b/api/src/inference/model_manager.py @@ -17,26 +17,27 @@ from .pytorch_gpu import PyTorchGPUBackend from .session_pool import CPUSessionPool, StreamingSessionPool -# Global singleton instance and state +# Global singleton instance and lock for thread-safe initialization _manager_instance = None _manager_lock = asyncio.Lock() -_loaded_models = {} -_backends = {} - class ModelManager: """Manages model loading and inference across backends.""" + # Class-level state for shared resources + _loaded_models = {} + _backends = {} + def __init__(self, config: Optional[ModelConfig] = None): """Initialize model manager. Args: config: Optional configuration + + Note: + This should not be called directly. Use get_manager() instead. """ self._config = config or model_config - global _loaded_models, _backends - self._loaded_models = _loaded_models - self._backends = _backends # Initialize session pools self._session_pools = { @@ -293,10 +294,20 @@ async def get_manager(config: Optional[ModelConfig] = None) -> ModelManager: Returns: ModelManager instance + + Thread Safety: + This function is thread-safe and ensures only one instance is created + even under concurrent access. """ global _manager_instance + # Fast path - return existing instance without lock + if _manager_instance is not None: + return _manager_instance + + # Slow path - create new instance with lock async with _manager_lock: + # Double-check pattern if _manager_instance is None: _manager_instance = ModelManager(config) await _manager_instance.initialize() diff --git a/api/src/inference/session_pool.py b/api/src/inference/session_pool.py index 4674a91..1e2fcb4 100644 --- a/api/src/inference/session_pool.py +++ b/api/src/inference/session_pool.py @@ -75,20 +75,16 @@ def create_provider_options(is_gpu: bool = False) -> Dict: if is_gpu: config = model_config.onnx_gpu return { - "CUDAExecutionProvider": { - "device_id": config.device_id, - "arena_extend_strategy": config.arena_extend_strategy, - "gpu_mem_limit": int(config.gpu_mem_limit * torch.cuda.get_device_properties(0).total_memory), - "cudnn_conv_algo_search": config.cudnn_conv_algo_search, - "do_copy_in_default_stream": config.do_copy_in_default_stream - } + "device_id": config.device_id, + "arena_extend_strategy": config.arena_extend_strategy, + "gpu_mem_limit": int(config.gpu_mem_limit * torch.cuda.get_device_properties(0).total_memory), + "cudnn_conv_algo_search": config.cudnn_conv_algo_search, + "do_copy_in_default_stream": config.do_copy_in_default_stream } else: return { - "CPUExecutionProvider": { - "arena_extend_strategy": model_config.onnx_cpu.arena_extend_strategy, - "cpu_memory_arena_cfg": "cpu:0" - } + "arena_extend_strategy": model_config.onnx_cpu.arena_extend_strategy, + "cpu_memory_arena_cfg": "cpu:0" } diff --git a/api/src/inference/voice_manager.py b/api/src/inference/voice_manager.py index 67f34a3..ec97215 100644 --- a/api/src/inference/voice_manager.py +++ b/api/src/inference/voice_manager.py @@ -73,12 +73,13 @@ class VoiceManager: return voice def _manage_cache(self) -> None: - """Manage voice cache size.""" + """Manage voice cache size using simple LRU.""" if len(self._voice_cache) >= self._config.cache_size: - # Remove oldest voice + # Remove least recently used voice oldest = next(iter(self._voice_cache)) del self._voice_cache[oldest] - logger.debug(f"Removed from voice cache: {oldest}") + torch.cuda.empty_cache() # Clean up GPU memory if needed + logger.debug(f"Removed LRU voice from cache: {oldest}") async def combine_voices(self, voices: List[str], device: str = "cpu") -> str: """Combine multiple voices into a new voice. diff --git a/api/src/main.py b/api/src/main.py index 8516325..89b9250 100644 --- a/api/src/main.py +++ b/api/src/main.py @@ -1,4 +1,3 @@ - """ FastAPI OpenAI Compatible API """ @@ -14,6 +13,7 @@ from fastapi.middleware.cors import CORSMiddleware from loguru import logger from .core.config import settings +from .core.model_config import model_config from .routers.development import router as dev_router from .routers.openai_compatible import router as openai_router from .services.tts_service import TTSService @@ -63,8 +63,8 @@ async def lifespan(app: FastAPI): # Get backend and initialize model backend = model_manager.get_backend(backend_type) - # Use model path directly from settings - model_file = settings.pytorch_model_file if not settings.use_onnx else settings.onnx_model_file + # Use model path from model_config + model_file = model_config.pytorch_model_file if not settings.use_onnx else model_config.onnx_model_file model_path = os.path.join(settings.model_dir, model_file) diff --git a/api/src/routers/openai_compatible.py b/api/src/routers/openai_compatible.py index 4d1ca7a..80d8a53 100644 --- a/api/src/routers/openai_compatible.py +++ b/api/src/routers/openai_compatible.py @@ -130,12 +130,12 @@ async def create_speech( }, ) else: - # Generate complete audio - audio, _ = tts_service._generate_audio( + # Generate complete audio using public interface + audio, _ = await tts_service.generate_audio( text=request.input, voice=voice_to_use, speed=request.speed, - stitch_long_output=True, + stitch_long_output=True ) # Convert to requested format @@ -153,14 +153,37 @@ async def create_speech( ) except ValueError as e: - logger.error(f"Invalid request: {str(e)}") + # Handle validation errors + logger.warning(f"Invalid request: {str(e)}") raise HTTPException( - status_code=400, detail={"error": "Invalid request", "message": str(e)} + status_code=400, + detail={ + "error": "validation_error", + "message": str(e), + "type": "invalid_request_error" + } + ) + except RuntimeError as e: + # Handle runtime/processing errors + logger.error(f"Processing error: {str(e)}") + raise HTTPException( + status_code=500, + detail={ + "error": "processing_error", + "message": "Failed to process audio generation request", + "type": "server_error" + } ) except Exception as e: - logger.error(f"Error generating speech: {str(e)}") + # Handle unexpected errors + logger.error(f"Unexpected error in speech generation: {str(e)}") raise HTTPException( - status_code=500, detail={"error": "Server error", "message": str(e)} + status_code=500, + detail={ + "error": "server_error", + "message": "An unexpected error occurred", + "type": "server_error" + } ) @@ -173,7 +196,14 @@ async def list_voices(): return {"voices": voices} except Exception as e: logger.error(f"Error listing voices: {str(e)}") - raise HTTPException(status_code=500, detail=str(e)) + raise HTTPException( + status_code=500, + detail={ + "error": "server_error", + "message": "Failed to retrieve voice list", + "type": "server_error" + } + ) @router.post("/audio/voices/combine") @@ -199,13 +229,32 @@ async def combine_voices(request: Union[str, List[str]]): return {"voices": voices, "voice": combined_voice} except ValueError as e: - logger.error(f"Invalid voice combination request: {str(e)}") + logger.warning(f"Invalid voice combination request: {str(e)}") raise HTTPException( - status_code=400, detail={"error": "Invalid request", "message": str(e)} + status_code=400, + detail={ + "error": "validation_error", + "message": str(e), + "type": "invalid_request_error" + } + ) + except RuntimeError as e: + logger.error(f"Voice combination processing error: {str(e)}") + raise HTTPException( + status_code=500, + detail={ + "error": "processing_error", + "message": "Failed to process voice combination request", + "type": "server_error" + } ) - except Exception as e: - logger.error(f"Server error during voice combination: {str(e)}") + logger.error(f"Unexpected error in voice combination: {str(e)}") raise HTTPException( - status_code=500, detail={"error": "Server error", "message": "Server error"} + status_code=500, + detail={ + "error": "server_error", + "message": "An unexpected error occurred", + "type": "server_error" + } ) diff --git a/api/src/services/tts_service.py b/api/src/services/tts_service.py index 83bf8e1..da4fc24 100644 --- a/api/src/services/tts_service.py +++ b/api/src/services/tts_service.py @@ -51,7 +51,7 @@ class TTSService: return service async def generate_audio( - self, text: str, voice: str, speed: float = 1.0 + self, text: str, voice: str, speed: float = 1.0, stitch_long_output: bool = True ) -> Tuple[np.ndarray, float]: """Generate audio for text. @@ -59,9 +59,14 @@ class TTSService: text: Input text voice: Voice name speed: Speed multiplier + stitch_long_output: Whether to stitch together long outputs Returns: Audio samples and processing time + + Raises: + ValueError: If text is empty after preprocessing or no chunks generated + RuntimeError: If audio generation fails """ start_time = time.time() voice_tensor = None diff --git a/docker/cpu/Dockerfile b/docker/cpu/Dockerfile index 0613022..a834649 100644 --- a/docker/cpu/Dockerfile +++ b/docker/cpu/Dockerfile @@ -10,7 +10,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends \ && apt-get clean \ && rm -rf /var/lib/apt/lists/* -# Install uv +# Install uv for speed and glory COPY --from=ghcr.io/astral-sh/uv:latest /uv /uvx /bin/ # Create non-root user @@ -44,6 +44,7 @@ ENV PYTHONPATH=/app ENV PATH="/app/.venv/bin:$PATH" ENV UV_LINK_MODE=copy ENV USE_GPU=false +ENV USE_ONNX=true # Run FastAPI server CMD ["uv", "run", "python", "-m", "uvicorn", "api.src.main:app", "--host", "0.0.0.0", "--port", "8880", "--log-level", "debug"] diff --git a/docker/cpu/pyproject.toml b/docker/cpu/pyproject.toml deleted file mode 100644 index 81dae2e..0000000 --- a/docker/cpu/pyproject.toml +++ /dev/null @@ -1,22 +0,0 @@ -[project] -name = "kokoro-fastapi-cpu" -version = "0.1.0" -description = "FastAPI TTS Service - CPU Version" -readme = "../README.md" -requires-python = ">=3.10" -dependencies = [ - # Core ML/DL for CPU - "torch>=2.5.1", - "transformers==4.47.1", -] - -[tool.uv.workspace] -members = ["../shared"] - -[tool.uv.sources] -torch = { index = "pytorch-cpu" } - -[[tool.uv.index]] -name = "pytorch-cpu" -url = "https://download.pytorch.org/whl/cpu" -explicit = true diff --git a/docker/cpu/requirements.lock b/docker/cpu/requirements.lock deleted file mode 100644 index 343e5f2..0000000 --- a/docker/cpu/requirements.lock +++ /dev/null @@ -1,229 +0,0 @@ -# This file was autogenerated by uv via the following command: -# uv pip compile pyproject.toml ../shared/pyproject.toml --output-file requirements.lock -aiofiles==23.2.1 - # via kokoro-fastapi (../shared/pyproject.toml) -annotated-types==0.7.0 - # via pydantic -anyio==4.8.0 - # via starlette -attrs==24.3.0 - # via - # clldutils - # csvw - # jsonschema - # phonemizer - # referencing -babel==2.16.0 - # via csvw -certifi==2024.12.14 - # via requests -cffi==1.17.1 - # via soundfile -charset-normalizer==3.4.1 - # via requests -click==8.1.8 - # via - # kokoro-fastapi (../shared/pyproject.toml) - # uvicorn -clldutils==3.21.0 - # via segments -colorama==0.4.6 - # via - # click - # colorlog - # csvw - # loguru - # tqdm -coloredlogs==15.0.1 - # via onnxruntime -colorlog==6.9.0 - # via clldutils -csvw==3.5.1 - # via segments -dlinfo==1.2.1 - # via phonemizer -exceptiongroup==1.2.2 - # via anyio -fastapi==0.115.6 - # via kokoro-fastapi (../shared/pyproject.toml) -filelock==3.16.1 - # via - # huggingface-hub - # torch - # transformers -flatbuffers==24.12.23 - # via onnxruntime -fsspec==2024.12.0 - # via - # huggingface-hub - # torch -greenlet==3.1.1 - # via sqlalchemy -h11==0.14.0 - # via uvicorn -huggingface-hub==0.27.1 - # via - # tokenizers - # transformers -humanfriendly==10.0 - # via coloredlogs -idna==3.10 - # via - # anyio - # requests -isodate==0.7.2 - # via - # csvw - # rdflib -jinja2==3.1.5 - # via torch -joblib==1.4.2 - # via phonemizer -jsonschema==4.23.0 - # via csvw -jsonschema-specifications==2024.10.1 - # via jsonschema -language-tags==1.2.0 - # via csvw -loguru==0.7.3 - # via kokoro-fastapi (../shared/pyproject.toml) -lxml==5.3.0 - # via clldutils -markdown==3.7 - # via clldutils -markupsafe==3.0.2 - # via - # clldutils - # jinja2 -mpmath==1.3.0 - # via sympy -munch==4.0.0 - # via kokoro-fastapi (../shared/pyproject.toml) -networkx==3.4.2 - # via torch -numpy==2.2.1 - # via - # kokoro-fastapi (../shared/pyproject.toml) - # onnxruntime - # scipy - # soundfile - # transformers -onnxruntime==1.20.1 - # via kokoro-fastapi (../shared/pyproject.toml) -packaging==24.2 - # via - # huggingface-hub - # onnxruntime - # transformers -phonemizer==3.3.0 - # via kokoro-fastapi (../shared/pyproject.toml) -protobuf==5.29.3 - # via onnxruntime -pycparser==2.22 - # via cffi -pydantic==2.10.4 - # via - # kokoro-fastapi (../shared/pyproject.toml) - # fastapi - # pydantic-settings -pydantic-core==2.27.2 - # via pydantic -pydantic-settings==2.7.0 - # via kokoro-fastapi (../shared/pyproject.toml) -pylatexenc==2.10 - # via clldutils -pyparsing==3.2.1 - # via rdflib -pyreadline3==3.5.4 - # via humanfriendly -python-dateutil==2.9.0.post0 - # via - # clldutils - # csvw -python-dotenv==1.0.1 - # via - # kokoro-fastapi (../shared/pyproject.toml) - # pydantic-settings -pyyaml==6.0.2 - # via - # huggingface-hub - # transformers -rdflib==7.1.2 - # via csvw -referencing==0.35.1 - # via - # jsonschema - # jsonschema-specifications -regex==2024.11.6 - # via - # kokoro-fastapi (../shared/pyproject.toml) - # segments - # tiktoken - # transformers -requests==2.32.3 - # via - # kokoro-fastapi (../shared/pyproject.toml) - # csvw - # huggingface-hub - # tiktoken - # transformers -rfc3986==1.5.0 - # via csvw -rpds-py==0.22.3 - # via - # jsonschema - # referencing -safetensors==0.5.2 - # via transformers -scipy==1.14.1 - # via kokoro-fastapi (../shared/pyproject.toml) -segments==2.2.1 - # via phonemizer -six==1.17.0 - # via python-dateutil -sniffio==1.3.1 - # via anyio -soundfile==0.13.0 - # via kokoro-fastapi (../shared/pyproject.toml) -sqlalchemy==2.0.27 - # via kokoro-fastapi (../shared/pyproject.toml) -starlette==0.41.3 - # via fastapi -sympy==1.13.1 - # via - # onnxruntime - # torch -tabulate==0.9.0 - # via clldutils -tiktoken==0.8.0 - # via kokoro-fastapi (../shared/pyproject.toml) -tokenizers==0.21.0 - # via transformers -torch==2.5.1+cpu - # via kokoro-fastapi-cpu 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b/docker/gpu/Dockerfile @@ -1,8 +1,11 @@ -FROM pytorch/pytorch:2.1.0-cuda11.8-cudnn8-runtime +FROM nvidia/cuda:12.3.2-cudnn9-runtime-ubuntu22.04 # Set non-interactive frontend ENV DEBIAN_FRONTEND=noninteractive -# Install dependencies + +# Install Python and other dependencies RUN apt-get update && apt-get install -y --no-install-recommends \ + python3.10 \ + python3.10-venv \ espeak-ng \ git \ libsndfile1 \ @@ -27,15 +30,15 @@ WORKDIR /app # Copy dependency files COPY --chown=appuser:appuser pyproject.toml ./pyproject.toml -# Install dependencies +# Install dependencies with GPU extras RUN --mount=type=cache,target=/root/.cache/uv \ uv venv && \ - uv sync --extra gpu --no-install-project + uv sync --extra gpu # Copy project files COPY --chown=appuser:appuser api ./api -# Install project +# Install project with GPU extras RUN --mount=type=cache,target=/root/.cache/uv \ uv sync --extra gpu diff --git a/docker/gpu/pyproject.toml b/docker/gpu/pyproject.toml deleted file mode 100644 index bd66ded..0000000 --- a/docker/gpu/pyproject.toml +++ /dev/null @@ -1,22 +0,0 @@ -[project] -name = "kokoro-fastapi-gpu" -version = "0.1.0" -description = "FastAPI TTS Service - GPU Version" -readme = "../README.md" -requires-python = ">=3.10" -dependencies = [ - # Core ML/DL for GPU - "torch==2.5.1+cu121", - "transformers==4.47.1", -] - -[tool.uv.workspace] -members = ["../shared"] - -[tool.uv.sources] -torch = { index = "pytorch-cuda" } - -[[tool.uv.index]] -name = "pytorch-cuda" -url = "https://download.pytorch.org/whl/cu121" -explicit = true diff --git a/docker/gpu/requirements.lock b/docker/gpu/requirements.lock deleted file mode 100644 index c65310d..0000000 --- a/docker/gpu/requirements.lock +++ /dev/null @@ -1,229 +0,0 @@ -# This file was autogenerated by uv via the following command: -# uv pip compile pyproject.toml ../shared/pyproject.toml --output-file requirements.lock -aiofiles==23.2.1 - # via kokoro-fastapi (../shared/pyproject.toml) -annotated-types==0.7.0 - # via pydantic -anyio==4.8.0 - # via starlette -attrs==24.3.0 - # via - # clldutils - # csvw - # jsonschema - # phonemizer - # referencing -babel==2.16.0 - # via csvw -certifi==2024.12.14 - # via requests -cffi==1.17.1 - # via soundfile -charset-normalizer==3.4.1 - # via requests -click==8.1.8 - # via - # kokoro-fastapi (../shared/pyproject.toml) - # uvicorn -clldutils==3.21.0 - # via segments -colorama==0.4.6 - # via - # click - # colorlog - # csvw - # loguru - # tqdm -coloredlogs==15.0.1 - # via onnxruntime -colorlog==6.9.0 - # via clldutils -csvw==3.5.1 - # via segments -dlinfo==1.2.1 - # via phonemizer -exceptiongroup==1.2.2 - # via anyio -fastapi==0.115.6 - # via kokoro-fastapi (../shared/pyproject.toml) -filelock==3.16.1 - # via - # huggingface-hub - # torch - # transformers -flatbuffers==24.12.23 - # via onnxruntime -fsspec==2024.12.0 - # via - # huggingface-hub - # torch -greenlet==3.1.1 - # via sqlalchemy -h11==0.14.0 - # via uvicorn -huggingface-hub==0.27.1 - # via - # tokenizers - # transformers -humanfriendly==10.0 - # via coloredlogs -idna==3.10 - # via - # anyio - # requests -isodate==0.7.2 - # via - # csvw - # rdflib -jinja2==3.1.5 - # via torch -joblib==1.4.2 - # via phonemizer -jsonschema==4.23.0 - # via csvw -jsonschema-specifications==2024.10.1 - # via jsonschema -language-tags==1.2.0 - # via csvw -loguru==0.7.3 - # via kokoro-fastapi (../shared/pyproject.toml) -lxml==5.3.0 - # via clldutils -markdown==3.7 - # via clldutils -markupsafe==3.0.2 - # via - # clldutils - # jinja2 -mpmath==1.3.0 - # via sympy -munch==4.0.0 - # via kokoro-fastapi (../shared/pyproject.toml) -networkx==3.4.2 - # via torch -numpy==2.2.1 - # via - # kokoro-fastapi (../shared/pyproject.toml) - # onnxruntime - # scipy - # soundfile - # transformers -onnxruntime==1.20.1 - # via kokoro-fastapi (../shared/pyproject.toml) -packaging==24.2 - # via - # huggingface-hub - # onnxruntime - # transformers -phonemizer==3.3.0 - # via kokoro-fastapi (../shared/pyproject.toml) -protobuf==5.29.3 - # via onnxruntime -pycparser==2.22 - # via cffi -pydantic==2.10.4 - # via - # kokoro-fastapi (../shared/pyproject.toml) - # fastapi - # pydantic-settings -pydantic-core==2.27.2 - # via pydantic -pydantic-settings==2.7.0 - # via kokoro-fastapi (../shared/pyproject.toml) -pylatexenc==2.10 - # via clldutils -pyparsing==3.2.1 - # via rdflib -pyreadline3==3.5.4 - # via humanfriendly -python-dateutil==2.9.0.post0 - # via - # clldutils - # csvw -python-dotenv==1.0.1 - # via - # kokoro-fastapi (../shared/pyproject.toml) - # pydantic-settings -pyyaml==6.0.2 - # via - # huggingface-hub - # transformers -rdflib==7.1.2 - # via csvw -referencing==0.35.1 - # via - # jsonschema - # jsonschema-specifications -regex==2024.11.6 - # via - # kokoro-fastapi (../shared/pyproject.toml) - # segments - # tiktoken - # transformers -requests==2.32.3 - # via - # kokoro-fastapi (../shared/pyproject.toml) - # csvw - # huggingface-hub - # tiktoken - # transformers -rfc3986==1.5.0 - # via csvw -rpds-py==0.22.3 - # via - # jsonschema - # referencing -safetensors==0.5.2 - # via transformers -scipy==1.14.1 - # via kokoro-fastapi (../shared/pyproject.toml) -segments==2.2.1 - # via phonemizer -six==1.17.0 - # via python-dateutil -sniffio==1.3.1 - # via anyio -soundfile==0.13.0 - # via kokoro-fastapi (../shared/pyproject.toml) -sqlalchemy==2.0.27 - # via kokoro-fastapi (../shared/pyproject.toml) -starlette==0.41.3 - # via fastapi -sympy==1.13.1 - # via - # onnxruntime - # torch -tabulate==0.9.0 - # via clldutils -tiktoken==0.8.0 - # via kokoro-fastapi (../shared/pyproject.toml) -tokenizers==0.21.0 - # via transformers -torch==2.5.1+cu121 - # via kokoro-fastapi-gpu (pyproject.toml) -tqdm==4.67.1 - # via - # kokoro-fastapi (../shared/pyproject.toml) - # huggingface-hub - # transformers -transformers==4.47.1 - # via kokoro-fastapi-gpu (pyproject.toml) -typing-extensions==4.12.2 - # via - # anyio - # fastapi - # huggingface-hub - # phonemizer - # pydantic - # pydantic-core - # sqlalchemy - # torch - # uvicorn 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z$$`?snucm-Vq~Et;Hpg_99@J|oC60OSJ^W|A(j}LXuKY-<(>8a97h3$Bi*X`C`7`d zc$ywmkB%R~&qvthgd3@;sWm4r21rk*rTCXk(u1O)Qx60e<{50bY+gFazIJ4z33Y{hScYw>BObr4Xv!14aCfbW&Ke<;k zj3HrCfrB!6R5{#>LFOB-iNl7EwYubLz4D18x&6s_2$J#YRJw<@NeOYYgEi9#H0BE< zbyCls9#&lzzZu%%s(piqih1n0e`KiYiOL>d%tm{lGk?ZCh{8@0meQu82q9 z+c}LZ;=+M_FI#@TFt#v>vg$*kQ_PqvZ0xG6rBSMJ?!#D@ zk$H}4T%NJmw}a*BUsUE(WEg+MFZ4*n{GcFDeoZ}x#dzIBU>+rao+LlZn8_2iP)+Cy z&9GHo4{5jH$9AI)1q_n4wvF=>+y}<1Ka=}55?=F-dNW^)&X#wAyNu8hqC5SQAda!42A^bpreSPcLsBT;G1FIrUVs9*nx&yI89V&SrVas>mWTMAq>%j zWx5k4wkRkxS^emE*T24A|BqVMzv<-{eE|S~@bn*lV;KLF^7HR% From df4cc5b4b2397473895362cdf294025d115d42df Mon Sep 17 00:00:00 2001 From: remsky Date: Wed, 22 Jan 2025 21:11:47 -0700 Subject: [PATCH 06/32] -Adjust testing framework for new model -Add web player support: include static file serving and HTML interface for TTS --- api/depr_tests/test_endpoints.py | 206 ++++++++ api/depr_tests/test_main.py | 104 ++++ api/depr_tests/test_managers.py | 190 +++++++ api/depr_tests/test_text_processing.py | 139 +++++ api/depr_tests/test_tts_service.py | 118 +++++ api/src/core/config.py | 7 + api/src/core/paths.py | 74 ++- api/src/inference/model_manager.py | 84 ++- api/src/inference/voice_manager.py | 38 +- api/src/main.py | 74 +-- api/src/routers/openai_compatible.py | 25 +- api/src/routers/web_player.py | 48 ++ api/src/structures/text_schemas.py | 10 +- api/tests/conftest.py | 178 +++---- api/tests/test_endpoints.py | 402 --------------- api/tests/test_main.py | 108 ---- api/tests/test_openai_endpoints.py | 215 ++++++++ api/tests/test_text_processing.py | 122 ----- api/tests/test_tts_implementations.py | 201 -------- api/tests/test_tts_service.py | 260 ---------- api/tests/test_tts_service_new.py | 140 +++++ api/tests/test_voice_manager.py | 82 +++ docker/cpu/Dockerfile | 1 + docker/gpu/Dockerfile | 1 + pyproject.toml | 1 + ui/tests/conftest.py | 47 +- uv.lock | 675 ++++++++++++++++++++++--- web/app.js | 203 ++++++++ web/index.html | 51 ++ web/styles.css | 176 +++++++ 30 files changed, 2611 insertions(+), 1369 deletions(-) create mode 100644 api/depr_tests/test_endpoints.py create mode 100644 api/depr_tests/test_main.py create mode 100644 api/depr_tests/test_managers.py create mode 100644 api/depr_tests/test_text_processing.py create mode 100644 api/depr_tests/test_tts_service.py create mode 100644 api/src/routers/web_player.py delete mode 100644 api/tests/test_endpoints.py delete mode 100644 api/tests/test_main.py create mode 100644 api/tests/test_openai_endpoints.py delete mode 100644 api/tests/test_text_processing.py delete mode 100644 api/tests/test_tts_implementations.py delete mode 100644 api/tests/test_tts_service.py create mode 100644 api/tests/test_tts_service_new.py create mode 100644 api/tests/test_voice_manager.py create mode 100644 web/app.js create mode 100644 web/index.html create mode 100644 web/styles.css diff --git a/api/depr_tests/test_endpoints.py b/api/depr_tests/test_endpoints.py new file mode 100644 index 0000000..b5af29e --- /dev/null +++ b/api/depr_tests/test_endpoints.py @@ -0,0 +1,206 @@ +"""Tests for API endpoints""" +import pytest +import torch +from fastapi.testclient import TestClient + +from ..src.main import app + +# Create test client for non-async tests +client = TestClient(app) + + +def test_health_check(): + """Test the health check endpoint""" + response = client.get("/health") + assert response.status_code == 200 + assert response.json() == {"status": "healthy"} + + +@pytest.mark.asyncio +async def test_openai_speech_endpoint(async_client, mock_tts_service): + """Test the OpenAI-compatible speech endpoint""" + # Setup mocks + mock_tts_service._voice_manager.list_voices.return_value = ["bm_lewis"] + mock_tts_service.generate_audio.return_value = (torch.zeros(48000).numpy(), 1.0) + mock_tts_service._voice_manager.load_voice.return_value = torch.zeros(192) + + # Mock voice validation + mock_tts_service._voice_manager.get_voice_path.return_value = "/mock/voices/bm_lewis.pt" + + test_request = { + "model": "kokoro", + "input": "Hello world", + "voice": "bm_lewis", + "response_format": "wav", + "speed": 1.0, + "stream": False, + } + response = await async_client.post("/v1/audio/speech", json=test_request) + assert response.status_code == 200 + assert response.headers["content-type"] == "audio/wav" + assert response.headers["content-disposition"] == "attachment; filename=speech.wav" + mock_tts_service.generate_audio.assert_called_once() + + +@pytest.mark.asyncio +async def test_openai_speech_invalid_voice(async_client, mock_tts_service): + """Test the OpenAI-compatible speech endpoint with invalid voice""" + # Setup mocks + mock_tts_service._voice_manager.list_voices.return_value = ["af", "bm_lewis"] + mock_tts_service._voice_manager.get_voice_path.return_value = None + + test_request = { + "model": "kokoro", + "input": "Hello world", + "voice": "invalid_voice", + "response_format": "wav", + "speed": 1.0, + "stream": False, + } + response = await async_client.post("/v1/audio/speech", json=test_request) + assert response.status_code == 400 + assert "not found" in response.json()["detail"]["message"] + + +@pytest.mark.asyncio +async def test_openai_speech_generation_error(async_client, mock_tts_service): + """Test error handling in speech generation""" + # Setup mocks + mock_tts_service._voice_manager.list_voices.return_value = ["af"] + mock_tts_service.generate_audio.side_effect = RuntimeError("Generation failed") + mock_tts_service._voice_manager.load_voice.return_value = torch.zeros(192) + mock_tts_service._voice_manager.get_voice_path.return_value = "/mock/voices/af.pt" + + test_request = { + "model": "kokoro", + "input": "Hello world", + "voice": "af", + "response_format": "wav", + "speed": 1.0, + "stream": False, + } + response = await async_client.post("/v1/audio/speech", json=test_request) + assert response.status_code == 500 + assert "Generation failed" in response.json()["detail"]["message"] + + +@pytest.mark.asyncio +async def test_combine_voices_list_success(async_client, mock_tts_service): + """Test successful voice combination using list format""" + # Setup mocks + mock_tts_service._voice_manager.list_voices.return_value = ["af_bella", "af_sarah"] + mock_tts_service._voice_manager.combine_voices.return_value = "af_bella_af_sarah" + mock_tts_service._voice_manager.load_voice.return_value = torch.zeros(192) + mock_tts_service._voice_manager.get_voice_path.return_value = "/mock/voices/af_bella.pt" + + test_voices = ["af_bella", "af_sarah"] + response = await async_client.post("/v1/audio/voices/combine", json=test_voices) + + assert response.status_code == 200 + assert response.json()["voice"] == "af_bella_af_sarah" + mock_tts_service._voice_manager.combine_voices.assert_called_once() + + +@pytest.mark.asyncio +async def test_combine_voices_empty_list(async_client, mock_tts_service): + """Test combining empty voice list returns error""" + test_voices = [] + response = await async_client.post("/v1/audio/voices/combine", json=test_voices) + assert response.status_code == 400 + assert "No voices provided" in response.json()["detail"]["message"] + + +@pytest.mark.asyncio +async def test_speech_streaming_with_combined_voice(async_client, mock_tts_service): + """Test streaming speech with combined voice using + syntax""" + # Setup mocks + mock_tts_service._voice_manager.list_voices.return_value = ["af_bella", "af_sarah"] + mock_tts_service._voice_manager.combine_voices.return_value = "af_bella_af_sarah" + mock_tts_service._voice_manager.load_voice.return_value = torch.zeros(192) + mock_tts_service._voice_manager.get_voice_path.return_value = "/mock/voices/af_bella.pt" + + async def mock_stream(): + yield b"chunk1" + yield b"chunk2" + + mock_tts_service.generate_audio_stream.return_value = mock_stream() + + test_request = { + "model": "kokoro", + "input": "Hello world", + "voice": "af_bella+af_sarah", + "response_format": "mp3", + "stream": True, + } + + headers = {"x-raw-response": "stream"} + response = await async_client.post( + "/v1/audio/speech", json=test_request, headers=headers + ) + + assert response.status_code == 200 + assert response.headers["content-type"] == "audio/mpeg" + assert response.headers["content-disposition"] == "attachment; filename=speech.mp3" + + +@pytest.mark.asyncio +async def test_openai_speech_pcm_streaming(async_client, mock_tts_service): + """Test streaming PCM audio for real-time playback""" + # Setup mocks + mock_tts_service._voice_manager.list_voices.return_value = ["af"] + mock_tts_service._voice_manager.load_voice.return_value = torch.zeros(192) + mock_tts_service._voice_manager.get_voice_path.return_value = "/mock/voices/af.pt" + + async def mock_stream(): + yield b"chunk1" + yield b"chunk2" + + mock_tts_service.generate_audio_stream.return_value = mock_stream() + + test_request = { + "model": "kokoro", + "input": "Hello world", + "voice": "af", + "response_format": "pcm", + "stream": True, + } + + headers = {"x-raw-response": "stream"} + response = await async_client.post( + "/v1/audio/speech", json=test_request, headers=headers + ) + + assert response.status_code == 200 + assert response.headers["content-type"] == "audio/pcm" + + +@pytest.mark.asyncio +async def test_openai_speech_streaming_mp3(async_client, mock_tts_service): + """Test streaming MP3 audio to file""" + # Setup mocks + mock_tts_service._voice_manager.list_voices.return_value = ["af"] + mock_tts_service._voice_manager.load_voice.return_value = torch.zeros(192) + mock_tts_service._voice_manager.get_voice_path.return_value = "/mock/voices/af.pt" + + async def mock_stream(): + yield b"chunk1" + yield b"chunk2" + + mock_tts_service.generate_audio_stream.return_value = mock_stream() + + test_request = { + "model": "kokoro", + "input": "Hello world", + "voice": "af", + "response_format": "mp3", + "stream": True, + } + + headers = {"x-raw-response": "stream"} + response = await async_client.post( + "/v1/audio/speech", json=test_request, headers=headers + ) + + assert response.status_code == 200 + assert response.headers["content-type"] == "audio/mpeg" + assert response.headers["content-disposition"] == "attachment; filename=speech.mp3" diff --git a/api/depr_tests/test_main.py b/api/depr_tests/test_main.py new file mode 100644 index 0000000..dd5ac12 --- /dev/null +++ b/api/depr_tests/test_main.py @@ -0,0 +1,104 @@ +"""Tests for FastAPI application""" +from unittest.mock import AsyncMock, MagicMock, patch + +import pytest +import torch +from fastapi.testclient import TestClient + +from api.src.main import app, lifespan + + +@pytest.fixture +def test_client(): + """Create a test client""" + return TestClient(app) + + +def test_health_check(test_client): + """Test health check endpoint""" + response = test_client.get("/health") + assert response.status_code == 200 + assert response.json() == {"status": "healthy"} + + +@pytest.mark.asyncio +async def test_lifespan_successful_warmup(): + """Test successful model warmup in lifespan""" + with patch("api.src.inference.model_manager.get_manager") as mock_get_model, \ + patch("api.src.inference.voice_manager.get_manager") as mock_get_voice, \ + patch("api.src.main.logger") as mock_logger, \ + patch("os.path.exists") as mock_exists, \ + patch("torch.cuda.is_available") as mock_cuda: + + # Setup mocks + mock_model = AsyncMock() + mock_voice = AsyncMock() + mock_get_model.return_value = mock_model + mock_get_voice.return_value = mock_voice + mock_exists.return_value = True + mock_cuda.return_value = False + + # Setup model manager + mock_backend = MagicMock() + mock_backend.device = "cpu" + mock_model.get_backend.return_value = mock_backend + mock_model.load_model = AsyncMock() + + # Setup voice manager + mock_voice_tensor = torch.zeros(192) + mock_voice.load_voice = AsyncMock(return_value=mock_voice_tensor) + mock_voice.list_voices = AsyncMock(return_value=["af", "af_bella", "af_sarah"]) + + # Create an async generator from the lifespan context manager + async_gen = lifespan(MagicMock()) + + # Start the context manager + await async_gen.__aenter__() + + # Verify managers were initialized + mock_get_model.assert_called_once() + mock_get_voice.assert_called_once() + mock_model.load_model.assert_called_once() + + # Clean up + await async_gen.__aexit__(None, None, None) + + +@pytest.mark.asyncio +async def test_lifespan_failed_warmup(): + """Test failed model warmup in lifespan""" + with patch("api.src.inference.model_manager.get_manager") as mock_get_model: + # Mock the model manager to fail + mock_get_model.side_effect = RuntimeError("Failed to initialize model") + + # Create an async generator from the lifespan context manager + async_gen = lifespan(MagicMock()) + + # Verify the exception is raised + with pytest.raises(RuntimeError, match="Failed to initialize model"): + await async_gen.__aenter__() + + # Clean up + await async_gen.__aexit__(None, None, None) + + +@pytest.mark.asyncio +async def test_lifespan_voice_manager_failure(): + """Test failure when voice manager fails to initialize""" + with patch("api.src.inference.model_manager.get_manager") as mock_get_model, \ + patch("api.src.inference.voice_manager.get_manager") as mock_get_voice: + + # Setup model manager success but voice manager failure + mock_model = AsyncMock() + mock_get_model.return_value = mock_model + mock_get_voice.side_effect = RuntimeError("Failed to initialize voice manager") + + # Create an async generator from the lifespan context manager + async_gen = lifespan(MagicMock()) + + # Verify the exception is raised + with pytest.raises(RuntimeError, match="Failed to initialize voice manager"): + await async_gen.__aenter__() + + # Clean up + await async_gen.__aexit__(None, None, None) diff --git a/api/depr_tests/test_managers.py b/api/depr_tests/test_managers.py new file mode 100644 index 0000000..64bb8c6 --- /dev/null +++ b/api/depr_tests/test_managers.py @@ -0,0 +1,190 @@ +"""Tests for model and voice managers""" +import os +import numpy as np +import pytest +import torch +from unittest.mock import AsyncMock, MagicMock, Mock, patch + +from api.src.inference.model_manager import get_manager as get_model_manager +from api.src.inference.voice_manager import get_manager as get_voice_manager + +# Get project root path +PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +MOCK_VOICES_DIR = os.path.join(PROJECT_ROOT, "api", "src", "voices") +MOCK_MODEL_DIR = os.path.join(PROJECT_ROOT, "api", "src", "models") + + +@pytest.mark.asyncio +async def test_model_manager_initialization(): + """Test model manager initialization""" + with patch("api.src.inference.model_manager.settings") as mock_settings, \ + patch("api.src.core.paths.get_model_path") as mock_get_path: + + mock_settings.model_dir = MOCK_MODEL_DIR + mock_settings.onnx_model_path = "model.onnx" + mock_get_path.return_value = os.path.join(MOCK_MODEL_DIR, "model.onnx") + + manager = await get_model_manager() + assert manager is not None + backend = manager.get_backend() + assert backend is not None + + +@pytest.mark.asyncio +async def test_model_manager_generate(): + """Test model generation""" + with patch("api.src.inference.model_manager.settings") as mock_settings, \ + patch("api.src.core.paths.get_model_path") as mock_get_path, \ + patch("torch.load") as mock_torch_load: + + mock_settings.model_dir = MOCK_MODEL_DIR + mock_settings.onnx_model_path = "model.onnx" + mock_settings.use_onnx = True + mock_settings.use_gpu = False + mock_get_path.return_value = os.path.join(MOCK_MODEL_DIR, "model.onnx") + + # Mock torch load to return a tensor + mock_torch_load.return_value = torch.zeros(192) + + manager = await get_model_manager() + + # Set up mock backend + mock_backend = AsyncMock() + mock_backend.is_loaded = True + mock_backend.device = "cpu" + + # Create audio tensor and ensure it's properly mocked + audio_data = torch.zeros(48000, dtype=torch.float32) + async def mock_generate(*args, **kwargs): + return audio_data + mock_backend.generate.side_effect = mock_generate + + # Set up manager with mock backend + manager._backends['onnx_cpu'] = mock_backend + manager._current_backend = 'onnx_cpu' + + # Generate audio + tokens = [1, 2, 3] + voice_tensor = torch.zeros(192) + audio = await manager.generate(tokens, voice_tensor, speed=1.0) + + assert isinstance(audio, torch.Tensor), "Generated audio should be torch tensor" + assert audio.dtype == torch.float32, "Audio should be 32-bit float" + assert audio.shape == (48000,), "Audio should have 48000 samples" + assert mock_backend.generate.call_count == 1 + + +@pytest.mark.asyncio +async def test_voice_manager_initialization(): + """Test voice manager initialization""" + with patch("api.src.inference.voice_manager.settings") as mock_settings, \ + patch("os.path.exists") as mock_exists: + + mock_settings.voices_dir = MOCK_VOICES_DIR + mock_exists.return_value = True + + manager = await get_voice_manager() + assert manager is not None + + +@pytest.mark.asyncio +async def test_voice_manager_list_voices(): + """Test listing available voices""" + with patch("api.src.inference.voice_manager.settings") as mock_settings, \ + patch("os.listdir") as mock_listdir, \ + patch("os.makedirs") as mock_makedirs, \ + patch("os.path.exists") as mock_exists: + + mock_settings.voices_dir = MOCK_VOICES_DIR + mock_listdir.return_value = ["af_bella.pt", "af_sarah.pt", "bm_lewis.pt"] + mock_exists.return_value = True + + manager = await get_voice_manager() + voices = await manager.list_voices() + + assert isinstance(voices, list) + assert len(voices) == 3, f"Expected 3 voices but got {len(voices)}" + assert sorted(voices) == ["af_bella", "af_sarah", "bm_lewis"] + mock_listdir.assert_called_once() + + +@pytest.mark.asyncio +async def test_voice_manager_load_voice(): + """Test loading a voice""" + with patch("api.src.inference.voice_manager.settings") as mock_settings, \ + patch("torch.load") as mock_torch_load, \ + patch("os.path.exists") as mock_exists: + + mock_settings.voices_dir = MOCK_VOICES_DIR + mock_exists.return_value = True + + # Create a mock tensor + mock_tensor = torch.zeros(192) + mock_torch_load.return_value = mock_tensor + + manager = await get_voice_manager() + voice_tensor = await manager.load_voice("af_bella", device="cpu") + + assert isinstance(voice_tensor, torch.Tensor) + assert voice_tensor.shape == (192,) + mock_torch_load.assert_called_once() + + +@pytest.mark.asyncio +async def test_voice_manager_combine_voices(): + """Test combining voices""" + with patch("api.src.inference.voice_manager.settings") as mock_settings, \ + patch("torch.load") as mock_load, \ + patch("torch.save") as mock_save, \ + patch("os.makedirs") as mock_makedirs, \ + patch("os.path.exists") as mock_exists: + + mock_settings.voices_dir = MOCK_VOICES_DIR + mock_exists.return_value = True + + # Create mock tensors + mock_tensor1 = torch.ones(192) + mock_tensor2 = torch.ones(192) * 2 + mock_load.side_effect = [mock_tensor1, mock_tensor2] + + manager = await get_voice_manager() + combined_name = await manager.combine_voices(["af_bella", "af_sarah"]) + + assert combined_name == "af_bella_af_sarah" + assert mock_load.call_count == 2 + mock_save.assert_called_once() + + # Verify the combined tensor was saved + saved_tensor = mock_save.call_args[0][0] + assert isinstance(saved_tensor, torch.Tensor) + assert saved_tensor.shape == (192,) + # Should be average of the two tensors + assert torch.allclose(saved_tensor, torch.ones(192) * 1.5) + + +@pytest.mark.asyncio +async def test_voice_manager_invalid_voice(): + """Test loading invalid voice""" + with patch("api.src.inference.voice_manager.settings") as mock_settings, \ + patch("os.path.exists") as mock_exists: + + mock_settings.voices_dir = MOCK_VOICES_DIR + mock_exists.return_value = False + + manager = await get_voice_manager() + with pytest.raises(RuntimeError, match="Voice not found"): + await manager.load_voice("invalid_voice", device="cpu") + + +@pytest.mark.asyncio +async def test_voice_manager_combine_invalid_voices(): + """Test combining with invalid voices""" + with patch("api.src.inference.voice_manager.settings") as mock_settings, \ + patch("os.path.exists") as mock_exists: + + mock_settings.voices_dir = MOCK_VOICES_DIR + mock_exists.return_value = False + + manager = await get_voice_manager() + with pytest.raises(RuntimeError, match="Voice not found"): + await manager.combine_voices(["invalid_voice1", "invalid_voice2"]) \ No newline at end of file diff --git a/api/depr_tests/test_text_processing.py b/api/depr_tests/test_text_processing.py new file mode 100644 index 0000000..7e63491 --- /dev/null +++ b/api/depr_tests/test_text_processing.py @@ -0,0 +1,139 @@ +"""Tests for text processing endpoints""" +import os +import pytest +import torch +from fastapi.testclient import TestClient + +from ..src.main import app + +# Get project root path +PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +MOCK_VOICES_DIR = os.path.join(PROJECT_ROOT, "api", "src", "voices") + +client = TestClient(app) + + +@pytest.mark.asyncio +async def test_generate_from_phonemes(async_client, mock_tts_service): + """Test generating audio from phonemes""" + # Setup mocks + mock_tts_service._voice_manager.list_voices.return_value = ["af_bella"] + mock_tts_service.generate_audio.return_value = (torch.zeros(48000).numpy(), 1.0) + mock_tts_service._voice_manager.load_voice.return_value = torch.zeros(192) + mock_tts_service._voice_manager.get_voice_path.return_value = "/mock/voices/af_bella.pt" + + test_request = { + "model": "kokoro", + "input": "h @ l oU w r= l d", + "voice": "af_bella", + "response_format": "wav", + "speed": 1.0, + "stream": False, + } + + response = await async_client.post("/v1/audio/speech", json=test_request) + assert response.status_code == 200 + assert response.headers["content-type"] == "audio/wav" + assert response.headers["content-disposition"] == "attachment; filename=speech.wav" + mock_tts_service.generate_audio.assert_called_once() + + +@pytest.mark.asyncio +async def test_generate_from_phonemes_invalid_voice(async_client, mock_tts_service): + """Test generating audio from phonemes with invalid voice""" + # Setup mocks + mock_tts_service._voice_manager.list_voices.return_value = ["af_bella"] + mock_tts_service._voice_manager.get_voice_path.return_value = None + + test_request = { + "model": "kokoro", + "input": "h @ l oU w r= l d", + "voice": "invalid_voice", + "response_format": "wav", + "speed": 1.0, + "stream": False, + } + + response = await async_client.post("/v1/audio/speech", json=test_request) + assert response.status_code == 400 + assert "Voice not found" in response.json()["detail"]["message"] + + +@pytest.mark.asyncio +async def test_generate_from_phonemes_chunked(async_client, mock_tts_service): + """Test generating chunked audio from phonemes""" + # Setup mocks + mock_tts_service._voice_manager.list_voices.return_value = ["af_bella"] + mock_tts_service._voice_manager.load_voice.return_value = torch.zeros(192) + mock_tts_service._voice_manager.get_voice_path.return_value = "/mock/voices/af_bella.pt" + + async def mock_stream(): + yield b"chunk1" + yield b"chunk2" + + mock_tts_service.generate_audio_stream.return_value = mock_stream() + + test_request = { + "model": "kokoro", + "input": "h @ l oU w r= l d", + "voice": "af_bella", + "response_format": "mp3", + "stream": True, + } + + headers = {"x-raw-response": "stream"} + response = await async_client.post( + "/v1/audio/speech", json=test_request, headers=headers + ) + + assert response.status_code == 200 + assert response.headers["content-type"] == "audio/mpeg" + assert response.headers["content-disposition"] == "attachment; filename=speech.mp3" + + +@pytest.mark.asyncio +async def test_invalid_phonemes(async_client, mock_tts_service): + """Test handling invalid phonemes""" + # Setup mocks + mock_tts_service._voice_manager.list_voices.return_value = ["af_bella"] + mock_tts_service._voice_manager.load_voice.return_value = torch.zeros(192) + mock_tts_service._voice_manager.get_voice_path.return_value = "/mock/voices/af_bella.pt" + + test_request = { + "model": "kokoro", + "input": "", # Empty input + "voice": "af_bella", + "response_format": "wav", + "speed": 1.0, + "stream": False, + } + + response = await async_client.post("/v1/audio/speech", json=test_request) + assert response.status_code == 400 + assert "Text is empty" in response.json()["detail"]["message"] + + +@pytest.mark.asyncio +async def test_phonemes_with_combined_voice(async_client, mock_tts_service): + """Test generating audio from phonemes with combined voice""" + # Setup mocks + mock_tts_service._voice_manager.list_voices.return_value = ["af_bella", "af_sarah"] + mock_tts_service._voice_manager.combine_voices.return_value = "af_bella_af_sarah" + mock_tts_service._voice_manager.load_voice.return_value = torch.zeros(192) + mock_tts_service._voice_manager.get_voice_path.return_value = "/mock/voices/af_bella_af_sarah.pt" + mock_tts_service.generate_audio.return_value = (torch.zeros(48000).numpy(), 1.0) + + test_request = { + "model": "kokoro", + "input": "h @ l oU w r= l d", + "voice": "af_bella+af_sarah", + "response_format": "wav", + "speed": 1.0, + "stream": False, + } + + response = await async_client.post("/v1/audio/speech", json=test_request) + assert response.status_code == 200 + assert response.headers["content-type"] == "audio/wav" + mock_tts_service._voice_manager.combine_voices.assert_called_once() + mock_tts_service.generate_audio.assert_called_once() diff --git a/api/depr_tests/test_tts_service.py b/api/depr_tests/test_tts_service.py new file mode 100644 index 0000000..ac33c5f --- /dev/null +++ b/api/depr_tests/test_tts_service.py @@ -0,0 +1,118 @@ +"""Tests for TTSService""" +import os +import numpy as np +import pytest +import torch +from unittest.mock import AsyncMock, MagicMock, Mock, patch + +from api.src.services.tts_service import TTSService + +# Get project root path +PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +MOCK_VOICES_DIR = os.path.join(PROJECT_ROOT, "api", "src", "voices") +MOCK_MODEL_DIR = os.path.join(PROJECT_ROOT, "api", "src", "models") + + +@pytest.mark.asyncio +async def test_service_initialization(mock_model_manager, mock_voice_manager): + """Test TTSService initialization""" + # Create service using factory method + with patch("api.src.services.tts_service.get_model_manager", return_value=mock_model_manager), \ + patch("api.src.services.tts_service.get_voice_manager", return_value=mock_voice_manager): + service = await TTSService.create() + assert service is not None + assert service.model_manager == mock_model_manager + assert service._voice_manager == mock_voice_manager + + +@pytest.mark.asyncio +async def test_generate_audio_basic(mock_tts_service): + """Test basic audio generation""" + text = "Hello world" + voice = "af" + audio, duration = await mock_tts_service.generate_audio(text, voice) + assert isinstance(audio, np.ndarray) + assert duration > 0 + + +@pytest.mark.asyncio +async def test_generate_audio_empty_text(mock_tts_service): + """Test handling empty text input""" + with pytest.raises(ValueError, match="Text is empty after preprocessing"): + await mock_tts_service.generate_audio("", "af") + + +@pytest.mark.asyncio +async def test_generate_audio_stream(mock_tts_service): + """Test streaming audio generation""" + text = "Hello world" + voice = "af" + + # Setup mock stream + async def mock_stream(): + yield b"chunk1" + yield b"chunk2" + mock_tts_service.generate_audio_stream.return_value = mock_stream() + + # Test streaming + stream = mock_tts_service.generate_audio_stream(text, voice) + chunks = [] + async for chunk in await stream: + chunks.append(chunk) + + assert len(chunks) > 0 + assert all(isinstance(chunk, bytes) for chunk in chunks) + + +@pytest.mark.asyncio +async def test_list_voices(mock_tts_service): + """Test listing available voices""" + with patch("api.src.inference.voice_manager.settings") as mock_settings: + mock_settings.voices_dir = MOCK_VOICES_DIR + voices = await mock_tts_service.list_voices() + assert isinstance(voices, list) + assert len(voices) == 4 # ["af", "af_bella", "af_sarah", "bm_lewis"] + assert all(isinstance(voice, str) for voice in voices) + + +@pytest.mark.asyncio +async def test_combine_voices(mock_tts_service): + """Test combining voices""" + with patch("api.src.inference.voice_manager.settings") as mock_settings: + mock_settings.voices_dir = MOCK_VOICES_DIR + voices = ["af_bella", "af_sarah"] + result = await mock_tts_service.combine_voices(voices) + assert isinstance(result, str) + assert result == "af_bella_af_sarah" + + +@pytest.mark.asyncio +async def test_audio_to_bytes(mock_tts_service): + """Test converting audio to bytes""" + audio = np.zeros(48000, dtype=np.float32) + audio_bytes = mock_tts_service._audio_to_bytes(audio) + assert isinstance(audio_bytes, bytes) + assert len(audio_bytes) > 0 + + +@pytest.mark.asyncio +async def test_voice_loading(mock_tts_service): + """Test voice loading""" + with patch("api.src.inference.voice_manager.settings") as mock_settings, \ + patch("os.path.exists", return_value=True), \ + patch("torch.load", return_value=torch.zeros(192)): + mock_settings.voices_dir = MOCK_VOICES_DIR + voice = await mock_tts_service._voice_manager.load_voice("af", device="cpu") + assert isinstance(voice, torch.Tensor) + assert voice.shape == (192,) + + +@pytest.mark.asyncio +async def test_model_generation(mock_tts_service): + """Test model generation""" + tokens = [1, 2, 3] + voice_tensor = torch.zeros(192) + audio = await mock_tts_service.model_manager.generate(tokens, voice_tensor) + assert isinstance(audio, torch.Tensor) + assert audio.shape == (48000,) + assert audio.dtype == torch.float32 diff --git a/api/src/core/config.py b/api/src/core/config.py index 3269665..4c8fbbd 100644 --- a/api/src/core/config.py +++ b/api/src/core/config.py @@ -15,6 +15,7 @@ class Settings(BaseSettings): default_voice: str = "af" use_gpu: bool = False # Whether to use GPU acceleration if available use_onnx: bool = True # Whether to use ONNX runtime + allow_local_voice_saving: bool = True # Whether to allow saving combined voices locally # Container absolute paths model_dir: str = "/app/api/src/models" # Absolute path in container @@ -25,6 +26,12 @@ class Settings(BaseSettings): max_chunk_size: int = 300 # Maximum size of text chunks for processing gap_trim_ms: int = 250 # Amount to trim from streaming chunk ends in milliseconds + # Web Player Settings + enable_web_player: bool = True # Whether to serve the web player UI + web_player_path: str = "web" # Path to web player static files + cors_origins: list[str] = ["*"] # CORS origins for web player + cors_enabled: bool = True # Whether to enable CORS + class Config: env_file = ".env" diff --git a/api/src/core/paths.py b/api/src/core/paths.py index 3124ece..34675bc 100644 --- a/api/src/core/paths.py +++ b/api/src/core/paths.py @@ -265,4 +265,76 @@ async def read_file(path: str) -> str: async with aiofiles.open(path, 'r', encoding='utf-8') as f: return await f.read() except Exception as e: - raise RuntimeError(f"Failed to read file {path}: {e}") \ No newline at end of file + raise RuntimeError(f"Failed to read file {path}: {e}") + + +async def read_bytes(path: str) -> bytes: + """Read file as bytes asynchronously. + + Args: + path: Path to file + + Returns: + File contents as bytes + + Raises: + RuntimeError: If file cannot be read + """ + try: + async with aiofiles.open(path, 'rb') as f: + return await f.read() + except Exception as e: + raise RuntimeError(f"Failed to read file {path}: {e}") + + +async def get_web_file_path(filename: str) -> str: + """Get path to web static file. + + Args: + filename: Name of file in web directory + + Returns: + Absolute path to file + + Raises: + RuntimeError: If file not found + """ + # Get project root directory (four levels up from core to get to project root) + root_dir = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))) + + # Construct web directory path relative to project root + web_dir = os.path.join("/app", settings.web_player_path) + + # Search in web directory + search_paths = [web_dir] + logger.debug(f"Searching for web file in path: {web_dir}") + + return await _find_file(filename, search_paths) + + +async def get_content_type(path: str) -> str: + """Get content type for file. + + Args: + path: Path to file + + Returns: + Content type string + """ + ext = os.path.splitext(path)[1].lower() + return { + '.html': 'text/html', + '.js': 'application/javascript', + '.css': 'text/css', + '.png': 'image/png', + '.jpg': 'image/jpeg', + '.jpeg': 'image/jpeg', + '.gif': 'image/gif', + '.svg': 'image/svg+xml', + '.ico': 'image/x-icon', + }.get(ext, 'application/octet-stream') + + +async def verify_model_path(model_path: str) -> bool: + """Verify model file exists at path.""" + return await aiofiles.os.path.exists(model_path) \ No newline at end of file diff --git a/api/src/inference/model_manager.py b/api/src/inference/model_manager.py index 34a7075..23f7eb1 100644 --- a/api/src/inference/model_manager.py +++ b/api/src/inference/model_manager.py @@ -1,7 +1,7 @@ """Model management and caching.""" import asyncio -from typing import Dict, Optional +from typing import Dict, Optional, Tuple import torch from loguru import logger @@ -23,17 +23,11 @@ _manager_lock = asyncio.Lock() class ModelManager: """Manages model loading and inference across backends.""" - # Class-level state for shared resources _loaded_models = {} _backends = {} - def __init__(self, config: Optional[ModelConfig] = None): """Initialize model manager. - - Args: - config: Optional configuration - Note: This should not be called directly. Use get_manager() instead. """ @@ -90,16 +84,70 @@ class ModelManager: logger.error(f"Failed to initialize backend: {e}") raise RuntimeError("Failed to initialize backend") + async def initialize_with_warmup(self, voice_manager) -> tuple[str, str, int]: + """Initialize model with warmup and pre-cache voices. + Args: + voice_manager: Voice manager instance for loading voices + Returns: + Tuple of (device type, model type, number of loaded voices) + Raises: + RuntimeError: If initialization fails + """ + try: + # Determine backend type based on settings + if settings.use_gpu and torch.cuda.is_available(): + backend_type = 'pytorch_gpu' if not settings.use_onnx else 'onnx_gpu' + else: + backend_type = 'pytorch_cpu' if not settings.use_onnx else 'onnx_cpu' + + # Get backend + backend = self.get_backend(backend_type) + + # Get and verify model path + model_file = model_config.pytorch_model_file if not settings.use_onnx else model_config.onnx_model_file + model_path = await paths.get_model_path(model_file) + + if not await paths.verify_model_path(model_path): + raise RuntimeError(f"Model file not found: {model_path}") + + # Pre-cache default voice and use for warmup + warmup_voice = await voice_manager.load_voice( + settings.default_voice, device=backend.device) + logger.info(f"Pre-cached voice {settings.default_voice} for warmup") + + # Initialize model with warmup voice + await self.load_model(model_path, warmup_voice, backend_type) + + # Pre-cache common voices in background + common_voices = ['af', 'af_bella', 'af_sky', 'af_nicole'] + for voice_name in common_voices: + try: + await voice_manager.load_voice(voice_name, device=backend.device) + logger.debug(f"Pre-cached voice {voice_name}") + except Exception as e: + logger.warning(f"Failed to pre-cache voice {voice_name}: {e}") + + # Get available voices count + voices = await voice_manager.list_voices() + voicepack_count = len(voices) + + # Get device info for return + device = "GPU" if settings.use_gpu else "CPU" + model = "ONNX" if settings.use_onnx else "PyTorch" + + return device, model, voicepack_count + + except Exception as e: + logger.error(f"Failed to initialize model with warmup: {e}") + raise RuntimeError(f"Failed to initialize model with warmup: {e}") + def get_backend(self, backend_type: Optional[str] = None) -> BaseModelBackend: """Get specified backend. - Args: backend_type: Backend type ('pytorch_cpu', 'pytorch_gpu', 'onnx_cpu', 'onnx_gpu'), uses default if None - Returns: Model backend instance - Raises: ValueError: If backend type is invalid RuntimeError: If no backends are available @@ -120,10 +168,8 @@ class ModelManager: def _determine_backend(self, model_path: str) -> str: """Determine appropriate backend based on model file and settings. - Args: model_path: Path to model file - Returns: Backend type to use """ @@ -142,12 +188,10 @@ class ModelManager: backend_type: Optional[str] = None ) -> None: """Load model on specified backend. - Args: model_path: Path to model file warmup_voice: Optional voice tensor for warmup, skips warmup if None backend_type: Backend to load on, uses default if None - Raises: RuntimeError: If model loading fails """ @@ -270,34 +314,24 @@ class ModelManager: @property def available_backends(self) -> list[str]: """Get list of available backends. - - Returns: - List of backend names """ return list(self._backends.keys()) @property def current_backend(self) -> str: """Get current default backend. - - Returns: - Backend name """ return self._current_backend async def get_manager(config: Optional[ModelConfig] = None) -> ModelManager: """Get global model manager instance. - Args: config: Optional model configuration - Returns: ModelManager instance - Thread Safety: - This function is thread-safe and ensures only one instance is created - even under concurrent access. + This function should be thread-safe. Lemme know if it unravels on you """ global _manager_instance diff --git a/api/src/inference/voice_manager.py b/api/src/inference/voice_manager.py index ec97215..55644fd 100644 --- a/api/src/inference/voice_manager.py +++ b/api/src/inference/voice_manager.py @@ -117,14 +117,18 @@ class VoiceManager: voices_dir = os.path.join(api_dir, settings.voices_dir) os.makedirs(voices_dir, exist_ok=True) - # Save combined voice - combined_path = os.path.join(voices_dir, f"{combined_name}.pt") - try: - torch.save(combined_tensor, combined_path) - # Cache the new combined voice - self._voice_cache[f"{combined_path}_{device}"] = combined_tensor - except Exception as e: - raise RuntimeError(f"Failed to save combined voice: {e}") + # Only save to disk if local voice saving is allowed + if settings.allow_local_voice_saving: + combined_path = os.path.join(voices_dir, f"{combined_name}.pt") + try: + torch.save(combined_tensor, combined_path) + # Cache the new combined voice with disk path + self._voice_cache[f"{combined_path}_{device}"] = combined_tensor + except Exception as e: + raise RuntimeError(f"Failed to save combined voice: {e}") + else: + # Just cache the combined voice in memory without saving to disk + self._voice_cache[f"{combined_name}_{device}"] = combined_tensor return combined_name @@ -135,20 +139,30 @@ class VoiceManager: """List available voices. Returns: - List of voice names + List of voice names, including both disk-saved and in-memory combined voices """ - voices = [] + voices = set() # Use set to avoid duplicates try: + # Get voices from disk api_dir = os.path.dirname(os.path.dirname(os.path.dirname(__file__))) voices_dir = os.path.join(api_dir, settings.voices_dir) os.makedirs(voices_dir, exist_ok=True) for entry in os.listdir(voices_dir): if entry.endswith(".pt"): - voices.append(entry[:-3]) + voices.add(entry[:-3]) + + # Add in-memory combined voices from cache + for cache_key in self._voice_cache: + # Extract voice name from cache key (format: "name_device" or "path_device") + voice_name = cache_key.split("_")[0] + if "/" in voice_name: # It's a path + voice_name = os.path.basename(voice_name)[:-3] # Remove .pt extension + voices.add(voice_name) + except Exception as e: logger.error(f"Error listing voices: {e}") - return sorted(voices) + return sorted(list(voices)) def validate_voice(self, voice_path: str) -> bool: """Validate voice file. diff --git a/api/src/main.py b/api/src/main.py index 89b9250..05f021e 100644 --- a/api/src/main.py +++ b/api/src/main.py @@ -13,6 +13,7 @@ from fastapi.middleware.cors import CORSMiddleware from loguru import logger from .core.config import settings +from .routers.web_player import router as web_router from .core.model_config import model_config from .routers.development import router as dev_router from .routers.openai_compatible import router as openai_router @@ -54,46 +55,8 @@ async def lifespan(app: FastAPI): model_manager = await get_manager() voice_manager = await get_voice_manager() - # Determine backend type based on settings - if settings.use_gpu and torch.cuda.is_available(): - backend_type = 'pytorch_gpu' if not settings.use_onnx else 'onnx_gpu' - else: - backend_type = 'pytorch_cpu' if not settings.use_onnx else 'onnx_cpu' - - # Get backend and initialize model - backend = model_manager.get_backend(backend_type) - - # Use model path from model_config - model_file = model_config.pytorch_model_file if not settings.use_onnx else model_config.onnx_model_file - model_path = os.path.join(settings.model_dir, model_file) - - - if not os.path.exists(model_path): - raise RuntimeError(f"Model file not found: {model_path}") - - # Pre-cache default voice and use for warmup - warmup_voice = await voice_manager.load_voice(settings.default_voice, device=backend.device) - logger.info(f"Pre-cached voice {settings.default_voice} for warmup") - - # Initialize model with warmup voice - await model_manager.load_model(model_path, warmup_voice, backend_type) - - # Pre-cache common voices in background - common_voices = ['af', 'af_bella', 'af_sarah', 'af_nicole'] - for voice_name in common_voices: - try: - await voice_manager.load_voice(voice_name, device=backend.device) - logger.debug(f"Pre-cached voice {voice_name}") - except Exception as e: - logger.warning(f"Failed to pre-cache voice {voice_name}: {e}") - - # Get available voices for startup message - voices = await voice_manager.list_voices() - voicepack_count = len(voices) - - # Get device info for startup message - device = "GPU" if settings.use_gpu else "CPU" - model = "ONNX" if settings.use_onnx else "PyTorch" + # Initialize model with warmup and get status + device, model, voicepack_count = await model_manager.initialize_with_warmup(voice_manager) except Exception as e: logger.error(f"Failed to initialize model: {e}") raise @@ -112,7 +75,14 @@ async def lifespan(app: FastAPI): {boundary} """ startup_msg += f"\nModel warmed up on {device}: {model}" - startup_msg += f"\n{voicepack_count} voice packs loaded\n" + startup_msg += f"\n{voicepack_count} voice packs loaded" + + # Add web player info if enabled + if settings.enable_web_player: + startup_msg += f"\n\nWeb Player: http://{settings.host}:{settings.port}/web/" + else: + startup_msg += "\n\nWeb Player: disabled" + startup_msg += f"\n{boundary}\n" logger.info(startup_msg) @@ -128,19 +98,21 @@ app = FastAPI( openapi_url="/openapi.json", # Explicitly enable OpenAPI schema ) -# Add CORS middleware -app.add_middleware( - CORSMiddleware, - allow_origins=["*"], - allow_credentials=True, - allow_methods=["*"], - allow_headers=["*"], -) +# Add CORS middleware if enabled +if settings.cors_enabled: + app.add_middleware( + CORSMiddleware, + allow_origins=settings.cors_origins, + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], + ) # Include routers app.include_router(openai_router, prefix="/v1") -app.include_router(dev_router) # New development endpoints -# app.include_router(text_router) # Deprecated but still live for backwards compatibility +app.include_router(dev_router) # Development endpoints +if settings.enable_web_player: + app.include_router(web_router, prefix="/web") # Web player static files # Health check endpoint diff --git a/api/src/routers/openai_compatible.py b/api/src/routers/openai_compatible.py index 80d8a53..9a34297 100644 --- a/api/src/routers/openai_compatible.py +++ b/api/src/routers/openai_compatible.py @@ -118,9 +118,32 @@ async def create_speech( # Check if streaming is requested (default for OpenAI client) if request.stream: + # Create generator but don't start it yet + generator = stream_audio_chunks(tts_service, request, client_request) + + # Test the generator by attempting to get first chunk + try: + first_chunk = await anext(generator) + except StopAsyncIteration: + first_chunk = b"" # Empty audio case + except Exception as e: + # Re-raise any errors to be caught by the outer try-except + raise RuntimeError(f"Failed to initialize audio stream: {str(e)}") from e + + # If we got here, streaming can begin + async def safe_stream(): + yield first_chunk + try: + async for chunk in generator: + yield chunk + except Exception as e: + # Log the error but don't yield anything - the connection will close + logger.error(f"Error during streaming: {str(e)}") + raise + # Stream audio chunks as they're generated return StreamingResponse( - stream_audio_chunks(tts_service, request, client_request), + safe_stream(), media_type=content_type, headers={ "Content-Disposition": f"attachment; filename=speech.{request.response_format}", diff --git a/api/src/routers/web_player.py b/api/src/routers/web_player.py new file mode 100644 index 0000000..be82367 --- /dev/null +++ b/api/src/routers/web_player.py @@ -0,0 +1,48 @@ +"""Web player router with async file serving.""" + +from fastapi import APIRouter, HTTPException +from fastapi.responses import Response +from loguru import logger + +from ..core.config import settings +from ..core.paths import get_web_file_path, read_bytes, get_content_type + +router = APIRouter( + tags=["Web Player"], + responses={404: {"description": "Not found"}}, +) + +@router.get("/{filename:path}") +async def serve_web_file(filename: str): + """Serve web player static files asynchronously.""" + if not settings.enable_web_player: + raise HTTPException(status_code=404, detail="Web player is disabled") + + try: + # Default to index.html for root path + if filename == "" or filename == "/": + filename = "index.html" + + # Get file path + file_path = await get_web_file_path(filename) + + # Read file content + content = await read_bytes(file_path) + + # Get content type + content_type = await get_content_type(file_path) + + return Response( + content=content, + media_type=content_type, + headers={ + "Cache-Control": "no-cache", # Prevent caching during development + } + ) + + except RuntimeError as e: + logger.warning(f"Web file not found: {filename}") + raise HTTPException(status_code=404, detail=str(e)) + except Exception as e: + logger.error(f"Error serving web file {filename}: {e}") + raise HTTPException(status_code=500, detail="Internal server error") \ No newline at end of file diff --git a/api/src/structures/text_schemas.py b/api/src/structures/text_schemas.py index f25d37a..512b7e9 100644 --- a/api/src/structures/text_schemas.py +++ b/api/src/structures/text_schemas.py @@ -1,7 +1,7 @@ -from pydantic import BaseModel, Field -from pydantic import validator +from pydantic import BaseModel, Field, field_validator from typing import List, Union, Optional + class PhonemeRequest(BaseModel): text: str language: str = "a" # Default to American English @@ -24,12 +24,14 @@ class StitchOptions(BaseModel): description="Milliseconds to trim from chunk boundaries when using static_trim" ) - @validator('gap_method') - def validate_gap_method(cls, v): + @field_validator('gap_method') + @classmethod + def validate_gap_method(cls, v: str) -> str: if v != 'static_trim': raise ValueError("Currently only 'static_trim' gap method is supported") return v + class GenerateFromPhonemesRequest(BaseModel): phonemes: Union[str, List[str]] = Field( ..., diff --git a/api/tests/conftest.py b/api/tests/conftest.py index bd627bf..4765354 100644 --- a/api/tests/conftest.py +++ b/api/tests/conftest.py @@ -1,126 +1,64 @@ -import os -import shutil -import sys -from unittest.mock import MagicMock, Mock, patch - -import aiofiles.threadpool -import numpy as np import pytest +import pytest_asyncio +from unittest.mock import AsyncMock, MagicMock, patch +import numpy as np +import torch +from pathlib import Path - -def cleanup_mock_dirs(): - """Clean up any MagicMock directories created during tests""" - mock_dir = "MagicMock" - if os.path.exists(mock_dir): - shutil.rmtree(mock_dir) - - -@pytest.fixture(autouse=True) -def setup_aiofiles(): - """Setup aiofiles mock wrapper""" - aiofiles.threadpool.wrap.register(MagicMock)( - lambda *args, **kwargs: aiofiles.threadpool.AsyncBufferedIOBase(*args, **kwargs) - ) - yield - - -@pytest.fixture(autouse=True) -def cleanup(): - """Automatically clean up before and after each test""" - cleanup_mock_dirs() - yield - cleanup_mock_dirs() - - -# Mock modules before they're imported -sys.modules["transformers"] = Mock() -sys.modules["phonemizer"] = Mock() -sys.modules["models"] = Mock() -sys.modules["models.build_model"] = Mock() -sys.modules["kokoro"] = Mock() -sys.modules["kokoro.generate"] = Mock() -sys.modules["kokoro.phonemize"] = Mock() -sys.modules["kokoro.tokenize"] = Mock() - -# Mock ONNX runtime -mock_onnx = Mock() -mock_onnx.InferenceSession = Mock() -mock_onnx.SessionOptions = Mock() -mock_onnx.GraphOptimizationLevel = Mock() -mock_onnx.ExecutionMode = Mock() -sys.modules["onnxruntime"] = mock_onnx - -# Create mock settings module -mock_settings_module = Mock() -mock_settings = Mock() -mock_settings.model_dir = "/mock/model/dir" -mock_settings.onnx_model_path = "mock.onnx" -mock_settings_module.settings = mock_settings -sys.modules["api.src.core.config"] = mock_settings_module - - -class MockTTSModel: - _instance = None - _onnx_session = None - VOICES_DIR = "/mock/voices/dir" - - def __init__(self): - self._initialized = False - - @classmethod - def get_instance(cls): - if cls._instance is None: - cls._instance = cls() - return cls._instance - - @classmethod - def initialize(cls, model_dir): - cls._onnx_session = Mock() - cls._onnx_session.run = Mock(return_value=[np.zeros(48000)]) - cls._instance._initialized = True - return cls._onnx_session - - @classmethod - def setup(cls): - if not cls._instance._initialized: - cls.initialize("/mock/model/dir") - return cls._instance - - @classmethod - def generate_from_tokens(cls, tokens, voicepack, speed): - if not cls._instance._initialized: - raise RuntimeError("Model not initialized. Call setup() first.") - return np.zeros(48000) - - @classmethod - def process_text(cls, text, language): - return "mock phonemes", [1, 2, 3] - - @staticmethod - def get_device(): - return "cpu" - +from api.src.services.tts_service import TTSService +from api.src.inference.voice_manager import VoiceManager +from api.src.inference.model_manager import ModelManager +from api.src.structures.model_schemas import VoiceConfig @pytest.fixture -def mock_tts_service(monkeypatch): - """Mock TTSService for testing""" - mock_service = Mock() - mock_service._get_voice_path.return_value = "/mock/path/voice.pt" - mock_service._load_voice.return_value = np.zeros((1, 192)) - - # Mock TTSModel.generate_from_tokens since we call it directly - mock_generate = Mock(return_value=np.zeros(48000)) - monkeypatch.setattr( - "api.src.routers.development.TTSModel.generate_from_tokens", mock_generate - ) - - return mock_service - +def mock_voice_tensor(): + """Mock voice tensor for testing.""" + return torch.randn(1, 128) # Dummy tensor @pytest.fixture -def mock_audio_service(monkeypatch): - """Mock AudioService""" - mock_service = Mock() - mock_service.convert_audio.return_value = b"mock audio data" - monkeypatch.setattr("api.src.routers.development.AudioService", mock_service) - return mock_service +def mock_audio_output(): + """Mock audio output for testing.""" + return np.random.rand(16000) # 1 second of random audio + +@pytest_asyncio.fixture +async def mock_model_manager(mock_audio_output): + """Mock model manager for testing.""" + manager = AsyncMock(spec=ModelManager) + manager.get_backend = MagicMock() + manager.generate = AsyncMock(return_value=mock_audio_output) + return manager + +@pytest_asyncio.fixture +async def mock_voice_manager(mock_voice_tensor): + """Mock voice manager for testing.""" + manager = AsyncMock(spec=VoiceManager) + manager.get_voice_path = MagicMock(return_value="/mock/path/voice.pt") + manager.load_voice = AsyncMock(return_value=mock_voice_tensor) + manager.list_voices = AsyncMock(return_value=["voice1", "voice2"]) + manager.combine_voices = AsyncMock(return_value="voice1_voice2") + return manager + +@pytest_asyncio.fixture +async def tts_service(mock_model_manager, mock_voice_manager): + """Get mocked TTS service instance.""" + service = TTSService() + service.model_manager = mock_model_manager + service._voice_manager = mock_voice_manager + return service + +@pytest.fixture +def test_voice(): + """Return a test voice name.""" + return "voice1" + +@pytest.fixture(scope="session") +def event_loop(): + """Create an instance of the default event loop for the test session.""" + import asyncio + try: + loop = asyncio.get_event_loop() + except RuntimeError: + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + yield loop + loop.close() diff --git a/api/tests/test_endpoints.py b/api/tests/test_endpoints.py deleted file mode 100644 index 8cc2d33..0000000 --- a/api/tests/test_endpoints.py +++ /dev/null @@ -1,402 +0,0 @@ -import asyncio -from unittest.mock import AsyncMock, Mock - -import pytest -import pytest_asyncio -from fastapi.testclient import TestClient -from httpx import AsyncClient - -from ..src.main import app - -# Create test client -client = TestClient(app) - - -# Create async client fixture -@pytest_asyncio.fixture -async def async_client(): - async with AsyncClient(app=app, base_url="http://test") as ac: - yield ac - - -# Mock services -@pytest.fixture -def mock_tts_service(monkeypatch): - mock_service = Mock() - mock_service._generate_audio.return_value = (bytes([0, 1, 2, 3]), 1.0) - - # Create proper async generator mock - async def mock_stream(*args, **kwargs): - for chunk in [b"chunk1", b"chunk2"]: - yield chunk - - mock_service.generate_audio_stream = mock_stream - - # Create async mocks - mock_service.list_voices = AsyncMock( - return_value=[ - "af", - "bm_lewis", - "bf_isabella", - "bf_emma", - "af_sarah", - "af_bella", - "am_adam", - "am_michael", - "bm_george", - ] - ) - mock_service.combine_voices = AsyncMock() - monkeypatch.setattr( - "api.src.routers.openai_compatible.TTSService", - lambda *args, **kwargs: mock_service, - ) - return mock_service - - -@pytest.fixture -def mock_audio_service(monkeypatch): - mock_service = Mock() - mock_service.convert_audio.return_value = b"converted mock audio data" - monkeypatch.setattr("api.src.routers.openai_compatible.AudioService", mock_service) - return mock_service - - -def test_health_check(): - """Test the health check endpoint""" - response = client.get("/health") - assert response.status_code == 200 - assert response.json() == {"status": "healthy"} - - -@pytest.mark.asyncio -async def test_openai_speech_endpoint( - mock_tts_service, mock_audio_service, async_client -): - """Test the OpenAI-compatible speech endpoint""" - test_request = { - "model": "kokoro", - "input": "Hello world", - "voice": "bm_lewis", - "response_format": "wav", - "speed": 1.0, - "stream": False, # Explicitly disable streaming - } - response = await async_client.post("/v1/audio/speech", json=test_request) - assert response.status_code == 200 - assert response.headers["content-type"] == "audio/wav" - assert response.headers["content-disposition"] == "attachment; filename=speech.wav" - mock_tts_service._generate_audio.assert_called_once_with( - text="Hello world", voice="bm_lewis", speed=1.0, stitch_long_output=True - ) - assert response.content == b"converted mock audio data" - - -@pytest.mark.asyncio -async def test_openai_speech_invalid_voice(mock_tts_service, async_client): - """Test the OpenAI-compatible speech endpoint with invalid voice""" - test_request = { - "model": "kokoro", - "input": "Hello world", - "voice": "invalid_voice", - "response_format": "wav", - "speed": 1.0, - "stream": False, # Explicitly disable streaming - } - response = await async_client.post("/v1/audio/speech", json=test_request) - assert response.status_code == 400 # Bad request - assert "not found" in response.json()["detail"]["message"] - - -@pytest.mark.asyncio -async def test_openai_speech_invalid_speed(mock_tts_service, async_client): - """Test the OpenAI-compatible speech endpoint with invalid speed""" - test_request = { - "model": "kokoro", - "input": "Hello world", - "voice": "af", - "response_format": "wav", - "speed": -1.0, # Invalid speed - "stream": False, # Explicitly disable streaming - } - response = await async_client.post("/v1/audio/speech", json=test_request) - assert response.status_code == 422 # Validation error - - -@pytest.mark.asyncio -async def test_openai_speech_generation_error(mock_tts_service, async_client): - """Test error handling in speech generation""" - mock_tts_service._generate_audio.side_effect = Exception("Generation failed") - test_request = { - "model": "kokoro", - "input": "Hello world", - "voice": "af", - "response_format": "wav", - "speed": 1.0, - "stream": False, # Explicitly disable streaming - } - response = await async_client.post("/v1/audio/speech", json=test_request) - assert response.status_code == 500 - assert "Generation failed" in response.json()["detail"]["message"] - - -@pytest.mark.asyncio -async def test_combine_voices_list_success(mock_tts_service, async_client): - """Test successful voice combination using list format""" - test_voices = ["af_bella", "af_sarah"] - mock_tts_service.combine_voices = AsyncMock(return_value="af_bella_af_sarah") - - response = await async_client.post("/v1/audio/voices/combine", json=test_voices) - - assert response.status_code == 200 - assert response.json()["voice"] == "af_bella_af_sarah" - mock_tts_service.combine_voices.assert_called_once_with(voices=test_voices) - - -@pytest.mark.asyncio -async def test_combine_voices_string_success(mock_tts_service, async_client): - """Test successful voice combination using string format with +""" - test_voices = "af_bella+af_sarah" - mock_tts_service.combine_voices = AsyncMock(return_value="af_bella_af_sarah") - - response = await async_client.post("/v1/audio/voices/combine", json=test_voices) - - assert response.status_code == 200 - assert response.json()["voice"] == "af_bella_af_sarah" - mock_tts_service.combine_voices.assert_called_once_with( - voices=["af_bella", "af_sarah"] - ) - - -@pytest.mark.asyncio -async def test_combine_voices_single_voice(mock_tts_service, async_client): - """Test combining single voice returns same voice""" - test_voices = ["af_bella"] - response = await async_client.post("/v1/audio/voices/combine", json=test_voices) - assert response.status_code == 200 - assert response.json()["voice"] == "af_bella" - - -@pytest.mark.asyncio -async def test_combine_voices_empty_list(mock_tts_service, async_client): - """Test combining empty voice list returns error""" - test_voices = [] - response = await async_client.post("/v1/audio/voices/combine", json=test_voices) - assert response.status_code == 400 - assert "No voices provided" in response.json()["detail"]["message"] - - -@pytest.mark.asyncio -async def test_combine_voices_error(mock_tts_service, async_client): - """Test error handling in voice combination""" - test_voices = ["af_bella", "af_sarah"] - mock_tts_service.combine_voices = AsyncMock( - side_effect=Exception("Combination failed") - ) - - response = await async_client.post("/v1/audio/voices/combine", json=test_voices) - assert response.status_code == 500 - assert "Server error" in response.json()["detail"]["message"] - - -@pytest.mark.asyncio -async def test_speech_with_combined_voice( - mock_tts_service, mock_audio_service, async_client -): - """Test speech generation with combined voice using + syntax""" - mock_tts_service.combine_voices = AsyncMock(return_value="af_bella_af_sarah") - - test_request = { - "model": "kokoro", - "input": "Hello world", - "voice": "af_bella+af_sarah", - "response_format": "wav", - "speed": 1.0, - "stream": False, - } - - response = await async_client.post("/v1/audio/speech", json=test_request) - - assert response.status_code == 200 - assert response.headers["content-type"] == "audio/wav" - mock_tts_service._generate_audio.assert_called_once_with( - text="Hello world", - voice="af_bella_af_sarah", - speed=1.0, - stitch_long_output=True, - ) - - -@pytest.mark.asyncio -async def test_speech_with_whitespace_in_voice( - mock_tts_service, mock_audio_service, async_client -): - """Test speech generation with whitespace in voice combination""" - mock_tts_service.combine_voices = AsyncMock(return_value="af_bella_af_sarah") - - test_request = { - "model": "kokoro", - "input": "Hello world", - "voice": " af_bella + af_sarah ", - "response_format": "wav", - "speed": 1.0, - "stream": False, - } - - response = await async_client.post("/v1/audio/speech", json=test_request) - - assert response.status_code == 200 - assert response.headers["content-type"] == "audio/wav" - mock_tts_service.combine_voices.assert_called_once_with( - voices=["af_bella", "af_sarah"] - ) - - -@pytest.mark.asyncio -async def test_speech_with_empty_voice_combination(mock_tts_service, async_client): - """Test speech generation with empty voice combination""" - test_request = { - "model": "kokoro", - "input": "Hello world", - "voice": "+", - "response_format": "wav", - "speed": 1.0, - "stream": False, - } - - response = await async_client.post("/v1/audio/speech", json=test_request) - assert response.status_code == 400 - assert "No voices provided" in response.json()["detail"]["message"] - - -@pytest.mark.asyncio -async def test_speech_with_invalid_combined_voice(mock_tts_service, async_client): - """Test speech generation with invalid voice combination""" - test_request = { - "model": "kokoro", - "input": "Hello world", - "voice": "invalid+combination", - "response_format": "wav", - "speed": 1.0, - "stream": False, - } - - response = await async_client.post("/v1/audio/speech", json=test_request) - assert response.status_code == 400 - assert "not found" in response.json()["detail"]["message"] - - -@pytest.mark.asyncio -async def test_speech_streaming_with_combined_voice(mock_tts_service, async_client): - """Test streaming speech with combined voice using + syntax""" - mock_tts_service.combine_voices = AsyncMock(return_value="af_bella_af_sarah") - - test_request = { - "model": "kokoro", - "input": "Hello world", - "voice": "af_bella+af_sarah", - "response_format": "mp3", - "stream": True, - } - - # Create streaming mock - async def mock_stream(*args, **kwargs): - for chunk in [b"mp3header", b"mp3data"]: - yield chunk - - mock_tts_service.generate_audio_stream = mock_stream - - # Add streaming header - headers = {"x-raw-response": "stream"} - response = await async_client.post( - "/v1/audio/speech", json=test_request, headers=headers - ) - - assert response.status_code == 200 - assert response.headers["content-type"] == "audio/mpeg" - assert response.headers["content-disposition"] == "attachment; filename=speech.mp3" - - -@pytest.mark.asyncio -async def test_openai_speech_pcm_streaming(mock_tts_service, async_client): - """Test streaming PCM audio for real-time playback""" - test_request = { - "model": "kokoro", - "input": "Hello world", - "voice": "af", - "response_format": "pcm", - "stream": True, - } - - # Create streaming mock for this test - async def mock_stream(*args, **kwargs): - for chunk in [b"chunk1", b"chunk2"]: - yield chunk - - mock_tts_service.generate_audio_stream = mock_stream - - # Add streaming header - headers = {"x-raw-response": "stream"} - response = await async_client.post( - "/v1/audio/speech", json=test_request, headers=headers - ) - - assert response.status_code == 200 - assert response.headers["content-type"] == "audio/pcm" - - -@pytest.mark.asyncio -async def test_openai_speech_streaming_mp3(mock_tts_service, async_client): - """Test streaming MP3 audio to file""" - test_request = { - "model": "kokoro", - "input": "Hello world", - "voice": "af", - "response_format": "mp3", - "stream": True, - } - - # Create streaming mock for this test - async def mock_stream(*args, **kwargs): - for chunk in [b"mp3header", b"mp3data"]: - yield chunk - - mock_tts_service.generate_audio_stream = mock_stream - - # Add streaming header - headers = {"x-raw-response": "stream"} - response = await async_client.post( - "/v1/audio/speech", json=test_request, headers=headers - ) - - assert response.status_code == 200 - assert response.headers["content-type"] == "audio/mpeg" - assert response.headers["content-disposition"] == "attachment; filename=speech.mp3" - - -@pytest.mark.asyncio -async def test_openai_speech_streaming_generator(mock_tts_service, async_client): - """Test streaming with async generator""" - test_request = { - "model": "kokoro", - "input": "Hello world", - "voice": "af", - "response_format": "pcm", - "stream": True, - } - - # Create streaming mock for this test - async def mock_stream(*args, **kwargs): - for chunk in [b"chunk1", b"chunk2"]: - yield chunk - - mock_tts_service.generate_audio_stream = mock_stream - - # Add streaming header - headers = {"x-raw-response": "stream"} - response = await async_client.post( - "/v1/audio/speech", json=test_request, headers=headers - ) - - assert response.status_code == 200 - assert response.headers["content-type"] == "audio/pcm" diff --git a/api/tests/test_main.py b/api/tests/test_main.py deleted file mode 100644 index f779483..0000000 --- a/api/tests/test_main.py +++ /dev/null @@ -1,108 +0,0 @@ -"""Tests for FastAPI application""" - -from unittest.mock import MagicMock, call, patch - -import pytest -from fastapi.testclient import TestClient - -from api.src.main import app, lifespan - - -@pytest.fixture -def test_client(): - """Create a test client""" - return TestClient(app) - - -def test_health_check(test_client): - """Test health check endpoint""" - response = test_client.get("/health") - assert response.status_code == 200 - assert response.json() == {"status": "healthy"} - - -@pytest.mark.asyncio -@patch("api.src.main.TTSModel") -@patch("api.src.main.logger") -async def test_lifespan_successful_warmup(mock_logger, mock_tts_model): - """Test successful model warmup in lifespan""" - # Mock file system for voice counting - mock_tts_model.VOICES_DIR = "/mock/voices" - - # Create async mock - async def async_setup(): - return 3 - - mock_tts_model.setup = MagicMock() - mock_tts_model.setup.side_effect = async_setup - mock_tts_model.get_device.return_value = "cuda" - - with patch("os.listdir", return_value=["voice1.pt", "voice2.pt", "voice3.pt"]): - # Create an async generator from the lifespan context manager - async_gen = lifespan(MagicMock()) - # Start the context manager - await async_gen.__aenter__() - - # Verify the expected logging sequence - mock_logger.info.assert_any_call("Loading TTS model and voice packs...") - - # Check for the startup message containing the required info - startup_calls = [call[0][0] for call in mock_logger.info.call_args_list] - startup_msg = next(msg for msg in startup_calls if "Model warmed up on" in msg) - assert "Model warmed up on" in startup_msg - assert "3 voice packs loaded" in startup_msg - - # Verify model setup was called - mock_tts_model.setup.assert_called_once() - - # Clean up - await async_gen.__aexit__(None, None, None) - - -@pytest.mark.asyncio -@patch("api.src.main.TTSModel") -@patch("api.src.main.logger") -async def test_lifespan_failed_warmup(mock_logger, mock_tts_model): - """Test failed model warmup in lifespan""" - # Mock the model setup to fail - mock_tts_model.setup.side_effect = RuntimeError("Failed to initialize model") - - # Create an async generator from the lifespan context manager - async_gen = lifespan(MagicMock()) - - # Verify the exception is raised - with pytest.raises(RuntimeError, match="Failed to initialize model"): - await async_gen.__aenter__() - - # Verify the expected logging sequence - mock_logger.info.assert_called_with("Loading TTS model and voice packs...") - - # Clean up - await async_gen.__aexit__(None, None, None) - - -@pytest.mark.asyncio -@patch("api.src.main.TTSModel") -async def test_lifespan_cuda_warmup(mock_tts_model): - """Test model warmup specifically on CUDA""" - # Mock file system for voice counting - mock_tts_model.VOICES_DIR = "/mock/voices" - - # Create async mock - async def async_setup(): - return 2 - - mock_tts_model.setup = MagicMock() - mock_tts_model.setup.side_effect = async_setup - mock_tts_model.get_device.return_value = "cuda" - - with patch("os.listdir", return_value=["voice1.pt", "voice2.pt"]): - # Create an async generator from the lifespan context manager - async_gen = lifespan(MagicMock()) - await async_gen.__aenter__() - - # Verify model setup was called - mock_tts_model.setup.assert_called_once() - - # Clean up - await async_gen.__aexit__(None, None, None) diff --git a/api/tests/test_openai_endpoints.py b/api/tests/test_openai_endpoints.py new file mode 100644 index 0000000..c5080e8 --- /dev/null +++ b/api/tests/test_openai_endpoints.py @@ -0,0 +1,215 @@ +import pytest +from unittest.mock import AsyncMock, patch, MagicMock +from fastapi.testclient import TestClient +import numpy as np +import asyncio +from typing import AsyncGenerator + +from api.src.main import app +from api.src.services.tts_service import TTSService + +client = TestClient(app) + +@pytest.fixture +def mock_audio_bytes(): + """Mock audio bytes for testing.""" + return b"mock audio data" + +@pytest.fixture +def mock_tts_service(mock_audio_bytes): + """Mock TTS service for testing.""" + with patch("api.src.routers.openai_compatible.get_tts_service") as mock_get: + service = AsyncMock(spec=TTSService) + service.generate_audio.return_value = (np.zeros(1000), 0.1) + + # Create a proper async generator for streaming + async def mock_stream(*args, **kwargs) -> AsyncGenerator[bytes, None]: + yield mock_audio_bytes + + service.generate_audio_stream = mock_stream + service.list_voices.return_value = ["voice1", "voice2"] + service.combine_voices.return_value = "voice1_voice2" + + # Return the same instance for all calls + mock_get.return_value = service + mock_get.side_effect = None + yield service + +def test_openai_speech_endpoint(mock_tts_service, test_voice): + """Test the OpenAI-compatible speech endpoint with basic MP3 generation""" + response = client.post( + "/v1/audio/speech", + json={ + "model": "kokoro", + "input": "Hello world", + "voice": test_voice, + "response_format": "mp3", + "stream": False + } + ) + assert response.status_code == 200 + assert response.headers["content-type"] == "audio/mpeg" + assert len(response.content) > 0 + +def test_openai_speech_streaming(mock_tts_service, test_voice, mock_audio_bytes): + """Test the OpenAI-compatible speech endpoint with streaming""" + response = client.post( + "/v1/audio/speech", + json={ + "model": "kokoro", + "input": "Hello world", + "voice": test_voice, + "response_format": "mp3", + "stream": True + } + ) + assert response.status_code == 200 + assert response.headers["content-type"] == "audio/mpeg" + assert "Transfer-Encoding" in response.headers + assert response.headers["Transfer-Encoding"] == "chunked" + + # For streaming responses, we need to read the content in chunks + content = b"" + for chunk in response.iter_bytes(): + content += chunk + assert content == mock_audio_bytes + +def test_openai_speech_pcm_streaming(mock_tts_service, test_voice, mock_audio_bytes): + """Test PCM streaming format""" + response = client.post( + "/v1/audio/speech", + json={ + "model": "kokoro", + "input": "Hello world", + "voice": test_voice, + "response_format": "pcm", + "stream": True + } + ) + assert response.status_code == 200 + assert response.headers["content-type"] == "audio/pcm" + + # For streaming responses, we need to read the content in chunks + content = b"" + for chunk in response.iter_bytes(): + content += chunk + assert content == mock_audio_bytes + +def test_openai_speech_invalid_voice(mock_tts_service): + """Test error handling for invalid voice""" + mock_tts_service.generate_audio.side_effect = ValueError("Voice 'invalid_voice' not found") + + response = client.post( + "/v1/audio/speech", + json={ + "model": "kokoro", + "input": "Hello world", + "voice": "invalid_voice", + "response_format": "mp3", + "stream": False + } + ) + assert response.status_code == 400 + error_response = response.json() + assert error_response["detail"]["error"] == "validation_error" + assert "Voice 'invalid_voice' not found" in error_response["detail"]["message"] + assert error_response["detail"]["type"] == "invalid_request_error" + +def test_openai_speech_empty_text(mock_tts_service, test_voice): + """Test error handling for empty text""" + mock_tts_service.generate_audio.side_effect = ValueError("Text is empty after preprocessing") + + response = client.post( + "/v1/audio/speech", + json={ + "model": "kokoro", + "input": "", + "voice": test_voice, + "response_format": "mp3", + "stream": False + } + ) + assert response.status_code == 400 + error_response = response.json() + assert error_response["detail"]["error"] == "validation_error" + assert "Text is empty after preprocessing" in error_response["detail"]["message"] + assert error_response["detail"]["type"] == "invalid_request_error" + +def test_openai_speech_invalid_format(mock_tts_service, test_voice): + """Test error handling for invalid format""" + response = client.post( + "/v1/audio/speech", + json={ + "model": "kokoro", + "input": "Hello world", + "voice": test_voice, + "response_format": "invalid_format", + "stream": False + } + ) + assert response.status_code == 422 # Validation error from Pydantic + +def test_list_voices(mock_tts_service): + """Test listing available voices""" + response = client.get("/v1/audio/voices") + assert response.status_code == 200 + data = response.json() + assert "voices" in data + assert len(data["voices"]) == 2 + assert "voice1" in data["voices"] + assert "voice2" in data["voices"] + +def test_combine_voices(mock_tts_service): + """Test combining voices endpoint""" + response = client.post( + "/v1/audio/voices/combine", + json="voice1+voice2" + ) + assert response.status_code == 200 + data = response.json() + assert "voice" in data + assert data["voice"] == "voice1_voice2" + +def test_server_error(mock_tts_service, test_voice): + """Test handling of server errors""" + mock_tts_service.generate_audio.side_effect = RuntimeError("Internal server error") + + response = client.post( + "/v1/audio/speech", + json={ + "model": "kokoro", + "input": "Hello world", + "voice": test_voice, + "response_format": "mp3", + "stream": False + } + ) + assert response.status_code == 500 + error_response = response.json() + assert error_response["detail"]["error"] == "processing_error" + assert error_response["detail"]["type"] == "server_error" + +def test_streaming_error(mock_tts_service, test_voice): + """Test handling streaming errors""" + # Create a proper async generator that raises an error + async def mock_error_stream(*args, **kwargs) -> AsyncGenerator[bytes, None]: + if False: # This makes it a proper generator + yield b"" + raise RuntimeError("Streaming failed") + + mock_tts_service.generate_audio_stream = mock_error_stream + + response = client.post( + "/v1/audio/speech", + json={ + "model": "kokoro", + "input": "Hello world", + "voice": test_voice, + "response_format": "mp3", + "stream": True + } + ) + assert response.status_code == 500 + error_response = response.json() + assert error_response["detail"]["error"] == "processing_error" + assert error_response["detail"]["type"] == "server_error" \ No newline at end of file diff --git a/api/tests/test_text_processing.py b/api/tests/test_text_processing.py deleted file mode 100644 index a5475a1..0000000 --- a/api/tests/test_text_processing.py +++ /dev/null @@ -1,122 +0,0 @@ -"""Tests for text processing endpoints""" - -from unittest.mock import Mock, patch - -import numpy as np -import pytest -import pytest_asyncio -from httpx import AsyncClient - -from ..src.main import app -from .conftest import MockTTSModel - - -@pytest_asyncio.fixture -async def async_client(): - async with AsyncClient(app=app, base_url="http://test") as ac: - yield ac - - -@pytest.mark.asyncio -async def test_phonemize_endpoint(async_client): - """Test phoneme generation endpoint""" - with patch("api.src.routers.development.phonemize") as mock_phonemize, patch( - "api.src.routers.development.tokenize" - ) as mock_tokenize: - # Setup mocks - mock_phonemize.return_value = "həlˈoʊ" - mock_tokenize.return_value = [1, 2, 3] - - # Test request - response = await async_client.post( - "/text/phonemize", json={"text": "hello", "language": "a"} - ) - - # Verify response - assert response.status_code == 200 - result = response.json() - assert result["phonemes"] == "həlˈoʊ" - assert result["tokens"] == [0, 1, 2, 3, 0] # Should add start/end tokens - - -@pytest.mark.asyncio -async def test_phonemize_empty_text(async_client): - """Test phoneme generation with empty text""" - response = await async_client.post( - "/text/phonemize", json={"text": "", "language": "a"} - ) - - assert response.status_code == 500 - assert "error" in response.json()["detail"] - - -@pytest.mark.asyncio -async def test_generate_from_phonemes( - async_client, mock_tts_service, mock_audio_service -): - """Test audio generation from phonemes""" - with patch( - "api.src.routers.development.TTSService", return_value=mock_tts_service - ): - response = await async_client.post( - "/text/generate_from_phonemes", - json={"phonemes": "həlˈoʊ", "voice": "af_bella", "speed": 1.0}, - ) - - assert response.status_code == 200 - assert response.headers["content-type"] == "audio/wav" - assert ( - response.headers["content-disposition"] == "attachment; filename=speech.wav" - ) - assert response.content == b"mock audio data" - - -@pytest.mark.asyncio -async def test_generate_from_phonemes_invalid_voice(async_client, mock_tts_service): - """Test audio generation with invalid voice""" - mock_tts_service._get_voice_path.return_value = None - with patch( - "api.src.routers.development.TTSService", return_value=mock_tts_service - ): - response = await async_client.post( - "/text/generate_from_phonemes", - json={"phonemes": "həlˈoʊ", "voice": "invalid_voice", "speed": 1.0}, - ) - - assert response.status_code == 400 - assert "Voice not found" in response.json()["detail"]["message"] - - -@pytest.mark.asyncio -async def test_generate_from_phonemes_invalid_speed(async_client, monkeypatch): - """Test audio generation with invalid speed""" - # Mock TTSModel initialization - mock_model = Mock() - mock_model.generate_from_tokens = Mock(return_value=np.zeros(48000)) - monkeypatch.setattr("api.src.services.tts_model.TTSModel._instance", mock_model) - monkeypatch.setattr( - "api.src.services.tts_model.TTSModel.get_instance", - Mock(return_value=mock_model), - ) - - response = await async_client.post( - "/text/generate_from_phonemes", - json={"phonemes": "həlˈoʊ", "voice": "af_bella", "speed": -1.0}, - ) - - assert response.status_code == 422 # Validation error - - -@pytest.mark.asyncio -async def test_generate_from_phonemes_empty_phonemes(async_client, mock_tts_service): - """Test audio generation with empty phonemes""" - with patch( - "api.src.routers.development.TTSService", return_value=mock_tts_service - ): - response = await async_client.post( - "/text/generate_from_phonemes", - json={"phonemes": "", "voice": "af_bella", "speed": 1.0}, - ) - - assert response.status_code == 400 - assert "Invalid request" in response.json()["detail"]["error"] diff --git a/api/tests/test_tts_implementations.py b/api/tests/test_tts_implementations.py deleted file mode 100644 index 9534dce..0000000 --- a/api/tests/test_tts_implementations.py +++ /dev/null @@ -1,201 +0,0 @@ -"""Tests for TTS model implementations""" - -import os -from unittest.mock import AsyncMock, MagicMock, patch - -import numpy as np -import pytest -import torch - -from api.src.services.tts_base import TTSBaseModel -from api.src.services.tts_cpu import TTSCPUModel -from api.src.services.tts_gpu import TTSGPUModel, length_to_mask - - -# Base Model Tests -def test_get_device_error(): - """Test get_device() raises error when not initialized""" - TTSBaseModel._device = None - with pytest.raises(RuntimeError, match="Model not initialized"): - TTSBaseModel.get_device() - - -@pytest.mark.asyncio -@patch("torch.cuda.is_available") -@patch("os.path.exists") -@patch("os.path.join") -@patch("os.listdir") -@patch("torch.load") -@patch("torch.save") -@patch("api.src.services.tts_base.settings") -@patch("api.src.services.warmup.WarmupService") -async def test_setup_cuda_available( - mock_warmup_class, mock_settings, mock_save, mock_load, mock_listdir, mock_join, mock_exists, mock_cuda_available -): - """Test setup with CUDA available""" - TTSBaseModel._device = None - # Mock CUDA as unavailable since we're using CPU PyTorch - mock_cuda_available.return_value = False - mock_exists.return_value = True - mock_load.return_value = torch.zeros(1) - mock_listdir.return_value = ["voice1.pt", "voice2.pt"] - mock_join.return_value = "/mocked/path" - - # Configure mock settings - mock_settings.model_dir = "/mock/model/dir" - mock_settings.onnx_model_path = "model.onnx" - mock_settings.voices_dir = "voices" - - # Configure mock warmup service - mock_warmup = MagicMock() - mock_warmup.load_voices.return_value = [torch.zeros(1)] - mock_warmup.warmup_voices = AsyncMock() - mock_warmup_class.return_value = mock_warmup - - # Create mock model - mock_model = MagicMock() - mock_model.bert = MagicMock() - mock_model.process_text = MagicMock(return_value=("dummy", [1, 2, 3])) - mock_model.generate_from_tokens = MagicMock(return_value=np.zeros(1000)) - - # Mock initialize to return our mock model - TTSBaseModel.initialize = MagicMock(return_value=mock_model) - TTSBaseModel._instance = mock_model - - voice_count = await TTSBaseModel.setup() - assert TTSBaseModel._device == "cpu" - assert voice_count == 2 - - -@pytest.mark.asyncio -@patch("torch.cuda.is_available") -@patch("os.path.exists") -@patch("os.path.join") -@patch("os.listdir") -@patch("torch.load") -@patch("torch.save") -@patch("api.src.services.tts_base.settings") -@patch("api.src.services.warmup.WarmupService") -async def test_setup_cuda_unavailable( - mock_warmup_class, mock_settings, mock_save, mock_load, mock_listdir, mock_join, mock_exists, mock_cuda_available -): - """Test setup with CUDA unavailable""" - TTSBaseModel._device = None - mock_cuda_available.return_value = False - mock_exists.return_value = True - mock_load.return_value = torch.zeros(1) - mock_listdir.return_value = ["voice1.pt", "voice2.pt"] - mock_join.return_value = "/mocked/path" - - # Configure mock settings - mock_settings.model_dir = "/mock/model/dir" - mock_settings.onnx_model_path = "model.onnx" - mock_settings.voices_dir = "voices" - - # Configure mock warmup service - mock_warmup = MagicMock() - mock_warmup.load_voices.return_value = [torch.zeros(1)] - mock_warmup.warmup_voices = AsyncMock() - mock_warmup_class.return_value = mock_warmup - - # Create mock model - mock_model = MagicMock() - mock_model.bert = MagicMock() - mock_model.process_text = MagicMock(return_value=("dummy", [1, 2, 3])) - mock_model.generate_from_tokens = MagicMock(return_value=np.zeros(1000)) - - # Mock initialize to return our mock model - TTSBaseModel.initialize = MagicMock(return_value=mock_model) - TTSBaseModel._instance = mock_model - - voice_count = await TTSBaseModel.setup() - assert TTSBaseModel._device == "cpu" - assert voice_count == 2 - - -# CPU Model Tests -def test_cpu_initialize_missing_model(): - """Test CPU initialize with missing model""" - TTSCPUModel._onnx_session = None # Reset the session - with patch("os.path.exists", return_value=False), patch( - "onnxruntime.InferenceSession", return_value=None - ): - result = TTSCPUModel.initialize("dummy_dir") - assert result is None - - -def test_cpu_generate_uninitialized(): - """Test CPU generate methods with uninitialized model""" - TTSCPUModel._onnx_session = None - - with pytest.raises(RuntimeError, match="ONNX model not initialized"): - TTSCPUModel.generate_from_text("test", torch.zeros(1), "en", 1.0) - - with pytest.raises(RuntimeError, match="ONNX model not initialized"): - TTSCPUModel.generate_from_tokens([1, 2, 3], torch.zeros(1), 1.0) - - -def test_cpu_process_text(): - """Test CPU process_text functionality""" - with patch("api.src.services.tts_cpu.phonemize") as mock_phonemize, patch( - "api.src.services.tts_cpu.tokenize" - ) as mock_tokenize: - mock_phonemize.return_value = "test phonemes" - mock_tokenize.return_value = [1, 2, 3] - - phonemes, tokens = TTSCPUModel.process_text("test", "en") - assert phonemes == "test phonemes" - assert tokens == [0, 1, 2, 3, 0] # Should add start/end tokens - - -# GPU Model Tests -@patch("torch.cuda.is_available") -def test_gpu_initialize_cuda_unavailable(mock_cuda_available): - """Test GPU initialize with CUDA unavailable""" - mock_cuda_available.return_value = False - TTSGPUModel._instance = None - - result = TTSGPUModel.initialize("dummy_dir", "dummy_path") - assert result is None - - -@patch("api.src.services.tts_gpu.length_to_mask") -def test_gpu_length_to_mask(mock_length_to_mask): - """Test length_to_mask function""" - # Setup mock return value - expected_mask = torch.tensor( - [[False, False, False, True, True], [False, False, False, False, False]] - ) - mock_length_to_mask.return_value = expected_mask - - # Call function with test input - lengths = torch.tensor([3, 5]) - mask = mock_length_to_mask(lengths) - - # Verify mock was called with correct input - mock_length_to_mask.assert_called_once() - assert torch.equal(mask, expected_mask) - - -def test_gpu_generate_uninitialized(): - """Test GPU generate methods with uninitialized model""" - TTSGPUModel._instance = None - - with pytest.raises(RuntimeError, match="GPU model not initialized"): - TTSGPUModel.generate_from_text("test", torch.zeros(1), "en", 1.0) - - with pytest.raises(RuntimeError, match="GPU model not initialized"): - TTSGPUModel.generate_from_tokens([1, 2, 3], torch.zeros(1), 1.0) - - -def test_gpu_process_text(): - """Test GPU process_text functionality""" - with patch("api.src.services.tts_gpu.phonemize") as mock_phonemize, patch( - "api.src.services.tts_gpu.tokenize" - ) as mock_tokenize: - mock_phonemize.return_value = "test phonemes" - mock_tokenize.return_value = [1, 2, 3] - - phonemes, tokens = TTSGPUModel.process_text("test", "en") - assert phonemes == "test phonemes" - assert tokens == [1, 2, 3] # GPU implementation doesn't add start/end tokens diff --git a/api/tests/test_tts_service.py b/api/tests/test_tts_service.py deleted file mode 100644 index 843008a..0000000 --- a/api/tests/test_tts_service.py +++ /dev/null @@ -1,260 +0,0 @@ -"""Tests for TTSService""" - -import os -from unittest.mock import MagicMock, call, patch - -import numpy as np -import pytest -import torch -from onnxruntime import InferenceSession - -from api.src.core.config import settings -from api.src.services.tts_cpu import TTSCPUModel -from api.src.services.tts_gpu import TTSGPUModel -from api.src.services.tts_model import TTSModel -from api.src.services.tts_service import TTSService - - -@pytest.fixture -def tts_service(monkeypatch): - """Create a TTSService instance for testing""" - # Mock TTSModel initialization - mock_model = MagicMock() - mock_model.generate_from_tokens = MagicMock(return_value=np.zeros(48000)) - mock_model.process_text = MagicMock(return_value=("mock phonemes", [1, 2, 3])) - - # Set up model instance - monkeypatch.setattr("api.src.services.tts_model.TTSModel._instance", mock_model) - monkeypatch.setattr( - "api.src.services.tts_model.TTSModel.get_instance", - MagicMock(return_value=mock_model), - ) - monkeypatch.setattr( - "api.src.services.tts_model.TTSModel.get_device", MagicMock(return_value="cpu") - ) - - return TTSService() - - -@pytest.fixture -def sample_audio(): - """Generate a simple sine wave for testing""" - sample_rate = 24000 - duration = 0.1 # 100ms - t = np.linspace(0, duration, int(sample_rate * duration)) - frequency = 440 # A4 note - return np.sin(2 * np.pi * frequency * t).astype(np.float32) - - -def test_audio_to_bytes(tts_service, sample_audio): - """Test converting audio tensor to bytes""" - audio_bytes = tts_service._audio_to_bytes(sample_audio) - assert isinstance(audio_bytes, bytes) - assert len(audio_bytes) > 0 - - -@pytest.mark.asyncio -async def test_list_voices(tts_service): - """Test listing available voices""" - - # Override list_voices for testing - # # TODO: - # Whatever aiofiles does here pathing aiofiles vs aiofiles.os - # I am thoroughly confused by it. - # Cheating the test as it seems to work in the real world (for now) - async def mock_list_voices(): - return ["voice1", "voice2"] - - tts_service.list_voices = mock_list_voices - - voices = await tts_service.list_voices() - assert len(voices) == 2 - assert "voice1" in voices - assert "voice2" in voices - - -@pytest.mark.asyncio -async def test_list_voices_error(tts_service): - """Test error handling in list_voices""" - - # Override list_voices for testing - # TODO: See above. - async def mock_list_voices(): - return [] - - tts_service.list_voices = mock_list_voices - - voices = await tts_service.list_voices() - assert voices == [] - - -def mock_model_setup(cuda_available=False): - """Helper function to mock model setup""" - # Reset model state - TTSModel._instance = None - TTSModel._device = None - TTSModel._voicepacks = {} - - # Create mock model instance with proper generate method - mock_model = MagicMock() - mock_model.generate.return_value = np.zeros(24000, dtype=np.float32) - TTSModel._instance = mock_model - - # Set device based on CUDA availability - TTSModel._device = "cuda" if cuda_available else "cpu" - - return 3 # Return voice count (including af.pt) - - -def test_model_initialization_cuda(): - """Test model initialization with CUDA""" - # Simulate CUDA availability - voice_count = mock_model_setup(cuda_available=True) - - assert TTSModel.get_device() == "cuda" - assert voice_count == 3 # voice1.pt, voice2.pt, af.pt - - -def test_model_initialization_cpu(): - """Test model initialization with CPU""" - # Simulate no CUDA availability - voice_count = mock_model_setup(cuda_available=False) - - assert TTSModel.get_device() == "cpu" - assert voice_count == 3 # voice1.pt, voice2.pt, af.pt - - -def test_generate_audio_empty_text(tts_service): - """Test generating audio with empty text""" - with pytest.raises(ValueError, match="Text is empty after preprocessing"): - tts_service._generate_audio("", "af", 1.0) - - -@pytest.fixture(autouse=True) -def mock_settings(): - """Mock settings for all tests""" - with patch("api.src.services.text_processing.chunker.settings") as mock_settings: - mock_settings.max_chunk_size = 300 - yield mock_settings - - -@patch("api.src.services.tts_model.TTSModel.get_instance") -@patch("api.src.services.tts_model.TTSModel.get_device") -@patch("os.path.exists") -@patch("kokoro.normalize_text") -@patch("kokoro.phonemize") -@patch("kokoro.tokenize") -@patch("kokoro.generate") -@patch("torch.load") -def test_generate_audio_phonemize_error( - mock_torch_load, - mock_generate, - mock_tokenize, - mock_phonemize, - mock_normalize, - mock_exists, - mock_get_device, - mock_instance, - tts_service, -): - """Test handling phonemization error""" - mock_normalize.return_value = "Test text" - mock_phonemize.side_effect = Exception("Phonemization failed") - mock_instance.return_value = ( - mock_generate, - "cpu", - ) # Use the same mock for consistency - mock_get_device.return_value = "cpu" - mock_exists.return_value = True - mock_torch_load.return_value = torch.zeros((10, 24000)) - mock_generate.return_value = (None, None) - - with pytest.raises(ValueError, match="No chunks were processed successfully"): - tts_service._generate_audio("Test text", "af", 1.0) - - -@patch("api.src.services.tts_model.TTSModel.get_instance") -@patch("api.src.services.tts_model.TTSModel.get_device") -@patch("os.path.exists") -@patch("kokoro.normalize_text") -@patch("kokoro.phonemize") -@patch("kokoro.tokenize") -@patch("kokoro.generate") -@patch("torch.load") -def test_generate_audio_error( - mock_torch_load, - mock_generate, - mock_tokenize, - mock_phonemize, - mock_normalize, - mock_exists, - mock_get_device, - mock_instance, - tts_service, -): - """Test handling generation error""" - mock_normalize.return_value = "Test text" - mock_phonemize.return_value = "Test text" - mock_tokenize.return_value = [1, 2] # Return integers instead of strings - mock_generate.side_effect = Exception("Generation failed") - mock_instance.return_value = ( - mock_generate, - "cpu", - ) # Use the same mock for consistency - mock_get_device.return_value = "cpu" - mock_exists.return_value = True - mock_torch_load.return_value = torch.zeros((10, 24000)) - - with pytest.raises(ValueError, match="No chunks were processed successfully"): - tts_service._generate_audio("Test text", "af", 1.0) - - -def test_save_audio(tts_service, sample_audio, tmp_path): - """Test saving audio to file""" - output_path = os.path.join(tmp_path, "test_output.wav") - tts_service._save_audio(sample_audio, output_path) - assert os.path.exists(output_path) - assert os.path.getsize(output_path) > 0 - - -@pytest.mark.asyncio -async def test_combine_voices(tts_service): - """Test combining multiple voices""" - # Setup mocks for torch operations - with patch("torch.load", return_value=torch.tensor([1.0, 2.0])), patch( - "torch.stack", return_value=torch.tensor([[1.0, 2.0], [3.0, 4.0]]) - ), patch("torch.mean", return_value=torch.tensor([2.0, 3.0])), patch( - "torch.save" - ), patch("os.path.exists", return_value=True): - # Test combining two voices - result = await tts_service.combine_voices(["voice1", "voice2"]) - - assert result == "voice1_voice2" - - -@pytest.mark.asyncio -async def test_combine_voices_invalid_input(tts_service): - """Test combining voices with invalid input""" - # Test with empty list - with pytest.raises(ValueError, match="At least 2 voices are required"): - await tts_service.combine_voices([]) - - # Test with single voice - with pytest.raises(ValueError, match="At least 2 voices are required"): - await tts_service.combine_voices(["voice1"]) - - -@patch("api.src.services.tts_service.TTSService._get_voice_path") -@patch("api.src.services.tts_model.TTSModel.get_instance") -def test_voicepack_loading_error(mock_get_instance, mock_get_voice_path): - """Test voicepack loading error handling""" - mock_get_voice_path.return_value = None - mock_instance = MagicMock() - mock_instance.generate.return_value = np.zeros(24000, dtype=np.float32) - mock_get_instance.return_value = (mock_instance, "cpu") - - TTSModel._voicepacks = {} # Reset voicepacks - - service = TTSService() - with pytest.raises(ValueError, match="Voice not found: nonexistent_voice"): - service._generate_audio("test", "nonexistent_voice", 1.0) diff --git a/api/tests/test_tts_service_new.py b/api/tests/test_tts_service_new.py new file mode 100644 index 0000000..f9a1cdd --- /dev/null +++ b/api/tests/test_tts_service_new.py @@ -0,0 +1,140 @@ +import pytest +import numpy as np +from unittest.mock import AsyncMock, patch + +@pytest.mark.asyncio +async def test_generate_audio(tts_service, mock_audio_output, test_voice): + """Test basic audio generation""" + audio, processing_time = await tts_service.generate_audio( + text="Hello world", + voice=test_voice, + speed=1.0 + ) + + assert isinstance(audio, np.ndarray) + assert np.array_equal(audio, mock_audio_output) + assert processing_time > 0 + tts_service.model_manager.generate.assert_called_once() + +@pytest.mark.asyncio +async def test_generate_audio_with_combined_voice(tts_service, mock_audio_output): + """Test audio generation with a combined voice""" + test_voices = ["voice1", "voice2"] + combined_id = await tts_service._voice_manager.combine_voices(test_voices) + + audio, processing_time = await tts_service.generate_audio( + text="Hello world", + voice=combined_id, + speed=1.0 + ) + + assert isinstance(audio, np.ndarray) + assert np.array_equal(audio, mock_audio_output) + assert processing_time > 0 + +@pytest.mark.asyncio +async def test_generate_audio_stream(tts_service, mock_audio_output, test_voice): + """Test streaming audio generation""" + tts_service.model_manager.generate.return_value = mock_audio_output + + chunks = [] + async for chunk in tts_service.generate_audio_stream( + text="Hello world", + voice=test_voice, + speed=1.0, + output_format="pcm" + ): + assert isinstance(chunk, bytes) + chunks.append(chunk) + + assert len(chunks) > 0 + tts_service.model_manager.generate.assert_called() + +@pytest.mark.asyncio +async def test_empty_text(tts_service, test_voice): + """Test handling empty text""" + with pytest.raises(ValueError) as exc_info: + await tts_service.generate_audio( + text="", + voice=test_voice, + speed=1.0 + ) + assert "Text is empty after preprocessing" in str(exc_info.value) + +@pytest.mark.asyncio +async def test_invalid_voice(tts_service): + """Test handling invalid voice""" + tts_service._voice_manager.load_voice.side_effect = ValueError("Voice not found") + + with pytest.raises(ValueError) as exc_info: + await tts_service.generate_audio( + text="Hello world", + voice="invalid_voice", + speed=1.0 + ) + assert "Voice not found" in str(exc_info.value) + +@pytest.mark.asyncio +async def test_model_generation_error(tts_service, test_voice): + """Test handling model generation error""" + # Make generate return None to simulate failed generation + tts_service.model_manager.generate.return_value = None + + with pytest.raises(ValueError) as exc_info: + await tts_service.generate_audio( + text="Hello world", + voice=test_voice, + speed=1.0 + ) + assert "No audio chunks were generated successfully" in str(exc_info.value) + +@pytest.mark.asyncio +async def test_streaming_generation_error(tts_service, test_voice): + """Test handling streaming generation error""" + # Make generate return None to simulate failed generation + tts_service.model_manager.generate.return_value = None + + chunks = [] + async for chunk in tts_service.generate_audio_stream( + text="Hello world", + voice=test_voice, + speed=1.0, + output_format="pcm" + ): + chunks.append(chunk) + + # Should get no chunks if generation fails + assert len(chunks) == 0 + +@pytest.mark.asyncio +async def test_list_voices(tts_service): + """Test listing available voices""" + voices = await tts_service.list_voices() + assert len(voices) == 2 + assert "voice1" in voices + assert "voice2" in voices + tts_service._voice_manager.list_voices.assert_called_once() + +@pytest.mark.asyncio +async def test_combine_voices(tts_service): + """Test combining voices""" + test_voices = ["voice1", "voice2"] + combined_id = await tts_service.combine_voices(test_voices) + assert combined_id == "voice1_voice2" + tts_service._voice_manager.combine_voices.assert_called_once_with(test_voices) + +@pytest.mark.asyncio +async def test_chunked_text_processing(tts_service, test_voice, mock_audio_output): + """Test processing chunked text""" + long_text = "First sentence. Second sentence. Third sentence." + + audio, processing_time = await tts_service.generate_audio( + text=long_text, + voice=test_voice, + speed=1.0, + stitch_long_output=True + ) + + assert tts_service.model_manager.generate.call_count > 1 + assert isinstance(audio, np.ndarray) + assert processing_time > 0 \ No newline at end of file diff --git a/api/tests/test_voice_manager.py b/api/tests/test_voice_manager.py new file mode 100644 index 0000000..2c129e8 --- /dev/null +++ b/api/tests/test_voice_manager.py @@ -0,0 +1,82 @@ +import pytest +from unittest.mock import AsyncMock, patch +import torch +from pathlib import Path + +@pytest.mark.asyncio +async def test_list_available_voices(mock_voice_manager): + """Test listing available voices""" + voices = await mock_voice_manager.list_voices() + assert len(voices) == 2 + assert "voice1" in voices + assert "voice2" in voices + +@pytest.mark.asyncio +async def test_get_voice_path(mock_voice_manager): + """Test getting path for a specific voice""" + voice_path = mock_voice_manager.get_voice_path("voice1") + assert voice_path == "/mock/path/voice.pt" + + # Test invalid voice + mock_voice_manager.get_voice_path.return_value = None + assert mock_voice_manager.get_voice_path("invalid_voice") is None + +@pytest.mark.asyncio +async def test_load_voice(mock_voice_manager, mock_voice_tensor): + """Test loading a voice tensor""" + voice_tensor = await mock_voice_manager.load_voice("voice1") + assert torch.equal(voice_tensor, mock_voice_tensor) + mock_voice_manager.load_voice.assert_called_once_with("voice1") + +@pytest.mark.asyncio +async def test_load_voice_not_found(mock_voice_manager): + """Test loading non-existent voice""" + mock_voice_manager.get_voice_path.return_value = None + mock_voice_manager.load_voice.side_effect = ValueError("Voice not found: invalid_voice") + + with pytest.raises(ValueError, match="Voice not found: invalid_voice"): + await mock_voice_manager.load_voice("invalid_voice") + +@pytest.mark.asyncio +async def test_combine_voices(mock_voice_manager): + """Test combining two voices""" + voices = ["voice1", "voice2"] + weights = [0.5, 0.5] + + combined_id = await mock_voice_manager.combine_voices(voices, weights) + assert combined_id == "voice1_voice2" + mock_voice_manager.combine_voices.assert_called_once_with(voices, weights) + +@pytest.mark.asyncio +async def test_combine_voices_invalid_weights(mock_voice_manager): + """Test combining voices with invalid weights""" + voices = ["voice1", "voice2"] + weights = [0.3, 0.3] # Doesn't sum to 1 + + mock_voice_manager.combine_voices.side_effect = ValueError("Weights must sum to 1") + with pytest.raises(ValueError, match="Weights must sum to 1"): + await mock_voice_manager.combine_voices(voices, weights) + +@pytest.mark.asyncio +async def test_combine_voices_single_voice(mock_voice_manager): + """Test combining with single voice""" + voices = ["voice1"] + weights = [1.0] + + mock_voice_manager.combine_voices.side_effect = ValueError("At least 2 voices are required") + with pytest.raises(ValueError, match="At least 2 voices are required"): + await mock_voice_manager.combine_voices(voices, weights) + +@pytest.mark.asyncio +async def test_cache_management(mock_voice_manager, mock_voice_tensor): + """Test voice cache management""" + # Mock cache info + mock_voice_manager.cache_info = {"size": 1, "max_size": 10} + + # Load voice to test caching + await mock_voice_manager.load_voice("voice1") + + # Check cache info + cache_info = mock_voice_manager.cache_info + assert cache_info["size"] == 1 + assert cache_info["max_size"] == 10 \ No newline at end of file diff --git a/docker/cpu/Dockerfile b/docker/cpu/Dockerfile index a834649..df4fa8b 100644 --- a/docker/cpu/Dockerfile +++ b/docker/cpu/Dockerfile @@ -33,6 +33,7 @@ RUN --mount=type=cache,target=/root/.cache/uv \ # Copy project files COPY --chown=appuser:appuser api ./api +COPY --chown=appuser:appuser web ./web # Install project RUN --mount=type=cache,target=/root/.cache/uv \ diff --git a/docker/gpu/Dockerfile b/docker/gpu/Dockerfile index 2e3c525..b1d6741 100644 --- a/docker/gpu/Dockerfile +++ b/docker/gpu/Dockerfile @@ -37,6 +37,7 @@ RUN --mount=type=cache,target=/root/.cache/uv \ # Copy project files COPY --chown=appuser:appuser api ./api +COPY --chown=appuser:appuser web ./web # Install project with GPU extras RUN --mount=type=cache,target=/root/.cache/uv \ diff --git a/pyproject.toml b/pyproject.toml index 5e03a0e..87bb9dd 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -33,6 +33,7 @@ dependencies = [ "ebooklib>=0.18", "html2text>=2024.2.26", "pydub>=0.25.1", + "matplotlib>=3.10.0", ] [project.optional-dependencies] diff --git a/ui/tests/conftest.py b/ui/tests/conftest.py index e9bc035..9b01e6b 100644 --- a/ui/tests/conftest.py +++ b/ui/tests/conftest.py @@ -1,9 +1,46 @@ -import gradio as gr import pytest +from unittest.mock import AsyncMock, Mock + +from api.src.services.tts_service import TTSService @pytest.fixture -def mock_gr_context(): - """Provides a context for testing Gradio components""" - with gr.Blocks(): - yield +async def mock_model_manager(): + """Mock model manager for UI tests""" + manager = AsyncMock() + manager.get_backend = Mock(return_value=Mock(device="cpu")) + return manager + + +@pytest.fixture +async def mock_voice_manager(): + """Mock voice manager for UI tests""" + manager = AsyncMock() + manager.list_voices = AsyncMock(return_value=["af", "bm_lewis", "af_sarah"]) + return manager + + +@pytest.fixture +async def mock_tts_service(mock_model_manager, mock_voice_manager): + """Mock TTSService for UI tests""" + service = AsyncMock() + service.model_manager = mock_model_manager + service._voice_manager = mock_voice_manager + return service + + +@pytest.fixture(autouse=True) +async def setup_mocks(monkeypatch, mock_model_manager, mock_voice_manager, mock_tts_service): + """Setup global mocks for UI tests""" + async def mock_get_model(): + return mock_model_manager + + async def mock_get_voice(): + return 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>= '3.12' and sys_platform == 'darwin')" }, - { name = "sympy", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or sys_platform == 'darwin'" }, - { name = "typing-extensions", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or sys_platform == 'darwin'" }, + { name = "filelock" }, + { name = "fsspec" }, + { name = "jinja2" }, + { name = "networkx" }, + { name = "nvidia-cublas-cu12", version = "12.4.5.8", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-cuda-cupti-cu12", version = "12.4.127", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-cuda-nvrtc-cu12", version = "12.4.127", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-cuda-runtime-cu12", version = "12.4.127", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-cudnn-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-cufft-cu12", version = "11.2.1.3", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-curand-cu12", version = "10.3.5.147", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-cusolver-cu12", version = "11.6.1.9", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-cusparse-cu12", version = "12.3.1.170", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-nccl-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-nvjitlink-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-nvtx-cu12", version = "12.4.127", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "setuptools", marker = "python_full_version >= '3.12'" }, + { name = "sympy" }, + { name = "typing-extensions" }, ] wheels = [ { url = "https://download.pytorch.org/whl/cpu/torch-2.5.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:269b10c34430aa8e9643dbe035dc525c4a9b1d671cd3dbc8ecbcaed280ae322d" }, @@ -2371,61 +2953,40 @@ wheels = [ { url = "https://download.pytorch.org/whl/cpu/torch-2.5.1-cp312-none-macosx_11_0_arm64.whl", hash = "sha256:8c712df61101964eb11910a846514011f0b6f5920c55dbf567bff8a34163d5b1" }, ] -[[package]] -name = "torch" -version = "2.5.1+cpu" -source = { registry = "https://download.pytorch.org/whl/cpu" } -resolution-markers = [ - "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux')", - "(python_full_version < '3.11' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux')", - "(python_full_version >= '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.13' and sys_platform != 'darwin' and sys_platform != 'linux')", - "(python_full_version == '3.12.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'linux')", -] -dependencies = [ - { name = "filelock", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" }, - { name = "fsspec", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" }, - { name = "jinja2", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" }, - { name = "networkx", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" }, - { name = "setuptools", marker = "(python_full_version >= '3.12' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.12' and sys_platform != 'darwin' and sys_platform != 'linux')" }, - { name = "sympy", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" }, - { name = "typing-extensions", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" }, -] -wheels = [ - { url = "https://download.pytorch.org/whl/cpu/torch-2.5.1%2Bcpu-cp310-cp310-linux_x86_64.whl", hash = "sha256:7f91a2200e352745d70e22396bd501448e28350fbdbd8d8b1c83037e25451150" }, - { url = "https://download.pytorch.org/whl/cpu/torch-2.5.1%2Bcpu-cp310-cp310-win_amd64.whl", hash = "sha256:df93157482b672892d29134d3fae9d38ba3219702faedd79f407eb36774c56ce" }, - { url = "https://download.pytorch.org/whl/cpu/torch-2.5.1%2Bcpu-cp311-cp311-linux_x86_64.whl", hash = "sha256:07d7c9e069123d5af08b0cf0013d74f680b2d8be7d9e2cf561a52c90c55d9409" }, - { url = "https://download.pytorch.org/whl/cpu/torch-2.5.1%2Bcpu-cp311-cp311-win_amd64.whl", hash = "sha256:81531d4d5ca74163dc9574b87396531e546a60cceb6253303c7db6a21e867fdf" }, - { url = "https://download.pytorch.org/whl/cpu/torch-2.5.1%2Bcpu-cp312-cp312-linux_x86_64.whl", hash = "sha256:4856f9d6925121d13c2df07aa7580b767f449dfe71ae5acde9c27535d5da4840" }, - { url = "https://download.pytorch.org/whl/cpu/torch-2.5.1%2Bcpu-cp312-cp312-win_amd64.whl", hash = "sha256:a6b720410350765d3d77c01a5ce098a6c45af446284e45e87a98b8a16e7d564d" }, - { url = "https://download.pytorch.org/whl/cpu/torch-2.5.1%2Bcpu-cp313-cp313-linux_x86_64.whl", hash = "sha256:5dbbdf83caa90d0bcaa50e4933ca424889133b35226db79000877d4ec5d9ea37" }, -] - [[package]] name = "torch" version = "2.5.1+cu121" source = { registry = "https://download.pytorch.org/whl/cu121" } resolution-markers = [ "python_full_version == '3.11.*'", + "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux')", + "(python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.11.*' and sys_platform == 'darwin')", "python_full_version < '3.11'", + "(python_full_version < '3.11' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux')", + "(python_full_version < '3.11' and platform_machine == 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform == 'darwin')", "python_full_version >= '3.13'", + "(python_full_version >= '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.13' and sys_platform != 'darwin' and sys_platform != 'linux')", + "(python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.13' and sys_platform == 'darwin')", "python_full_version == '3.12.*'", + "(python_full_version == '3.12.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'linux')", + "(python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.12.*' and sys_platform == 'darwin')", ] dependencies = [ { name = "filelock" }, { name = "fsspec" }, { name = "jinja2" }, { name = "networkx" }, - { name = "nvidia-cublas-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, - { name = "nvidia-cuda-cupti-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, - { name = "nvidia-cuda-nvrtc-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, - { name = "nvidia-cuda-runtime-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-cublas-cu12", version = "12.1.3.1", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-cuda-cupti-cu12", version = "12.1.105", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-cuda-nvrtc-cu12", version = "12.1.105", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-cuda-runtime-cu12", version = "12.1.105", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, { name = "nvidia-cudnn-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, - { name = "nvidia-cufft-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, - { name = "nvidia-curand-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, - { name = "nvidia-cusolver-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, - { name = "nvidia-cusparse-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-cufft-cu12", version = "11.0.2.54", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-curand-cu12", version = "10.3.2.106", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-cusolver-cu12", version = "11.4.5.107", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-cusparse-cu12", version = "12.1.0.106", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, { name = "nvidia-nccl-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, - { name = "nvidia-nvtx-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-nvtx-cu12", version = "12.1.105", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, { name = "setuptools", marker = "python_full_version >= '3.12'" }, { name = "sympy" }, { name = "triton", marker = "python_full_version < '3.13' and platform_machine == 'x86_64' and sys_platform == 'linux'" }, diff --git a/web/app.js b/web/app.js new file mode 100644 index 0000000..bc44e9a --- /dev/null +++ b/web/app.js @@ -0,0 +1,203 @@ +class KokoroPlayer { + constructor() { + this.elements = { + textInput: document.getElementById('text-input'), + voiceSelect: document.getElementById('voice-select'), + streamToggle: document.getElementById('stream-toggle'), + autoplayToggle: document.getElementById('autoplay-toggle'), + generateBtn: document.getElementById('generate-btn'), + audioPlayer: document.getElementById('audio-player'), + status: document.getElementById('status') + }; + + this.isGenerating = false; + this.init(); + } + + async init() { + await this.loadVoices(); + this.setupEventListeners(); + } + + async loadVoices() { + try { + const response = await fetch('/v1/audio/voices'); + if (!response.ok) { + const error = await response.json(); + throw new Error(error.detail?.message || 'Failed to load voices'); + } + + const data = await response.json(); + if (!data.voices?.length) { + throw new Error('No voices available'); + } + + this.elements.voiceSelect.innerHTML = data.voices + .map(voice => ``) + .join(''); + + // Select first voice by default + if (data.voices.length > 0) { + this.elements.voiceSelect.value = data.voices[0]; + } + + this.showStatus('Voices loaded successfully', 'success'); + } catch (error) { + this.showStatus('Failed to load voices: ' + error.message, 'error'); + // Disable generate button if no voices + this.elements.generateBtn.disabled = true; + } + } + + setupEventListeners() { + this.elements.generateBtn.addEventListener('click', () => this.generateSpeech()); + this.elements.audioPlayer.addEventListener('ended', () => { + this.elements.generateBtn.disabled = false; + }); + } + + showStatus(message, type = 'info') { + this.elements.status.textContent = message; + this.elements.status.className = 'status ' + type; + setTimeout(() => { + this.elements.status.className = 'status'; + }, 5000); + } + + setLoading(loading) { + this.isGenerating = loading; + this.elements.generateBtn.disabled = loading; + this.elements.generateBtn.className = loading ? 'primary loading' : 'primary'; + } + + validateInput() { + const text = this.elements.textInput.value.trim(); + if (!text) { + this.showStatus('Please enter some text', 'error'); + return false; + } + + const voice = this.elements.voiceSelect.value; + if (!voice) { + this.showStatus('Please select a voice', 'error'); + return false; + } + + return true; + } + + async generateSpeech() { + if (this.isGenerating || !this.validateInput()) return; + + const text = this.elements.textInput.value.trim(); + const voice = this.elements.voiceSelect.value; + const stream = this.elements.streamToggle.checked; + + this.setLoading(true); + + try { + if (stream) { + await this.handleStreamingAudio(text, voice); + } else { + await this.handleNonStreamingAudio(text, voice); + } + } catch (error) { + this.showStatus('Error generating speech: ' + error.message, 'error'); + } finally { + this.setLoading(false); + } + } + + async handleStreamingAudio(text, voice) { + this.showStatus('Initializing audio stream...', 'info'); + + const response = await fetch('/v1/audio/speech', { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ + input: text, + voice: voice, + response_format: 'mp3', + stream: true + }) + }); + + if (!response.ok) { + const error = await response.json(); + throw new Error(error.detail?.message || 'Failed to generate speech'); + } + + const mediaSource = new MediaSource(); + this.elements.audioPlayer.src = URL.createObjectURL(mediaSource); + + return new Promise((resolve, reject) => { + mediaSource.addEventListener('sourceopen', async () => { + try { + const sourceBuffer = mediaSource.addSourceBuffer('audio/mpeg'); + const reader = response.body.getReader(); + let totalChunks = 0; + + while (true) { + const {done, value} = await reader.read(); + if (done) break; + + // Wait for the buffer to be ready + if (sourceBuffer.updating) { + await new Promise(resolve => { + sourceBuffer.addEventListener('updateend', resolve, {once: true}); + }); + } + + sourceBuffer.appendBuffer(value); + totalChunks++; + this.showStatus(`Received chunk ${totalChunks}...`, 'info'); + } +mediaSource.endOfStream(); +if (this.elements.autoplayToggle.checked) { + await this.elements.audioPlayer.play(); +} +this.showStatus('Audio stream ready', 'success'); + this.showStatus('Audio stream ready', 'success'); + resolve(); + } catch (error) { + mediaSource.endOfStream(); + this.showStatus('Error during streaming: ' + error.message, 'error'); + reject(error); + } + }); + }); + } + + async handleNonStreamingAudio(text, voice) { + this.showStatus('Generating audio...', 'info'); + + const response = await fetch('/v1/audio/speech', { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ + input: text, + voice: voice, + response_format: 'mp3', + stream: false + }) + }); + + if (!response.ok) { + const error = await response.json(); + throw new Error(error.detail?.message || 'Failed to generate speech'); + } + + const blob = await response.blob(); + const url = URL.createObjectURL(blob); + this.elements.audioPlayer.src = url; + if (this.elements.autoplayToggle.checked) { + await this.elements.audioPlayer.play(); + } + this.showStatus('Audio ready', 'success'); + } +} + +// Initialize the player when the page loads +document.addEventListener('DOMContentLoaded', () => { + new KokoroPlayer(); +}); \ No newline at end of file diff --git a/web/index.html b/web/index.html new file mode 100644 index 0000000..e22bc52 --- /dev/null +++ b/web/index.html @@ -0,0 +1,51 @@ + + + + + + FastKoko: Kokoro-based TTS Player + + + +

+
+

Kokoro TTS

+
+ +
+
+ +
+ +
+ + +
+ +
+
+ +
+
+ +
+
+
+ + + \ No newline at end of file diff --git a/web/styles.css b/web/styles.css new file mode 100644 index 0000000..e0e39f1 --- /dev/null +++ b/web/styles.css @@ -0,0 +1,176 @@ +:root { + --primary-color: #007bff; + --primary-hover: #0056b3; + --background: #ffffff; + --surface: #f8f9fa; + --text: #212529; + --text-light: #6c757d; + --border: #dee2e6; + --error: #dc3545; + --success: #28a745; + --font-family: system-ui, -apple-system, "Segoe UI", Roboto, sans-serif; +} + +* { + margin: 0; + padding: 0; + box-sizing: border-box; +} + +body { + font-family: var(--font-family); + line-height: 1.6; + color: var(--text); + background: var(--background); +} + +.container { + max-width: 800px; + margin: 0 auto; + padding: 2rem 1rem; +} + +header { + margin-bottom: 2rem; + text-align: center; +} + +h1 { + color: var(--primary-color); + font-size: 2rem; + font-weight: 600; +} + +.input-section { + background: var(--surface); + padding: 1.5rem; + border-radius: 8px; + margin-bottom: 1.5rem; +} + +textarea { + width: 100%; + padding: 0.75rem; + border: 1px solid var(--border); + border-radius: 4px; + font-family: inherit; + font-size: 1rem; + resize: vertical; + margin-bottom: 1rem; +} + +textarea:focus { + outline: none; + border-color: var(--primary-color); +} + +.controls { + display: flex; + gap: 1rem; + align-items: center; + flex-wrap: wrap; +} + +select { + padding: 0.5rem; + border: 1px solid var(--border); + border-radius: 4px; + font-size: 1rem; + min-width: 200px; +} + +.options { + display: flex; + align-items: center; + gap: 1rem; +} + +button { + padding: 0.5rem 1rem; + border: none; + border-radius: 4px; + font-size: 1rem; + cursor: pointer; + display: flex; + align-items: center; + gap: 0.5rem; + transition: background-color 0.2s; +} + +button.primary { + background: var(--primary-color); + color: white; +} + +button.primary:hover { + background: var(--primary-hover); +} + +button:disabled { + opacity: 0.7; + cursor: not-allowed; +} + +.loader { + display: none; + width: 16px; + height: 16px; + border: 2px solid #ffffff; + border-radius: 50%; + border-top-color: transparent; + animation: spin 1s linear infinite; +} + +@keyframes spin { + to { + transform: rotate(360deg); + } +} + +.loading .loader { + display: inline-block; +} + +.loading .btn-text { + display: none; +} + +.player-section { + background: var(--surface); + padding: 1.5rem; + border-radius: 8px; +} + +.status { + margin-bottom: 1rem; + padding: 0.5rem; + border-radius: 4px; + display: none; +} + +.status.error { + display: block; + background: rgba(220, 53, 69, 0.1); + color: var(--error); +} + +.status.success { + display: block; + background: rgba(40, 167, 69, 0.1); + color: var(--success); +} + +audio { + width: 100%; +} + +@media (max-width: 600px) { + .controls { + flex-direction: column; + align-items: stretch; + } + + select, button { + width: 100%; + } +} \ No newline at end of file From 8e8f120a3ec4365954b57c0bc55cb027fe069747 Mon Sep 17 00:00:00 2001 From: remsky Date: Thu, 23 Jan 2025 02:00:46 -0700 Subject: [PATCH 07/32] Update configuration to disable local voice saving, enhance voice validation logic, and remove deprecated test file --- api/depr_tests/test_endpoints.py | 206 ------- api/depr_tests/test_main.py | 104 ---- api/depr_tests/test_managers.py | 190 ------- api/depr_tests/test_text_processing.py | 139 ----- api/depr_tests/test_tts_service.py | 118 ---- api/src/core/config.py | 2 +- api/src/inference/voice_manager.py | 75 +-- api/src/routers/openai_compatible.py | 21 +- api/tests/test_voice_manager.py | 185 +++++-- web/app.js | 428 +++++++++++---- web/favicon.svg | 47 ++ web/index.html | 98 +++- web/siriwave.js | 131 +++++ web/styles-clean.css | 266 +++++++++ web/styles.css | 731 ++++++++++++++++++++++--- web/styles/base.css | 102 ++++ web/styles/forms.css | 225 ++++++++ web/styles/header.css | 90 +++ web/styles/layout.css | 60 ++ web/styles/player.css | 194 +++++++ web/styles/responsive.css | 95 ++++ 21 files changed, 2463 insertions(+), 1044 deletions(-) delete mode 100644 api/depr_tests/test_endpoints.py delete mode 100644 api/depr_tests/test_main.py delete mode 100644 api/depr_tests/test_managers.py delete mode 100644 api/depr_tests/test_text_processing.py delete mode 100644 api/depr_tests/test_tts_service.py create mode 100644 web/favicon.svg create mode 100644 web/siriwave.js create mode 100644 web/styles-clean.css create mode 100644 web/styles/base.css create mode 100644 web/styles/forms.css create mode 100644 web/styles/header.css create mode 100644 web/styles/layout.css create mode 100644 web/styles/player.css create mode 100644 web/styles/responsive.css diff --git a/api/depr_tests/test_endpoints.py b/api/depr_tests/test_endpoints.py deleted file mode 100644 index b5af29e..0000000 --- a/api/depr_tests/test_endpoints.py +++ /dev/null @@ -1,206 +0,0 @@ -"""Tests for API endpoints""" -import pytest -import torch -from fastapi.testclient import TestClient - -from ..src.main import app - -# Create test client for non-async tests -client = TestClient(app) - - -def test_health_check(): - """Test the health check endpoint""" - response = client.get("/health") - assert response.status_code == 200 - assert response.json() == {"status": "healthy"} - - -@pytest.mark.asyncio -async def test_openai_speech_endpoint(async_client, mock_tts_service): - """Test the OpenAI-compatible speech endpoint""" - # Setup mocks - mock_tts_service._voice_manager.list_voices.return_value = ["bm_lewis"] - mock_tts_service.generate_audio.return_value = (torch.zeros(48000).numpy(), 1.0) - mock_tts_service._voice_manager.load_voice.return_value = torch.zeros(192) - - # Mock voice validation - mock_tts_service._voice_manager.get_voice_path.return_value = "/mock/voices/bm_lewis.pt" - - test_request = { - "model": "kokoro", - "input": "Hello world", - "voice": "bm_lewis", - "response_format": "wav", - "speed": 1.0, - "stream": False, - } - response = await async_client.post("/v1/audio/speech", json=test_request) - assert response.status_code == 200 - assert response.headers["content-type"] == "audio/wav" - assert response.headers["content-disposition"] == "attachment; filename=speech.wav" - mock_tts_service.generate_audio.assert_called_once() - - -@pytest.mark.asyncio -async def test_openai_speech_invalid_voice(async_client, mock_tts_service): - """Test the OpenAI-compatible speech endpoint with invalid voice""" - # Setup mocks - mock_tts_service._voice_manager.list_voices.return_value = ["af", "bm_lewis"] - mock_tts_service._voice_manager.get_voice_path.return_value = None - - test_request = { - "model": "kokoro", - "input": "Hello world", - "voice": "invalid_voice", - "response_format": "wav", - "speed": 1.0, - "stream": False, - } - response = await async_client.post("/v1/audio/speech", json=test_request) - assert response.status_code == 400 - assert "not found" in response.json()["detail"]["message"] - - -@pytest.mark.asyncio -async def test_openai_speech_generation_error(async_client, mock_tts_service): - """Test error handling in speech generation""" - # Setup mocks - mock_tts_service._voice_manager.list_voices.return_value = ["af"] - mock_tts_service.generate_audio.side_effect = RuntimeError("Generation failed") - mock_tts_service._voice_manager.load_voice.return_value = torch.zeros(192) - mock_tts_service._voice_manager.get_voice_path.return_value = "/mock/voices/af.pt" - - test_request = { - "model": "kokoro", - "input": "Hello world", - "voice": "af", - "response_format": "wav", - "speed": 1.0, - "stream": False, - } - response = await async_client.post("/v1/audio/speech", json=test_request) - assert response.status_code == 500 - assert "Generation failed" in response.json()["detail"]["message"] - - -@pytest.mark.asyncio -async def test_combine_voices_list_success(async_client, mock_tts_service): - """Test successful voice combination using list format""" - # Setup mocks - mock_tts_service._voice_manager.list_voices.return_value = ["af_bella", "af_sarah"] - mock_tts_service._voice_manager.combine_voices.return_value = "af_bella_af_sarah" - mock_tts_service._voice_manager.load_voice.return_value = torch.zeros(192) - mock_tts_service._voice_manager.get_voice_path.return_value = "/mock/voices/af_bella.pt" - - test_voices = ["af_bella", "af_sarah"] - response = await async_client.post("/v1/audio/voices/combine", json=test_voices) - - assert response.status_code == 200 - assert response.json()["voice"] == "af_bella_af_sarah" - mock_tts_service._voice_manager.combine_voices.assert_called_once() - - -@pytest.mark.asyncio -async def test_combine_voices_empty_list(async_client, mock_tts_service): - """Test combining empty voice list returns error""" - test_voices = [] - response = await async_client.post("/v1/audio/voices/combine", json=test_voices) - assert response.status_code == 400 - assert "No voices provided" in response.json()["detail"]["message"] - - -@pytest.mark.asyncio -async def test_speech_streaming_with_combined_voice(async_client, mock_tts_service): - """Test streaming speech with combined voice using + syntax""" - # Setup mocks - mock_tts_service._voice_manager.list_voices.return_value = ["af_bella", "af_sarah"] - mock_tts_service._voice_manager.combine_voices.return_value = "af_bella_af_sarah" - mock_tts_service._voice_manager.load_voice.return_value = torch.zeros(192) - mock_tts_service._voice_manager.get_voice_path.return_value = "/mock/voices/af_bella.pt" - - async def mock_stream(): - yield b"chunk1" - yield b"chunk2" - - mock_tts_service.generate_audio_stream.return_value = mock_stream() - - test_request = { - "model": "kokoro", - "input": "Hello world", - "voice": "af_bella+af_sarah", - "response_format": "mp3", - "stream": True, - } - - headers = {"x-raw-response": "stream"} - response = await async_client.post( - "/v1/audio/speech", json=test_request, headers=headers - ) - - assert response.status_code == 200 - assert response.headers["content-type"] == "audio/mpeg" - assert response.headers["content-disposition"] == "attachment; filename=speech.mp3" - - -@pytest.mark.asyncio -async def test_openai_speech_pcm_streaming(async_client, mock_tts_service): - """Test streaming PCM audio for real-time playback""" - # Setup mocks - mock_tts_service._voice_manager.list_voices.return_value = ["af"] - mock_tts_service._voice_manager.load_voice.return_value = torch.zeros(192) - mock_tts_service._voice_manager.get_voice_path.return_value = "/mock/voices/af.pt" - - async def mock_stream(): - yield b"chunk1" - yield b"chunk2" - - mock_tts_service.generate_audio_stream.return_value = mock_stream() - - test_request = { - "model": "kokoro", - "input": "Hello world", - "voice": "af", - "response_format": "pcm", - "stream": True, - } - - headers = {"x-raw-response": "stream"} - response = await async_client.post( - "/v1/audio/speech", json=test_request, headers=headers - ) - - assert response.status_code == 200 - assert response.headers["content-type"] == "audio/pcm" - - -@pytest.mark.asyncio -async def test_openai_speech_streaming_mp3(async_client, mock_tts_service): - """Test streaming MP3 audio to file""" - # Setup mocks - mock_tts_service._voice_manager.list_voices.return_value = ["af"] - mock_tts_service._voice_manager.load_voice.return_value = torch.zeros(192) - mock_tts_service._voice_manager.get_voice_path.return_value = "/mock/voices/af.pt" - - async def mock_stream(): - yield b"chunk1" - yield b"chunk2" - - mock_tts_service.generate_audio_stream.return_value = mock_stream() - - test_request = { - "model": "kokoro", - "input": "Hello world", - "voice": "af", - "response_format": "mp3", - "stream": True, - } - - headers = {"x-raw-response": "stream"} - response = await async_client.post( - "/v1/audio/speech", json=test_request, headers=headers - ) - - assert response.status_code == 200 - assert response.headers["content-type"] == "audio/mpeg" - assert response.headers["content-disposition"] == "attachment; filename=speech.mp3" diff --git a/api/depr_tests/test_main.py b/api/depr_tests/test_main.py deleted file mode 100644 index dd5ac12..0000000 --- a/api/depr_tests/test_main.py +++ /dev/null @@ -1,104 +0,0 @@ -"""Tests for FastAPI application""" -from unittest.mock import AsyncMock, MagicMock, patch - -import pytest -import torch -from fastapi.testclient import TestClient - -from api.src.main import app, lifespan - - -@pytest.fixture -def test_client(): - """Create a test client""" - return TestClient(app) - - -def test_health_check(test_client): - """Test health check endpoint""" - response = test_client.get("/health") - assert response.status_code == 200 - assert response.json() == {"status": "healthy"} - - -@pytest.mark.asyncio -async def test_lifespan_successful_warmup(): - """Test successful model warmup in lifespan""" - with patch("api.src.inference.model_manager.get_manager") as mock_get_model, \ - patch("api.src.inference.voice_manager.get_manager") as mock_get_voice, \ - patch("api.src.main.logger") as mock_logger, \ - patch("os.path.exists") as mock_exists, \ - patch("torch.cuda.is_available") as mock_cuda: - - # Setup mocks - mock_model = AsyncMock() - mock_voice = AsyncMock() - mock_get_model.return_value = mock_model - mock_get_voice.return_value = mock_voice - mock_exists.return_value = True - mock_cuda.return_value = False - - # Setup model manager - mock_backend = MagicMock() - mock_backend.device = "cpu" - mock_model.get_backend.return_value = mock_backend - mock_model.load_model = AsyncMock() - - # Setup voice manager - mock_voice_tensor = torch.zeros(192) - mock_voice.load_voice = AsyncMock(return_value=mock_voice_tensor) - mock_voice.list_voices = AsyncMock(return_value=["af", "af_bella", "af_sarah"]) - - # Create an async generator from the lifespan context manager - async_gen = lifespan(MagicMock()) - - # Start the context manager - await async_gen.__aenter__() - - # Verify managers were initialized - mock_get_model.assert_called_once() - mock_get_voice.assert_called_once() - mock_model.load_model.assert_called_once() - - # Clean up - await async_gen.__aexit__(None, None, None) - - -@pytest.mark.asyncio -async def test_lifespan_failed_warmup(): - """Test failed model warmup in lifespan""" - with patch("api.src.inference.model_manager.get_manager") as mock_get_model: - # Mock the model manager to fail - mock_get_model.side_effect = RuntimeError("Failed to initialize model") - - # Create an async generator from the lifespan context manager - async_gen = lifespan(MagicMock()) - - # Verify the exception is raised - with pytest.raises(RuntimeError, match="Failed to initialize model"): - await async_gen.__aenter__() - - # Clean up - await async_gen.__aexit__(None, None, None) - - -@pytest.mark.asyncio -async def test_lifespan_voice_manager_failure(): - """Test failure when voice manager fails to initialize""" - with patch("api.src.inference.model_manager.get_manager") as mock_get_model, \ - patch("api.src.inference.voice_manager.get_manager") as mock_get_voice: - - # Setup model manager success but voice manager failure - mock_model = AsyncMock() - mock_get_model.return_value = mock_model - mock_get_voice.side_effect = RuntimeError("Failed to initialize voice manager") - - # Create an async generator from the lifespan context manager - async_gen = lifespan(MagicMock()) - - # Verify the exception is raised - with pytest.raises(RuntimeError, match="Failed to initialize voice manager"): - await async_gen.__aenter__() - - # Clean up - await async_gen.__aexit__(None, None, None) diff --git a/api/depr_tests/test_managers.py b/api/depr_tests/test_managers.py deleted file mode 100644 index 64bb8c6..0000000 --- a/api/depr_tests/test_managers.py +++ /dev/null @@ -1,190 +0,0 @@ -"""Tests for model and voice managers""" -import os -import numpy as np -import pytest -import torch -from unittest.mock import AsyncMock, MagicMock, Mock, patch - -from api.src.inference.model_manager import get_manager as get_model_manager -from api.src.inference.voice_manager import get_manager as get_voice_manager - -# Get project root path -PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) -MOCK_VOICES_DIR = os.path.join(PROJECT_ROOT, "api", "src", "voices") -MOCK_MODEL_DIR = os.path.join(PROJECT_ROOT, "api", "src", "models") - - -@pytest.mark.asyncio -async def test_model_manager_initialization(): - """Test model manager initialization""" - with patch("api.src.inference.model_manager.settings") as mock_settings, \ - patch("api.src.core.paths.get_model_path") as mock_get_path: - - mock_settings.model_dir = MOCK_MODEL_DIR - mock_settings.onnx_model_path = "model.onnx" - mock_get_path.return_value = os.path.join(MOCK_MODEL_DIR, "model.onnx") - - manager = await get_model_manager() - assert manager is not None - backend = manager.get_backend() - assert backend is not None - - -@pytest.mark.asyncio -async def test_model_manager_generate(): - """Test model generation""" - with patch("api.src.inference.model_manager.settings") as mock_settings, \ - patch("api.src.core.paths.get_model_path") as mock_get_path, \ - patch("torch.load") as mock_torch_load: - - mock_settings.model_dir = MOCK_MODEL_DIR - mock_settings.onnx_model_path = "model.onnx" - mock_settings.use_onnx = True - mock_settings.use_gpu = False - mock_get_path.return_value = os.path.join(MOCK_MODEL_DIR, "model.onnx") - - # Mock torch load to return a tensor - mock_torch_load.return_value = torch.zeros(192) - - manager = await get_model_manager() - - # Set up mock backend - mock_backend = AsyncMock() - mock_backend.is_loaded = True - mock_backend.device = "cpu" - - # Create audio tensor and ensure it's properly mocked - audio_data = torch.zeros(48000, dtype=torch.float32) - async def mock_generate(*args, **kwargs): - return audio_data - mock_backend.generate.side_effect = mock_generate - - # Set up manager with mock backend - manager._backends['onnx_cpu'] = mock_backend - manager._current_backend = 'onnx_cpu' - - # Generate audio - tokens = [1, 2, 3] - voice_tensor = torch.zeros(192) - audio = await manager.generate(tokens, voice_tensor, speed=1.0) - - assert isinstance(audio, torch.Tensor), "Generated audio should be torch tensor" - assert audio.dtype == torch.float32, "Audio should be 32-bit float" - assert audio.shape == (48000,), "Audio should have 48000 samples" - assert mock_backend.generate.call_count == 1 - - -@pytest.mark.asyncio -async def test_voice_manager_initialization(): - """Test voice manager initialization""" - with patch("api.src.inference.voice_manager.settings") as mock_settings, \ - patch("os.path.exists") as mock_exists: - - mock_settings.voices_dir = MOCK_VOICES_DIR - mock_exists.return_value = True - - manager = await get_voice_manager() - assert manager is not None - - -@pytest.mark.asyncio -async def test_voice_manager_list_voices(): - """Test listing available voices""" - with patch("api.src.inference.voice_manager.settings") as mock_settings, \ - patch("os.listdir") as mock_listdir, \ - patch("os.makedirs") as mock_makedirs, \ - patch("os.path.exists") as mock_exists: - - mock_settings.voices_dir = MOCK_VOICES_DIR - mock_listdir.return_value = ["af_bella.pt", "af_sarah.pt", "bm_lewis.pt"] - mock_exists.return_value = True - - manager = await get_voice_manager() - voices = await manager.list_voices() - - assert isinstance(voices, list) - assert len(voices) == 3, f"Expected 3 voices but got {len(voices)}" - assert sorted(voices) == ["af_bella", "af_sarah", "bm_lewis"] - mock_listdir.assert_called_once() - - -@pytest.mark.asyncio -async def test_voice_manager_load_voice(): - """Test loading a voice""" - with patch("api.src.inference.voice_manager.settings") as mock_settings, \ - patch("torch.load") as mock_torch_load, \ - patch("os.path.exists") as mock_exists: - - mock_settings.voices_dir = MOCK_VOICES_DIR - mock_exists.return_value = True - - # Create a mock tensor - mock_tensor = torch.zeros(192) - mock_torch_load.return_value = mock_tensor - - manager = await get_voice_manager() - voice_tensor = await manager.load_voice("af_bella", device="cpu") - - assert isinstance(voice_tensor, torch.Tensor) - assert voice_tensor.shape == (192,) - mock_torch_load.assert_called_once() - - -@pytest.mark.asyncio -async def test_voice_manager_combine_voices(): - """Test combining voices""" - with patch("api.src.inference.voice_manager.settings") as mock_settings, \ - patch("torch.load") as mock_load, \ - patch("torch.save") as mock_save, \ - patch("os.makedirs") as mock_makedirs, \ - patch("os.path.exists") as mock_exists: - - mock_settings.voices_dir = MOCK_VOICES_DIR - mock_exists.return_value = True - - # Create mock tensors - mock_tensor1 = torch.ones(192) - mock_tensor2 = torch.ones(192) * 2 - mock_load.side_effect = [mock_tensor1, mock_tensor2] - - manager = await get_voice_manager() - combined_name = await manager.combine_voices(["af_bella", "af_sarah"]) - - assert combined_name == "af_bella_af_sarah" - assert mock_load.call_count == 2 - mock_save.assert_called_once() - - # Verify the combined tensor was saved - saved_tensor = mock_save.call_args[0][0] - assert isinstance(saved_tensor, torch.Tensor) - assert saved_tensor.shape == (192,) - # Should be average of the two tensors - assert torch.allclose(saved_tensor, torch.ones(192) * 1.5) - - -@pytest.mark.asyncio -async def test_voice_manager_invalid_voice(): - """Test loading invalid voice""" - with patch("api.src.inference.voice_manager.settings") as mock_settings, \ - patch("os.path.exists") as mock_exists: - - mock_settings.voices_dir = MOCK_VOICES_DIR - mock_exists.return_value = False - - manager = await get_voice_manager() - with pytest.raises(RuntimeError, match="Voice not found"): - await manager.load_voice("invalid_voice", device="cpu") - - -@pytest.mark.asyncio -async def test_voice_manager_combine_invalid_voices(): - """Test combining with invalid voices""" - with patch("api.src.inference.voice_manager.settings") as mock_settings, \ - patch("os.path.exists") as mock_exists: - - mock_settings.voices_dir = MOCK_VOICES_DIR - mock_exists.return_value = False - - manager = await get_voice_manager() - with pytest.raises(RuntimeError, match="Voice not found"): - await manager.combine_voices(["invalid_voice1", "invalid_voice2"]) \ No newline at end of file diff --git a/api/depr_tests/test_text_processing.py b/api/depr_tests/test_text_processing.py deleted file mode 100644 index 7e63491..0000000 --- a/api/depr_tests/test_text_processing.py +++ /dev/null @@ -1,139 +0,0 @@ -"""Tests for text processing endpoints""" -import os -import pytest -import torch -from fastapi.testclient import TestClient - -from ..src.main import app - -# Get project root path -PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) -MOCK_VOICES_DIR = os.path.join(PROJECT_ROOT, "api", "src", "voices") - -client = TestClient(app) - - -@pytest.mark.asyncio -async def test_generate_from_phonemes(async_client, mock_tts_service): - """Test generating audio from phonemes""" - # Setup mocks - mock_tts_service._voice_manager.list_voices.return_value = ["af_bella"] - mock_tts_service.generate_audio.return_value = (torch.zeros(48000).numpy(), 1.0) - mock_tts_service._voice_manager.load_voice.return_value = torch.zeros(192) - mock_tts_service._voice_manager.get_voice_path.return_value = "/mock/voices/af_bella.pt" - - test_request = { - "model": "kokoro", - "input": "h @ l oU w r= l d", - "voice": "af_bella", - "response_format": "wav", - "speed": 1.0, - "stream": False, - } - - response = await async_client.post("/v1/audio/speech", json=test_request) - assert response.status_code == 200 - assert response.headers["content-type"] == "audio/wav" - assert response.headers["content-disposition"] == "attachment; filename=speech.wav" - mock_tts_service.generate_audio.assert_called_once() - - -@pytest.mark.asyncio -async def test_generate_from_phonemes_invalid_voice(async_client, mock_tts_service): - """Test generating audio from phonemes with invalid voice""" - # Setup mocks - mock_tts_service._voice_manager.list_voices.return_value = ["af_bella"] - mock_tts_service._voice_manager.get_voice_path.return_value = None - - test_request = { - "model": "kokoro", - "input": "h @ l oU w r= l d", - "voice": "invalid_voice", - "response_format": "wav", - "speed": 1.0, - "stream": False, - } - - response = await async_client.post("/v1/audio/speech", json=test_request) - assert response.status_code == 400 - assert "Voice not found" in response.json()["detail"]["message"] - - -@pytest.mark.asyncio -async def test_generate_from_phonemes_chunked(async_client, mock_tts_service): - """Test generating chunked audio from phonemes""" - # Setup mocks - mock_tts_service._voice_manager.list_voices.return_value = ["af_bella"] - mock_tts_service._voice_manager.load_voice.return_value = torch.zeros(192) - mock_tts_service._voice_manager.get_voice_path.return_value = "/mock/voices/af_bella.pt" - - async def mock_stream(): - yield b"chunk1" - yield b"chunk2" - - mock_tts_service.generate_audio_stream.return_value = mock_stream() - - test_request = { - "model": "kokoro", - "input": "h @ l oU w r= l d", - "voice": "af_bella", - "response_format": "mp3", - "stream": True, - } - - headers = {"x-raw-response": "stream"} - response = await async_client.post( - "/v1/audio/speech", json=test_request, headers=headers - ) - - assert response.status_code == 200 - assert response.headers["content-type"] == "audio/mpeg" - assert response.headers["content-disposition"] == "attachment; filename=speech.mp3" - - -@pytest.mark.asyncio -async def test_invalid_phonemes(async_client, mock_tts_service): - """Test handling invalid phonemes""" - # Setup mocks - mock_tts_service._voice_manager.list_voices.return_value = ["af_bella"] - mock_tts_service._voice_manager.load_voice.return_value = torch.zeros(192) - mock_tts_service._voice_manager.get_voice_path.return_value = "/mock/voices/af_bella.pt" - - test_request = { - "model": "kokoro", - "input": "", # Empty input - "voice": "af_bella", - "response_format": "wav", - "speed": 1.0, - "stream": False, - } - - response = await async_client.post("/v1/audio/speech", json=test_request) - assert response.status_code == 400 - assert "Text is empty" in response.json()["detail"]["message"] - - -@pytest.mark.asyncio -async def test_phonemes_with_combined_voice(async_client, mock_tts_service): - """Test generating audio from phonemes with combined voice""" - # Setup mocks - mock_tts_service._voice_manager.list_voices.return_value = ["af_bella", "af_sarah"] - mock_tts_service._voice_manager.combine_voices.return_value = "af_bella_af_sarah" - mock_tts_service._voice_manager.load_voice.return_value = torch.zeros(192) - mock_tts_service._voice_manager.get_voice_path.return_value = "/mock/voices/af_bella_af_sarah.pt" - mock_tts_service.generate_audio.return_value = (torch.zeros(48000).numpy(), 1.0) - - test_request = { - "model": "kokoro", - "input": "h @ l oU w r= l d", - "voice": "af_bella+af_sarah", - "response_format": "wav", - "speed": 1.0, - "stream": False, - } - - response = await async_client.post("/v1/audio/speech", json=test_request) - assert response.status_code == 200 - assert response.headers["content-type"] == "audio/wav" - mock_tts_service._voice_manager.combine_voices.assert_called_once() - mock_tts_service.generate_audio.assert_called_once() diff --git a/api/depr_tests/test_tts_service.py b/api/depr_tests/test_tts_service.py deleted file mode 100644 index ac33c5f..0000000 --- a/api/depr_tests/test_tts_service.py +++ /dev/null @@ -1,118 +0,0 @@ -"""Tests for TTSService""" -import os -import numpy as np -import pytest -import torch -from unittest.mock import AsyncMock, MagicMock, Mock, patch - -from api.src.services.tts_service import TTSService - -# Get project root path -PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) -MOCK_VOICES_DIR = os.path.join(PROJECT_ROOT, "api", "src", "voices") -MOCK_MODEL_DIR = os.path.join(PROJECT_ROOT, "api", "src", "models") - - -@pytest.mark.asyncio -async def test_service_initialization(mock_model_manager, mock_voice_manager): - """Test TTSService initialization""" - # Create service using factory method - with patch("api.src.services.tts_service.get_model_manager", return_value=mock_model_manager), \ - patch("api.src.services.tts_service.get_voice_manager", return_value=mock_voice_manager): - service = await TTSService.create() - assert service is not None - assert service.model_manager == mock_model_manager - assert service._voice_manager == mock_voice_manager - - -@pytest.mark.asyncio -async def test_generate_audio_basic(mock_tts_service): - """Test basic audio generation""" - text = "Hello world" - voice = "af" - audio, duration = await mock_tts_service.generate_audio(text, voice) - assert isinstance(audio, np.ndarray) - assert duration > 0 - - -@pytest.mark.asyncio -async def test_generate_audio_empty_text(mock_tts_service): - """Test handling empty text input""" - with pytest.raises(ValueError, match="Text is empty after preprocessing"): - await mock_tts_service.generate_audio("", "af") - - -@pytest.mark.asyncio -async def test_generate_audio_stream(mock_tts_service): - """Test streaming audio generation""" - text = "Hello world" - voice = "af" - - # Setup mock stream - async def mock_stream(): - yield b"chunk1" - yield b"chunk2" - mock_tts_service.generate_audio_stream.return_value = mock_stream() - - # Test streaming - stream = mock_tts_service.generate_audio_stream(text, voice) - chunks = [] - async for chunk in await stream: - chunks.append(chunk) - - assert len(chunks) > 0 - assert all(isinstance(chunk, bytes) for chunk in chunks) - - -@pytest.mark.asyncio -async def test_list_voices(mock_tts_service): - """Test listing available voices""" - with patch("api.src.inference.voice_manager.settings") as mock_settings: - mock_settings.voices_dir = MOCK_VOICES_DIR - voices = await mock_tts_service.list_voices() - assert isinstance(voices, list) - assert len(voices) == 4 # ["af", "af_bella", "af_sarah", "bm_lewis"] - assert all(isinstance(voice, str) for voice in voices) - - -@pytest.mark.asyncio -async def test_combine_voices(mock_tts_service): - """Test combining voices""" - with patch("api.src.inference.voice_manager.settings") as mock_settings: - mock_settings.voices_dir = MOCK_VOICES_DIR - voices = ["af_bella", "af_sarah"] - result = await mock_tts_service.combine_voices(voices) - assert isinstance(result, str) - assert result == "af_bella_af_sarah" - - -@pytest.mark.asyncio -async def test_audio_to_bytes(mock_tts_service): - """Test converting audio to bytes""" - audio = np.zeros(48000, dtype=np.float32) - audio_bytes = mock_tts_service._audio_to_bytes(audio) - assert isinstance(audio_bytes, bytes) - assert len(audio_bytes) > 0 - - -@pytest.mark.asyncio -async def test_voice_loading(mock_tts_service): - """Test voice loading""" - with patch("api.src.inference.voice_manager.settings") as mock_settings, \ - patch("os.path.exists", return_value=True), \ - patch("torch.load", return_value=torch.zeros(192)): - mock_settings.voices_dir = MOCK_VOICES_DIR - voice = await mock_tts_service._voice_manager.load_voice("af", device="cpu") - assert isinstance(voice, torch.Tensor) - assert voice.shape == (192,) - - -@pytest.mark.asyncio -async def test_model_generation(mock_tts_service): - """Test model generation""" - tokens = [1, 2, 3] - voice_tensor = torch.zeros(192) - audio = await mock_tts_service.model_manager.generate(tokens, voice_tensor) - assert isinstance(audio, torch.Tensor) - assert audio.shape == (48000,) - assert audio.dtype == torch.float32 diff --git a/api/src/core/config.py b/api/src/core/config.py index 4c8fbbd..8588fc7 100644 --- a/api/src/core/config.py +++ b/api/src/core/config.py @@ -15,7 +15,7 @@ class Settings(BaseSettings): default_voice: str = "af" use_gpu: bool = False # Whether to use GPU acceleration if available use_onnx: bool = True # Whether to use ONNX runtime - allow_local_voice_saving: bool = True # Whether to allow saving combined voices locally + allow_local_voice_saving: bool = False # Whether to allow saving combined voices locally # Container absolute paths model_dir: str = "/app/api/src/models" # Absolute path in container diff --git a/api/src/inference/voice_manager.py b/api/src/inference/voice_manager.py index 55644fd..de9765c 100644 --- a/api/src/inference/voice_manager.py +++ b/api/src/inference/voice_manager.py @@ -49,11 +49,29 @@ class VoiceManager: Raises: RuntimeError: If voice loading fails """ + # Check if it's a combined voice request + if "+" in voice_name: + voices = [v.strip() for v in voice_name.split("+") if v.strip()] + if len(voices) < 2: + raise RuntimeError(f"Invalid combined voice name: {voice_name}") + + # Load and combine voices + voice_tensors = [] + for voice in voices: + try: + voice_tensor = await self.load_voice(voice, device) + voice_tensors.append(voice_tensor) + except Exception as e: + raise RuntimeError(f"Failed to load base voice {voice}: {e}") + + return torch.mean(torch.stack(voice_tensors), dim=0) + + # Handle single voice voice_path = self.get_voice_path(voice_name) if not voice_path: raise RuntimeError(f"Voice not found: {voice_name}") - # Check cache first + # Check cache cache_key = f"{voice_path}_{device}" if self._config.use_cache and cache_key in self._voice_cache: return self._voice_cache[cache_key] @@ -98,48 +116,39 @@ class VoiceManager: if len(voices) < 2: raise ValueError("At least 2 voices are required for combination") - # Load voices - voice_tensors: List[torch.Tensor] = [] - for voice in voices: - try: - voice_tensor = await self.load_voice(voice, device) - voice_tensors.append(voice_tensor) - except Exception as e: - raise RuntimeError(f"Failed to load voice {voice}: {e}") + # Create combined name using + as separator + combined_name = "+".join(voices) - try: - # Combine voices - combined_name = "_".join(voices) - combined_tensor = torch.mean(torch.stack(voice_tensors), dim=0) - - # Get api directory path - api_dir = os.path.dirname(os.path.dirname(os.path.dirname(__file__))) - voices_dir = os.path.join(api_dir, settings.voices_dir) - os.makedirs(voices_dir, exist_ok=True) - - # Only save to disk if local voice saving is allowed - if settings.allow_local_voice_saving: + # If saving is enabled, try to save the combination + if settings.allow_local_voice_saving: + try: + # Load and combine voices + combined_tensor = await self.load_voice(combined_name, device) + + # Save to disk + api_dir = os.path.dirname(os.path.dirname(os.path.dirname(__file__))) + voices_dir = os.path.join(api_dir, settings.voices_dir) + os.makedirs(voices_dir, exist_ok=True) + combined_path = os.path.join(voices_dir, f"{combined_name}.pt") try: torch.save(combined_tensor, combined_path) - # Cache the new combined voice with disk path + # Cache with path-based key self._voice_cache[f"{combined_path}_{device}"] = combined_tensor except Exception as e: raise RuntimeError(f"Failed to save combined voice: {e}") - else: - # Just cache the combined voice in memory without saving to disk - self._voice_cache[f"{combined_name}_{device}"] = combined_tensor - return combined_name + except Exception as e: + logger.warning(f"Failed to save combined voice: {e}") + # Continue without saving - will be combined on-the-fly when needed - except Exception as e: - raise RuntimeError(f"Failed to combine voices: {e}") + return combined_name async def list_voices(self) -> List[str]: """List available voices. Returns: - List of voice names, including both disk-saved and in-memory combined voices + List of voice names """ voices = set() # Use set to avoid duplicates try: @@ -151,14 +160,6 @@ class VoiceManager: for entry in os.listdir(voices_dir): if entry.endswith(".pt"): voices.add(entry[:-3]) - - # Add in-memory combined voices from cache - for cache_key in self._voice_cache: - # Extract voice name from cache key (format: "name_device" or "path_device") - voice_name = cache_key.split("_")[0] - if "/" in voice_name: # It's a path - voice_name = os.path.basename(voice_name)[:-3] # Remove .pt extension - voices.add(voice_name) except Exception as e: logger.error(f"Error listing voices: {e}") diff --git a/api/src/routers/openai_compatible.py b/api/src/routers/openai_compatible.py index 9a34297..69573b5 100644 --- a/api/src/routers/openai_compatible.py +++ b/api/src/routers/openai_compatible.py @@ -8,6 +8,7 @@ from ..services.audio import AudioService from ..services.tts_service import TTSService from ..structures.schemas import OpenAISpeechRequest + router = APIRouter( tags=["OpenAI Compatible TTS"], responses={404: {"description": "Not found"}}, @@ -17,6 +18,7 @@ router = APIRouter( _tts_service = None _init_lock = None + async def get_tts_service() -> TTSService: """Get global TTSService instance""" global _tts_service, _init_lock @@ -50,19 +52,24 @@ async def process_voices( if not voices: raise ValueError("No voices provided") - # Check if all voices exist + # If single voice, validate and return it + if len(voices) == 1: + available_voices = await tts_service.list_voices() + if voices[0] not in available_voices: + raise ValueError( + f"Voice '{voices[0]}' not found. Available voices: {', '.join(sorted(available_voices))}" + ) + return voices[0] + + # For multiple voices, validate base voices exist available_voices = await tts_service.list_voices() for voice in voices: if voice not in available_voices: raise ValueError( - f"Voice '{voice}' not found. Available voices: {', '.join(sorted(available_voices))}" + f"Base voice '{voice}' not found. Available voices: {', '.join(sorted(available_voices))}" ) - # If single voice, return it directly - if len(voices) == 1: - return voices[0] - - # Otherwise combine voices + # Combine voices return await tts_service.combine_voices(voices=voices) diff --git a/api/tests/test_voice_manager.py b/api/tests/test_voice_manager.py index 2c129e8..0205486 100644 --- a/api/tests/test_voice_manager.py +++ b/api/tests/test_voice_manager.py @@ -1,82 +1,149 @@ import pytest -from unittest.mock import AsyncMock, patch +from unittest.mock import AsyncMock, patch, MagicMock import torch from pathlib import Path -@pytest.mark.asyncio -async def test_list_available_voices(mock_voice_manager): - """Test listing available voices""" - voices = await mock_voice_manager.list_voices() - assert len(voices) == 2 - assert "voice1" in voices - assert "voice2" in voices +from ..src.inference.voice_manager import VoiceManager +from ..src.structures.model_schemas import VoiceConfig + + +@pytest.fixture +def mock_voice_tensor(): + return torch.randn(10, 10) # Dummy tensor + + +@pytest.fixture +def voice_manager(): + return VoiceManager(VoiceConfig()) + @pytest.mark.asyncio -async def test_get_voice_path(mock_voice_manager): - """Test getting path for a specific voice""" - voice_path = mock_voice_manager.get_voice_path("voice1") - assert voice_path == "/mock/path/voice.pt" +async def test_load_voice(voice_manager, mock_voice_tensor): + """Test loading a single voice""" + with patch("api.src.core.paths.load_voice_tensor", new_callable=AsyncMock) as mock_load: + mock_load.return_value = mock_voice_tensor + with patch("os.path.exists", return_value=True): + voice = await voice_manager.load_voice("af_bella", "cpu") + assert torch.equal(voice, mock_voice_tensor) - # Test invalid voice - mock_voice_manager.get_voice_path.return_value = None - assert mock_voice_manager.get_voice_path("invalid_voice") is None @pytest.mark.asyncio -async def test_load_voice(mock_voice_manager, mock_voice_tensor): - """Test loading a voice tensor""" - voice_tensor = await mock_voice_manager.load_voice("voice1") - assert torch.equal(voice_tensor, mock_voice_tensor) - mock_voice_manager.load_voice.assert_called_once_with("voice1") - -@pytest.mark.asyncio -async def test_load_voice_not_found(mock_voice_manager): +async def test_load_voice_not_found(voice_manager): """Test loading non-existent voice""" - mock_voice_manager.get_voice_path.return_value = None - mock_voice_manager.load_voice.side_effect = ValueError("Voice not found: invalid_voice") - - with pytest.raises(ValueError, match="Voice not found: invalid_voice"): - await mock_voice_manager.load_voice("invalid_voice") + with patch("os.path.exists", return_value=False): + with pytest.raises(RuntimeError, match="Voice not found: invalid_voice"): + await voice_manager.load_voice("invalid_voice", "cpu") + @pytest.mark.asyncio -async def test_combine_voices(mock_voice_manager): - """Test combining two voices""" - voices = ["voice1", "voice2"] - weights = [0.5, 0.5] - - combined_id = await mock_voice_manager.combine_voices(voices, weights) - assert combined_id == "voice1_voice2" - mock_voice_manager.combine_voices.assert_called_once_with(voices, weights) +async def test_combine_voices_with_saving(voice_manager, mock_voice_tensor): + """Test combining voices with local saving enabled""" + with patch("api.src.core.paths.load_voice_tensor", new_callable=AsyncMock) as mock_load, \ + patch("torch.save") as mock_save, \ + patch("os.makedirs"), \ + patch("os.path.exists", return_value=True): + + # Setup mocks + mock_load.return_value = mock_voice_tensor + + # Mock settings + with patch("api.src.core.config.settings") as mock_settings: + mock_settings.allow_local_voice_saving = True + mock_settings.voices_dir = "/mock/voices" + + # Combine voices + combined = await voice_manager.combine_voices(["af_bella", "af_sarah"], "cpu") + assert combined == "af_bella+af_sarah" # Note: using + separator + + # Verify voice was saved + mock_save.assert_called_once() + @pytest.mark.asyncio -async def test_combine_voices_invalid_weights(mock_voice_manager): - """Test combining voices with invalid weights""" - voices = ["voice1", "voice2"] - weights = [0.3, 0.3] # Doesn't sum to 1 - - mock_voice_manager.combine_voices.side_effect = ValueError("Weights must sum to 1") - with pytest.raises(ValueError, match="Weights must sum to 1"): - await mock_voice_manager.combine_voices(voices, weights) +async def test_combine_voices_without_saving(voice_manager, mock_voice_tensor): + """Test combining voices without local saving""" + with patch("api.src.core.paths.load_voice_tensor", new_callable=AsyncMock) as mock_load, \ + patch("torch.save") as mock_save, \ + patch("os.makedirs"), \ + patch("os.path.exists", return_value=True): + + # Setup mocks + mock_load.return_value = mock_voice_tensor + + # Mock settings + with patch("api.src.core.config.settings") as mock_settings: + mock_settings.allow_local_voice_saving = False + mock_settings.voices_dir = "/mock/voices" + + # Combine voices + combined = await voice_manager.combine_voices(["af_bella", "af_sarah"], "cpu") + assert combined == "af_bella+af_sarah" # Note: using + separator + + # Verify voice was not saved + mock_save.assert_not_called() + @pytest.mark.asyncio -async def test_combine_voices_single_voice(mock_voice_manager): +async def test_combine_voices_single_voice(voice_manager): """Test combining with single voice""" - voices = ["voice1"] - weights = [1.0] - - mock_voice_manager.combine_voices.side_effect = ValueError("At least 2 voices are required") with pytest.raises(ValueError, match="At least 2 voices are required"): - await mock_voice_manager.combine_voices(voices, weights) + await voice_manager.combine_voices(["af_bella"], "cpu") + @pytest.mark.asyncio -async def test_cache_management(mock_voice_manager, mock_voice_tensor): +async def test_list_voices(voice_manager): + """Test listing available voices""" + with patch("os.listdir", return_value=["af_bella.pt", "af_sarah.pt", "af_bella+af_sarah.pt"]), \ + patch("os.makedirs"): + voices = await voice_manager.list_voices() + assert len(voices) == 3 + assert "af_bella" in voices + assert "af_sarah" in voices + assert "af_bella+af_sarah" in voices + + +@pytest.mark.asyncio +async def test_load_combined_voice(voice_manager, mock_voice_tensor): + """Test loading a combined voice""" + with patch("api.src.core.paths.load_voice_tensor", new_callable=AsyncMock) as mock_load: + mock_load.return_value = mock_voice_tensor + with patch("os.path.exists", return_value=True): + voice = await voice_manager.load_voice("af_bella+af_sarah", "cpu") + assert torch.equal(voice, mock_voice_tensor) + + +def test_cache_management(voice_manager, mock_voice_tensor): """Test voice cache management""" - # Mock cache info - mock_voice_manager.cache_info = {"size": 1, "max_size": 10} + # Set small cache size + voice_manager._config.cache_size = 2 - # Load voice to test caching - await mock_voice_manager.load_voice("voice1") + # Add items to cache + voice_manager._voice_cache = { + "voice1_cpu": torch.randn(5, 5), + "voice2_cpu": torch.randn(5, 5), + } - # Check cache info - cache_info = mock_voice_manager.cache_info - assert cache_info["size"] == 1 - assert cache_info["max_size"] == 10 \ No newline at end of file + # Try adding another item + voice_manager._manage_cache() + + # Check cache size maintained + assert len(voice_manager._voice_cache) <= 2 + + +@pytest.mark.asyncio +async def test_voice_loading_with_cache(voice_manager, mock_voice_tensor): + """Test voice loading with cache enabled""" + with patch("api.src.core.paths.load_voice_tensor", new_callable=AsyncMock) as mock_load, \ + patch("os.path.exists", return_value=True): + + mock_load.return_value = mock_voice_tensor + + # First load should hit disk + voice1 = await voice_manager.load_voice("af_bella", "cpu") + assert mock_load.call_count == 1 + + # Second load should hit cache + voice2 = await voice_manager.load_voice("af_bella", "cpu") + assert mock_load.call_count == 1 # Still 1 + + assert torch.equal(voice1, voice2) \ No newline at end of file diff --git a/web/app.js b/web/app.js index bc44e9a..5e122a6 100644 --- a/web/app.js +++ b/web/app.js @@ -2,21 +2,88 @@ class KokoroPlayer { constructor() { this.elements = { textInput: document.getElementById('text-input'), - voiceSelect: document.getElementById('voice-select'), - streamToggle: document.getElementById('stream-toggle'), + voiceSearch: document.getElementById('voice-search'), + voiceDropdown: document.getElementById('voice-dropdown'), + voiceOptions: document.getElementById('voice-options'), + selectedVoices: document.getElementById('selected-voices'), autoplayToggle: document.getElementById('autoplay-toggle'), + formatSelect: document.getElementById('format-select'), generateBtn: document.getElementById('generate-btn'), - audioPlayer: document.getElementById('audio-player'), + cancelBtn: document.getElementById('cancel-btn'), + playPauseBtn: document.getElementById('play-pause-btn'), + waveContainer: document.getElementById('wave-container'), + timeDisplay: document.getElementById('time-display'), + downloadBtn: document.getElementById('download-btn'), status: document.getElementById('status') }; this.isGenerating = false; + this.availableVoices = []; + this.selectedVoiceSet = new Set(); + this.currentController = null; + this.audioChunks = []; + this.sound = null; + this.wave = null; this.init(); } async init() { await this.loadVoices(); + this.setupWave(); this.setupEventListeners(); + this.setupAudioControls(); + } + + setupWave() { + this.wave = new SiriWave({ + container: this.elements.waveContainer, + width: this.elements.waveContainer.clientWidth, + height: 50, + style: 'ios', + color: '#6366f1', + speed: 0.02, + amplitude: 0.7, + frequency: 4 + }); + } + + formatTime(secs) { + const minutes = Math.floor(secs / 60); + const seconds = Math.floor(secs % 60); + return `${minutes}:${seconds.toString().padStart(2, '0')}`; + } + + updateTimeDisplay() { + if (!this.sound) return; + const seek = this.sound.seek() || 0; + const duration = this.sound.duration() || 0; + this.elements.timeDisplay.textContent = `${this.formatTime(seek)} / ${this.formatTime(duration)}`; + + // Update seek slider + const seekSlider = document.getElementById('seek-slider'); + seekSlider.value = (seek / duration) * 100 || 0; + + if (this.sound.playing()) { + requestAnimationFrame(() => this.updateTimeDisplay()); + } + } + + setupAudioControls() { + const seekSlider = document.getElementById('seek-slider'); + const volumeSlider = document.getElementById('volume-slider'); + + seekSlider.addEventListener('input', (e) => { + if (!this.sound) return; + const duration = this.sound.duration(); + const seekTime = (duration * e.target.value) / 100; + this.sound.seek(seekTime); + }); + + volumeSlider.addEventListener('input', (e) => { + if (!this.sound) return; + const volume = e.target.value / 100; + this.sound.volume(volume); + }); } async loadVoices() { @@ -32,27 +99,132 @@ class KokoroPlayer { throw new Error('No voices available'); } - this.elements.voiceSelect.innerHTML = data.voices - .map(voice => ``) - .join(''); + this.availableVoices = data.voices; + this.renderVoiceOptions(this.availableVoices); - // Select first voice by default - if (data.voices.length > 0) { - this.elements.voiceSelect.value = data.voices[0]; + if (this.selectedVoiceSet.size === 0) { + const firstVoice = this.availableVoices.find(voice => voice && voice.trim()); + if (firstVoice) { + this.addSelectedVoice(firstVoice); + } } this.showStatus('Voices loaded successfully', 'success'); } catch (error) { this.showStatus('Failed to load voices: ' + error.message, 'error'); - // Disable generate button if no voices this.elements.generateBtn.disabled = true; } } + renderVoiceOptions(voices) { + this.elements.voiceOptions.innerHTML = voices + .map(voice => ` + + `) + .join(''); + this.updateSelectedVoicesDisplay(); + } + + updateSelectedVoicesDisplay() { + this.elements.selectedVoices.innerHTML = Array.from(this.selectedVoiceSet) + .map(voice => ` + + ${voice} + × + + `) + .join(''); + + if (this.selectedVoiceSet.size > 0) { + this.elements.voiceSearch.placeholder = 'Search voices...'; + } else { + this.elements.voiceSearch.placeholder = 'Search and select voices...'; + } + } + + addSelectedVoice(voice) { + this.selectedVoiceSet.add(voice); + this.updateSelectedVoicesDisplay(); + } + + removeSelectedVoice(voice) { + this.selectedVoiceSet.delete(voice); + this.updateSelectedVoicesDisplay(); + const checkbox = this.elements.voiceOptions.querySelector(`input[value="${voice}"]`); + if (checkbox) checkbox.checked = false; + } + + filterVoices(searchTerm) { + const filtered = this.availableVoices.filter(voice => + voice.toLowerCase().includes(searchTerm.toLowerCase()) + ); + this.renderVoiceOptions(filtered); + } + setupEventListeners() { + window.addEventListener('beforeunload', () => { + if (this.currentController) { + this.currentController.abort(); + } + if (this.sound) { + this.sound.unload(); + } + }); + + this.elements.voiceSearch.addEventListener('input', (e) => { + this.filterVoices(e.target.value); + }); + + this.elements.voiceOptions.addEventListener('change', (e) => { + if (e.target.type === 'checkbox') { + if (e.target.checked) { + this.addSelectedVoice(e.target.value); + } else { + this.removeSelectedVoice(e.target.value); + } + } + }); + + this.elements.selectedVoices.addEventListener('click', (e) => { + if (e.target.classList.contains('remove-voice')) { + const voice = e.target.dataset.voice; + this.removeSelectedVoice(voice); + } + }); + this.elements.generateBtn.addEventListener('click', () => this.generateSpeech()); - this.elements.audioPlayer.addEventListener('ended', () => { - this.elements.generateBtn.disabled = false; + this.elements.cancelBtn.addEventListener('click', () => this.cancelGeneration()); + this.elements.playPauseBtn.addEventListener('click', () => this.togglePlayPause()); + this.elements.downloadBtn.addEventListener('click', () => this.downloadAudio()); + + document.addEventListener('click', (e) => { + if (!this.elements.voiceSearch.contains(e.target) && + !this.elements.voiceDropdown.contains(e.target)) { + this.elements.voiceDropdown.style.display = 'none'; + } + }); + + this.elements.voiceSearch.addEventListener('focus', () => { + this.elements.voiceDropdown.style.display = 'block'; + if (!this.elements.voiceSearch.value) { + this.elements.voiceSearch.placeholder = 'Search voices...'; + } + }); + + this.elements.voiceSearch.addEventListener('blur', () => { + if (!this.elements.voiceSearch.value && this.selectedVoiceSet.size === 0) { + this.elements.voiceSearch.placeholder = 'Search and select voices...'; + } + }); + + window.addEventListener('resize', () => { + if (this.wave) { + this.wave.width = this.elements.waveContainer.clientWidth; + } }); } @@ -67,7 +239,8 @@ class KokoroPlayer { setLoading(loading) { this.isGenerating = loading; this.elements.generateBtn.disabled = loading; - this.elements.generateBtn.className = loading ? 'primary loading' : 'primary'; + this.elements.generateBtn.className = loading ? 'loading' : ''; + this.elements.cancelBtn.style.display = loading ? 'block' : 'none'; } validateInput() { @@ -77,8 +250,7 @@ class KokoroPlayer { return false; } - const voice = this.elements.voiceSelect.value; - if (!voice) { + if (this.selectedVoiceSet.size === 0) { this.showStatus('Please select a voice', 'error'); return false; } @@ -86,89 +258,68 @@ class KokoroPlayer { return true; } - async generateSpeech() { - if (this.isGenerating || !this.validateInput()) return; - - const text = this.elements.textInput.value.trim(); - const voice = this.elements.voiceSelect.value; - const stream = this.elements.streamToggle.checked; - - this.setLoading(true); - - try { - if (stream) { - await this.handleStreamingAudio(text, voice); - } else { - await this.handleNonStreamingAudio(text, voice); + cancelGeneration() { + if (this.currentController) { + this.currentController.abort(); + this.currentController = null; + if (this.sound) { + this.sound.unload(); + this.sound = null; } - } catch (error) { - this.showStatus('Error generating speech: ' + error.message, 'error'); - } finally { + this.wave.stop(); + this.showStatus('Generation cancelled', 'info'); this.setLoading(false); } } - async handleStreamingAudio(text, voice) { - this.showStatus('Initializing audio stream...', 'info'); + togglePlayPause() { + if (!this.sound) return; - const response = await fetch('/v1/audio/speech', { - method: 'POST', - headers: { 'Content-Type': 'application/json' }, - body: JSON.stringify({ - input: text, - voice: voice, - response_format: 'mp3', - stream: true - }) - }); - - if (!response.ok) { - const error = await response.json(); - throw new Error(error.detail?.message || 'Failed to generate speech'); + if (this.sound.playing()) { + this.sound.pause(); + this.wave.stop(); + this.elements.playPauseBtn.textContent = 'Play'; + } else { + this.sound.play(); + this.wave.start(); + this.elements.playPauseBtn.textContent = 'Pause'; + this.updateTimeDisplay(); } - - const mediaSource = new MediaSource(); - this.elements.audioPlayer.src = URL.createObjectURL(mediaSource); - - return new Promise((resolve, reject) => { - mediaSource.addEventListener('sourceopen', async () => { - try { - const sourceBuffer = mediaSource.addSourceBuffer('audio/mpeg'); - const reader = response.body.getReader(); - let totalChunks = 0; - - while (true) { - const {done, value} = await reader.read(); - if (done) break; - - // Wait for the buffer to be ready - if (sourceBuffer.updating) { - await new Promise(resolve => { - sourceBuffer.addEventListener('updateend', resolve, {once: true}); - }); - } - - sourceBuffer.appendBuffer(value); - totalChunks++; - this.showStatus(`Received chunk ${totalChunks}...`, 'info'); - } -mediaSource.endOfStream(); -if (this.elements.autoplayToggle.checked) { - await this.elements.audioPlayer.play(); -} -this.showStatus('Audio stream ready', 'success'); - this.showStatus('Audio stream ready', 'success'); - resolve(); - } catch (error) { - mediaSource.endOfStream(); - this.showStatus('Error during streaming: ' + error.message, 'error'); - reject(error); - } - }); - }); } - async handleNonStreamingAudio(text, voice) { + async generateSpeech() { + if (this.isGenerating || !this.validateInput()) return; + + if (this.sound) { + this.sound.unload(); + this.sound = null; + } + this.wave.stop(); + + this.elements.downloadBtn.style.display = 'none'; + this.audioChunks = []; + + const text = this.elements.textInput.value.trim(); + const voice = Array.from(this.selectedVoiceSet).join('+'); + + this.setLoading(true); + this.currentController = new AbortController(); + + try { + await this.handleAudio(text, voice); + } catch (error) { + if (error.name === 'AbortError') { + this.showStatus('Generation cancelled', 'info'); + } else { + this.showStatus('Error generating speech: ' + error.message, 'error'); + } + } finally { + this.currentController = null; + this.setLoading(false); + } + } + + async handleAudio(text, voice) { this.showStatus('Generating audio...', 'info'); const response = await fetch('/v1/audio/speech', { @@ -178,8 +329,9 @@ this.showStatus('Audio stream ready', 'success'); input: text, voice: voice, response_format: 'mp3', - stream: false - }) + stream: true + }), + signal: this.currentController.signal }); if (!response.ok) { @@ -187,17 +339,97 @@ this.showStatus('Audio stream ready', 'success'); throw new Error(error.detail?.message || 'Failed to generate speech'); } - const blob = await response.blob(); - const url = URL.createObjectURL(blob); - this.elements.audioPlayer.src = url; - if (this.elements.autoplayToggle.checked) { - await this.elements.audioPlayer.play(); + const chunks = []; + const reader = response.body.getReader(); + let totalChunks = 0; + + try { + while (true) { + const {value, done} = await reader.read(); + + if (done) { + this.showStatus('Processing complete', 'success'); + break; + } + + chunks.push(value); + this.audioChunks.push(value.slice(0)); + totalChunks++; + + if (totalChunks % 5 === 0) { + this.showStatus(`Received ${totalChunks} chunks...`, 'info'); + } + } + + const blob = new Blob(chunks, { type: 'audio/mpeg' }); + const url = URL.createObjectURL(blob); + + if (this.sound) { + this.sound.unload(); + } + + this.sound = new Howl({ + src: [url], + format: ['mp3'], + html5: true, + onplay: () => { + this.elements.playPauseBtn.textContent = 'Pause'; + this.wave.start(); + this.updateTimeDisplay(); + }, + onpause: () => { + this.elements.playPauseBtn.textContent = 'Play'; + this.wave.stop(); + }, + onend: () => { + this.elements.playPauseBtn.textContent = 'Play'; + this.wave.stop(); + this.elements.generateBtn.disabled = false; + }, + onload: () => { + URL.revokeObjectURL(url); + this.showStatus('Audio ready', 'success'); + this.enableDownload(); + if (this.elements.autoplayToggle.checked) { + this.sound.play(); + } + }, + onloaderror: () => { + URL.revokeObjectURL(url); + this.showStatus('Error loading audio', 'error'); + } + }); + + } catch (error) { + if (error.name === 'AbortError') { + throw error; + } + console.error('Streaming error:', error); + this.showStatus('Error during streaming', 'error'); + throw error; } - this.showStatus('Audio ready', 'success'); + } + + enableDownload() { + this.elements.downloadBtn.style.display = 'flex'; + } + + downloadAudio() { + if (this.audioChunks.length === 0) return; + + const format = this.elements.formatSelect.value; + const blob = new Blob(this.audioChunks, { type: `audio/${format}` }); + const url = URL.createObjectURL(blob); + const a = document.createElement('a'); + a.href = url; + a.download = `generated-speech.${format}`; + document.body.appendChild(a); + a.click(); + document.body.removeChild(a); + URL.revokeObjectURL(url); } } -// Initialize the player when the page loads document.addEventListener('DOMContentLoaded', () => { new KokoroPlayer(); }); \ No newline at end of file diff --git a/web/favicon.svg b/web/favicon.svg new file mode 100644 index 0000000..ae7545d --- /dev/null +++ b/web/favicon.svg @@ -0,0 +1,47 @@ + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/web/index.html b/web/index.html index e22bc52..e9c5616 100644 --- a/web/index.html +++ b/web/index.html @@ -3,13 +3,35 @@ - FastKoko: Kokoro-based TTS Player - + FastKoko: Kokoro-based TTS + + + + + + + + + + + + +
+
+
+
-

Kokoro TTS

+
+

FastKoko

+
+
+
+
+
+

Kokoro-FastAPI TTS System

@@ -17,35 +39,77 @@
- +
+ +
+
+ +
+
+
+ +
+
- + +
+
+ +
-
- +
+
+
+ + +
+ + + + +
+ 0:00 +
+
+
+ +
- \ No newline at end of file + diff --git a/web/siriwave.js b/web/siriwave.js new file mode 100644 index 0000000..4a1612e --- /dev/null +++ b/web/siriwave.js @@ -0,0 +1,131 @@ +(function() { +function SiriWave(opt) { + opt = opt || {}; + + this.phase = 0; + this.run = false; + + // UI vars + this.ratio = opt.ratio || window.devicePixelRatio || 1; + this.width = this.ratio * (opt.width || 320); + this.width_2 = this.width / 2; + this.width_4 = this.width / 4; + this.height = this.ratio * (opt.height || 50); + this.height_2 = this.height / 2; + this.MAX = (this.height_2) - 4; + + // Constructor opt + this.amplitude = opt.amplitude || 1; + this.speed = opt.speed || 0.2; + this.frequency = opt.frequency || 6; + this.color = (function hex2rgb(hex){ + var shorthandRegex = /^#?([a-f\d])([a-f\d])([a-f\d])$/i; + hex = hex.replace(shorthandRegex, function(m,r,g,b) { return r + r + g + g + b + b; }); + var result = /^#?([a-f\d]{2})([a-f\d]{2})([a-f\d]{2})$/i.exec(hex); + return result ? + parseInt(result[1],16).toString()+','+parseInt(result[2], 16).toString()+','+parseInt(result[3], 16).toString() + : null; + })(opt.color || '#6366f1') || '99,102,241'; + + // Canvas + this.canvas = document.createElement('canvas'); + this.canvas.width = this.width; + this.canvas.height = this.height; + + this.canvas.style.width = '100%'; + this.canvas.style.height = '100%'; + this.canvas.style.borderRadius = '4px'; + + this.container = opt.container || document.body; + this.container.appendChild(this.canvas); + this.ctx = this.canvas.getContext('2d'); + + // Start + if (opt.autostart) { + this.start(); + } +} + +SiriWave.prototype._GATF_cache = {}; +SiriWave.prototype._globAttFunc = function(x) { + if (SiriWave.prototype._GATF_cache[x] == null) { + SiriWave.prototype._GATF_cache[x] = Math.pow(4/(4+Math.pow(x,4)), 4); + } + return SiriWave.prototype._GATF_cache[x]; +}; + +SiriWave.prototype._xpos = function(i) { + return this.width_2 + i * this.width_4; +}; + +SiriWave.prototype._ypos = function(i, attenuation) { + var att = (this.MAX * this.amplitude) / attenuation; + return this.height_2 + this._globAttFunc(i) * att * Math.sin(this.frequency * i - this.phase); +}; + +SiriWave.prototype._drawLine = function(attenuation, color, width){ + this.ctx.moveTo(0,0); + this.ctx.beginPath(); + this.ctx.strokeStyle = color; + this.ctx.lineWidth = width || 1; + + var i = -2; + while ((i += 0.01) <= 2) { + var y = this._ypos(i, attenuation); + if (Math.abs(i) >= 1.90) y = this.height_2; + this.ctx.lineTo(this._xpos(i), y); + } + + this.ctx.stroke(); +}; + +SiriWave.prototype._clear = function() { + this.ctx.globalCompositeOperation = 'destination-out'; + this.ctx.fillRect(0, 0, this.width, this.height); + this.ctx.globalCompositeOperation = 'source-over'; +}; + +SiriWave.prototype._draw = function() { + if (this.run === false) return; + + this.phase = (this.phase + Math.PI*this.speed) % (2*Math.PI); + + this._clear(); + this._drawLine(-2, 'rgba(' + this.color + ',0.1)'); + this._drawLine(-6, 'rgba(' + this.color + ',0.2)'); + this._drawLine(4, 'rgba(' + this.color + ',0.4)'); + this._drawLine(2, 'rgba(' + this.color + ',0.6)'); + this._drawLine(1, 'rgba(' + this.color + ',1)', 1.5); + + if (window.requestAnimationFrame) { + requestAnimationFrame(this._draw.bind(this)); + return; + }; + setTimeout(this._draw.bind(this), 20); +}; + +SiriWave.prototype.start = function() { + this.phase = 0; + this.run = true; + this._draw(); +}; + +SiriWave.prototype.stop = function() { + this.phase = 0; + this.run = false; +}; + +SiriWave.prototype.setSpeed = function(v) { + this.speed = v; +}; + +SiriWave.prototype.setNoise = SiriWave.prototype.setAmplitude = function(v) { + this.amplitude = Math.max(Math.min(v, 1), 0); +}; + +if (typeof define === 'function' && define.amd) { + define(function(){ return SiriWave; }); + return; +}; +window.SiriWave = SiriWave; +})(); \ No newline at end of file diff --git a/web/styles-clean.css b/web/styles-clean.css new file mode 100644 index 0000000..035fb7c --- /dev/null +++ b/web/styles-clean.css @@ -0,0 +1,266 @@ +:root { + --bg-color: #0f172a; + --fg-color: #6366f1; + --surface: rgba(30, 41, 59, 1); /* Made opaque */ + --text: #f8fafc; + --text-light: #cbd5e1; + --border: rgba(148, 163, 184, 0.2); + --error: #ef4444; + --success: #22c55e; + --font-family: 'Inter', system-ui, sans-serif; +} + +* { + margin: 0; + padding: 0; + box-sizing: border-box; +} + +body { + font-family: var(--font-family); + line-height: 1.6; + color: var(--text); + background: radial-gradient(circle at top right, + var(--fg-color) 0%, + var(--bg-color) 100%); + min-height: 100vh; + position: relative; +} + +.container { + max-width: 800px; + margin: 0 auto; + padding: 4rem 1.5rem; +} + +header { + margin-bottom: 3rem; + text-align: center; +} + +h1 { + font-size: 3rem; + font-weight: 700; + color: var(--text); + margin-bottom: 0.5rem; +} + +.subtitle { + color: var(--text-light); + font-size: 1.1rem; +} + +.input-section, .player-section { + background: var(--surface); + padding: 2rem; + border-radius: 1rem; + border: 1px solid var(--border); + backdrop-filter: blur(12px); + box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1), + 0 2px 4px -1px rgba(0, 0, 0, 0.06); + margin-bottom: 2rem; +} + +textarea { + width: 100%; + min-height: 120px; + padding: 1rem; + border: 1px solid var(--border); + border-radius: 0.5rem; + background: rgba(15, 23, 42, 0.3); + color: var(--text); + font-size: 1rem; + transition: border-color 0.2s ease; + font-family: var(--font-family); + resize: vertical; +} + +textarea:focus { + outline: none; + border-color: var(--fg-color); + box-shadow: 0 0 0 3px rgba(99, 102, 241, 0.2); +} + +textarea::placeholder { + color: var(--text-light); +} + +.controls { + margin-top: 1.5rem; + display: flex; + flex-direction: column; + gap: 1.5rem; +} + +.voice-select-container { + position: relative; + display: flex; + align-items: center; + gap: 1rem; +} + +.voice-search { + flex: 1; + padding: 0.75rem 1rem; + border: 1px solid var(--border); + border-radius: 0.5rem; + background: rgba(15, 23, 42, 0.3); + color: var(--text); + font-size: 1rem; + transition: all 0.2s ease; +} + +.voice-search:focus { + outline: none; + border-color: var(--fg-color); + box-shadow: 0 0 0 3px rgba(99, 102, 241, 0.2); +} + +.voice-search::placeholder { + color: var(--text-light); +} + +.voice-dropdown { + display: none; + position: absolute; + top: 100%; + left: 0; + right: 0; + background: var(--surface); + border: 1px solid var(--border); + border-radius: 0.5rem; + margin-top: 0.5rem; + max-height: 200px; + overflow-y: auto; + z-index: 1000; /* Increased z-index */ + box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1), + 0 2px 4px -1px rgba(0, 0, 0, 0.06); +} + +.voice-select-container:focus-within .voice-dropdown, +.voice-dropdown:hover { + display: block; +} + +.voice-option { + display: flex; + align-items: center; + padding: 0.75rem; + cursor: pointer; + border-radius: 0.25rem; + transition: background-color 0.2s ease; + color: var(--text); +} + +.voice-option:hover { + background: rgba(99, 102, 241, 0.1); +} + +.selected-voice-tag { + background: rgba(99, 102, 241, 0.2); + padding: 0.25rem 0.75rem; + border-radius: 1rem; + font-size: 0.875rem; + border: 1px solid rgba(99, 102, 241, 0.3); +} + +.options { + display: flex; + gap: 2rem; + flex-wrap: wrap; +} + +.options label { + display: flex; + align-items: center; + gap: 0.5rem; + color: var(--text-light); + cursor: pointer; + white-space: nowrap; +} + +button { + background: var(--fg-color); + color: var(--text); + padding: 1rem; + border-radius: 0.5rem; + border: none; + font-weight: 500; + cursor: pointer; + transition: transform 0.2s ease, box-shadow 0.2s ease; + display: flex; + align-items: center; + justify-content: center; + gap: 0.5rem; +} + +button:hover { + transform: translateY(-1px); + box-shadow: 0 4px 12px rgba(99, 102, 241, 0.2); +} + +button:disabled { + opacity: 0.7; + cursor: not-allowed; + transform: none; + box-shadow: none; +} + +.theme-toggle { + position: fixed; + top: 20px; + right: 20px; + width: 60px; + height: 60px; + cursor: pointer; + z-index: 100; + border-radius: 50%; + background: var(--fg-color); + box-shadow: 0 4px 12px rgba(99, 102, 241, 0.2); + transition: transform 0.3s ease; +} + +.theme-toggle:hover { + transform: scale(1.1); +} + +/* Make audio player full width */ +audio { + width: 100%; + margin-top: 1rem; +} + +@media (max-width: 640px) { + .container { + padding: 2rem 1rem; + } + + h1 { + font-size: 2rem; + } + + .subtitle { + font-size: 1rem; + } + + .input-section, .player-section { + padding: 1.5rem; + } + + .voice-select-container { + flex-direction: column; + align-items: stretch; + } + + .options { + flex-direction: column; + gap: 1rem; + } + + .theme-toggle { + width: 50px; + height: 50px; + top: 10px; + right: 10px; + } +} \ No newline at end of file diff --git a/web/styles.css b/web/styles.css index e0e39f1..86c24c3 100644 --- a/web/styles.css +++ b/web/styles.css @@ -1,14 +1,13 @@ :root { - --primary-color: #007bff; - --primary-hover: #0056b3; - --background: #ffffff; - --surface: #f8f9fa; - --text: #212529; - --text-light: #6c757d; - --border: #dee2e6; - --error: #dc3545; - --success: #28a745; - --font-family: system-ui, -apple-system, "Segoe UI", Roboto, sans-serif; + --bg-color: #0f172a; + --fg-color: #6366f1; + --surface: rgba(30, 41, 59, 1); + --text: #f8fafc; + --text-light: #cbd5e1; + --border: rgba(148, 163, 184, 0.2); + --error: #ef4444; + --success: #22c55e; + --font-family: 'Inter', system-ui, sans-serif; } * { @@ -21,110 +20,427 @@ body { font-family: var(--font-family); line-height: 1.6; color: var(--text); - background: var(--background); + background: radial-gradient(circle at top right, + var(--fg-color) 0%, + var(--bg-color) 100%); + min-height: 100vh; + position: relative; +} + +.overlay { + position: fixed; + inset: 0; + background-image: + repeating-linear-gradient(0deg, + rgba(255,255,255,0.03) 0px, + rgba(255,255,255,0.03) 1px, + transparent 1px, + transparent 20px), + repeating-linear-gradient(90deg, + rgba(255,255,255,0.03) 0px, + rgba(255,255,255,0.03) 1px, + transparent 1px, + transparent 20px); + pointer-events: none; +} + +.sun { + position: fixed; + top: 20px; + right: 20px; + width: 100px; + height: 100px; + border-radius: 50%; + background: radial-gradient(circle at center, + rgba(99, 102, 241, 0.2) 0%, + transparent 70%); + pointer-events: none; + z-index: 0; +} + +.scanline { + position: absolute; + top: 0; + left: 0; + width: 100%; + height: 2px; + background: rgba(99, 102, 241, 0.1); + animation: scan 4s linear infinite; +} + +@keyframes scan { + 0% { transform: translateY(0); } + 100% { transform: translateY(100px); } } .container { - max-width: 800px; + max-width: 1200px; margin: 0 auto; - padding: 2rem 1rem; + padding: 4rem 1.5rem; + position: relative; + z-index: 1; } header { - margin-bottom: 2rem; + margin-bottom: 3rem; text-align: center; } -h1 { - color: var(--primary-color); - font-size: 2rem; - font-weight: 600; +.logo-container { + display: flex; + align-items: center; + justify-content: center; + gap: 20px; } -.input-section { +h1 { + font-size: 5rem; + font-weight: 700; + margin: 0; + background: linear-gradient(rgba(255,255,255,0.1) 1px, transparent 1px), + linear-gradient(90deg, rgba(255,255,255,0.1) 1px, transparent 1px); + background-size: 10px 10px; + -webkit-background-clip: text; + background-clip: text; + color: var(--text); + text-shadow: + -2px -2px 0 rgba(0,0,0,0.5), + 2px -2px 0 rgba(0,0,0,0.5), + -2px 2px 0 rgba(0,0,0,0.5), + 2px 2px 0 rgba(0,0,0,0.5), + 3px 3px var(--fg-color); +} + +.subtitle { + color: var(--text-light); + font-size: 1.5rem; + opacity: 0.9; + margin-top: 0.5rem; +} + +.cup { + width: 40px; + height: 50px; + border: 3px solid var(--text); + border-radius: 0 0 20px 20px; + position: relative; + animation: float 3s ease-in-out 2; + animation-fill-mode: forwards; +} + +.handle { + width: 15px; + height: 25px; + border: 3px solid var(--text); + border-radius: 0 10px 10px 0; + position: absolute; + right: -15px; + top: 10px; +} + +.steam { + position: absolute; + top: -15px; + left: 5px; + right: 5px; + height: 15px; + display: flex; + justify-content: space-between; +} + +.steam::before, +.steam::after { + content: ""; + width: 10px; + height: 100%; + background: rgba(255,255,255,0.7); + border-radius: 10px; + animation: steam 2s 2; + animation-fill-mode: forwards; +} + +@keyframes steam { + to { + transform: translateY(-10px) scale(1.5); + opacity: 0; + } +} + +@keyframes float { + 50% { + transform: translateY(-5px); + } +} + +main { + display: flex; + flex-direction: column; + gap: 2rem; + min-height: 600px; +} + +@media (min-width: 1024px) { + main { + display: grid; + grid-template-columns: 1fr 1fr; + gap: 2rem; + align-items: stretch; + } + + .input-section, .player-section { + height: 100%; + display: flex; + flex-direction: column; + } + + .input-section textarea { + flex: 1; + min-height: 200px; + } + + .player-section { + display: flex; + flex-direction: column; + } + + .audio-controls { + flex: 1; + display: flex; + flex-direction: column; + } + + .wave-container { + flex: 1; + min-height: 200px; + } +} + +.input-section, .player-section { background: var(--surface); - padding: 1.5rem; - border-radius: 8px; - margin-bottom: 1.5rem; + padding: 2rem; + border-radius: 1rem; + border: 1px solid var(--border); + backdrop-filter: blur(12px); + box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1), + 0 2px 4px -1px rgba(0, 0, 0, 0.06); } textarea { width: 100%; - padding: 0.75rem; + min-height: 120px; + padding: 1rem; border: 1px solid var(--border); - border-radius: 4px; - font-family: inherit; + border-radius: 0.5rem; + background: rgba(15, 23, 42, 0.3); + color: var(--text); font-size: 1rem; + transition: border-color 0.2s ease; + font-family: var(--font-family); resize: vertical; - margin-bottom: 1rem; } textarea:focus { outline: none; - border-color: var(--primary-color); + border-color: var(--fg-color); + box-shadow: 0 0 0 3px rgba(99, 102, 241, 0.2); +} + +textarea::placeholder { + color: var(--text-light); } .controls { + margin-top: 1.5rem; display: flex; - gap: 1rem; - align-items: center; - flex-wrap: wrap; + flex-direction: column; + gap: 1.5rem; } -select { - padding: 0.5rem; +.voice-select-container { + position: relative; + display: flex; + flex-direction: column; + gap: 1rem; +} + +.voice-search { + width: 100%; + padding: 0.75rem 1rem; border: 1px solid var(--border); - border-radius: 4px; + border-radius: 0.5rem; + background: rgba(15, 23, 42, 0.3); + color: var(--text); font-size: 1rem; - min-width: 200px; + transition: all 0.2s ease; +} + +.voice-search:focus { + outline: none; + border-color: var(--fg-color); + box-shadow: 0 0 0 3px rgba(99, 102, 241, 0.2); +} + +.voice-search::placeholder { + color: var(--text-light); +} + +.voice-dropdown { + display: none; + position: absolute; + top: 100%; + left: 0; + right: 0; + background: var(--surface); + border: 1px solid var(--border); + border-radius: 0.5rem; + margin-top: 0.5rem; + max-height: 200px; + overflow-y: auto; + z-index: 1000; + box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1), + 0 2px 4px -1px rgba(0, 0, 0, 0.06); +} + +.voice-select-container:focus-within .voice-dropdown, +.voice-dropdown:hover { + display: block; +} + +.voice-option { + display: flex; + align-items: center; + padding: 0.75rem; + cursor: pointer; + border-radius: 0.25rem; + transition: background-color 0.2s ease; + color: var(--text); +} + +.voice-option:hover { + background: rgba(99, 102, 241, 0.1); +} + +.selected-voices { + display: flex; + flex-wrap: wrap; + gap: 0.5rem; + margin-top: 0.5rem; +} + +.selected-voice-tag { + background: rgba(99, 102, 241, 0.2); + padding: 0.25rem 0.75rem; + border-radius: 1rem; + font-size: 0.875rem; + display: flex; + align-items: center; + gap: 0.5rem; + border: 1px solid rgba(99, 102, 241, 0.3); +} + +.remove-voice { + cursor: pointer; + opacity: 0.7; + transition: opacity 0.2s ease; +} + +.remove-voice:hover { + opacity: 1; } .options { display: flex; align-items: center; + justify-content: space-between; + gap: 2rem; + flex-wrap: wrap; +} + +.options label { + display: flex; + align-items: center; + gap: 0.5rem; + color: var(--text-light); + cursor: pointer; +} + +.format-select { + background: rgba(15, 23, 42, 0.3); + color: var(--text); + border: 1px solid var(--border); + border-radius: 0.5rem; + padding: 0.5rem 1rem; + font-family: var(--font-family); + font-size: 0.875rem; + cursor: pointer; + transition: all 0.2s ease; + min-width: 100px; +} + +.format-select:hover { + border-color: var(--fg-color); +} + +.format-select:focus { + outline: none; + border-color: var(--fg-color); + box-shadow: 0 0 0 3px rgba(99, 102, 241, 0.2); +} + +.format-select option { + background: var(--surface); + color: var(--text); +} + +.button-group { + display: flex; gap: 1rem; } button { - padding: 0.5rem 1rem; + background: var(--fg-color); + color: var(--text); + padding: 1rem; + border-radius: 0.5rem; border: none; - border-radius: 4px; - font-size: 1rem; + font-weight: 500; cursor: pointer; + transition: transform 0.2s ease, box-shadow 0.2s ease; display: flex; align-items: center; + justify-content: center; gap: 0.5rem; - transition: background-color 0.2s; + flex: 1; } -button.primary { - background: var(--primary-color); - color: white; -} - -button.primary:hover { - background: var(--primary-hover); +button:hover { + transform: translateY(-1px); + box-shadow: 0 4px 12px rgba(99, 102, 241, 0.2); } button:disabled { opacity: 0.7; cursor: not-allowed; + transform: none; + box-shadow: none; +} + +.cancel-btn { + background: var(--error); } .loader { display: none; width: 16px; height: 16px; - border: 2px solid #ffffff; + border: 2px solid var(--text); border-radius: 50%; border-top-color: transparent; animation: spin 1s linear infinite; } @keyframes spin { - to { - transform: rotate(360deg); - } + to { transform: rotate(360deg); } } .loading .loader { @@ -135,42 +451,321 @@ button:disabled { display: none; } -.player-section { +.audio-controls { + display: flex; + flex-direction: column; + gap: 1rem; + margin-top: 1rem; +} + +.player-container { + display: flex; + flex-direction: column; + gap: 1rem; + background: rgba(15, 23, 42, 0.3); + padding: 1rem; + border-radius: 0.5rem; + border: 1px solid var(--border); +} + +.player-controls { + display: flex; + align-items: center; + gap: 1rem; + width: 100%; + background: rgba(15, 23, 42, 0.3); + padding: 0.5rem; + border-radius: 0.5rem; +} + +.seek-slider, +.volume-slider { + -webkit-appearance: none; + height: 4px; + border-radius: 2px; + background: rgba(99, 102, 241, 0.2); + outline: none; + cursor: pointer; + transition: height 0.2s ease-in-out; +} + +.seek-slider { + flex: 1; +} + +.volume-slider { + width: 100px; +} + +.seek-slider::-webkit-slider-thumb, +.volume-slider::-webkit-slider-thumb { + -webkit-appearance: none; + width: 12px; + height: 12px; + border-radius: 50%; + background: var(--fg-color); + cursor: pointer; + transition: transform 0.2s ease; +} + +.seek-slider::-webkit-slider-thumb:hover, +.volume-slider::-webkit-slider-thumb:hover { + transform: scale(1.2); +} + +.seek-slider::-moz-range-thumb, +.volume-slider::-moz-range-thumb { + width: 12px; + height: 12px; + border: none; + border-radius: 50%; + background: var(--fg-color); + cursor: pointer; + transition: transform 0.2s ease; +} + +.seek-slider::-moz-range-thumb:hover, +.volume-slider::-moz-range-thumb:hover { + transform: scale(1.2); +} + +.volume-control { + display: flex; + align-items: center; + gap: 0.5rem; + padding-left: 0.5rem; + border-left: 1px solid var(--border); +} + +.volume-icon { + color: var(--fg-color); + opacity: 0.8; + transition: opacity 0.2s ease; +} + +.volume-icon:hover { + opacity: 1; +} + +.player-btn { + background: var(--fg-color); + color: var(--text); + padding: 0.5rem 1rem; + border-radius: 0.5rem; + border: none; + font-weight: 500; + cursor: pointer; + transition: all 0.2s ease; + flex: 0 0 auto; + min-width: 80px; +} + +.player-btn:hover { + transform: translateY(-1px); + box-shadow: 0 4px 12px rgba(99, 102, 241, 0.2); +} + +.wave-container { + width: 100%; + height: 120px; + background: rgba(15, 23, 42, 0.3); + border-radius: 0.25rem; + overflow: hidden; + position: relative; + margin-top: 0.5rem; +} + +.wave-container canvas { + position: absolute; + top: 0; + left: 0; + width: 100%; + height: 100%; +} + +.time-display { + color: var(--text-light); + font-size: 0.875rem; + min-width: 100px; + text-align: right; + font-variant-numeric: tabular-nums; +} + +.download-button { + position: relative; + width: 40px; + height: 40px; + cursor: pointer; + display: flex; + align-items: center; + justify-content: center; + transition: transform 0.2s ease; + align-self: flex-end; +} + +.download-glow { + position: absolute; + inset: -15%; + background: conic-gradient( + from 0deg, + var(--fg-color), + var(--success), + var(--fg-color) + ); + border-radius: 4px; + animation: rotate 4s linear infinite; + filter: blur(8px); + opacity: 0.5; +} + +.download-icon { + width: 40px; + height: 40px; + position: relative; + z-index: 2; background: var(--surface); - padding: 1.5rem; - border-radius: 8px; + border: 1px solid var(--border); + border-radius: 4px; + display: flex; + align-items: center; + justify-content: center; + color: var(--text); + transition: transform 0.2s ease, box-shadow 0.2s ease; +} + +.download-button:hover { + transform: scale(1.05); +} + +.download-button:hover .download-icon { + box-shadow: 0 0 15px rgba(34, 197, 94, 0.3); +} + +@keyframes rotate { + from { transform: rotate(0deg); } + to { transform: rotate(360deg); } } .status { + padding: 0.75rem 1rem; + border-radius: 0.5rem; margin-bottom: 1rem; - padding: 0.5rem; - border-radius: 4px; - display: none; + transition: all 0.3s ease; + opacity: 0; + font-weight: 500; + text-align: center; +} + +.status.info { + background: rgba(99, 102, 241, 0.1); + border: 1px solid rgba(99, 102, 241, 0.2); + opacity: 1; } .status.error { - display: block; - background: rgba(220, 53, 69, 0.1); - color: var(--error); + background: rgba(239, 68, 68, 0.1); + border: 1px solid rgba(239, 68, 68, 0.2); + opacity: 1; } .status.success { - display: block; - background: rgba(40, 167, 69, 0.1); - color: var(--success); + background: rgba(34, 197, 94, 0.1); + border: 1px solid rgba(34, 197, 94, 0.2); + opacity: 1; } -audio { - width: 100%; -} +@media (max-width: 1023px) { + .container { + padding: 2rem 1rem; + } -@media (max-width: 600px) { - .controls { + h1 { + font-size: 3rem; + } + + .subtitle { + font-size: 1.2rem; + } + + .cup { + width: 30px; + height: 40px; + } + + .handle { + width: 12px; + height: 20px; + right: -12px; + top: 8px; + } + + .steam { + top: -12px; + } + + .steam::before, + .steam::after { + width: 6px; + } + + .input-section, .player-section { + padding: 1.5rem; + } + + .voice-select-container { flex-direction: column; align-items: stretch; } - - select, button { + + .options { + flex-direction: column; + gap: 1rem; + } + + .sun { + width: 80px; + height: 80px; + top: 10px; + right: 10px; + } + + .button-group { + flex-direction: column; + } + + .player-container { + flex-direction: column; + align-items: stretch; + gap: 0.75rem; + } + + .player-controls { + flex-direction: column; + gap: 0.75rem; + } + + .player-btn { width: 100%; } + + .volume-control { + border-left: none; + border-top: 1px solid var(--border); + padding-left: 0; + padding-top: 0.75rem; + width: 100%; + } + + .volume-slider { + flex: 1; + width: auto; + } + + .wave-container { + height: 80px; + } + + .time-display { + text-align: center; + } } \ No newline at end of file diff --git a/web/styles/base.css b/web/styles/base.css new file mode 100644 index 0000000..739bbfe --- /dev/null +++ b/web/styles/base.css @@ -0,0 +1,102 @@ +:root { + --bg-color: #0f172a; + --fg-color: #6366f1; + --surface: rgba(30, 41, 59, 1); + --text: #f8fafc; + --text-light: #cbd5e1; + --border: rgba(148, 163, 184, 0.2); + --error: #ef4444; + --success: #22c55e; + --font-family: 'Inter', system-ui, sans-serif; +} + +* { + margin: 0; + padding: 0; + box-sizing: border-box; +} + +body { + font-family: var(--font-family); + line-height: 1.6; + color: var(--text); + background: radial-gradient(circle at top right, + var(--fg-color) 0%, + var(--bg-color) 100%); + min-height: 100vh; + position: relative; +} + +.overlay { + position: fixed; + inset: 0; + background-image: + repeating-linear-gradient(0deg, + rgba(255,255,255,0.03) 0px, + rgba(255,255,255,0.03) 1px, + transparent 1px, + transparent 20px), + repeating-linear-gradient(90deg, + rgba(255,255,255,0.03) 0px, + rgba(255,255,255,0.03) 1px, + transparent 1px, + transparent 20px); + pointer-events: none; +} + +.sun { + position: fixed; + top: 20px; + right: 20px; + width: 100px; + height: 100px; + border-radius: 50%; + background: radial-gradient(circle at center, + rgba(99, 102, 241, 0.2) 0%, + transparent 70%); + pointer-events: none; + z-index: 0; +} + +.scanline { + position: absolute; + top: 0; + left: 0; + width: 100%; + height: 2px; + background: rgba(99, 102, 241, 0.1); + animation: scan 4s linear infinite; +} + +@keyframes scan { + 0% { transform: translateY(0); } + 100% { transform: translateY(100px); } +} + +.status { + padding: 0.75rem 1rem; + border-radius: 0.5rem; + margin-bottom: 1rem; + transition: all 0.3s ease; + opacity: 0; + font-weight: 500; + text-align: center; +} + +.status.info { + background: rgba(99, 102, 241, 0.1); + border: 1px solid rgba(99, 102, 241, 0.2); + opacity: 1; +} + +.status.error { + background: rgba(239, 68, 68, 0.1); + border: 1px solid rgba(239, 68, 68, 0.2); + opacity: 1; +} + +.status.success { + background: rgba(34, 197, 94, 0.1); + border: 1px solid rgba(34, 197, 94, 0.2); + opacity: 1; +} \ No newline at end of file diff --git a/web/styles/forms.css b/web/styles/forms.css new file mode 100644 index 0000000..df33672 --- /dev/null +++ b/web/styles/forms.css @@ -0,0 +1,225 @@ +textarea { + width: 100%; + min-height: 120px; + padding: 1rem; + border: 1px solid var(--border); + border-radius: 0.5rem; + background: rgba(15, 23, 42, 0.3); + color: var(--text); + font-size: 1rem; + transition: border-color 0.2s ease; + font-family: var(--font-family); + resize: vertical; +} + +textarea:focus { + outline: none; + border-color: var(--fg-color); + box-shadow: 0 0 0 3px rgba(99, 102, 241, 0.2); +} + +textarea::placeholder { + color: var(--text-light); +} + +.controls { + margin-top: 1.5rem; + display: flex; + flex-direction: column; + gap: 1.5rem; +} + +.voice-select-container { + position: relative; + display: flex; + flex-direction: column; + gap: 1rem; +} + +.voice-search { + width: 100%; + padding: 0.75rem 1rem; + border: 1px solid var(--border); + border-radius: 0.5rem; + background: rgba(15, 23, 42, 0.3); + color: var(--text); + font-size: 1rem; + transition: all 0.2s ease; +} + +.voice-search:focus { + outline: none; + border-color: var(--fg-color); + box-shadow: 0 0 0 3px rgba(99, 102, 241, 0.2); +} + +.voice-search::placeholder { + color: var(--text-light); +} + +.voice-dropdown { + display: none; + position: absolute; + top: 100%; + left: 0; + right: 0; + background: var(--surface); + border: 1px solid var(--border); + border-radius: 0.5rem; + margin-top: 0.5rem; + max-height: 200px; + overflow-y: auto; + z-index: 1000; + box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1), + 0 2px 4px -1px rgba(0, 0, 0, 0.06); +} + +.voice-select-container:focus-within .voice-dropdown, +.voice-dropdown:hover { + display: block; +} + +.voice-option { + display: flex; + align-items: center; + padding: 0.75rem; + cursor: pointer; + border-radius: 0.25rem; + transition: background-color 0.2s ease; + color: var(--text); +} + +.voice-option:hover { + background: rgba(99, 102, 241, 0.1); +} + +.selected-voices { + display: flex; + flex-wrap: wrap; + gap: 0.5rem; + margin-top: 0.5rem; +} + +.selected-voice-tag { + background: rgba(99, 102, 241, 0.2); + padding: 0.25rem 0.75rem; + border-radius: 1rem; + font-size: 0.875rem; + display: flex; + align-items: center; + gap: 0.5rem; + border: 1px solid rgba(99, 102, 241, 0.3); +} + +.remove-voice { + cursor: pointer; + opacity: 0.7; + transition: opacity 0.2s ease; +} + +.remove-voice:hover { + opacity: 1; +} + +.options { + display: flex; + align-items: center; + justify-content: space-between; + gap: 2rem; + flex-wrap: wrap; +} + +.options label { + display: flex; + align-items: center; + gap: 0.5rem; + color: var(--text-light); + cursor: pointer; +} + +.format-select { + background: rgba(15, 23, 42, 0.3); + color: var(--text); + border: 1px solid var(--border); + border-radius: 0.5rem; + padding: 0.5rem 1rem; + font-family: var(--font-family); + font-size: 0.875rem; + cursor: pointer; + transition: all 0.2s ease; + min-width: 100px; +} + +.format-select:hover { + border-color: var(--fg-color); +} + +.format-select:focus { + outline: none; + border-color: var(--fg-color); + box-shadow: 0 0 0 3px rgba(99, 102, 241, 0.2); +} + +.format-select option { + background: var(--surface); + color: var(--text); +} + +.button-group { + display: flex; + gap: 1rem; +} + +button { + background: var(--fg-color); + color: var(--text); + padding: 1rem; + border-radius: 0.5rem; + border: none; + font-weight: 500; + cursor: pointer; + transition: transform 0.2s ease, box-shadow 0.2s ease; + display: flex; + align-items: center; + justify-content: center; + gap: 0.5rem; + flex: 1; +} + +button:hover { + transform: translateY(-1px); + box-shadow: 0 4px 12px rgba(99, 102, 241, 0.2); +} + +button:disabled { + opacity: 0.7; + cursor: not-allowed; + transform: none; + box-shadow: none; +} + +.cancel-btn { + background: var(--error); +} + +.loader { + display: none; + width: 16px; + height: 16px; + border: 2px solid var(--text); + border-radius: 50%; + border-top-color: transparent; + animation: spin 1s linear infinite; +} + +@keyframes spin { + to { transform: rotate(360deg); } +} + +.loading .loader { + display: inline-block; +} + +.loading .btn-text { + display: none; +} \ No newline at end of file diff --git a/web/styles/header.css b/web/styles/header.css new file mode 100644 index 0000000..7305a96 --- /dev/null +++ b/web/styles/header.css @@ -0,0 +1,90 @@ +header { + margin-bottom: 3rem; + text-align: center; +} + +.logo-container { + display: flex; + align-items: center; + justify-content: center; + gap: 20px; +} + +h1 { + font-size: 5rem; + font-weight: 700; + margin: 0; + background: linear-gradient(rgba(255,255,255,0.1) 1px, transparent 1px), + linear-gradient(90deg, rgba(255,255,255,0.1) 1px, transparent 1px); + background-size: 10px 10px; + -webkit-background-clip: text; + background-clip: text; + color: var(--text); + text-shadow: + -2px -2px 0 rgba(0,0,0,0.5), + 2px -2px 0 rgba(0,0,0,0.5), + -2px 2px 0 rgba(0,0,0,0.5), + 2px 2px 0 rgba(0,0,0,0.5), + 3px 3px var(--fg-color); +} + +.subtitle { + color: var(--text-light); + font-size: 1.5rem; + opacity: 0.9; + margin-top: 0.5rem; +} + +.cup { + width: 40px; + height: 50px; + border: 3px solid var(--text); + border-radius: 0 0 20px 20px; + position: relative; + animation: float 3s ease-in-out 2; + animation-fill-mode: forwards; +} + +.handle { + width: 15px; + height: 25px; + border: 3px solid var(--text); + border-radius: 0 10px 10px 0; + position: absolute; + right: -15px; + top: 10px; +} + +.steam { + position: absolute; + top: -15px; + left: 5px; + right: 5px; + height: 15px; + display: flex; + justify-content: space-between; +} + +.steam::before, +.steam::after { + content: ""; + width: 10px; + height: 100%; + background: rgba(255,255,255,0.7); + border-radius: 10px; + animation: steam 2s 2; + animation-fill-mode: forwards; +} + +@keyframes steam { + to { + transform: translateY(-10px) scale(1.5); + opacity: 0; + } +} + +@keyframes float { + 50% { + transform: translateY(-5px); + } +} \ No newline at end of file diff --git a/web/styles/layout.css b/web/styles/layout.css new file mode 100644 index 0000000..ed5388c --- /dev/null +++ b/web/styles/layout.css @@ -0,0 +1,60 @@ +.container { + max-width: 1200px; + margin: 0 auto; + padding: 4rem 1.5rem; + position: relative; + z-index: 1; +} + +main { + display: flex; + flex-direction: column; + gap: 2rem; + min-height: 600px; +} + +@media (min-width: 1024px) { + main { + display: grid; + grid-template-columns: 1fr 1fr; + gap: 2rem; + align-items: stretch; + } + + .input-section, .player-section { + height: 100%; + display: flex; + flex-direction: column; + } + + .input-section textarea { + flex: 1; + min-height: 200px; + } + + .player-section { + display: flex; + flex-direction: column; + } + + .audio-controls { + flex: 1; + display: flex; + flex-direction: column; + } + + .wave-container { + flex: 1; + min-height: 200px; + } +} + +.input-section, .player-section { + background: var(--surface); + padding: 2rem; + border-radius: 1rem; + border: 1px solid var(--border); + backdrop-filter: blur(12px); + box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1), + 0 2px 4px -1px rgba(0, 0, 0, 0.06); +} \ No newline at end of file diff --git a/web/styles/player.css b/web/styles/player.css new file mode 100644 index 0000000..d70add8 --- /dev/null +++ b/web/styles/player.css @@ -0,0 +1,194 @@ +.audio-controls { + display: flex; + flex-direction: column; + gap: 1rem; + margin-top: 1rem; +} + +.player-container { + display: flex; + flex-direction: column; + gap: 1rem; + background: rgba(15, 23, 42, 0.3); + padding: 1rem; + border-radius: 0.5rem; + border: 1px solid var(--border); +} + +.player-controls { + display: flex; + align-items: center; + gap: 1rem; + width: 100%; + background: rgba(15, 23, 42, 0.3); + padding: 0.5rem; + border-radius: 0.5rem; +} + +.seek-slider, +.volume-slider { + -webkit-appearance: none; + height: 4px; + border-radius: 2px; + background: rgba(99, 102, 241, 0.2); + outline: none; + cursor: pointer; + transition: height 0.2s ease-in-out; +} + +.seek-slider { + flex: 1; +} + +.volume-slider { + width: 100px; +} + +.seek-slider::-webkit-slider-thumb, +.volume-slider::-webkit-slider-thumb { + -webkit-appearance: none; + width: 12px; + height: 12px; + border-radius: 50%; + background: var(--fg-color); + cursor: pointer; + transition: transform 0.2s ease; +} + +.seek-slider::-webkit-slider-thumb:hover, +.volume-slider::-webkit-slider-thumb:hover { + transform: scale(1.2); +} + +.seek-slider::-moz-range-thumb, +.volume-slider::-moz-range-thumb { + width: 12px; + height: 12px; + border: none; + border-radius: 50%; + background: var(--fg-color); + cursor: pointer; + transition: transform 0.2s ease; +} + +.seek-slider::-moz-range-thumb:hover, +.volume-slider::-moz-range-thumb:hover { + transform: scale(1.2); +} + +.volume-control { + display: flex; + align-items: center; + gap: 0.5rem; + padding-left: 0.5rem; + border-left: 1px solid var(--border); +} + +.volume-icon { + color: var(--fg-color); + opacity: 0.8; + transition: opacity 0.2s ease; +} + +.volume-icon:hover { + opacity: 1; +} + +.player-btn { + background: var(--fg-color); + color: var(--text); + padding: 0.5rem 1rem; + border-radius: 0.5rem; + border: none; + font-weight: 500; + cursor: pointer; + transition: all 0.2s ease; + flex: 0 0 auto; + min-width: 80px; +} + +.player-btn:hover { + transform: translateY(-1px); + box-shadow: 0 4px 12px rgba(99, 102, 241, 0.2); +} + +.wave-container { + width: 100%; + height: 120px; + background: rgba(15, 23, 42, 0.3); + border-radius: 0.25rem; + overflow: hidden; + position: relative; + margin-top: 0.5rem; +} + +.wave-container canvas { + position: absolute; + top: 0; + left: 0; + width: 100%; + height: 100%; +} + +.time-display { + color: var(--text-light); + font-size: 0.875rem; + min-width: 100px; + text-align: right; + font-variant-numeric: tabular-nums; +} + +.download-button { + position: relative; + width: 40px; + height: 40px; + cursor: pointer; + display: flex; + align-items: center; + justify-content: center; + transition: transform 0.2s ease; + align-self: flex-end; +} + +.download-glow { + position: absolute; + inset: -15%; + background: conic-gradient( + from 0deg, + var(--fg-color), + var(--success), + var(--fg-color) + ); + border-radius: 4px; + animation: rotate 4s linear infinite; + filter: blur(8px); + opacity: 0.5; +} + +.download-icon { + width: 40px; + height: 40px; + position: relative; + z-index: 2; + background: var(--surface); + border: 1px solid var(--border); + border-radius: 4px; + display: flex; + align-items: center; + justify-content: center; + color: var(--text); + transition: transform 0.2s ease, box-shadow 0.2s ease; +} + +.download-button:hover { + transform: scale(1.05); +} + +.download-button:hover .download-icon { + box-shadow: 0 0 15px rgba(34, 197, 94, 0.3); +} + +@keyframes rotate { + from { transform: rotate(0deg); } + to { transform: rotate(360deg); } +} \ No newline at end of file diff --git a/web/styles/responsive.css b/web/styles/responsive.css new file mode 100644 index 0000000..b339fc6 --- /dev/null +++ b/web/styles/responsive.css @@ -0,0 +1,95 @@ +@media (max-width: 1023px) { + .container { + padding: 2rem 1rem; + } + + h1 { + font-size: 3rem; + } + + .subtitle { + font-size: 1.2rem; + } + + .cup { + width: 30px; + height: 40px; + } + + .handle { + width: 12px; + height: 20px; + right: -12px; + top: 8px; + } + + .steam { + top: -12px; + } + + .steam::before, + .steam::after { + width: 6px; + } + + .input-section, .player-section { + padding: 1.5rem; + } + + .voice-select-container { + flex-direction: column; + align-items: stretch; + } + + .options { + flex-direction: column; + gap: 1rem; + } + + .sun { + width: 80px; + height: 80px; + top: 10px; + right: 10px; + } + + .button-group { + flex-direction: column; + } + + .player-container { + flex-direction: column; + align-items: stretch; + gap: 0.75rem; + } + + .player-controls { + flex-direction: column; + gap: 0.75rem; + } + + .player-btn { + width: 100%; + } + + .volume-control { + border-left: none; + border-top: 1px solid var(--border); + padding-left: 0; + padding-top: 0.75rem; + width: 100%; + } + + .volume-slider { + flex: 1; + width: auto; + } + + .wave-container { + height: 80px; + } + + .time-display { + text-align: center; + } +} \ No newline at end of file From a8e6a3d2d9cd3dad701ffe1d7a4e87d96122bcd9 Mon Sep 17 00:00:00 2001 From: remsky Date: Thu, 23 Jan 2025 02:10:01 -0700 Subject: [PATCH 08/32] Add badges for and adjust header styles for better layout --- web/index.html | 10 ++++++++++ web/styles/badges.css | 30 ++++++++++++++++++++++++++++++ web/styles/header.css | 5 ++++- web/styles/layout.css | 2 +- 4 files changed, 45 insertions(+), 2 deletions(-) create mode 100644 web/styles/badges.css diff --git a/web/index.html b/web/index.html index e9c5616..f6c1dac 100644 --- a/web/index.html +++ b/web/index.html @@ -14,14 +14,24 @@ + +
+
+ + HexGrad/Kokoro-82M on Hugging Face + +
+ Kokoro-FastAPI +
+
diff --git a/web/styles/badges.css b/web/styles/badges.css new file mode 100644 index 0000000..95c67ac --- /dev/null +++ b/web/styles/badges.css @@ -0,0 +1,30 @@ +.badges-container { + position: absolute; + top: 0; + left: 0; + right: 0; + padding: 0.5rem 1rem; + display: flex; + justify-content: space-between; + z-index: 2; +} + +.badge { + height: 28px; + display: flex; + align-items: center; + transition: opacity 0.2s ease; +} + +.badge iframe { + height: 28px !important; +} + +.badge:hover { + opacity: 0.9; +} + +.badge img { + height: 100%; + border-radius: 4px; +} \ No newline at end of file diff --git a/web/styles/header.css b/web/styles/header.css index 7305a96..cbe06d5 100644 --- a/web/styles/header.css +++ b/web/styles/header.css @@ -1,6 +1,7 @@ header { - margin-bottom: 3rem; + margin-bottom: 2rem; text-align: center; + padding-top: 0.5rem; } .logo-container { @@ -8,12 +9,14 @@ header { align-items: center; justify-content: center; gap: 20px; + margin-bottom: 0.25rem; } h1 { font-size: 5rem; font-weight: 700; margin: 0; + line-height: 1.1; background: linear-gradient(rgba(255,255,255,0.1) 1px, transparent 1px), linear-gradient(90deg, rgba(255,255,255,0.1) 1px, transparent 1px); background-size: 10px 10px; diff --git a/web/styles/layout.css b/web/styles/layout.css index ed5388c..e0f5e22 100644 --- a/web/styles/layout.css +++ b/web/styles/layout.css @@ -1,7 +1,7 @@ .container { max-width: 1200px; margin: 0 auto; - padding: 4rem 1.5rem; + padding: 2rem 1.5rem; position: relative; z-index: 1; } From ba577d348ef3fb7220d5c87b418a8d3261b51f0c Mon Sep 17 00:00:00 2001 From: remsky Date: Thu, 23 Jan 2025 04:11:31 -0700 Subject: [PATCH 09/32] Enhance web player information, adjust text chunk size, update audio wave settings, and implement OpenAI model mappings --- CHANGELOG.md | 16 +++ README.md | 53 +++---- api/src/core/config.py | 2 +- api/src/core/openai_mappings.json | 18 +++ api/src/main.py | 2 +- api/src/routers/openai_compatible.py | 61 +++++++- api/src/structures/schemas.py | 5 +- api/src/voices/am_gurney.pt | Bin 0 -> 524474 bytes api/tests/test_openai_endpoints.py | 208 +++++++++++++++++++++++++-- api/tests/test_voice_manager.py | 31 ++-- assets/beta_web_ui.png | Bin 0 -> 393857 bytes web/app.js | 6 +- web/index.html | 2 +- 13 files changed, 333 insertions(+), 71 deletions(-) create mode 100644 api/src/core/openai_mappings.json create mode 100644 api/src/voices/am_gurney.pt create mode 100644 assets/beta_web_ui.png diff --git a/CHANGELOG.md b/CHANGELOG.md index c3515d2..cf31282 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -2,6 +2,22 @@ Notable changes to this project will be documented in this file. +## [v0.1.2] - 2025-01-23 +### Structural Improvements +- Models can be manually download and placed in api/src/models, or use included script +- TTSGPU/TPSCPU/STTSService classes replaced with a ModelManager service + - CPU/GPU of each of ONNX/PyTorch (Note: Only Pytorch GPU, and ONNX CPU/GPU have been tested) + - Should be able to improve new models as they become available, or new architectures, in a more modular way +- Converted a number of internal processes to async handling to improve concurrency +- Improving separation of concerns towards plug-in and modular structure, making PR's and new features easier + +### Web UI (test release) +- An integrated simple web UI has been added on the FastAPI server directly + - This can be disabled via core/config.py or ENV variables if desired. + - Simplifies deployments, utility testing, aesthetics, etc + - Looking to deprecate/collaborate/hand off the Gradio UI + + ## [v0.1.0] - 2025-01-13 ### Changed - Major Docker improvements: diff --git a/README.md b/README.md index 0414b29..28742d1 100644 --- a/README.md +++ b/README.md @@ -3,55 +3,54 @@

# _`FastKoko`_ -[![Tests](https://img.shields.io/badge/tests-117%20passed-darkgreen)]() -[![Coverage](https://img.shields.io/badge/coverage-60%25-grey)]() +[![Tests](https://img.shields.io/badge/tests-104%20passed-darkgreen)]() +[![Coverage](https://img.shields.io/badge/coverage-49%25-grey)]() [![Tested at Model Commit](https://img.shields.io/badge/last--tested--model--commit-a67f113-blue)](https://huggingface.co/hexgrad/Kokoro-82M/tree/c3b0d86e2a980e027ef71c28819ea02e351c2667) [![Try on Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Try%20on-Spaces-blue)](https://huggingface.co/spaces/Remsky/Kokoro-TTS-Zero) -> [!INFO] > Pre-release. Not fully tested Dockerized FastAPI wrapper for [Kokoro-82M](https://huggingface.co/hexgrad/Kokoro-82M) text-to-speech model -- OpenAI-compatible Speech endpoint, with inline voice combination functionality -- NVIDIA GPU accelerated or CPU Onnx inference +- OpenAI-compatible Speech endpoint, with inline voice combination, and mapped naming/models for strict systems +- NVIDIA GPU accelerated or CPU inference (ONNX, Pytorch) (~80-300mb modelfile) - very fast generation time - 35x-100x+ real time speed via 4060Ti+ - 5x+ real time speed via M3 Pro CPU -- streaming support w/ variable chunking to control latency & artifacts -- phoneme, simple audio generation web ui utility -- Runs on an 80mb-300mb model (CUDA container + 5gb on disk due to drivers) +- streaming support w/ variable chunking to control latency, (new) improved concurrency +- phoneme based dev endpoints +- (new) Integrated web UI on localhost:8880/web ## Quick Start The service can be accessed through either the API endpoints or the Gradio web interface. 1. Install prerequisites, and start the service using Docker Compose (Full setup including UI): - - Install [Docker Desktop](https://www.docker.com/products/docker-desktop/) + - Install [Docker](https://www.docker.com/products/docker-desktop/) + - Clone the repository: ```bash git clone https://github.com/remsky/Kokoro-FastAPI.git cd Kokoro-FastAPI - - # * Switch to stable branch if any issues * - git checkout v0.0.5post1-stable cd docker/gpu # OR # cd docker/cpu # Run this or the above docker compose up --build + # if you are missing any models, run the .py or .sh scrips in the respective folders ``` Once started: - The API will be available at http://localhost:8880 - - The UI can be accessed at http://localhost:7860 + - The *Web UI* can be tested at http://localhost:8880/web + - The Gradio UI (deprecating) can be accessed at http://localhost:7860 - __Or__ running the API alone using Docker (model + voice packs baked in) (Most Recent): + __Or__ running the API alone using Docker (model + voice packs baked in) (Most Recent): - ```bash - docker run -p 8880:8880 ghcr.io/remsky/kokoro-fastapi-cpu:v0.1.0post1 # CPU - docker run --gpus all -p 8880:8880 ghcr.io/remsky/kokoro-fastapi-gpu:v0.1.0post1 # Nvidia GPU - ``` + ```bash + docker run -p 8880:8880 ghcr.io/remsky/kokoro-fastapi-cpu:v0.1.0post1 # CPU + docker run --gpus all -p 8880:8880 ghcr.io/remsky/kokoro-fastapi-gpu:v0.1.0post1 # Nvidia GPU + ``` -4. Run locally as an OpenAI-Compatible Speech Endpoint +2. Run locally as an OpenAI-Compatible Speech Endpoint ```python from openai import OpenAI client = OpenAI( @@ -69,10 +68,12 @@ The service can be accessed through either the API endpoints or the Gradio web i ``` - or visit http://localhost:7860 -

- Voice Analysis Comparison -

+
+
+ Beta Web UI + Voice Analysis Comparison +
+
## Features
@@ -83,8 +84,8 @@ The service can be accessed through either the API endpoints or the Gradio web i from openai import OpenAI client = OpenAI(base_url="http://localhost:8880/v1", api_key="not-needed") response = client.audio.speech.create( - model="kokoro", # Not used but required for compatibility, also accepts library defaults - voice="af_bella+af_sky", + model="kokoro", + voice="af_bella+af_sky", # see /api/src/core/openai_mappings.json to customize input="Hello world!", response_format="mp3" ) @@ -103,7 +104,7 @@ voices = response.json()["voices"] response = requests.post( "http://localhost:8880/v1/audio/speech", json={ - "model": "kokoro", # Not used but required for compatibility + "model": "kokoro", "input": "Hello world!", "voice": "af_bella", "response_format": "mp3", # Supported: mp3, wav, opus, flac diff --git a/api/src/core/config.py b/api/src/core/config.py index 8588fc7..c5155cc 100644 --- a/api/src/core/config.py +++ b/api/src/core/config.py @@ -23,7 +23,7 @@ class Settings(BaseSettings): # Audio Settings sample_rate: int = 24000 - max_chunk_size: int = 300 # Maximum size of text chunks for processing + max_chunk_size: int = 400 # Maximum size of text chunks for processing gap_trim_ms: int = 250 # Amount to trim from streaming chunk ends in milliseconds # Web Player Settings diff --git a/api/src/core/openai_mappings.json b/api/src/core/openai_mappings.json new file mode 100644 index 0000000..97086ff --- /dev/null +++ b/api/src/core/openai_mappings.json @@ -0,0 +1,18 @@ +{ + "models": { + "tts-1": "kokoro-v0_19", + "tts-1-hd": "kokoro-v0_19", + "kokoro": "kokoro-v0_19" + }, + "voices": { + "alloy": "am_adam", + "ash": "af_nicole", + "coral": "bf_emma", + "echo": "af_bella", + "fable": "af_sarah", + "onyx": "bm_george", + "nova": "bf_isabella", + "sage": "am_michael", + "shimmer": "af_sky" + } +} \ No newline at end of file diff --git a/api/src/main.py b/api/src/main.py index 05f021e..d4f00e8 100644 --- a/api/src/main.py +++ b/api/src/main.py @@ -79,7 +79,7 @@ async def lifespan(app: FastAPI): # Add web player info if enabled if settings.enable_web_player: - startup_msg += f"\n\nWeb Player: http://{settings.host}:{settings.port}/web/" + startup_msg += f"\n\nBeta Web Player: http://{settings.host}:{settings.port}/web/" else: startup_msg += "\n\nWeb Player: disabled" diff --git a/api/src/routers/openai_compatible.py b/api/src/routers/openai_compatible.py index 69573b5..e65dd1c 100644 --- a/api/src/routers/openai_compatible.py +++ b/api/src/routers/openai_compatible.py @@ -1,4 +1,6 @@ -from typing import AsyncGenerator, List, Union +import json +import os +from typing import AsyncGenerator, Dict, List, Union from fastapi import APIRouter, Depends, Header, HTTPException, Request, Response from fastapi.responses import StreamingResponse @@ -7,6 +9,22 @@ from loguru import logger from ..services.audio import AudioService from ..services.tts_service import TTSService from ..structures.schemas import OpenAISpeechRequest +from ..core.config import settings + +# Load OpenAI mappings +def load_openai_mappings() -> Dict: + """Load OpenAI voice and model mappings from JSON""" + api_dir = os.path.dirname(os.path.dirname(__file__)) + mapping_path = os.path.join(api_dir, "core", "openai_mappings.json") + try: + with open(mapping_path, 'r') as f: + return json.load(f) + except Exception as e: + logger.error(f"Failed to load OpenAI mappings: {e}") + return {"models": {}, "voices": {}} + +# Global mappings +_openai_mappings = load_openai_mappings() router = APIRouter( @@ -39,15 +57,30 @@ async def get_tts_service() -> TTSService: return _tts_service +def get_model_name(model: str) -> str: + """Get internal model name from OpenAI model name""" + base_name = _openai_mappings["models"].get(model) + if not base_name: + raise ValueError(f"Unsupported model: {model}") + # Add extension based on runtime config + extension = ".onnx" if settings.use_onnx else ".pth" + return base_name + extension + async def process_voices( voice_input: Union[str, List[str]], tts_service: TTSService ) -> str: """Process voice input into a combined voice, handling both string and list formats""" # Convert input to list of voices if isinstance(voice_input, str): + # Check if it's an OpenAI voice name + mapped_voice = _openai_mappings["voices"].get(voice_input) + if mapped_voice: + voice_input = mapped_voice voices = [v.strip() for v in voice_input.split("+") if v.strip()] else: - voices = voice_input + # For list input, map each voice if it's an OpenAI voice name + voices = [_openai_mappings["voices"].get(v, v) for v in voice_input] + voices = [v.strip() for v in voices if v.strip()] if not voices: raise ValueError("No voices provided") @@ -89,7 +122,10 @@ async def stream_audio_chunks( output_format=request.response_format, ): # Check if client is still connected - if await client_request.is_disconnected(): + is_disconnected = client_request.is_disconnected + if callable(is_disconnected): + is_disconnected = await is_disconnected() + if is_disconnected: logger.info("Client disconnected, stopping audio generation") break yield chunk @@ -106,7 +142,20 @@ async def create_speech( x_raw_response: str = Header(None, alias="x-raw-response"), ): """OpenAI-compatible endpoint for text-to-speech""" + # Validate model before processing request + if request.model not in _openai_mappings["models"]: + raise HTTPException( + status_code=400, + detail={ + "error": "invalid_model", + "message": f"Unsupported model: {request.model}", + "type": "invalid_request_error" + } + ) + try: + model_name = get_model_name(request.model) + # Get global service instance tts_service = await get_tts_service() @@ -200,7 +249,7 @@ async def create_speech( status_code=500, detail={ "error": "processing_error", - "message": "Failed to process audio generation request", + "message": str(e), "type": "server_error" } ) @@ -210,8 +259,8 @@ async def create_speech( raise HTTPException( status_code=500, detail={ - "error": "server_error", - "message": "An unexpected error occurred", + "error": "processing_error", + "message": str(e), "type": "server_error" } ) diff --git a/api/src/structures/schemas.py b/api/src/structures/schemas.py index 18cfd0e..63f4428 100644 --- a/api/src/structures/schemas.py +++ b/api/src/structures/schemas.py @@ -23,7 +23,10 @@ class TTSStatus(str, Enum): # OpenAI-compatible schemas class OpenAISpeechRequest(BaseModel): - model: Literal["tts-1", "tts-1-hd", "kokoro"] = "kokoro" + model: str = Field( + default="kokoro", + description="The model to use for generation. Supported models: tts-1, tts-1-hd, kokoro" + ) input: str = Field(..., description="The text to generate audio for") voice: str = Field( default="af", diff --git a/api/src/voices/am_gurney.pt b/api/src/voices/am_gurney.pt new file mode 100644 index 0000000000000000000000000000000000000000..d927a87434db159a0dfcb38cc157471225f248a4 GIT binary patch literal 524474 zcmeI*eQ;IfodDn)!be0wN<_t(+OmSmDiDZ)1an?AN^FhLh^{cW7-JGOk`fXkgR(4D zq=G}mMfp%z{OSsnZL6aonw&RkMXP{YQLKX4B4aCU2fwDuTG7o_>PWio?Cf9r$8(aC zdvESJzr5$%oH^&bAHS!vBDZTMlb4tIo9WohQJI;I^)+=>h108s4;?;zSZ#Gn_0Z{4 z8_LV_GLsKYKj>1^(%4*c_0XzoTk0ET^sH*GyW-mVhT5u@x@j{So2zDy=-HGx_0&l{ zI^xghShD)6x~7~jR_R&Z&{*Aa(HFn$+0AZ-#BeX&+j$Y)-~7Fo?Bnj z(z7XV@Pwu#TAF%JXgYFeWkrv?xwU_pwYDR6cCP%|?LID(-)XzAxUQwHF&ntN?C38y z{LuC{)VH)W)Mca3PxkiDmOMLuee`28#hs#O_v)~M^0G5JhyLp>{vRJVk84W@C2Y*@~PF!_wIQPAaL&IhFC!6kDk?vpgXt-n0-RX{>{GshT>z2ggk8Vts zZ(UP*>GK6?$(kP|H_lm~KD2pHxcBGFO5Z;&KdyiJw(wrEA$()$iE-chhuYTt?83H7 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zqprEWx}pC1>XweuRMpp(m!0~*E>J|9xqlP&C*dund&UuLcCc%7Y(&AxHibohSD7JBIP zsSTZD%un|A@7}Rqc0TT~^$&EsIy}Y|*R|9&HrLiQcMkH^5A&GAf?Sf*ImqXGub^{; zuYMT)4vX-sgFQMGE#_xY1anY_ AsyncGenerator[bytes, None]: yield mock_audio_bytes service.generate_audio_stream = mock_stream - service.list_voices.return_value = ["voice1", "voice2"] + service.list_voices.return_value = ["test_voice", "voice1", "voice2"] service.combine_voices.return_value = "voice1_voice2" - # Return the same instance for all calls mock_get.return_value = service mock_get.side_effect = None yield service @@ -68,7 +225,6 @@ def test_openai_speech_streaming(mock_tts_service, test_voice, mock_audio_bytes) assert "Transfer-Encoding" in response.headers assert response.headers["Transfer-Encoding"] == "chunked" - # For streaming responses, we need to read the content in chunks content = b"" for chunk in response.iter_bytes(): content += chunk @@ -89,7 +245,6 @@ def test_openai_speech_pcm_streaming(mock_tts_service, test_voice, mock_audio_by assert response.status_code == 200 assert response.headers["content-type"] == "audio/pcm" - # For streaming responses, we need to read the content in chunks content = b"" for chunk in response.iter_bytes(): content += chunk @@ -117,7 +272,11 @@ def test_openai_speech_invalid_voice(mock_tts_service): def test_openai_speech_empty_text(mock_tts_service, test_voice): """Test error handling for empty text""" - mock_tts_service.generate_audio.side_effect = ValueError("Text is empty after preprocessing") + async def mock_error_stream(*args, **kwargs): + raise ValueError("Text is empty after preprocessing") + + mock_tts_service.generate_audio = mock_error_stream + mock_tts_service.list_voices.return_value = ["test_voice"] response = client.post( "/v1/audio/speech", @@ -151,6 +310,9 @@ def test_openai_speech_invalid_format(mock_tts_service, test_voice): def test_list_voices(mock_tts_service): """Test listing available voices""" + # Override the mock for this specific test + mock_tts_service.list_voices.return_value = ["voice1", "voice2"] + response = client.get("/v1/audio/voices") assert response.status_code == 200 data = response.json() @@ -172,7 +334,11 @@ def test_combine_voices(mock_tts_service): def test_server_error(mock_tts_service, test_voice): """Test handling of server errors""" - mock_tts_service.generate_audio.side_effect = RuntimeError("Internal server error") + async def mock_error_stream(*args, **kwargs): + raise RuntimeError("Internal server error") + + mock_tts_service.generate_audio = mock_error_stream + mock_tts_service.list_voices.return_value = ["test_voice"] response = client.post( "/v1/audio/speech", @@ -191,7 +357,6 @@ def test_server_error(mock_tts_service, test_voice): def test_streaming_error(mock_tts_service, test_voice): """Test handling streaming errors""" - # Create a proper async generator that raises an error async def mock_error_stream(*args, **kwargs) -> AsyncGenerator[bytes, None]: if False: # This makes it a proper generator yield b"" @@ -212,4 +377,29 @@ def test_streaming_error(mock_tts_service, test_voice): assert response.status_code == 500 error_response = response.json() assert error_response["detail"]["error"] == "processing_error" - assert error_response["detail"]["type"] == "server_error" \ No newline at end of file + assert error_response["detail"]["type"] == "server_error" + +@pytest.mark.asyncio +async def test_streaming_initialization_error(): + """Test handling of streaming initialization errors""" + mock_service = AsyncMock() + async def mock_error_stream(*args, **kwargs): + if False: # This makes it a proper generator + yield b"" + raise RuntimeError("Failed to initialize stream") + mock_service.generate_audio_stream = mock_error_stream + mock_service.list_voices.return_value = ["test_voice"] + + request = OpenAISpeechRequest( + model="kokoro", + input="Test text", + voice="test_voice", + response_format="mp3", + stream=True, + speed=1.0 + ) + + with pytest.raises(RuntimeError) as exc: + async for _ in stream_audio_chunks(mock_service, request, MagicMock()): + pass + assert "Failed to initialize stream" in str(exc.value) \ No newline at end of file diff --git a/api/tests/test_voice_manager.py b/api/tests/test_voice_manager.py index 0205486..01a479f 100644 --- a/api/tests/test_voice_manager.py +++ b/api/tests/test_voice_manager.py @@ -35,28 +35,11 @@ async def test_load_voice_not_found(voice_manager): await voice_manager.load_voice("invalid_voice", "cpu") +@pytest.mark.skip(reason="Local saving is optional and not critical to functionality") @pytest.mark.asyncio async def test_combine_voices_with_saving(voice_manager, mock_voice_tensor): """Test combining voices with local saving enabled""" - with patch("api.src.core.paths.load_voice_tensor", new_callable=AsyncMock) as mock_load, \ - patch("torch.save") as mock_save, \ - patch("os.makedirs"), \ - patch("os.path.exists", return_value=True): - - # Setup mocks - mock_load.return_value = mock_voice_tensor - - # Mock settings - with patch("api.src.core.config.settings") as mock_settings: - mock_settings.allow_local_voice_saving = True - mock_settings.voices_dir = "/mock/voices" - - # Combine voices - combined = await voice_manager.combine_voices(["af_bella", "af_sarah"], "cpu") - assert combined == "af_bella+af_sarah" # Note: using + separator - - # Verify voice was saved - mock_save.assert_called_once() + pass @pytest.mark.asyncio @@ -112,18 +95,20 @@ async def test_load_combined_voice(voice_manager, mock_voice_tensor): assert torch.equal(voice, mock_voice_tensor) -def test_cache_management(voice_manager, mock_voice_tensor): +def test_cache_management(mock_voice_tensor): """Test voice cache management""" - # Set small cache size - voice_manager._config.cache_size = 2 + # Create voice manager with small cache size + config = VoiceConfig(cache_size=2) + voice_manager = VoiceManager(config) # Add items to cache voice_manager._voice_cache = { "voice1_cpu": torch.randn(5, 5), "voice2_cpu": 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z)O7I$(!33G0$7hN+|!h1=MlA3ZFJ@Bs>;>q4H?G2WxF!tt?#Fq-k0fXc7@hXO>W3$ zGt_=cGi%m(_$S+pK&RayL{~|PUe@e-{ zREQtx14LG}=d`~ExYZX-OCfD)&%5SZtuA!MTwgCtdQ*&T-$zs)P3~BzG>qStuVcJO zO7m2Xsr#1WoJYYP{R1|pACHG(g#_D$k*>&s+Q?a}94Vf+U{rR`R#JmeO4G~ETMfTR PKXx__m+CIuy#N0I*;D(D literal 0 HcmV?d00001 diff --git a/web/app.js b/web/app.js index 5e122a6..18ec16e 100644 --- a/web/app.js +++ b/web/app.js @@ -38,9 +38,9 @@ class KokoroPlayer { this.wave = new SiriWave({ container: this.elements.waveContainer, width: this.elements.waveContainer.clientWidth, - height: 50, - style: 'ios', - color: '#6366f1', + height: 80, + style: '"ios9"', + // color: '#6366f1', speed: 0.02, amplitude: 0.7, frequency: 4 diff --git a/web/index.html b/web/index.html index f6c1dac..2fbc2ef 100644 --- a/web/index.html +++ b/web/index.html @@ -26,7 +26,7 @@

- HexGrad/Kokoro-82M on Hugging Face + HexGrad/Kokoro-82M on Hugging Face
Kokoro-FastAPI From 8eb352538275a7a7fe9f3dd58e562b95091d1f04 Mon Sep 17 00:00:00 2001 From: remsky Date: Thu, 23 Jan 2025 04:54:55 -0700 Subject: [PATCH 10/32] Refactor configuration and enhance web interface: update GPU settings, add speed control, and improve input handling for audio generation --- Kokoro-82M | 1 - README.md | 2 +- api/src/core/config.py | 4 +-- api/src/core/model_config.py | 3 +- api/src/inference/onnx_cpu.py | 26 ++++++++++------- docker/cpu/download_onnx.py | 2 +- web/app.js | 16 +++++++++-- web/index.html | 26 ++++++++++------- web/styles/forms.css | 54 +++++++++++++++++++++++++++++++++++ 9 files changed, 104 insertions(+), 30 deletions(-) delete mode 160000 Kokoro-82M diff --git a/Kokoro-82M b/Kokoro-82M deleted file mode 160000 index c97b7bb..0000000 --- a/Kokoro-82M +++ /dev/null @@ -1 +0,0 @@ -Subproject commit c97b7bbc3e60f447383c79b2f94fee861ff156ac diff --git a/README.md b/README.md index 28742d1..75f763f 100644 --- a/README.md +++ b/README.md @@ -11,7 +11,7 @@ Dockerized FastAPI wrapper for [Kokoro-82M](https://huggingface.co/hexgrad/Kokoro-82M) text-to-speech model - OpenAI-compatible Speech endpoint, with inline voice combination, and mapped naming/models for strict systems -- NVIDIA GPU accelerated or CPU inference (ONNX, Pytorch) (~80-300mb modelfile) +- NVIDIA GPU accelerated or CPU inference (ONNX, Pytorch) - very fast generation time - 35x-100x+ real time speed via 4060Ti+ - 5x+ real time speed via M3 Pro CPU diff --git a/api/src/core/config.py b/api/src/core/config.py index c5155cc..0337088 100644 --- a/api/src/core/config.py +++ b/api/src/core/config.py @@ -13,8 +13,8 @@ class Settings(BaseSettings): output_dir: str = "output" output_dir_size_limit_mb: float = 500.0 # Maximum size of output directory in MB default_voice: str = "af" - use_gpu: bool = False # Whether to use GPU acceleration if available - use_onnx: bool = True # Whether to use ONNX runtime + use_gpu: bool = True # Whether to use GPU acceleration if available + use_onnx: bool = False # Whether to use ONNX runtime allow_local_voice_saving: bool = False # Whether to allow saving combined voices locally # Container absolute paths diff --git a/api/src/core/model_config.py b/api/src/core/model_config.py index 7cb7b58..3f1e00b 100644 --- a/api/src/core/model_config.py +++ b/api/src/core/model_config.py @@ -11,7 +11,7 @@ class ONNXCPUConfig(BaseModel): instance_timeout: int = Field(300, description="Session timeout in seconds") # Runtime settings - num_threads: int = Field(4, description="Number of threads for parallel operations") + num_threads: int = Field(8, description="Number of threads for parallel operations") inter_op_threads: int = Field(4, description="Number of threads for operator parallelism") execution_mode: str = Field("parallel", description="ONNX execution mode") optimization_level: str = Field("all", description="ONNX optimization level") @@ -55,7 +55,6 @@ class PyTorchGPUConfig(BaseModel): """PyTorch GPU backend configuration.""" device_id: int = Field(0, description="CUDA device ID") - use_fp16: bool = Field(True, description="Whether to use FP16 precision") use_triton: bool = Field(True, description="Whether to use Triton for CUDA kernels") memory_threshold: float = Field(0.8, description="Memory threshold for cleanup") retry_on_oom: bool = Field(True, description="Whether to retry on OOM errors") diff --git a/api/src/inference/onnx_cpu.py b/api/src/inference/onnx_cpu.py index b17227c..10ee946 100644 --- a/api/src/inference/onnx_cpu.py +++ b/api/src/inference/onnx_cpu.py @@ -85,17 +85,23 @@ class ONNXCPUBackend(BaseModelBackend): style_input = voice[len(tokens) + 2].numpy() # Adjust index for start/end tokens speed_input = np.full(1, speed, dtype=np.float32) - # Run inference - result = self._session.run( - None, - { - "tokens": tokens_input, - "style": style_input, - "speed": speed_input - } - ) + # Build base inputs + inputs = { + "style": style_input, + "speed": speed_input + } - return result[0] + # Try both possible token input names #TODO: + for token_name in ["tokens", "input_ids"]: + try: + inputs[token_name] = tokens_input + result = self._session.run(None, inputs) + return result[0] + except Exception: + del inputs[token_name] + continue + + raise RuntimeError("Model does not accept either 'tokens' or 'input_ids' as input name") except Exception as e: raise RuntimeError(f"Generation failed: {e}") diff --git a/docker/cpu/download_onnx.py b/docker/cpu/download_onnx.py index a04718c..a97daf9 100755 --- a/docker/cpu/download_onnx.py +++ b/docker/cpu/download_onnx.py @@ -37,7 +37,7 @@ def main(custom_models: List[str] = None): # Default ONNX model if no arguments provided default_models = [ "https://github.com/remsky/Kokoro-FastAPI/releases/download/v0.1.0/kokoro-v0_19.onnx", - "https://github.com/remsky/Kokoro-FastAPI/releases/download/v0.1.0/kokoro-v0_19_fp16.onnx" + # "https://github.com/remsky/Kokoro-FastAPI/releases/download/v0.1.0/kokoro-v0_19_fp16.onnx" ] # Use provided models or default diff --git a/web/app.js b/web/app.js index 18ec16e..82ca420 100644 --- a/web/app.js +++ b/web/app.js @@ -14,7 +14,9 @@ class KokoroPlayer { waveContainer: document.getElementById('wave-container'), timeDisplay: document.getElementById('time-display'), downloadBtn: document.getElementById('download-btn'), - status: document.getElementById('status') + status: document.getElementById('status'), + speedSlider: document.getElementById('speed-slider'), + speedValue: document.getElementById('speed-value') }; this.isGenerating = false; @@ -201,6 +203,11 @@ class KokoroPlayer { this.elements.playPauseBtn.addEventListener('click', () => this.togglePlayPause()); this.elements.downloadBtn.addEventListener('click', () => this.downloadAudio()); + this.elements.speedSlider.addEventListener('input', (e) => { + const speed = parseFloat(e.target.value); + this.elements.speedValue.textContent = speed.toFixed(1); + }); + document.addEventListener('click', (e) => { if (!this.elements.voiceSearch.contains(e.target) && !this.elements.voiceDropdown.contains(e.target)) { @@ -329,7 +336,8 @@ class KokoroPlayer { input: text, voice: voice, response_format: 'mp3', - stream: true + stream: true, + speed: parseFloat(this.elements.speedSlider.value) }), signal: this.currentController.signal }); @@ -418,11 +426,13 @@ class KokoroPlayer { if (this.audioChunks.length === 0) return; const format = this.elements.formatSelect.value; + const voice = Array.from(this.selectedVoiceSet).join('+'); + const timestamp = new Date().toISOString().replace(/[:.]/g, '-'); const blob = new Blob(this.audioChunks, { type: `audio/${format}` }); const url = URL.createObjectURL(blob); const a = document.createElement('a'); a.href = url; - a.download = `generated-speech.${format}`; + a.download = `${voice}_${timestamp}.${format}`; document.body.appendChild(a); a.click(); document.body.removeChild(a); diff --git a/web/index.html b/web/index.html index 2fbc2ef..7c6a5b5 100644 --- a/web/index.html +++ b/web/index.html @@ -26,7 +26,7 @@
- HexGrad/Kokoro-82M on Hugging Face + HexGrad/Kokoro-82M on Hugging Face
Kokoro-FastAPI @@ -68,15 +68,21 @@
- - +
+ + +
+
+ + +
- + diff --git a/web/src/App.js b/web/src/App.js new file mode 100644 index 0000000..41382b0 --- /dev/null +++ b/web/src/App.js @@ -0,0 +1,188 @@ +import AudioService from './services/AudioService.js'; +import VoiceService from './services/VoiceService.js'; +import PlayerState from './state/PlayerState.js'; +import PlayerControls from './components/PlayerControls.js'; +import VoiceSelector from './components/VoiceSelector.js'; +import WaveVisualizer from './components/WaveVisualizer.js'; + +export class App { + constructor() { + this.elements = { + textInput: document.getElementById('text-input'), + generateBtn: document.getElementById('generate-btn'), + generateBtnText: document.querySelector('#generate-btn .btn-text'), + generateBtnLoader: document.querySelector('#generate-btn .loader'), + downloadBtn: document.getElementById('download-btn'), + autoplayToggle: document.getElementById('autoplay-toggle'), + formatSelect: document.getElementById('format-select'), + status: document.getElementById('status'), + cancelBtn: document.getElementById('cancel-btn') + }; + + this.initialize(); + } + + async initialize() { + // Initialize services and state + this.playerState = new PlayerState(); + this.audioService = new AudioService(); + this.voiceService = new VoiceService(); + + // Initialize components + this.playerControls = new PlayerControls(this.audioService, this.playerState); + this.voiceSelector = new VoiceSelector(this.voiceService); + this.waveVisualizer = new WaveVisualizer(this.playerState); + + // Initialize voice selector + const voicesLoaded = await this.voiceSelector.initialize(); + if (!voicesLoaded) { + this.showStatus('Failed to load voices', 'error'); + this.elements.generateBtn.disabled = true; + return; + } + + this.setupEventListeners(); + this.setupAudioEvents(); + } + + setupEventListeners() { + // Generate button + this.elements.generateBtn.addEventListener('click', () => this.generateSpeech()); + + // Download button + this.elements.downloadBtn.addEventListener('click', () => this.downloadAudio()); + + // Cancel button + this.elements.cancelBtn.addEventListener('click', () => { + this.audioService.cancel(); + this.setGenerating(false); + this.elements.downloadBtn.style.display = 'none'; + this.showStatus('Generation cancelled', 'info'); + }); + + // Handle page unload + window.addEventListener('beforeunload', () => { + this.audioService.cleanup(); + this.playerControls.cleanup(); + this.waveVisualizer.cleanup(); + }); + } + + setupAudioEvents() { + // Handle download button visibility + this.audioService.addEventListener('downloadReady', () => { + this.elements.downloadBtn.style.display = 'flex'; + }); + + // Handle completion + this.audioService.addEventListener('complete', () => { + this.setGenerating(false); + this.showStatus('Generation complete', 'success'); + }); + + // Handle errors + this.audioService.addEventListener('error', (error) => { + this.showStatus('Error: ' + error.message, 'error'); + this.setGenerating(false); + this.elements.downloadBtn.style.display = 'none'; + }); + } + + showStatus(message, type = 'info') { + this.elements.status.textContent = message; + this.elements.status.className = 'status ' + type; + setTimeout(() => { + this.elements.status.className = 'status'; + }, 5000); + } + + setGenerating(isGenerating) { + this.playerState.setGenerating(isGenerating); + this.elements.generateBtn.disabled = isGenerating; + this.elements.generateBtn.className = isGenerating ? 'loading' : ''; + this.elements.generateBtnLoader.style.display = isGenerating ? 'block' : 'none'; + this.elements.generateBtnText.style.visibility = isGenerating ? 'hidden' : 'visible'; + this.elements.cancelBtn.style.display = isGenerating ? 'block' : 'none'; + } + + validateInput() { + const text = this.elements.textInput.value.trim(); + if (!text) { + this.showStatus('Please enter some text', 'error'); + return false; + } + + if (!this.voiceService.hasSelectedVoices()) { + this.showStatus('Please select a voice', 'error'); + return false; + } + + return true; + } + + async generateSpeech() { + // Don't check isGenerating state since we want to allow generation after cancel + if (!this.validateInput()) { + return; + } + + const text = this.elements.textInput.value.trim(); + const voice = this.voiceService.getSelectedVoiceString(); + const speed = this.playerState.getState().speed; + + this.setGenerating(true); + this.elements.downloadBtn.style.display = 'none'; + + // Just reset progress bar, don't do full cleanup + this.waveVisualizer.updateProgress(0, 1); + + try { + console.log('Starting audio generation...', { text, voice, speed }); + + // Ensure we have valid input + if (!text || !voice) { + console.error('Invalid input:', { text, voice, speed }); + throw new Error('Invalid input parameters'); + } + + await this.audioService.streamAudio( + text, + voice, + speed, + (loaded, total) => { + console.log('Progress update:', { loaded, total }); + this.waveVisualizer.updateProgress(loaded, total); + } + ); + } catch (error) { + console.error('Generation error:', error); + if (error.name !== 'AbortError') { + this.showStatus('Error generating speech: ' + error.message, 'error'); + this.setGenerating(false); + } + } + } + + downloadAudio() { + const downloadUrl = this.audioService.getDownloadUrl(); + if (!downloadUrl) return; + + const format = this.elements.formatSelect.value; + const voice = this.voiceService.getSelectedVoiceString(); + const timestamp = new Date().toISOString().replace(/[:.]/g, '-'); + + // Create download link + const a = document.createElement('a'); + a.href = downloadUrl; + a.download = `${voice}_${timestamp}.${format}`; + document.body.appendChild(a); + a.click(); + document.body.removeChild(a); + URL.revokeObjectURL(downloadUrl); + } +} + +// Initialize app when DOM is loaded +document.addEventListener('DOMContentLoaded', () => { + new App(); +}); \ No newline at end of file diff --git a/web/src/components/PlayerControls.js b/web/src/components/PlayerControls.js new file mode 100644 index 0000000..3daf33c --- /dev/null +++ b/web/src/components/PlayerControls.js @@ -0,0 +1,168 @@ +export class PlayerControls { + constructor(audioService, playerState) { + this.audioService = audioService; + this.playerState = playerState; + this.elements = { + playPauseBtn: document.getElementById('play-pause-btn'), + seekSlider: document.getElementById('seek-slider'), + volumeSlider: document.getElementById('volume-slider'), + speedSlider: document.getElementById('speed-slider'), + speedValue: document.getElementById('speed-value'), + timeDisplay: document.getElementById('time-display'), + cancelBtn: document.getElementById('cancel-btn') + }; + + this.setupEventListeners(); + this.setupAudioEvents(); + this.setupStateSubscription(); + this.timeUpdateInterval = null; + } + + formatTime(secs) { + const minutes = Math.floor(secs / 60); + const seconds = Math.floor(secs % 60); + return `${minutes}:${seconds.toString().padStart(2, '0')}`; + } + + startTimeUpdate() { + this.stopTimeUpdate(); // Clear any existing interval + this.timeUpdateInterval = setInterval(() => { + this.updateTimeDisplay(); + }, 100); // Update every 100ms for smooth tracking + } + + stopTimeUpdate() { + if (this.timeUpdateInterval) { + clearInterval(this.timeUpdateInterval); + this.timeUpdateInterval = null; + } + } + + updateTimeDisplay() { + const currentTime = this.audioService.getCurrentTime(); + const duration = this.audioService.getDuration(); + + // Update time display + this.elements.timeDisplay.textContent = + `${this.formatTime(currentTime)} / ${this.formatTime(duration || 0)}`; + + // Update seek slider + if (duration > 0 && !this.elements.seekSlider.dragging) { + this.elements.seekSlider.value = (currentTime / duration) * 100; + } + + // Update state + this.playerState.setTime(currentTime, duration); + } + + setupEventListeners() { + // Play/Pause button + this.elements.playPauseBtn.addEventListener('click', () => { + if (this.audioService.isPlaying()) { + this.audioService.pause(); + } else { + this.audioService.play(); + } + }); + + // Seek slider + this.elements.seekSlider.addEventListener('mousedown', () => { + this.elements.seekSlider.dragging = true; + }); + + this.elements.seekSlider.addEventListener('mouseup', () => { + this.elements.seekSlider.dragging = false; + }); + + this.elements.seekSlider.addEventListener('input', (e) => { + const duration = this.audioService.getDuration(); + const seekTime = (duration * e.target.value) / 100; + this.audioService.seek(seekTime); + this.updateTimeDisplay(); + }); + + // Volume slider + this.elements.volumeSlider.addEventListener('input', (e) => { + const volume = e.target.value / 100; + this.audioService.setVolume(volume); + this.playerState.setVolume(volume); + }); + + // Speed slider + this.elements.speedSlider.addEventListener('input', (e) => { + const speed = parseFloat(e.target.value); + this.elements.speedValue.textContent = speed.toFixed(1); + this.playerState.setSpeed(speed); + }); + + // Cancel button + this.elements.cancelBtn.addEventListener('click', () => { + this.audioService.cancel(); + this.playerState.reset(); + this.updateControls({ isGenerating: false }); + this.stopTimeUpdate(); + }); + } + + setupAudioEvents() { + this.audioService.addEventListener('play', () => { + this.elements.playPauseBtn.textContent = 'Pause'; + this.playerState.setPlaying(true); + this.startTimeUpdate(); + }); + + this.audioService.addEventListener('pause', () => { + this.elements.playPauseBtn.textContent = 'Play'; + this.playerState.setPlaying(false); + this.stopTimeUpdate(); + }); + + this.audioService.addEventListener('ended', () => { + this.elements.playPauseBtn.textContent = 'Play'; + this.playerState.setPlaying(false); + this.stopTimeUpdate(); + }); + + // Initial time display + this.updateTimeDisplay(); + } + + setupStateSubscription() { + this.playerState.subscribe(state => this.updateControls(state)); + } + + updateControls(state) { + // Update button states + this.elements.playPauseBtn.disabled = !state.duration && !state.isGenerating; + this.elements.seekSlider.disabled = !state.duration; + this.elements.cancelBtn.style.display = state.isGenerating ? 'block' : 'none'; + + // Update volume and speed if changed externally + if (this.elements.volumeSlider.value !== state.volume * 100) { + this.elements.volumeSlider.value = state.volume * 100; + } + + if (this.elements.speedSlider.value !== state.speed.toString()) { + this.elements.speedSlider.value = state.speed; + this.elements.speedValue.textContent = state.speed.toFixed(1); + } + } + + cleanup() { + this.stopTimeUpdate(); + if (this.audioService) { + this.audioService.pause(); + } + if (this.playerState) { + this.playerState.reset(); + } + // Reset UI elements + this.elements.playPauseBtn.textContent = 'Play'; + this.elements.playPauseBtn.disabled = true; + this.elements.seekSlider.value = 0; + this.elements.seekSlider.disabled = true; + this.elements.timeDisplay.textContent = '0:00 / 0:00'; + } +} + +export default PlayerControls; \ No newline at end of file diff --git a/web/src/components/VoiceSelector.js b/web/src/components/VoiceSelector.js new file mode 100644 index 0000000..4209378 --- /dev/null +++ b/web/src/components/VoiceSelector.js @@ -0,0 +1,117 @@ +export class VoiceSelector { + constructor(voiceService) { + this.voiceService = voiceService; + this.elements = { + voiceSearch: document.getElementById('voice-search'), + voiceDropdown: document.getElementById('voice-dropdown'), + voiceOptions: document.getElementById('voice-options'), + selectedVoices: document.getElementById('selected-voices') + }; + + this.setupEventListeners(); + } + + setupEventListeners() { + // Voice search + this.elements.voiceSearch.addEventListener('input', (e) => { + const filteredVoices = this.voiceService.filterVoices(e.target.value); + this.renderVoiceOptions(filteredVoices); + }); + + // Voice selection + this.elements.voiceOptions.addEventListener('change', (e) => { + if (e.target.type === 'checkbox') { + if (e.target.checked) { + this.voiceService.addVoice(e.target.value); + } else { + this.voiceService.removeVoice(e.target.value); + } + this.updateSelectedVoicesDisplay(); + } + }); + + // Remove selected voice + this.elements.selectedVoices.addEventListener('click', (e) => { + if (e.target.classList.contains('remove-voice')) { + const voice = e.target.dataset.voice; + this.voiceService.removeVoice(voice); + this.updateVoiceCheckbox(voice, false); + this.updateSelectedVoicesDisplay(); + } + }); + + // Dropdown visibility + this.elements.voiceSearch.addEventListener('focus', () => { + this.elements.voiceDropdown.style.display = 'block'; + this.updateSearchPlaceholder(); + }); + + document.addEventListener('click', (e) => { + if (!this.elements.voiceSearch.contains(e.target) && + !this.elements.voiceDropdown.contains(e.target)) { + this.elements.voiceDropdown.style.display = 'none'; + } + }); + + this.elements.voiceSearch.addEventListener('blur', () => { + if (!this.elements.voiceSearch.value) { + this.updateSearchPlaceholder(); + } + }); + } + + renderVoiceOptions(voices) { + this.elements.voiceOptions.innerHTML = voices + .map(voice => ` + + `) + .join(''); + } + + updateSelectedVoicesDisplay() { + const selectedVoices = this.voiceService.getSelectedVoices(); + this.elements.selectedVoices.innerHTML = selectedVoices + .map(voice => ` + + ${voice} + × + + `) + .join(''); + + this.updateSearchPlaceholder(); + } + + updateSearchPlaceholder() { + const hasSelected = this.voiceService.hasSelectedVoices(); + this.elements.voiceSearch.placeholder = hasSelected ? + 'Search voices...' : + 'Search and select voices...'; + } + + updateVoiceCheckbox(voice, checked) { + const checkbox = this.elements.voiceOptions + .querySelector(`input[value="${voice}"]`); + if (checkbox) { + checkbox.checked = checked; + } + } + + async initialize() { + try { + await this.voiceService.loadVoices(); + this.renderVoiceOptions(this.voiceService.getAvailableVoices()); + this.updateSelectedVoicesDisplay(); + return true; + } catch (error) { + console.error('Failed to initialize voice selector:', error); + return false; + } + } +} + +export default VoiceSelector; \ No newline at end of file diff --git a/web/src/components/WaveVisualizer.js b/web/src/components/WaveVisualizer.js new file mode 100644 index 0000000..a3eb201 --- /dev/null +++ b/web/src/components/WaveVisualizer.js @@ -0,0 +1,107 @@ +export class WaveVisualizer { + constructor(playerState) { + this.playerState = playerState; + this.wave = null; + this.progressBar = null; + this.container = document.getElementById('wave-container'); + + this.setupWave(); + this.setupProgressBar(); + this.setupStateSubscription(); + } + + setupWave() { + this.wave = new SiriWave({ + container: this.container, + style: 'ios9', + width: this.container.clientWidth, + height: 100, // Increased height + autostart: false, + amplitude: 1, + speed: 0.1 + }); + + // Handle window resize + window.addEventListener('resize', () => { + if (this.wave) { + this.wave.width = this.container.clientWidth; + } + }); + } + + setupProgressBar() { + this.progressBar = document.createElement('progress'); + this.progressBar.style.width = '100%'; + this.progressBar.max = 100; + this.progressBar.value = 0; + this.progressBar.className = 'generation-progress'; + // Insert inside player-container, after wave-container + const playerContainer = this.container.closest('.player-container'); + playerContainer.insertBefore(this.progressBar, playerContainer.lastElementChild); + this.progressBar.style.display = 'none'; + } + + setupStateSubscription() { + this.playerState.subscribe(state => { + // Handle generation progress + if (state.isGenerating) { + this.progressBar.style.display = 'block'; + this.progressBar.value = state.progress; + } else if (state.progress >= 100) { + // Hide progress bar after completion + setTimeout(() => { + this.progressBar.style.display = 'none'; + this.progressBar.value = 0; + }, 500); + } + + // Only animate when playing, stop otherwise + if (state.isPlaying) { + this.wave.start(); + } else { + this.wave.stop(); + } + }); + } + + updateProgress(receivedChunks, totalChunks) { + if (!totalChunks) return; + + // Calculate progress percentage based on chunks + const progress = Math.min((receivedChunks / totalChunks) * 100, 99); + + // Always update on 0 progress or when progress increases + if (receivedChunks === 0 || progress > this.progressBar.value) { + this.progressBar.style.display = 'block'; + this.progressBar.value = progress; + this.playerState.setProgress(receivedChunks, totalChunks); + } + } + + cleanup() { + if (this.wave) { + this.wave.stop(); + this.wave.dispose(); + this.wave = null; + } + + if (this.progressBar) { + this.progressBar.style.display = 'none'; + this.progressBar.value = 0; + if (this.progressBar.parentNode) { + this.progressBar.parentNode.removeChild(this.progressBar); + } + this.progressBar = null; + } + + // Re-setup wave and progress bar + this.setupWave(); + this.setupProgressBar(); + + if (this.playerState) { + this.playerState.setProgress(0, 1); // Reset progress in state + } + } +} + +export default WaveVisualizer; \ No newline at end of file diff --git a/web/src/services/AudioService.js b/web/src/services/AudioService.js new file mode 100644 index 0000000..adbd20a --- /dev/null +++ b/web/src/services/AudioService.js @@ -0,0 +1,295 @@ +export class AudioService { + constructor() { + this.mediaSource = null; + this.sourceBuffer = null; + this.audio = null; + this.controller = null; + this.eventListeners = new Map(); + this.chunks = []; + this.minimumPlaybackSize = 50000; // 50KB minimum before playback + this.textLength = 0; + this.shouldAutoplay = false; + this.CHARS_PER_CHUNK = 600; // Estimated chars per chunk + } + + async streamAudio(text, voice, speed, onProgress) { + try { + console.log('AudioService: Starting stream...', { text, voice, speed }); + + // Only abort if there's an active controller + if (this.controller) { + this.controller.abort(); + this.controller = null; + } + + // Create new controller before cleanup to prevent race conditions + this.controller = new AbortController(); + + // Clean up previous audio state + this.cleanup(); + onProgress?.(0, 1); // Reset progress to 0 + this.chunks = []; + this.textLength = text.length; + this.shouldAutoplay = document.getElementById('autoplay-toggle').checked; + + // Calculate expected number of chunks based on text length + const estimatedChunks = Math.max(1, Math.ceil(this.textLength / this.CHARS_PER_CHUNK)); + + console.log('AudioService: Making API call...', { text, voice, speed }); + + const response = await fetch('/v1/audio/speech', { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ + input: text, + voice: voice, + response_format: 'mp3', + stream: true, + speed: speed + }), + signal: this.controller.signal + }); + + console.log('AudioService: Got response', { status: response.status }); + + if (!response.ok) { + const error = await response.json(); + console.error('AudioService: API error', error); + throw new Error(error.detail?.message || 'Failed to generate speech'); + } + + await this.setupAudioStream(response, onProgress, estimatedChunks); + return this.audio; + } catch (error) { + this.cleanup(); + throw error; + } + } + + async setupAudioStream(response, onProgress, estimatedTotalSize) { + this.audio = new Audio(); + this.mediaSource = new MediaSource(); + this.audio.src = URL.createObjectURL(this.mediaSource); + + return new Promise((resolve, reject) => { + this.mediaSource.addEventListener('sourceopen', async () => { + try { + this.sourceBuffer = this.mediaSource.addSourceBuffer('audio/mpeg'); + await this.processStream(response.body, onProgress, estimatedTotalSize); + resolve(); + } catch (error) { + reject(error); + } + }); + }); + } + + async processStream(stream, onProgress, estimatedChunks) { + const reader = stream.getReader(); + let hasStartedPlaying = false; + let receivedChunks = 0; + + try { + while (true) { + const {value, done} = await reader.read(); + + if (done) { + if (this.mediaSource.readyState === 'open') { + this.mediaSource.endOfStream(); + } + // Ensure we show 100% at completion + onProgress?.(estimatedChunks, estimatedChunks); + this.dispatchEvent('complete'); + this.dispatchEvent('downloadReady'); + return; + } + + this.chunks.push(value); + receivedChunks++; + + await this.appendChunk(value); + + // Update progress based on received chunks + onProgress?.(receivedChunks, estimatedChunks); + + // Start playback if we have enough chunks + if (!hasStartedPlaying && receivedChunks >= 1) { + hasStartedPlaying = true; + if (this.shouldAutoplay) { + // Small delay to ensure buffer is ready + setTimeout(() => this.play(), 100); + } + } + } + } catch (error) { + if (error.name !== 'AbortError') { + throw error; + } + } + } + + async appendChunk(chunk) { + return new Promise((resolve) => { + const appendChunk = () => { + this.sourceBuffer.appendBuffer(chunk); + this.sourceBuffer.addEventListener('updateend', resolve, { once: true }); + }; + + if (!this.sourceBuffer.updating) { + appendChunk(); + } else { + this.sourceBuffer.addEventListener('updateend', appendChunk, { once: true }); + } + }); + } + + play() { + if (this.audio && this.audio.readyState >= 2) { + const playPromise = this.audio.play(); + if (playPromise) { + playPromise.catch(error => { + if (error.name !== 'AbortError') { + console.error('Playback error:', error); + } + }); + } + this.dispatchEvent('play'); + } + } + + pause() { + if (this.audio) { + this.audio.pause(); + this.dispatchEvent('pause'); + } + } + + seek(time) { + if (this.audio) { + const wasPlaying = !this.audio.paused; + this.audio.currentTime = time; + if (wasPlaying) { + this.play(); + } + } + } + + setVolume(volume) { + if (this.audio) { + this.audio.volume = Math.max(0, Math.min(1, volume)); + } + } + + getCurrentTime() { + return this.audio ? this.audio.currentTime : 0; + } + + getDuration() { + return this.audio ? this.audio.duration : 0; + } + + isPlaying() { + return this.audio ? !this.audio.paused : false; + } + + addEventListener(event, callback) { + if (!this.eventListeners.has(event)) { + this.eventListeners.set(event, new Set()); + } + this.eventListeners.get(event).add(callback); + + if (this.audio && ['play', 'pause', 'ended', 'timeupdate'].includes(event)) { + this.audio.addEventListener(event, callback); + } + } + + removeEventListener(event, callback) { + const listeners = this.eventListeners.get(event); + if (listeners) { + listeners.delete(callback); + } + if (this.audio) { + this.audio.removeEventListener(event, callback); + } + } + + dispatchEvent(event, data) { + const listeners = this.eventListeners.get(event); + if (listeners) { + listeners.forEach(callback => callback(data)); + } + } + + cancel() { + if (this.controller) { + this.controller.abort(); + this.controller = null; + } + + // Full cleanup of all resources + if (this.audio) { + this.audio.pause(); + this.audio.src = ''; + this.audio = null; + } + + if (this.mediaSource && this.mediaSource.readyState === 'open') { + try { + this.mediaSource.endOfStream(); + } catch (e) { + // Ignore errors during cleanup + } + } + + this.mediaSource = null; + this.sourceBuffer = null; + this.chunks = []; + this.textLength = 0; + + // Force a hard refresh of the page to ensure clean state + window.location.reload(); + } + + cleanup() { + // Clean up audio elements + if (this.audio) { + // Remove all event listeners + this.eventListeners.forEach((listeners, event) => { + listeners.forEach(callback => { + this.audio.removeEventListener(event, callback); + }); + }); + + this.audio.pause(); + this.audio.src = ''; + this.audio = null; + } + + if (this.mediaSource && this.mediaSource.readyState === 'open') { + try { + this.mediaSource.endOfStream(); + } catch (e) { + // Ignore errors during cleanup + } + } + + this.mediaSource = null; + this.sourceBuffer = null; + this.chunks = []; + this.textLength = 0; + } + + getDownloadUrl() { + if (!this.audio || !this.sourceBuffer || this.chunks.length === 0) return null; + + // Get the buffered data from MediaSource + const buffered = this.sourceBuffer.buffered; + if (buffered.length === 0) return null; + + // Create blob from the original chunks + const blob = new Blob(this.chunks, { type: 'audio/mpeg' }); + return URL.createObjectURL(blob); + } +} + +export default AudioService; diff --git a/web/src/services/VoiceService.js b/web/src/services/VoiceService.js new file mode 100644 index 0000000..92cd4db --- /dev/null +++ b/web/src/services/VoiceService.js @@ -0,0 +1,81 @@ +export class VoiceService { + constructor() { + this.availableVoices = []; + this.selectedVoices = new Set(); + } + + async loadVoices() { + try { + const response = await fetch('/v1/audio/voices'); + if (!response.ok) { + const error = await response.json(); + throw new Error(error.detail?.message || 'Failed to load voices'); + } + + const data = await response.json(); + if (!data.voices?.length) { + throw new Error('No voices available'); + } + + this.availableVoices = data.voices; + + // Select first voice if none selected + if (this.selectedVoices.size === 0) { + const firstVoice = this.availableVoices.find(voice => voice && voice.trim()); + if (firstVoice) { + this.addVoice(firstVoice); + } + } + + return this.availableVoices; + } catch (error) { + console.error('Failed to load voices:', error); + throw error; + } + } + + getAvailableVoices() { + return this.availableVoices; + } + + getSelectedVoices() { + return Array.from(this.selectedVoices); + } + + getSelectedVoiceString() { + return Array.from(this.selectedVoices).join('+'); + } + + addVoice(voice) { + if (this.availableVoices.includes(voice)) { + this.selectedVoices.add(voice); + return true; + } + return false; + } + + removeVoice(voice) { + return this.selectedVoices.delete(voice); + } + + clearSelectedVoices() { + this.selectedVoices.clear(); + } + + filterVoices(searchTerm) { + if (!searchTerm) { + return this.availableVoices; + } + + const term = searchTerm.toLowerCase(); + return this.availableVoices.filter(voice => + voice.toLowerCase().includes(term) + ); + } + + hasSelectedVoices() { + return this.selectedVoices.size > 0; + } +} + +export default VoiceService; \ No newline at end of file diff --git a/web/src/state/PlayerState.js b/web/src/state/PlayerState.js new file mode 100644 index 0000000..a2ea7b1 --- /dev/null +++ b/web/src/state/PlayerState.js @@ -0,0 +1,88 @@ +export class PlayerState { + constructor() { + this.state = { + isPlaying: false, + isGenerating: false, + currentTime: 0, + duration: 0, + volume: 1, + speed: 1, + progress: 0, + error: null + }; + this.listeners = new Set(); + } + + subscribe(listener) { + this.listeners.add(listener); + return () => this.listeners.delete(listener); + } + + notify() { + this.listeners.forEach(listener => listener(this.state)); + } + + setState(updates) { + this.state = { + ...this.state, + ...updates + }; + this.notify(); + } + + setPlaying(isPlaying) { + this.setState({ isPlaying }); + } + + setGenerating(isGenerating) { + this.setState({ isGenerating }); + } + + setProgress(loaded, total) { + const progress = total > 0 ? (loaded / total) * 100 : 0; + this.setState({ progress }); + } + + setTime(currentTime, duration) { + this.setState({ currentTime, duration }); + } + + setVolume(volume) { + this.setState({ volume }); + } + + setSpeed(speed) { + this.setState({ speed }); + } + + setError(error) { + this.setState({ error }); + } + + clearError() { + this.setState({ error: null }); + } + + reset() { + // Keep current speed setting but reset everything else + const currentSpeed = this.state.speed; + const currentVolume = this.state.volume; + + this.setState({ + isPlaying: false, + isGenerating: false, + currentTime: 0, + duration: 0, + progress: 0, + error: null, + speed: currentSpeed, + volume: currentVolume + }); + } + + getState() { + return { ...this.state }; + } +} + +export default PlayerState; \ No newline at end of file diff --git a/web/styles/player.css b/web/styles/player.css index d70add8..214f865 100644 --- a/web/styles/player.css +++ b/web/styles/player.css @@ -122,6 +122,45 @@ margin-top: 0.5rem; } +/* Progress bar styles */ +.generation-progress { + -webkit-appearance: none; + appearance: none; + width: 100%; + height: 6px; + border: none; + background: rgba(99, 102, 241, 0.1); + border-radius: 3px; + margin: 1rem 0; + display: block; +} + +.generation-progress::-webkit-progress-bar { + background: rgba(99, 102, 241, 0.1); + border-radius: 3px; +} + +.generation-progress::-webkit-progress-value { + background: var(--fg-color); + border-radius: 3px; + transition: width 0.2s ease; + box-shadow: 0 0 10px rgba(99, 102, 241, 0.3); +} + +.generation-progress::-moz-progress-bar { + background: var(--fg-color); + border-radius: 3px; + transition: width 0.2s ease; + box-shadow: 0 0 10px rgba(99, 102, 241, 0.3); +} + +.generation-progress::-ms-fill { + background: var(--fg-color); + border-radius: 3px; + transition: width 0.2s ease; + box-shadow: 0 0 10px rgba(99, 102, 241, 0.3); +} + .wave-container canvas { position: absolute; top: 0; From 1345b6c81a6a87d6938786a9c3d0b8f8d9448b23 Mon Sep 17 00:00:00 2001 From: remsky Date: Tue, 28 Jan 2025 03:25:50 -0700 Subject: [PATCH 23/32] Added v1.0.0 voices --- api/src/voices/v1_0/af_alloy.pt | Bin 0 -> 523425 bytes api/src/voices/v1_0/af_aoede.pt | Bin 0 -> 523425 bytes api/src/voices/v1_0/af_jessica.pt | Bin 0 -> 523435 bytes api/src/voices/v1_0/af_kore.pt | Bin 0 -> 523420 bytes api/src/voices/v1_0/af_nova.pt | Bin 0 -> 523420 bytes api/src/voices/v1_0/af_river.pt | Bin 0 -> 523425 bytes api/src/voices/v1_0/am_echo.pt | Bin 0 -> 523420 bytes api/src/voices/v1_0/am_eric.pt | Bin 0 -> 523420 bytes api/src/voices/v1_0/am_fenrir.pt | Bin 0 -> 523430 bytes api/src/voices/v1_0/am_liam.pt | Bin 0 -> 523420 bytes api/src/voices/v1_0/am_onyx.pt | Bin 0 -> 523420 bytes api/src/voices/v1_0/am_puck.pt | Bin 0 -> 523420 bytes api/src/voices/v1_0/bf_alice.pt | Bin 0 -> 523425 bytes api/src/voices/v1_0/bf_lily.pt | Bin 0 -> 523420 bytes api/src/voices/v1_0/bm_daniel.pt | Bin 0 -> 523430 bytes api/src/voices/v1_0/bm_fable.pt | Bin 0 -> 523425 bytes 16 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 api/src/voices/v1_0/af_alloy.pt create mode 100644 api/src/voices/v1_0/af_aoede.pt create mode 100644 api/src/voices/v1_0/af_jessica.pt create mode 100644 api/src/voices/v1_0/af_kore.pt create mode 100644 api/src/voices/v1_0/af_nova.pt create mode 100644 api/src/voices/v1_0/af_river.pt create mode 100644 api/src/voices/v1_0/am_echo.pt create mode 100644 api/src/voices/v1_0/am_eric.pt create mode 100644 api/src/voices/v1_0/am_fenrir.pt create mode 100644 api/src/voices/v1_0/am_liam.pt create mode 100644 api/src/voices/v1_0/am_onyx.pt create mode 100644 api/src/voices/v1_0/am_puck.pt create mode 100644 api/src/voices/v1_0/bf_alice.pt create mode 100644 api/src/voices/v1_0/bf_lily.pt create mode 100644 api/src/voices/v1_0/bm_daniel.pt create mode 100644 api/src/voices/v1_0/bm_fable.pt diff --git a/api/src/voices/v1_0/af_alloy.pt b/api/src/voices/v1_0/af_alloy.pt new 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zCEL$)Y3F8TV1CYFw3qLJUYAyhWxqM1j=>okYb#A5JykSIx0m>{NK6gSdEGU#i|uPU z!2C6H#1W6$>DKvbIP^0CCs*c+o>}GzfgwgX+rJom*Dj$mZ}wBm)IGSpM-25Fc5%LL zGK?05az^@)uA8k zz@g*l9Jh#~6T|UY{6+BFQi{c2RPg!kQ*2(k4E*%FGUU57~(r*Ph3;&p5G+sh9BmsywDwR*jqQA$9M~7TfR|=UXGL->eiyo20GK z$EaQu+t7=Sb3Bv;l5rh%$3(%Tbve3ECB$Z_d)AAJX#`qG@&Kb^Z&sN}{U*B1h+grBjlLk!_ zg|oh!7eU|-O`N&qkMLt_IVDStqVvu&q`iC{HP+1mJ&WD+W=mgkwC&r^ZvUxViLSm9 z|J;tYy`>~1`t_6e-!26SD~VN`+}5mIwQ==?ovXKdd2ZP}%L*ly{C{1>1ODkjMZ(&J zz4$BsbN}CaFj`{b|LTE($4c*&6TDV$_guNrbI(d|-WxYhk6Bjw|8E^KG@N8M(a6}$ zY~mDCew&$0HZnEj*F>X9#-?UwMw5*V&G@&`KfU~+D+<%$J^bf(w3Ym)v;R3#a;ts! zr1HoA+=Kr4#lKzuId1=S+Fh-&#BM{7dWq z^ZVs!D=j_#KN^kdt^c3R`~1gG|MOpK{#X2Sb@Bi5pNqHq?>|`n`X7(~`L&~MpMQo= Oq7VQ2=lTERx&IADAQN{0 literal 0 HcmV?d00001 From 9867fc398fd03813a262a7534255f9e810ca0e0e Mon Sep 17 00:00:00 2001 From: remsky Date: Tue, 28 Jan 2025 13:52:57 -0700 Subject: [PATCH 24/32] WIP: v1_0_0 migration --- README.md | 8 +- api/src/inference/model_manager.py | 3 +- api/src/main.py | 17 ++- api/src/routers/openai_compatible.py | 6 +- api/src/services/audio.py | 22 ++-- api/src/services/streaming_audio_writer.py | 143 ++++++++++++++------- api/tests/test_audio_service.py | 76 +++++++---- api/tests/test_tts_service_new.py | 10 +- docker/cpu/Dockerfile | 14 +- docker/cpu/download_onnx.py | 53 -------- docker/cpu/download_onnx.sh | 32 ----- docker/gpu/Dockerfile | 13 ++ docker/gpu/download_pth.py | 57 -------- docker/gpu/download_pth.sh | 31 ----- docker/scripts/download_model.py | 97 ++++++++++++++ docker/scripts/download_model.sh | 109 ++++++++++++++++ 16 files changed, 422 insertions(+), 269 deletions(-) delete mode 100755 docker/cpu/download_onnx.py delete mode 100755 docker/cpu/download_onnx.sh delete mode 100755 docker/gpu/download_pth.py delete mode 100755 docker/gpu/download_pth.sh create mode 100644 docker/scripts/download_model.py create mode 100644 docker/scripts/download_model.sh diff --git a/README.md b/README.md index a8557af..44d9600 100644 --- a/README.md +++ b/README.md @@ -53,10 +53,12 @@ The service can be accessed through either the API endpoints or the Gradio web i git clone https://github.com/remsky/Kokoro-FastAPI.git cd Kokoro-FastAPI - cd docker/gpu # OR + cd docker/gpu # OR # cd docker/cpu # Run this or the above - docker compose up --build - # if you are missing any models, run the .py or .sh scrips in the respective folders + docker compose up --build + # if you are missing any models, run: + # python ../scripts/download_model.py --type pth # for GPU + # python ../scripts/download_model.py --type onnx # for CPU ``` Once started: diff --git a/api/src/inference/model_manager.py b/api/src/inference/model_manager.py index 5920843..5fe53c9 100644 --- a/api/src/inference/model_manager.py +++ b/api/src/inference/model_manager.py @@ -335,4 +335,5 @@ async def get_manager(config: Optional[ModelConfig] = None) -> ModelManager: if _manager_instance is None: _manager_instance = ModelManager(config) await _manager_instance.initialize() - return _manager_instance \ No newline at end of file + return _manager_instance + diff --git a/api/src/main.py b/api/src/main.py index d4f00e8..759bbd7 100644 --- a/api/src/main.py +++ b/api/src/main.py @@ -5,6 +5,7 @@ FastAPI OpenAI Compatible API import os import sys from contextlib import asynccontextmanager +from pathlib import Path import torch import uvicorn @@ -57,16 +58,30 @@ async def lifespan(app: FastAPI): # Initialize model with warmup and get status device, model, voicepack_count = await model_manager.initialize_with_warmup(voice_manager) + except FileNotFoundError: + logger.error(""" +Model files not found! You need to either: + +1. Download models using the scripts: + GPU: python docker/scripts/download_model.py --type pth + CPU: python docker/scripts/download_model.py --type onnx + +2. Set environment variables in docker-compose: + GPU: DOWNLOAD_PTH=true + CPU: DOWNLOAD_ONNX=true +""") + raise except Exception as e: logger.error(f"Failed to initialize model: {e}") raise + boundary = "░" * 2*12 startup_msg = f""" {boundary} ╔═╗┌─┐┌─┐┌┬┐ - ╠╣ ├─┤└─┐ │ + ╠╣ ├─┤└─┐ │ ╚ ┴ ┴└─┘ ┴ ╦╔═┌─┐┬┌─┌─┐ ╠╩╗│ │├┴┐│ │ diff --git a/api/src/routers/openai_compatible.py b/api/src/routers/openai_compatible.py index 5908a56..f4b8684 100644 --- a/api/src/routers/openai_compatible.py +++ b/api/src/routers/openai_compatible.py @@ -1,3 +1,5 @@ +"""OpenAI-compatible router for text-to-speech""" + import json import os from typing import AsyncGenerator, Dict, List, Union @@ -217,9 +219,9 @@ async def create_speech( stitch_long_output=True ) - # Convert to requested format + # Convert to requested format - removed stream parameter content = await AudioService.convert_audio( - audio, 24000, request.response_format, is_first_chunk=True, stream=False + audio, 24000, request.response_format, is_first_chunk=True ) return Response( diff --git a/api/src/services/audio.py b/api/src/services/audio.py index bfe419b..2633594 100644 --- a/api/src/services/audio.py +++ b/api/src/services/audio.py @@ -16,7 +16,6 @@ class AudioNormalizer: """Handles audio normalization state for a single stream""" def __init__(self): - self.int16_max = np.iinfo(np.int16).max self.chunk_trim_ms = settings.gap_trim_ms self.sample_rate = 24000 # Sample rate of the audio self.samples_to_trim = int(self.chunk_trim_ms * self.sample_rate / 1000) @@ -30,20 +29,23 @@ class AudioNormalizer: Returns: Normalized and trimmed audio data """ - # Convert to float32 for processing - audio_float = audio_data.astype(np.float32) - + if len(audio_data) == 0: + raise ValueError("Empty audio data") + # Trim start and end if enough samples - if len(audio_float) > (2 * self.samples_to_trim): - audio_float = audio_float[self.samples_to_trim:-self.samples_to_trim] + if len(audio_data) > (2 * self.samples_to_trim): + audio_data = audio_data[self.samples_to_trim:-self.samples_to_trim] - # Scale to int16 range - return (audio_float * 32767).astype(np.int16) + # Scale directly to int16 range with clipping + return np.clip(audio_data * 32767, -32768, 32767).astype(np.int16) class AudioService: """Service for audio format conversions with streaming support""" + # Supported formats + SUPPORTED_FORMATS = {"wav", "mp3", "opus", "flac", "aac", "pcm", "ogg"} + # Default audio format settings balanced for speed and compression DEFAULT_SETTINGS = { "mp3": { @@ -86,6 +88,10 @@ class AudioService: Bytes of the converted audio chunk """ try: + # Validate format + if output_format not in AudioService.SUPPORTED_FORMATS: + raise ValueError(f"Format {output_format} not supported") + # Always normalize audio to ensure proper amplitude scaling if normalizer is None: normalizer = AudioNormalizer() diff --git a/api/src/services/streaming_audio_writer.py b/api/src/services/streaming_audio_writer.py index 23d63c0..1084c91 100644 --- a/api/src/services/streaming_audio_writer.py +++ b/api/src/services/streaming_audio_writer.py @@ -17,26 +17,41 @@ class StreamingAudioWriter: self.sample_rate = sample_rate self.channels = channels self.bytes_written = 0 + self.buffer = BytesIO() # Format-specific setup if self.format == "wav": self._write_wav_header() - elif self.format == "ogg": + elif self.format in ["ogg", "opus"]: + # For OGG/Opus, write to memory buffer self.writer = sf.SoundFile( - file=BytesIO(), + file=self.buffer, mode='w', samplerate=sample_rate, channels=channels, format='OGG', - subtype='VORBIS' + subtype='VORBIS' if self.format == "ogg" else "OPUS" ) - elif self.format == "mp3": - # For MP3, we'll use pydub's incremental writer - self.buffer = BytesIO() + elif self.format == "flac": + # For FLAC, write to memory buffer + self.writer = sf.SoundFile( + file=self.buffer, + mode='w', + samplerate=sample_rate, + channels=channels, + format='FLAC' + ) + elif self.format in ["mp3", "aac"]: + # For MP3/AAC, we'll use pydub's incremental writer self.segments = [] # Store segments until we have enough data self.total_duration = 0 # Track total duration in milliseconds # Initialize an empty AudioSegment as our encoder self.encoder = AudioSegment.silent(duration=0, frame_rate=self.sample_rate) + elif self.format == "pcm": + # PCM doesn't need initialization, we'll write raw bytes + pass + else: + raise ValueError(f"Unsupported format: {format}") def _write_wav_header(self) -> bytes: """Write WAV header with correct streaming format""" @@ -63,42 +78,48 @@ class StreamingAudioWriter: audio_data: Audio data to write, or None if finalizing finalize: Whether this is the final write to close the stream """ - buffer = BytesIO() + output_buffer = BytesIO() if finalize: if self.format == "wav": # Write final WAV header with correct sizes - buffer.write(b'RIFF') - buffer.write(struct.pack(' 0: # Export with duration metadata + format_args = { + "mp3": {"format": "mp3", "codec": "libmp3lame"}, + "aac": {"format": "adts", "codec": "aac"} + }[self.format] + self.encoder.export( - buffer, - format="mp3", + output_buffer, + **format_args, bitrate="192k", parameters=[ "-q:a", "2", - "-write_xing", "1", # Force XING/LAME header + "-write_xing", "1" if self.format == "mp3" else "0", # XING header for MP3 only "-metadata", f"duration={self.total_duration/1000}" # Duration in seconds ] ) self.encoder = None - return buffer.getvalue() + return output_buffer.getvalue() if audio_data is None or len(audio_data) == 0: return b'' @@ -106,22 +127,22 @@ class StreamingAudioWriter: if self.format == "wav": # For WAV, write raw PCM after the first chunk if self.bytes_written == 0: - buffer.write(self._write_wav_header()) - buffer.write(audio_data.tobytes()) + output_buffer.write(self._write_wav_header()) + output_buffer.write(audio_data.tobytes()) self.bytes_written += len(audio_data.tobytes()) - elif self.format == "ogg": - # OGG/Vorbis handles streaming naturally + elif self.format in ["ogg", "opus", "flac"]: + # Write to soundfile buffer self.writer.write(audio_data) self.writer.flush() - buffer = self.writer.file - buffer.seek(0, 2) # Seek to end - chunk = buffer.getvalue() - buffer.seek(0) - buffer.truncate() - return chunk + # Get current buffer contents + data = self.buffer.getvalue() + # Clear buffer for next chunk + self.buffer.seek(0) + self.buffer.truncate() + return data - elif self.format == "mp3": + elif self.format in ["mp3", "aac"]: # Convert chunk to AudioSegment and encode segment = AudioSegment( audio_data.tobytes(), @@ -137,21 +158,30 @@ class StreamingAudioWriter: self.encoder = self.encoder + segment # Export current state to buffer - self.encoder.export(buffer, format="mp3", bitrate="192k", parameters=[ + format_args = { + "mp3": {"format": "mp3", "codec": "libmp3lame"}, + "aac": {"format": "adts", "codec": "aac"} + }[self.format] + + self.encoder.export(output_buffer, **format_args, bitrate="192k", parameters=[ "-q:a", "2", - "-write_xing", "1", # Force XING/LAME header + "-write_xing", "1" if self.format == "mp3" else "0", # XING header for MP3 only "-metadata", f"duration={self.total_duration/1000}" # Duration in seconds ]) # Get the encoded data - encoded_data = buffer.getvalue() + encoded_data = output_buffer.getvalue() # Reset encoder to prevent memory growth self.encoder = AudioSegment.silent(duration=0, frame_rate=self.sample_rate) return encoded_data - return buffer.getvalue() + elif self.format == "pcm": + # For PCM, just write raw bytes + return audio_data.tobytes() + + return output_buffer.getvalue() def close(self) -> Optional[bytes]: """Finish the audio file and return any remaining data""" @@ -161,16 +191,31 @@ class StreamingAudioWriter: buffer.write(b'RIFF') buffer.write(struct.pack(' 0: + format_args = { + "mp3": {"format": "mp3", "codec": "libmp3lame"}, + "aac": {"format": "adts", "codec": "aac"} + }[self.format] + self.encoder.export(buffer, **format_args) + return buffer.getvalue() + + return None \ No newline at end of file diff --git a/api/tests/test_audio_service.py b/api/tests/test_audio_service.py index 8131c9f..b201c92 100644 --- a/api/tests/test_audio_service.py +++ b/api/tests/test_audio_service.py @@ -26,106 +26,128 @@ def sample_audio(): return np.sin(2 * np.pi * frequency * t).astype(np.float32), sample_rate -def test_convert_to_wav(sample_audio): +@pytest.mark.asyncio +async def test_convert_to_wav(sample_audio): """Test converting to WAV format""" audio_data, sample_rate = sample_audio - result = AudioService.convert_audio(audio_data, sample_rate, "wav") + result = await AudioService.convert_audio(audio_data, sample_rate, "wav") assert isinstance(result, bytes) assert len(result) > 0 + # Check WAV header + assert result.startswith(b'RIFF') + assert b'WAVE' in result[:12] -def test_convert_to_mp3(sample_audio): +@pytest.mark.asyncio +async def test_convert_to_mp3(sample_audio): """Test converting to MP3 format""" audio_data, sample_rate = sample_audio - result = AudioService.convert_audio(audio_data, sample_rate, "mp3") + result = await AudioService.convert_audio(audio_data, sample_rate, "mp3") assert isinstance(result, bytes) assert len(result) > 0 + # Check MP3 header (ID3 or MPEG frame sync) + assert result.startswith(b'ID3') or result.startswith(b'\xff\xfb') -def test_convert_to_opus(sample_audio): +@pytest.mark.asyncio +async def test_convert_to_opus(sample_audio): """Test converting to Opus format""" audio_data, sample_rate = sample_audio - result = AudioService.convert_audio(audio_data, sample_rate, "opus") + result = await AudioService.convert_audio(audio_data, sample_rate, "opus") assert isinstance(result, bytes) assert len(result) > 0 + # Check OGG header + assert result.startswith(b'OggS') -def test_convert_to_flac(sample_audio): +@pytest.mark.asyncio +async def test_convert_to_flac(sample_audio): """Test converting to FLAC format""" audio_data, sample_rate = sample_audio - result = AudioService.convert_audio(audio_data, sample_rate, "flac") + result = await AudioService.convert_audio(audio_data, sample_rate, "flac") assert isinstance(result, bytes) assert len(result) > 0 + # Check FLAC header + assert result.startswith(b'fLaC') -def test_convert_to_aac(sample_audio): +@pytest.mark.asyncio +async def test_convert_to_aac(sample_audio): """Test converting to AAC format""" audio_data, sample_rate = sample_audio - result = AudioService.convert_audio(audio_data, sample_rate, "aac") + result = await AudioService.convert_audio(audio_data, sample_rate, "aac") assert isinstance(result, bytes) assert len(result) > 0 - # AAC files typically start with an ADTS header - assert result.startswith(b'\xff\xf1') or result.startswith(b'\xff\xf9') + # Check ADTS header (AAC) + assert result.startswith(b'\xff\xf0') or result.startswith(b'\xff\xf1') -def test_convert_to_pcm(sample_audio): +@pytest.mark.asyncio +async def test_convert_to_pcm(sample_audio): """Test converting to PCM format""" audio_data, sample_rate = sample_audio - result = AudioService.convert_audio(audio_data, sample_rate, "pcm") + result = await AudioService.convert_audio(audio_data, sample_rate, "pcm") assert isinstance(result, bytes) assert len(result) > 0 + # PCM is raw bytes, so no header to check -def test_convert_to_invalid_format_raises_error(sample_audio): +@pytest.mark.asyncio +async def test_convert_to_invalid_format_raises_error(sample_audio): """Test that converting to an invalid format raises an error""" audio_data, sample_rate = sample_audio with pytest.raises(ValueError, match="Format invalid not supported"): - AudioService.convert_audio(audio_data, sample_rate, "invalid") + await AudioService.convert_audio(audio_data, sample_rate, "invalid") -def test_normalization_wav(sample_audio): +@pytest.mark.asyncio +async def test_normalization_wav(sample_audio): """Test that WAV output is properly normalized to int16 range""" audio_data, sample_rate = sample_audio # Create audio data outside int16 range large_audio = audio_data * 1e5 - result = AudioService.convert_audio(large_audio, sample_rate, "wav") + result = await AudioService.convert_audio(large_audio, sample_rate, "wav") assert isinstance(result, bytes) assert len(result) > 0 -def test_normalization_pcm(sample_audio): +@pytest.mark.asyncio +async def test_normalization_pcm(sample_audio): """Test that PCM output is properly normalized to int16 range""" audio_data, sample_rate = sample_audio # Create audio data outside int16 range large_audio = audio_data * 1e5 - result = AudioService.convert_audio(large_audio, sample_rate, "pcm") + result = await AudioService.convert_audio(large_audio, sample_rate, "pcm") assert isinstance(result, bytes) assert len(result) > 0 -def test_invalid_audio_data(): +@pytest.mark.asyncio +async def test_invalid_audio_data(): """Test handling of invalid audio data""" invalid_audio = np.array([]) # Empty array sample_rate = 24000 with pytest.raises(ValueError): - AudioService.convert_audio(invalid_audio, sample_rate, "wav") + await AudioService.convert_audio(invalid_audio, sample_rate, "wav") -def test_different_sample_rates(sample_audio): +@pytest.mark.asyncio +async def test_different_sample_rates(sample_audio): """Test converting audio with different sample rates""" audio_data, _ = sample_audio sample_rates = [8000, 16000, 44100, 48000] for rate in sample_rates: - result = AudioService.convert_audio(audio_data, rate, "wav") + result = await AudioService.convert_audio(audio_data, rate, "wav") assert isinstance(result, bytes) assert len(result) > 0 -def test_buffer_position_after_conversion(sample_audio): +@pytest.mark.asyncio +async def test_buffer_position_after_conversion(sample_audio): """Test that buffer position is reset after writing""" audio_data, sample_rate = sample_audio - result = AudioService.convert_audio(audio_data, sample_rate, "wav") + result = await AudioService.convert_audio(audio_data, sample_rate, "wav") # Convert again to ensure buffer was properly reset - result2 = AudioService.convert_audio(audio_data, sample_rate, "wav") + result2 = await AudioService.convert_audio(audio_data, sample_rate, "wav") assert len(result) == len(result2) diff --git a/api/tests/test_tts_service_new.py b/api/tests/test_tts_service_new.py index f9a1cdd..4b2887f 100644 --- a/api/tests/test_tts_service_new.py +++ b/api/tests/test_tts_service_new.py @@ -59,7 +59,7 @@ async def test_empty_text(tts_service, test_voice): voice=test_voice, speed=1.0 ) - assert "Text is empty after preprocessing" in str(exc_info.value) + assert "No audio chunks were generated successfully" in str(exc_info.value) @pytest.mark.asyncio async def test_invalid_voice(tts_service): @@ -126,15 +126,17 @@ async def test_combine_voices(tts_service): @pytest.mark.asyncio async def test_chunked_text_processing(tts_service, test_voice, mock_audio_output): """Test processing chunked text""" - long_text = "First sentence. Second sentence. Third sentence." + # Create text that will force chunking by exceeding max tokens + long_text = "This is a test sentence." * 100 # Should be way over 500 tokens + # Don't mock smart_split - let it actually split the text audio, processing_time = await tts_service.generate_audio( text=long_text, voice=test_voice, - speed=1.0, - stitch_long_output=True + speed=1.0 ) + # Should be called multiple times due to chunking assert tts_service.model_manager.generate.call_count > 1 assert isinstance(audio, np.ndarray) assert processing_time > 0 \ No newline at end of file diff --git a/docker/cpu/Dockerfile b/docker/cpu/Dockerfile index ccd42cd..8363ed3 100644 --- a/docker/cpu/Dockerfile +++ b/docker/cpu/Dockerfile @@ -34,6 +34,7 @@ RUN --mount=type=cache,target=/root/.cache/uv \ # Copy project files including models COPY --chown=appuser:appuser api ./api COPY --chown=appuser:appuser web ./web +COPY --chown=appuser:appuser docker/scripts/download_model.* ./ # Install project RUN --mount=type=cache,target=/root/.cache/uv \ @@ -44,8 +45,19 @@ ENV PYTHONUNBUFFERED=1 ENV PYTHONPATH=/app ENV PATH="/app/.venv/bin:$PATH" ENV UV_LINK_MODE=copy + ENV USE_GPU=false -ENV USE_ONNX=false +ENV USE_ONNX=true +ENV DOWNLOAD_ONNX=true +ENV DOWNLOAD_PTH=false + +# Download models based on environment variables +RUN if [ "$DOWNLOAD_ONNX" = "true" ]; then \ + python download_model.py --type onnx; \ + fi && \ + if [ "$DOWNLOAD_PTH" = "true" ]; then \ + python download_model.py --type pth; \ + fi # Run FastAPI server CMD ["uv", "run", "python", "-m", "uvicorn", "api.src.main:app", "--host", "0.0.0.0", "--port", "8880", "--log-level", "debug"] diff --git a/docker/cpu/download_onnx.py b/docker/cpu/download_onnx.py deleted file mode 100755 index a97daf9..0000000 --- a/docker/cpu/download_onnx.py +++ /dev/null @@ -1,53 +0,0 @@ -#!/usr/bin/env python3 -import os -import sys -import requests -from pathlib import Path -from typing import List - -def download_file(url: str, output_dir: Path) -> None: - """Download a file from URL to the specified directory.""" - filename = os.path.basename(url) - output_path = output_dir / filename - - print(f"Downloading {filename}...") - response = requests.get(url, stream=True) - response.raise_for_status() - - with open(output_path, 'wb') as f: - for chunk in response.iter_content(chunk_size=8192): - f.write(chunk) - -def find_project_root() -> Path: - """Find project root by looking for api directory.""" - max_steps = 5 - current = Path(__file__).resolve() - for _ in range(max_steps): - if (current / 'api').is_dir(): - return current - current = current.parent - raise RuntimeError("Could not find project root (no api directory found)") - -def main(custom_models: List[str] = None): - # Always use top-level models directory relative to project root - project_root = find_project_root() - models_dir = project_root / 'api' / 'src' / 'models' - models_dir.mkdir(exist_ok=True, parents=True) - - # Default ONNX model if no arguments provided - default_models = [ - "https://github.com/remsky/Kokoro-FastAPI/releases/download/v0.1.0/kokoro-v0_19.onnx", - # "https://github.com/remsky/Kokoro-FastAPI/releases/download/v0.1.0/kokoro-v0_19_fp16.onnx" - ] - - # Use provided models or default - models_to_download = custom_models if custom_models else default_models - - for model_url in models_to_download: - try: - download_file(model_url, models_dir) - except Exception as e: - print(f"Error downloading {model_url}: {e}") - -if __name__ == "__main__": - main(sys.argv[1:] if len(sys.argv) > 1 else None) \ No newline at end of file diff --git a/docker/cpu/download_onnx.sh b/docker/cpu/download_onnx.sh deleted file mode 100755 index c0a250b..0000000 --- a/docker/cpu/download_onnx.sh +++ /dev/null @@ -1,32 +0,0 @@ -#!/bin/bash - -# Ensure models directory exists -mkdir -p api/src/models - -# Function to download a file -download_file() { - local url="$1" - local filename=$(basename "$url") - echo "Downloading $filename..." - curl -L "$url" -o "api/src/models/$filename" -} - -# Default ONNX model if no arguments provided -DEFAULT_MODELS=( - "https://github.com/remsky/Kokoro-FastAPI/releases/download/v0.1.0/kokoro-v0_19.onnx" - "https://github.com/remsky/Kokoro-FastAPI/releases/download/v0.1.0/kokoro-v0_19_fp16.onnx" -) - -# Use provided models or default -if [ $# -gt 0 ]; then - MODELS=("$@") -else - MODELS=("${DEFAULT_MODELS[@]}") -fi - -# Download all models -for model in "${MODELS[@]}"; do - download_file "$model" -done - -echo "ONNX model download complete!" \ No newline at end of file diff --git a/docker/gpu/Dockerfile b/docker/gpu/Dockerfile index 93613aa..3134f4e 100644 --- a/docker/gpu/Dockerfile +++ b/docker/gpu/Dockerfile @@ -38,6 +38,7 @@ RUN --mount=type=cache,target=/root/.cache/uv \ # Copy project files including models COPY --chown=appuser:appuser api ./api COPY --chown=appuser:appuser web ./web +COPY --chown=appuser:appuser docker/scripts/download_model.* ./ # Install project with GPU extras RUN --mount=type=cache,target=/root/.cache/uv \ @@ -48,7 +49,19 @@ ENV PYTHONUNBUFFERED=1 ENV PYTHONPATH=/app ENV PATH="/app/.venv/bin:$PATH" ENV UV_LINK_MODE=copy + ENV USE_GPU=true +ENV USE_ONNX=false +ENV DOWNLOAD_PTH=true +ENV DOWNLOAD_ONNX=false + +# Download models based on environment variables +RUN if [ "$DOWNLOAD_PTH" = "true" ]; then \ + python download_model.py --type pth; \ + fi && \ + if [ "$DOWNLOAD_ONNX" = "true" ]; then \ + python download_model.py --type onnx; \ + fi # Run FastAPI server CMD ["uv", "run", "python", "-m", "uvicorn", "api.src.main:app", "--host", "0.0.0.0", "--port", "8880", "--log-level", "debug"] diff --git a/docker/gpu/download_pth.py b/docker/gpu/download_pth.py deleted file mode 100755 index f58f29d..0000000 --- a/docker/gpu/download_pth.py +++ /dev/null @@ -1,57 +0,0 @@ -#!/usr/bin/env python3 -import os -import sys -import requests -from pathlib import Path -from typing import List - -def download_file(url: str, output_dir: Path) -> None: - """Download a file from URL to the specified directory.""" - filename = os.path.basename(url) - if not filename.endswith('.pth'): - print(f"Warning: {filename} is not a .pth file") - return - - output_path = output_dir / filename - - print(f"Downloading {filename}...") - response = requests.get(url, stream=True) - response.raise_for_status() - - with open(output_path, 'wb') as f: - for chunk in response.iter_content(chunk_size=8192): - f.write(chunk) - -def find_project_root() -> Path: - """Find project root by looking for api directory.""" - max_steps = 5 - current = Path(__file__).resolve() - for _ in range(max_steps): - if (current / 'api').is_dir(): - return current - current = current.parent - raise RuntimeError("Could not find project root (no api directory found)") - -def main(custom_models: List[str] = None): - # Find project root and ensure models directory exists - project_root = find_project_root() - models_dir = project_root / 'api' / 'src' / 'models' - print(f"Downloading models to {models_dir}") - models_dir.mkdir(exist_ok=True) - - # Default PTH model if no arguments provided - default_models = [ - "https://github.com/remsky/Kokoro-FastAPI/releases/download/v0.1.0/kokoro-v0_19.pth" - ] - - # Use provided models or default - models_to_download = custom_models if custom_models else default_models - - for model_url in models_to_download: - try: - download_file(model_url, models_dir) - except Exception as e: - print(f"Error downloading {model_url}: {e}") - -if __name__ == "__main__": - main(sys.argv[1:] if len(sys.argv) > 1 else None) \ No newline at end of file diff --git a/docker/gpu/download_pth.sh b/docker/gpu/download_pth.sh deleted file mode 100755 index c8bda83..0000000 --- a/docker/gpu/download_pth.sh +++ /dev/null @@ -1,31 +0,0 @@ -#!/bin/bash - -# Ensure models directory exists -mkdir -p api/src/models - -# Function to download a file -download_file() { - local url="$1" - local filename=$(basename "$url") - echo "Downloading $filename..." - curl -L "$url" -o "api/src/models/$filename" -} - -# Default PTH model if no arguments provided -DEFAULT_MODELS=( - "https://github.com/remsky/Kokoro-FastAPI/releases/download/v0.1.0/kokoro-v0_19.pth" -) - -# Use provided models or default -if [ $# -gt 0 ]; then - MODELS=("$@") -else - MODELS=("${DEFAULT_MODELS[@]}") -fi - -# Download all models -for model in "${MODELS[@]}"; do - download_file "$model" -done - -echo "PyTorch model download complete!" \ No newline at end of file diff --git a/docker/scripts/download_model.py b/docker/scripts/download_model.py new file mode 100644 index 0000000..bc808df --- /dev/null +++ b/docker/scripts/download_model.py @@ -0,0 +1,97 @@ +#!/usr/bin/env python3 +import os +import sys +import argparse +import requests +from pathlib import Path +from typing import List + +def download_file(url: str, output_dir: Path, model_type: str) -> bool: + """Download a file from URL to the specified directory. + + Returns: + bool: True if download succeeded, False otherwise + """ + filename = os.path.basename(url) + if not filename.endswith(f'.{model_type}'): + print(f"Warning: {filename} is not a .{model_type} file", file=sys.stderr) + return False + + output_path = output_dir / filename + + print(f"Downloading {filename}...") + try: + response = requests.get(url, stream=True) + response.raise_for_status() + + with open(output_path, 'wb') as f: + for chunk in response.iter_content(chunk_size=8192): + f.write(chunk) + print(f"Successfully downloaded {filename}") + return True + except Exception as e: + print(f"Error downloading {filename}: {e}", file=sys.stderr) + return False + +def find_project_root() -> Path: + """Find project root by looking for api directory.""" + max_steps = 5 + current = Path(__file__).resolve() + for _ in range(max_steps): + if (current / 'api').is_dir(): + return current + current = current.parent + raise RuntimeError("Could not find project root (no api directory found)") + +def main() -> int: + """Download models to the project. + + Returns: + int: Exit code (0 for success, 1 for failure) + """ + parser = argparse.ArgumentParser(description='Download model files') + parser.add_argument('--type', choices=['pth', 'onnx'], required=True, + help='Model type to download (pth or onnx)') + parser.add_argument('urls', nargs='*', help='Optional model URLs to download') + args = parser.parse_args() + + try: + # Find project root and ensure models directory exists + project_root = find_project_root() + models_dir = project_root / 'api' / 'src' / 'models' + print(f"Downloading models to {models_dir}") + models_dir.mkdir(exist_ok=True) + + # Default models if no arguments provided + default_models = { + 'pth': [ + "https://github.com/remsky/Kokoro-FastAPI/releases/download/v0.1.0/kokoro-v0_19.pth" + ], + 'onnx': [ + "https://github.com/remsky/Kokoro-FastAPI/releases/download/v0.1.0/kokoro-v0_19.onnx", + "https://github.com/remsky/Kokoro-FastAPI/releases/download/v0.1.0/kokoro-v0_19_fp16.onnx" + ] + } + + # Use provided models or default + models_to_download = args.urls if args.urls else default_models[args.type] + + # Download all models + success = True + for model_url in models_to_download: + if not download_file(model_url, models_dir, args.type): + success = False + + if success: + print(f"{args.type.upper()} model download complete!") + return 0 + else: + print("Some downloads failed", file=sys.stderr) + return 1 + + except Exception as e: + print(f"Error: {e}", file=sys.stderr) + return 1 + +if __name__ == "__main__": + sys.exit(main()) \ No newline at end of file diff --git a/docker/scripts/download_model.sh b/docker/scripts/download_model.sh new file mode 100644 index 0000000..926a831 --- /dev/null +++ b/docker/scripts/download_model.sh @@ -0,0 +1,109 @@ +#!/bin/bash + +# Find project root by looking for api directory +find_project_root() { + local current_dir="$PWD" + local max_steps=5 + local steps=0 + + while [ $steps -lt $max_steps ]; do + if [ -d "$current_dir/api" ]; then + echo "$current_dir" + return 0 + fi + current_dir="$(dirname "$current_dir")" + ((steps++)) + done + + echo "Error: Could not find project root (no api directory found)" >&2 + exit 1 +} + +# Function to download a file +download_file() { + local url="$1" + local output_dir="$2" + local model_type="$3" + local filename=$(basename "$url") + + # Validate file extension + if [[ ! "$filename" =~ \.$model_type$ ]]; then + echo "Warning: $filename is not a .$model_type file" >&2 + return 1 + } + + echo "Downloading $filename..." + if curl -L "$url" -o "$output_dir/$filename"; then + echo "Successfully downloaded $filename" + return 0 + else + echo "Error downloading $filename" >&2 + return 1 + fi +} + +# Parse arguments +MODEL_TYPE="" +while [[ $# -gt 0 ]]; do + case $1 in + --type) + MODEL_TYPE="$2" + shift 2 + ;; + *) + # If no flag specified, treat remaining args as model URLs + break + ;; + esac +done + +# Validate model type +if [ "$MODEL_TYPE" != "pth" ] && [ "$MODEL_TYPE" != "onnx" ]; then + echo "Error: Must specify model type with --type (pth or onnx)" >&2 + exit 1 +fi + +# Find project root and ensure models directory exists +PROJECT_ROOT=$(find_project_root) +if [ $? -ne 0 ]; then + exit 1 +fi + +MODELS_DIR="$PROJECT_ROOT/api/src/models" +echo "Downloading models to $MODELS_DIR" +mkdir -p "$MODELS_DIR" + +# Default models if no arguments provided +if [ "$MODEL_TYPE" = "pth" ]; then + DEFAULT_MODELS=( + "https://github.com/remsky/Kokoro-FastAPI/releases/download/v0.1.0/kokoro-v0_19.pth" + ) +else + DEFAULT_MODELS=( + "https://github.com/remsky/Kokoro-FastAPI/releases/download/v0.1.0/kokoro-v0_19.onnx" + "https://github.com/remsky/Kokoro-FastAPI/releases/download/v0.1.0/kokoro-v0_19_fp16.onnx" + ) +fi + +# Use provided models or default +if [ $# -gt 0 ]; then + MODELS=("$@") +else + MODELS=("${DEFAULT_MODELS[@]}") +fi + +# Download all models +success=true +for model in "${MODELS[@]}"; do + if ! download_file "$model" "$MODELS_DIR" "$MODEL_TYPE"; then + success=false + fi +done + +if [ "$success" = true ]; then + echo "${MODEL_TYPE^^} model download complete!" + exit 0 +else + echo "Some downloads failed" >&2 + exit 1 +fi \ No newline at end of file From 66ebd0e33ce5461ac9e07a6ee9d84a1af27c3514 Mon Sep 17 00:00:00 2001 From: Fireblade Date: Tue, 28 Jan 2025 16:44:20 -0500 Subject: [PATCH 25/32] Fixes auto downloading models and adds the option to the python script to overwrite existing files --- docker/gpu/Dockerfile | 14 +++++--------- docker/scripts/download_model.py | 12 +++++++++--- docker/scripts/download_model.sh | 1 + docker/scripts/entrypoint.sh | 12 ++++++++++++ 4 files changed, 27 insertions(+), 12 deletions(-) create mode 100644 docker/scripts/entrypoint.sh diff --git a/docker/gpu/Dockerfile b/docker/gpu/Dockerfile index 3134f4e..f1cae1a 100644 --- a/docker/gpu/Dockerfile +++ b/docker/gpu/Dockerfile @@ -44,6 +44,10 @@ COPY --chown=appuser:appuser docker/scripts/download_model.* ./ RUN --mount=type=cache,target=/root/.cache/uv \ uv sync --extra gpu +COPY --chown=appuser:appuser docker/scripts/ /app/docker/scripts/ +RUN chmod +x docker/scripts/entrypoint.sh +RUN chmod +x docker/scripts/download_model.sh + # Set environment variables ENV PYTHONUNBUFFERED=1 ENV PYTHONPATH=/app @@ -55,13 +59,5 @@ ENV USE_ONNX=false ENV DOWNLOAD_PTH=true ENV DOWNLOAD_ONNX=false -# Download models based on environment variables -RUN if [ "$DOWNLOAD_PTH" = "true" ]; then \ - python download_model.py --type pth; \ - fi && \ - if [ "$DOWNLOAD_ONNX" = "true" ]; then \ - python download_model.py --type onnx; \ - fi - # Run FastAPI server -CMD ["uv", "run", "python", "-m", "uvicorn", "api.src.main:app", "--host", "0.0.0.0", "--port", "8880", "--log-level", "debug"] +CMD ["/app/docker/scripts/entrypoint.sh"] diff --git a/docker/scripts/download_model.py b/docker/scripts/download_model.py index bc808df..c27f802 100644 --- a/docker/scripts/download_model.py +++ b/docker/scripts/download_model.py @@ -6,7 +6,7 @@ import requests from pathlib import Path from typing import List -def download_file(url: str, output_dir: Path, model_type: str) -> bool: +def download_file(url: str, output_dir: Path, model_type: str, overwrite:str) -> bool: """Download a file from URL to the specified directory. Returns: @@ -19,6 +19,10 @@ def download_file(url: str, output_dir: Path, model_type: str) -> bool: output_path = output_dir / filename + if os.path.exists(output_path): + print(f"{filename} exists. Canceling download") + return True + print(f"Downloading {filename}...") try: response = requests.get(url, stream=True) @@ -52,6 +56,7 @@ def main() -> int: parser = argparse.ArgumentParser(description='Download model files') parser.add_argument('--type', choices=['pth', 'onnx'], required=True, help='Model type to download (pth or onnx)') + parser.add_argument('--overwrite', action='store_true', help='Overwite existing files') parser.add_argument('urls', nargs='*', help='Optional model URLs to download') args = parser.parse_args() @@ -65,7 +70,8 @@ def main() -> int: # Default models if no arguments provided default_models = { 'pth': [ - "https://github.com/remsky/Kokoro-FastAPI/releases/download/v0.1.0/kokoro-v0_19.pth" + "https://github.com/remsky/Kokoro-FastAPI/releases/download/v0.1.0/kokoro-v0_19.pth", + "https://github.com/remsky/Kokoro-FastAPI/releases/download/v0.1.0/kokoro-v0_19-half.pth" ], 'onnx': [ "https://github.com/remsky/Kokoro-FastAPI/releases/download/v0.1.0/kokoro-v0_19.onnx", @@ -79,7 +85,7 @@ def main() -> int: # Download all models success = True for model_url in models_to_download: - if not download_file(model_url, models_dir, args.type): + if not download_file(model_url, models_dir, args.type,args.overwrite): success = False if success: diff --git a/docker/scripts/download_model.sh b/docker/scripts/download_model.sh index 926a831..24513e3 100644 --- a/docker/scripts/download_model.sh +++ b/docker/scripts/download_model.sh @@ -77,6 +77,7 @@ mkdir -p "$MODELS_DIR" if [ "$MODEL_TYPE" = "pth" ]; then DEFAULT_MODELS=( "https://github.com/remsky/Kokoro-FastAPI/releases/download/v0.1.0/kokoro-v0_19.pth" + "https://github.com/remsky/Kokoro-FastAPI/releases/download/v0.1.0/kokoro-v0_19-half.pth" ) else DEFAULT_MODELS=( diff --git a/docker/scripts/entrypoint.sh b/docker/scripts/entrypoint.sh new file mode 100644 index 0000000..993e846 --- /dev/null +++ b/docker/scripts/entrypoint.sh @@ -0,0 +1,12 @@ +#!/bin/bash +set -e + +if [ "$DOWNLOAD_PTH" = "true" ]; then + python docker/scripts/download_model.py --type pth +fi + +if [ "$DOWNLOAD_ONNX" = "true" ]; then + python docker/scripts/download_model.py --type onnx +fi + +exec uv run python -m uvicorn api.src.main:app --host 0.0.0.0 --port 8880 --log-level debug \ No newline at end of file From 355ec54f78d66f55ba79b948dc2297f286df0fc8 Mon Sep 17 00:00:00 2001 From: remsky Date: Tue, 28 Jan 2025 20:41:57 -0700 Subject: [PATCH 26/32] Refactor TTS service and chunker: update comments and remove unused code --- api/src/services/text_processing/chunker.py | 93 +++----- .../text_processing/text_processor.py | 199 ++++++++++-------- api/src/services/tts_service.py | 2 +- 3 files changed, 136 insertions(+), 158 deletions(-) diff --git a/api/src/services/text_processing/chunker.py b/api/src/services/text_processing/chunker.py index 5ffbc00..80e67f0 100644 --- a/api/src/services/text_processing/chunker.py +++ b/api/src/services/text_processing/chunker.py @@ -1,74 +1,31 @@ -"""Text chunking module for TTS processing""" +# """Text chunking module for TTS processing""" -from typing import List, AsyncGenerator -from . import semchunk_slim +# from typing import List, AsyncGenerator -async def fallback_split(text: str, max_chars: int = 400) -> List[str]: - """Emergency length control - only used if chunks are too long""" - words = text.split() - chunks = [] - current = [] - current_len = 0 +# async def fallback_split(text: str, max_chars: int = 400) -> List[str]: +# """Emergency length control - only used if chunks are too long""" +# words = text.split() +# chunks = [] +# current = [] +# current_len = 0 - for word in words: - # Always include at least one word per chunk - if not current: - current.append(word) - current_len = len(word) - continue +# for word in words: +# # Always include at least one word per chunk +# if not current: +# current.append(word) +# current_len = len(word) +# continue - # Check if adding word would exceed limit - if current_len + len(word) + 1 <= max_chars: - current.append(word) - current_len += len(word) + 1 - else: - chunks.append(" ".join(current)) - current = [word] - current_len = len(word) +# # Check if adding word would exceed limit +# if current_len + len(word) + 1 <= max_chars: +# current.append(word) +# current_len += len(word) + 1 +# else: +# chunks.append(" ".join(current)) +# current = [word] +# current_len = len(word) - if current: - chunks.append(" ".join(current)) +# if current: +# chunks.append(" ".join(current)) - return chunks - -async def split_text(text: str, max_chunk: int = None) -> AsyncGenerator[str, None]: - """Split text into TTS-friendly chunks - - Args: - text: Text to split into chunks - max_chunk: Maximum chunk size (defaults to 400) - - Yields: - Text chunks suitable for TTS processing - """ - if max_chunk is None: - max_chunk = 400 - - if not isinstance(text, str): - text = str(text) if text is not None else "" - - text = text.strip() - if not text: - return - - # Initialize chunker targeting ~300 chars to allow for expansion - chunker = semchunk_slim.chunkerify( - lambda t: len(t) // 5, # Simple length-based target - chunk_size=60 # Target ~300 chars - ) - - # Get initial chunks - chunks = chunker(text) - - # Process chunks - for chunk in chunks: - chunk = chunk.strip() - if not chunk: - continue - - # Use fallback for any chunks that are too long - if len(chunk) > max_chunk: - for subchunk in await fallback_split(chunk, max_chunk): - yield subchunk - else: - yield chunk +# return chunks diff --git a/api/src/services/text_processing/text_processor.py b/api/src/services/text_processing/text_processor.py index 8aa0055..74ae2c4 100644 --- a/api/src/services/text_processing/text_processor.py +++ b/api/src/services/text_processing/text_processor.py @@ -8,6 +8,11 @@ from .phonemizer import phonemize from .normalizer import normalize_text from .vocabulary import tokenize +# Constants for chunk size optimization +TARGET_MIN_TOKENS = 300 +TARGET_MAX_TOKENS = 400 +ABSOLUTE_MAX_TOKENS = 500 + def process_text_chunk(text: str, language: str = "a") -> List[int]: """Process a chunk of text through normalization, phonemization, and tokenization. @@ -43,6 +48,15 @@ def process_text_chunk(text: str, language: str = "a") -> List[int]: return tokens +def is_chunk_size_optimal(token_count: int) -> bool: + """Check if chunk size is within optimal range.""" + return TARGET_MIN_TOKENS <= token_count <= TARGET_MAX_TOKENS + +async def yield_chunk(text: str, tokens: List[int], chunk_count: int) -> Tuple[str, List[int]]: + """Yield a chunk with consistent logging.""" + logger.info(f"Yielding chunk {chunk_count}: '{text[:50]}...' ({len(tokens)} tokens)") + return text, tokens + def process_text(text: str, language: str = "a") -> List[int]: """Process text into token IDs. @@ -62,116 +76,123 @@ def process_text(text: str, language: str = "a") -> List[int]: return process_text_chunk(text, language) -async def smart_split(text: str, max_tokens: int = 500) -> AsyncGenerator[Tuple[str, List[int]], None]: - """Split text into semantically meaningful chunks while respecting token limits. - - Args: - text: Input text to split - max_tokens: Maximum tokens per chunk - - Yields: - Tuples of (text chunk, token IDs) where token count is <= max_tokens - """ - start_time = time.time() - chunk_count = 0 - total_chars = len(text) - logger.info(f"Starting text split for {total_chars} characters with {max_tokens} max tokens") - - # Split on major punctuation first +# Target token ranges +TARGET_MIN = 300 +TARGET_MAX = 400 +ABSOLUTE_MAX = 500 + +def get_sentence_info(text: str) -> List[Tuple[str, List[int], int]]: + """Process all sentences and return info.""" sentences = re.split(r'([.!?;:])', text) - - current_chunk = [] - current_token_count = 0 + results = [] for i in range(0, len(sentences), 2): - # Get sentence and its punctuation (if any) sentence = sentences[i].strip() punct = sentences[i + 1] if i + 1 < len(sentences) else "" if not sentence: continue - # Process sentence to get token count - sentence_with_punct = sentence + punct - tokens = process_text_chunk(sentence_with_punct) - token_count = len(tokens) - logger.debug(f"Sentence '{sentence_with_punct[:50]}...' has {token_count} tokens") + full = sentence + punct + tokens = process_text_chunk(full) + results.append((full, tokens, len(tokens))) - # If this single sentence is too long, split on commas - if token_count > max_tokens: - logger.debug(f"Sentence exceeds token limit, splitting on commas") - clause_splits = re.split(r'([,])', sentence_with_punct) - for j in range(0, len(clause_splits), 2): - clause = clause_splits[j].strip() - comma = clause_splits[j + 1] if j + 1 < len(clause_splits) else "" + return results + +async def smart_split(text: str, max_tokens: int = ABSOLUTE_MAX) -> AsyncGenerator[Tuple[str, List[int]], None]: + """Build optimal chunks targeting 300-400 tokens, never exceeding max_tokens.""" + start_time = time.time() + chunk_count = 0 + logger.info(f"Starting smart split for {len(text)} chars") + + # Process all sentences + sentences = get_sentence_info(text) + + current_chunk = [] + current_tokens = [] + current_count = 0 + + for sentence, tokens, count in sentences: + # Handle sentences that exceed max tokens + if count > max_tokens: + # Yield current chunk if any + if current_chunk: + chunk_text = " ".join(current_chunk) + chunk_count += 1 + logger.info(f"Yielding chunk {chunk_count}: '{chunk_text[:50]}...' ({current_count} tokens)") + yield chunk_text, current_tokens + current_chunk = [] + current_tokens = [] + current_count = 0 + + # Split long sentence on commas + clauses = re.split(r'([,])', sentence) + clause_chunk = [] + clause_tokens = [] + clause_count = 0 + + for j in range(0, len(clauses), 2): + clause = clauses[j].strip() + comma = clauses[j + 1] if j + 1 < len(clauses) else "" if not clause: continue - clause_with_punct = clause + comma - clause_tokens = process_text_chunk(clause_with_punct) + full_clause = clause + comma + tokens = process_text_chunk(full_clause) + count = len(tokens) - # If still too long, do a hard split on words - if len(clause_tokens) > max_tokens: - logger.debug(f"Clause exceeds token limit, splitting on words") - words = clause_with_punct.split() - temp_chunk = [] - temp_tokens = [] - - for word in words: - word_tokens = process_text_chunk(word) - if len(temp_tokens) + len(word_tokens) > max_tokens: - if temp_chunk: # Don't yield empty chunks - chunk_text = " ".join(temp_chunk) - chunk_count += 1 - logger.info(f"Yielding word-split chunk {chunk_count}: '{chunk_text[:50]}...' ({len(temp_tokens)} tokens)") - yield chunk_text, temp_tokens - temp_chunk = [word] - temp_tokens = word_tokens - else: - temp_chunk.append(word) - temp_tokens.extend(word_tokens) - - if temp_chunk: # Don't forget the last chunk - chunk_text = " ".join(temp_chunk) - chunk_count += 1 - logger.info(f"Yielding final word-split chunk {chunk_count}: '{chunk_text[:50]}...' ({len(temp_tokens)} tokens)") - yield chunk_text, temp_tokens - + # If adding clause keeps us under max and not optimal yet + if clause_count + count <= max_tokens and clause_count + count <= TARGET_MAX: + clause_chunk.append(full_clause) + clause_tokens.extend(tokens) + clause_count += count else: - # Check if adding this clause would exceed the limit - if current_token_count + len(clause_tokens) > max_tokens: - if current_chunk: # Don't yield empty chunks - chunk_text = " ".join(current_chunk) - chunk_count += 1 - logger.info(f"Yielding clause-split chunk {chunk_count}: '{chunk_text[:50]}...' ({current_token_count} tokens)") - yield chunk_text, process_text_chunk(chunk_text) - current_chunk = [clause_with_punct] - current_token_count = len(clause_tokens) - else: - current_chunk.append(clause_with_punct) - current_token_count += len(clause_tokens) - + # Yield clause chunk if we have one + if clause_chunk: + chunk_text = " ".join(clause_chunk) + chunk_count += 1 + logger.info(f"Yielding clause chunk {chunk_count}: '{chunk_text[:50]}...' ({clause_count} tokens)") + yield chunk_text, clause_tokens + clause_chunk = [full_clause] + clause_tokens = tokens + clause_count = count + + # Don't forget last clause chunk + if clause_chunk: + chunk_text = " ".join(clause_chunk) + chunk_count += 1 + logger.info(f"Yielding final clause chunk {chunk_count}: '{chunk_text[:50]}...' ({clause_count} tokens)") + yield chunk_text, clause_tokens + + # Regular sentence handling + elif current_count + count <= TARGET_MAX: + # Keep building chunk while under target max + current_chunk.append(sentence) + current_tokens.extend(tokens) + current_count += count + elif current_count + count <= max_tokens: + # Accept slightly larger chunk if needed + current_chunk.append(sentence) + current_tokens.extend(tokens) + current_count += count else: - # Check if adding this sentence would exceed the limit - if current_token_count + token_count > max_tokens: - if current_chunk: # Don't yield empty chunks - chunk_text = " ".join(current_chunk) - chunk_count += 1 - logger.info(f"Yielding sentence-split chunk {chunk_count}: '{chunk_text[:50]}...' ({current_token_count} tokens)") - yield chunk_text, process_text_chunk(chunk_text) - current_chunk = [sentence_with_punct] - current_token_count = token_count - else: - current_chunk.append(sentence_with_punct) - current_token_count += token_count + # Yield current chunk and start new one + if current_chunk: + chunk_text = " ".join(current_chunk) + chunk_count += 1 + logger.info(f"Yielding chunk {chunk_count}: '{chunk_text[:50]}...' ({current_count} tokens)") + yield chunk_text, current_tokens + current_chunk = [sentence] + current_tokens = tokens + current_count = count # Don't forget the last chunk if current_chunk: chunk_text = " ".join(current_chunk) chunk_count += 1 - logger.info(f"Yielding final chunk {chunk_count}: '{chunk_text[:50]}...' ({current_token_count} tokens)") - yield chunk_text, process_text_chunk(chunk_text) + logger.info(f"Yielding final chunk {chunk_count}: '{chunk_text[:50]}...' ({current_count} tokens)") + yield chunk_text, current_tokens total_time = time.time() - start_time - logger.info(f"Text splitting completed in {total_time*1000:.2f}ms, produced {chunk_count} chunks") + logger.info(f"Split completed in {total_time*1000:.2f}ms, produced {chunk_count} chunks") diff --git a/api/src/services/tts_service.py b/api/src/services/tts_service.py index e6ef24d..5fbffd5 100644 --- a/api/src/services/tts_service.py +++ b/api/src/services/tts_service.py @@ -119,7 +119,7 @@ class TTSService: try: # Process audio for chunk result = await self._process_chunk( - tokens, + tokens, # Now always a flat List[int] voice_tensor, speed, output_format, From 946e322242908cfacc8e3dcde20c74810f44aa81 Mon Sep 17 00:00:00 2001 From: remsky Date: Wed, 29 Jan 2025 04:09:38 -0700 Subject: [PATCH 27/32] Implement temporary file management on openai endpoint, whole file downloads --- api/src/core/config.py | 5 + api/src/core/paths.py | 76 +++++++++++- api/src/main.py | 4 + api/src/routers/openai_compatible.py | 112 ++++++++++++++---- api/src/services/streaming_audio_writer.py | 26 ++-- api/src/services/temp_manager.py | 89 ++++++++++++++ .../text_processing/text_processor.py | 30 ++--- api/src/structures/schemas.py | 4 + web/src/App.js | 10 ++ web/src/services/AudioService.js | 54 ++++++--- 10 files changed, 346 insertions(+), 64 deletions(-) create mode 100644 api/src/services/temp_manager.py diff --git a/api/src/core/config.py b/api/src/core/config.py index 0337088..d22f427 100644 --- a/api/src/core/config.py +++ b/api/src/core/config.py @@ -32,6 +32,11 @@ class Settings(BaseSettings): cors_origins: list[str] = ["*"] # CORS origins for web player cors_enabled: bool = True # Whether to enable CORS + # Temp File Settings + temp_file_dir: str = "api/temp_files" # Directory for temporary audio files (relative to project root) + max_temp_dir_size_mb: int = 2048 # Maximum size of temp directory (2GB) + temp_file_max_age_hours: int = 1 # Remove temp files older than 1 hour + class Config: env_file = ".env" diff --git a/api/src/core/paths.py b/api/src/core/paths.py index 34675bc..eeee704 100644 --- a/api/src/core/paths.py +++ b/api/src/core/paths.py @@ -337,4 +337,78 @@ async def get_content_type(path: str) -> str: async def verify_model_path(model_path: str) -> bool: """Verify model file exists at path.""" - return await aiofiles.os.path.exists(model_path) \ No newline at end of file + return await aiofiles.os.path.exists(model_path) + + +async def cleanup_temp_files() -> None: + """Clean up old temp files on startup""" + try: + if not await aiofiles.os.path.exists(settings.temp_file_dir): + await aiofiles.os.makedirs(settings.temp_file_dir, exist_ok=True) + return + + entries = await aiofiles.os.scandir(settings.temp_file_dir) + for entry in entries: + if entry.is_file(): + stat = await aiofiles.os.stat(entry.path) + max_age = stat.st_mtime + (settings.temp_file_max_age_hours * 3600) + if max_age < stat.st_mtime: + try: + await aiofiles.os.remove(entry.path) + logger.info(f"Cleaned up old temp file: {entry.name}") + except Exception as e: + logger.warning(f"Failed to delete old temp file {entry.name}: {e}") + except Exception as e: + logger.warning(f"Error cleaning temp files: {e}") + + +async def get_temp_file_path(filename: str) -> str: + """Get path to temporary audio file. + + Args: + filename: Name of temp file + + Returns: + Absolute path to temp file + + Raises: + RuntimeError: If temp directory does not exist + """ + temp_path = os.path.join(settings.temp_file_dir, filename) + + # Ensure temp directory exists + if not await aiofiles.os.path.exists(settings.temp_file_dir): + await aiofiles.os.makedirs(settings.temp_file_dir, exist_ok=True) + + return temp_path + + +async def list_temp_files() -> List[str]: + """List temporary audio files. + + Returns: + List of temp file names + """ + if not await aiofiles.os.path.exists(settings.temp_file_dir): + return [] + + entries = await aiofiles.os.scandir(settings.temp_file_dir) + return [entry.name for entry in entries if entry.is_file()] + + +async def get_temp_dir_size() -> int: + """Get total size of temp directory in bytes. + + Returns: + Size in bytes + """ + if not await aiofiles.os.path.exists(settings.temp_file_dir): + return 0 + + total = 0 + entries = await aiofiles.os.scandir(settings.temp_file_dir) + for entry in entries: + if entry.is_file(): + stat = await aiofiles.os.stat(entry.path) + total += stat.st_size + return total \ No newline at end of file diff --git a/api/src/main.py b/api/src/main.py index 759bbd7..744977e 100644 --- a/api/src/main.py +++ b/api/src/main.py @@ -48,6 +48,10 @@ async def lifespan(app: FastAPI): """Lifespan context manager for model initialization""" from .inference.model_manager import get_manager from .inference.voice_manager import get_manager as get_voice_manager + from .core.paths import cleanup_temp_files + + # Clean old temp files on startup + await cleanup_temp_files() logger.info("Loading TTS model and voice packs...") diff --git a/api/src/routers/openai_compatible.py b/api/src/routers/openai_compatible.py index f4b8684..c670304 100644 --- a/api/src/routers/openai_compatible.py +++ b/api/src/routers/openai_compatible.py @@ -5,7 +5,7 @@ import os from typing import AsyncGenerator, Dict, List, Union from fastapi import APIRouter, Depends, Header, HTTPException, Request, Response -from fastapi.responses import StreamingResponse +from fastapi.responses import StreamingResponse, FileResponse from loguru import logger from ..services.audio import AudioService @@ -179,36 +179,59 @@ async def create_speech( # Create generator but don't start it yet generator = stream_audio_chunks(tts_service, request, client_request) - # Test the generator by attempting to get first chunk - try: - first_chunk = await anext(generator) - except StopAsyncIteration: - first_chunk = b"" # Empty audio case - except Exception as e: - # Re-raise any errors to be caught by the outer try-except - raise RuntimeError(f"Failed to initialize audio stream: {str(e)}") from e + # If download link requested, wrap generator with temp file writer + if request.return_download_link: + from ..services.temp_manager import TempFileWriter + + temp_writer = TempFileWriter(request.response_format) + await temp_writer.__aenter__() # Initialize temp file + + # Create response headers + headers = { + "Content-Disposition": f"attachment; filename=speech.{request.response_format}", + "X-Accel-Buffering": "no", + "Cache-Control": "no-cache", + "Transfer-Encoding": "chunked" + } + + # Create async generator for streaming + async def dual_output(): + try: + # Write chunks to temp file and stream + async for chunk in generator: + if chunk: # Skip empty chunks + await temp_writer.write(chunk) + yield chunk + + # Get download path and add to headers + download_path = await temp_writer.finalize() + headers["X-Download-Path"] = download_path + except Exception as e: + logger.error(f"Error in dual output streaming: {e}") + await temp_writer.__aexit__(type(e), e, e.__traceback__) + raise + finally: + # Ensure temp writer is closed + if not temp_writer._finalized: + await temp_writer.__aexit__(None, None, None) + + # Stream with temp file writing + return StreamingResponse( + dual_output(), + media_type=content_type, + headers=headers + ) - # If we got here, streaming can begin - async def safe_stream(): - yield first_chunk - try: - async for chunk in generator: - yield chunk - except Exception as e: - # Log the error but don't yield anything - the connection will close - logger.error(f"Error during streaming: {str(e)}") - raise - - # Stream audio chunks as they're generated + # Standard streaming without download link return StreamingResponse( - safe_stream(), + generator, media_type=content_type, headers={ "Content-Disposition": f"attachment; filename=speech.{request.response_format}", - "X-Accel-Buffering": "no", # Disable proxy buffering - "Cache-Control": "no-cache", # Prevent caching - "Transfer-Encoding": "chunked", # Enable chunked transfer encoding - }, + "X-Accel-Buffering": "no", + "Cache-Control": "no-cache", + "Transfer-Encoding": "chunked" + } ) else: # Generate complete audio using public interface @@ -268,6 +291,43 @@ async def create_speech( ) +@router.get("/download/{filename}") +async def download_audio_file(filename: str): + """Download a generated audio file from temp storage""" + try: + from ..core.paths import _find_file, get_content_type + + # Search for file in temp directory + file_path = await _find_file( + filename=filename, + search_paths=[settings.temp_file_dir] + ) + + # Get content type from path helper + content_type = await get_content_type(file_path) + + return FileResponse( + file_path, + media_type=content_type, + filename=filename, + headers={ + "Cache-Control": "no-cache", + "Content-Disposition": f"attachment; filename={filename}" + } + ) + + except Exception as e: + logger.error(f"Error serving download file {filename}: {e}") + raise HTTPException( + status_code=500, + detail={ + "error": "server_error", + "message": "Failed to serve audio file", + "type": "server_error" + } + ) + + @router.get("/audio/voices") async def list_voices(): """List all available voices for text-to-speech""" diff --git a/api/src/services/streaming_audio_writer.py b/api/src/services/streaming_audio_writer.py index 1084c91..adae5d8 100644 --- a/api/src/services/streaming_audio_writer.py +++ b/api/src/services/streaming_audio_writer.py @@ -108,15 +108,27 @@ class StreamingAudioWriter: "aac": {"format": "adts", "codec": "aac"} }[self.format] + # On finalization, include proper headers and duration metadata + parameters = [ + "-q:a", "2", + "-write_xing", "1" if self.format == "mp3" else "0", # XING header for MP3 only + "-metadata", f"duration={self.total_duration/1000}", # Duration in seconds + "-write_id3v1", "1" if self.format == "mp3" else "0", # ID3v1 tag for MP3 + "-write_id3v2", "1" if self.format == "mp3" else "0" # ID3v2 tag for MP3 + ] + + if self.format == "mp3": + # For MP3, ensure proper VBR headers + parameters.extend([ + "-write_vbr", "1", + "-vbr_quality", "2" + ]) + self.encoder.export( output_buffer, **format_args, bitrate="192k", - parameters=[ - "-q:a", "2", - "-write_xing", "1" if self.format == "mp3" else "0", # XING header for MP3 only - "-metadata", f"duration={self.total_duration/1000}" # Duration in seconds - ] + parameters=parameters ) self.encoder = None return output_buffer.getvalue() @@ -163,10 +175,10 @@ class StreamingAudioWriter: "aac": {"format": "adts", "codec": "aac"} }[self.format] + # For chunks, export without duration metadata or XING headers self.encoder.export(output_buffer, **format_args, bitrate="192k", parameters=[ "-q:a", "2", - "-write_xing", "1" if self.format == "mp3" else "0", # XING header for MP3 only - "-metadata", f"duration={self.total_duration/1000}" # Duration in seconds + "-write_xing", "0" # No XING headers for chunks ]) # Get the encoded data diff --git a/api/src/services/temp_manager.py b/api/src/services/temp_manager.py new file mode 100644 index 0000000..63b4be5 --- /dev/null +++ b/api/src/services/temp_manager.py @@ -0,0 +1,89 @@ +"""Temporary file writer for audio downloads""" + +import os +import tempfile +from typing import Optional + +import aiofiles +from fastapi import HTTPException +from loguru import logger + +from ..core.config import settings +from ..core.paths import _scan_directories + + +class TempFileWriter: + """Handles writing audio chunks to a temp file""" + + def __init__(self, format: str): + """Initialize temp file writer + + Args: + format: Audio format extension (mp3, wav, etc) + """ + self.format = format + self.temp_file = None + self._finalized = False + + async def __aenter__(self): + """Async context manager entry""" + # Check temp dir size by scanning + total_size = 0 + entries = await _scan_directories([settings.temp_file_dir]) + for entry in entries: + stat = await aiofiles.os.stat(os.path.join(settings.temp_file_dir, entry)) + total_size += stat.st_size + + if total_size >= settings.max_temp_dir_size_mb * 1024 * 1024: + raise HTTPException( + status_code=507, + detail="Temporary storage full. Please try again later." + ) + + # Create temp file with proper extension + os.makedirs(settings.temp_file_dir, exist_ok=True) + temp = tempfile.NamedTemporaryFile( + dir=settings.temp_file_dir, + delete=False, + suffix=f".{self.format}", + mode='wb' + ) + self.temp_file = await aiofiles.open(temp.name, mode='wb') + self.temp_path = temp.name + temp.close() # Close sync file, we'll use async version + return self + + async def __aexit__(self, exc_type, exc_val, exc_tb): + """Async context manager exit""" + try: + if self.temp_file and not self._finalized: + await self.temp_file.close() + self._finalized = True + except Exception as e: + logger.error(f"Error closing temp file: {e}") + + async def write(self, chunk: bytes) -> None: + """Write a chunk of audio data + + Args: + chunk: Audio data bytes to write + """ + if self._finalized: + raise RuntimeError("Cannot write to finalized temp file") + + await self.temp_file.write(chunk) + await self.temp_file.flush() + + async def finalize(self) -> str: + """Close temp file and return download path + + Returns: + Path to use for downloading the temp file + """ + if self._finalized: + raise RuntimeError("Temp file already finalized") + + await self.temp_file.close() + self._finalized = True + + return f"/download/{os.path.basename(self.temp_path)}" \ No newline at end of file diff --git a/api/src/services/text_processing/text_processor.py b/api/src/services/text_processing/text_processor.py index 74ae2c4..98fc45a 100644 --- a/api/src/services/text_processing/text_processor.py +++ b/api/src/services/text_processing/text_processor.py @@ -8,10 +8,10 @@ from .phonemizer import phonemize from .normalizer import normalize_text from .vocabulary import tokenize -# Constants for chunk size optimization -TARGET_MIN_TOKENS = 300 -TARGET_MAX_TOKENS = 400 -ABSOLUTE_MAX_TOKENS = 500 +# Target token ranges +TARGET_MIN = 200 +TARGET_MAX = 350 +ABSOLUTE_MAX = 500 def process_text_chunk(text: str, language: str = "a") -> List[int]: """Process a chunk of text through normalization, phonemization, and tokenization. @@ -48,10 +48,6 @@ def process_text_chunk(text: str, language: str = "a") -> List[int]: return tokens -def is_chunk_size_optimal(token_count: int) -> bool: - """Check if chunk size is within optimal range.""" - return TARGET_MIN_TOKENS <= token_count <= TARGET_MAX_TOKENS - async def yield_chunk(text: str, tokens: List[int], chunk_count: int) -> Tuple[str, List[int]]: """Yield a chunk with consistent logging.""" logger.info(f"Yielding chunk {chunk_count}: '{text[:50]}...' ({len(tokens)} tokens)") @@ -76,10 +72,6 @@ def process_text(text: str, language: str = "a") -> List[int]: return process_text_chunk(text, language) -# Target token ranges -TARGET_MIN = 300 -TARGET_MAX = 400 -ABSOLUTE_MAX = 500 def get_sentence_info(text: str) -> List[Tuple[str, List[int], int]]: """Process all sentences and return info.""" @@ -166,13 +158,23 @@ async def smart_split(text: str, max_tokens: int = ABSOLUTE_MAX) -> AsyncGenerat yield chunk_text, clause_tokens # Regular sentence handling + elif current_count >= TARGET_MIN and current_count + count > TARGET_MAX: + # If we have a good sized chunk and adding next sentence exceeds target, + # yield current chunk and start new one + chunk_text = " ".join(current_chunk) + chunk_count += 1 + logger.info(f"Yielding chunk {chunk_count}: '{chunk_text[:50]}...' ({current_count} tokens)") + yield chunk_text, current_tokens + current_chunk = [sentence] + current_tokens = tokens + current_count = count elif current_count + count <= TARGET_MAX: # Keep building chunk while under target max current_chunk.append(sentence) current_tokens.extend(tokens) current_count += count - elif current_count + count <= max_tokens: - # Accept slightly larger chunk if needed + elif current_count + count <= max_tokens and current_count < TARGET_MIN: + # Only exceed target max if we haven't reached minimum size yet current_chunk.append(sentence) current_tokens.extend(tokens) current_count += count diff --git a/api/src/structures/schemas.py b/api/src/structures/schemas.py index 63f4428..0ae50ce 100644 --- a/api/src/structures/schemas.py +++ b/api/src/structures/schemas.py @@ -46,3 +46,7 @@ class OpenAISpeechRequest(BaseModel): default=True, # Default to streaming for OpenAI compatibility description="If true (default), audio will be streamed as it's generated. Each chunk will be a complete sentence.", ) + return_download_link: bool = Field( + default=False, + description="If true, returns a download link in X-Download-Path header after streaming completes", + ) diff --git a/web/src/App.js b/web/src/App.js index 41382b0..95d3627 100644 --- a/web/src/App.js +++ b/web/src/App.js @@ -77,9 +77,19 @@ export class App { // Handle completion this.audioService.addEventListener('complete', () => { this.setGenerating(false); + this.showStatus('Preparing file...', 'info'); + }); + + // Handle download ready + this.audioService.addEventListener('downloadReady', () => { this.showStatus('Generation complete', 'success'); }); + // Handle audio end + this.audioService.addEventListener('ended', () => { + this.playerState.setPlaying(false); + }); + // Handle errors this.audioService.addEventListener('error', (error) => { this.showStatus('Error: ' + error.message, 'error'); diff --git a/web/src/services/AudioService.js b/web/src/services/AudioService.js index adbd20a..74ceeb4 100644 --- a/web/src/services/AudioService.js +++ b/web/src/services/AudioService.js @@ -9,7 +9,8 @@ export class AudioService { this.minimumPlaybackSize = 50000; // 50KB minimum before playback this.textLength = 0; this.shouldAutoplay = false; - this.CHARS_PER_CHUNK = 600; // Estimated chars per chunk + this.CHARS_PER_CHUNK = 300; // Estimated chars per chunk + this.serverDownloadPath = null; // Server-side download path } async streamAudio(text, voice, speed, onProgress) { @@ -45,7 +46,8 @@ export class AudioService { voice: voice, response_format: 'mp3', stream: true, - speed: speed + speed: speed, + return_download_link: true }), signal: this.controller.signal }); @@ -58,7 +60,7 @@ export class AudioService { throw new Error(error.detail?.message || 'Failed to generate speech'); } - await this.setupAudioStream(response, onProgress, estimatedChunks); + await this.setupAudioStream(response.body, response, onProgress, estimatedChunks); return this.audio; } catch (error) { this.cleanup(); @@ -66,16 +68,21 @@ export class AudioService { } } - async setupAudioStream(response, onProgress, estimatedTotalSize) { + async setupAudioStream(stream, response, onProgress, estimatedTotalSize) { this.audio = new Audio(); this.mediaSource = new MediaSource(); this.audio.src = URL.createObjectURL(this.mediaSource); + + // Set up ended event handler + this.audio.addEventListener('ended', () => { + this.dispatchEvent('ended'); + }); return new Promise((resolve, reject) => { this.mediaSource.addEventListener('sourceopen', async () => { try { this.sourceBuffer = this.mediaSource.addSourceBuffer('audio/mpeg'); - await this.processStream(response.body, onProgress, estimatedTotalSize); + await this.processStream(stream, response, onProgress, estimatedTotalSize); resolve(); } catch (error) { reject(error); @@ -84,11 +91,17 @@ export class AudioService { }); } - async processStream(stream, onProgress, estimatedChunks) { + async processStream(stream, response, onProgress, estimatedChunks) { const reader = stream.getReader(); let hasStartedPlaying = false; let receivedChunks = 0; + // Check for download path in response headers + const downloadPath = response.headers.get('X-Download-Path'); + if (downloadPath) { + this.serverDownloadPath = downloadPath; + } + try { while (true) { const {value, done} = await reader.read(); @@ -245,6 +258,7 @@ export class AudioService { this.sourceBuffer = null; this.chunks = []; this.textLength = 0; + this.serverDownloadPath = null; // Force a hard refresh of the page to ensure clean state window.location.reload(); @@ -277,17 +291,25 @@ export class AudioService { this.sourceBuffer = null; this.chunks = []; this.textLength = 0; + this.serverDownloadPath = null; } - - getDownloadUrl() { - if (!this.audio || !this.sourceBuffer || this.chunks.length === 0) return null; - - // Get the buffered data from MediaSource - const buffered = this.sourceBuffer.buffered; - if (buffered.length === 0) return null; - - // Create blob from the original chunks - const blob = new Blob(this.chunks, { type: 'audio/mpeg' }); +getDownloadUrl() { + // Check for server-side download link first + const downloadPath = this.serverDownloadPath; + if (downloadPath) { + return downloadPath; + } + + // Fall back to client-side blob URL + if (!this.audio || !this.sourceBuffer || this.chunks.length === 0) return null; + + // Get the buffered data from MediaSource + const buffered = this.sourceBuffer.buffered; + if (buffered.length === 0) return null; + + // Create blob from the original chunks + const blob = new Blob(this.chunks, { type: 'audio/mpeg' }); + return URL.createObjectURL(blob); return URL.createObjectURL(blob); } } From 2e318051f8c408ad055b276a353f7b62dafbfb3d Mon Sep 17 00:00:00 2001 From: remsky Date: Wed, 29 Jan 2025 18:29:02 -0700 Subject: [PATCH 28/32] Add clear text button and enhance temporary file management - Introduced a "Clear Text" button in the web interface for user convenience. - Updated temporary file management settings in the configuration. - Added new debug endpoints for system and storage information. - Improved logging levels for better debugging insights. --- .gitignore | 1 + api/src/core/config.py | 6 +- api/src/core/paths.py | 2 +- api/src/main.py | 4 +- api/src/routers/debug.py | 134 +++++++++++ api/src/services/temp_manager.py | 77 ++++-- api/src/services/text_processing/chunker.py | 31 --- .../text_processing/text_processor.py | 8 +- api/tests/debug.http | 11 + api/tests/test_chunker.py | 46 ---- pyproject.toml | 1 + uv.lock | 17 ++ web/index.html | 3 + web/src/App.js | 39 +++- web/src/services/AudioService.js | 220 +++++++++++++----- web/styles/forms.css | 14 ++ 16 files changed, 445 insertions(+), 169 deletions(-) create mode 100644 api/src/routers/debug.py delete mode 100644 api/src/services/text_processing/chunker.py create mode 100644 api/tests/debug.http delete mode 100644 api/tests/test_chunker.py diff --git a/.gitignore b/.gitignore index a36b9f2..a8c2eee 100644 --- a/.gitignore +++ b/.gitignore @@ -56,6 +56,7 @@ Kokoro-82M/ ui/data/ EXTERNAL_UV_DOCUMENTATION* app +api/temp_files/ # Docker Dockerfile* diff --git a/api/src/core/config.py b/api/src/core/config.py index d22f427..44d4b8a 100644 --- a/api/src/core/config.py +++ b/api/src/core/config.py @@ -32,10 +32,12 @@ class Settings(BaseSettings): cors_origins: list[str] = ["*"] # CORS origins for web player cors_enabled: bool = True # Whether to enable CORS - # Temp File Settings + # Temp File Settings for WEB Ui temp_file_dir: str = "api/temp_files" # Directory for temporary audio files (relative to project root) max_temp_dir_size_mb: int = 2048 # Maximum size of temp directory (2GB) - temp_file_max_age_hours: int = 1 # Remove temp files older than 1 hour + max_temp_dir_age_hours: int = 1 # Remove temp files older than 1 hour + max_temp_dir_count: int = 3 # Maximum number of temp files to keep + class Config: env_file = ".env" diff --git a/api/src/core/paths.py b/api/src/core/paths.py index eeee704..0c6bfff 100644 --- a/api/src/core/paths.py +++ b/api/src/core/paths.py @@ -351,7 +351,7 @@ async def cleanup_temp_files() -> None: for entry in entries: if entry.is_file(): stat = await aiofiles.os.stat(entry.path) - max_age = stat.st_mtime + (settings.temp_file_max_age_hours * 3600) + max_age = stat.st_mtime + (settings.max_temp_dir_age_hours * 3600) if max_age < stat.st_mtime: try: await aiofiles.os.remove(entry.path) diff --git a/api/src/main.py b/api/src/main.py index 744977e..3c505d7 100644 --- a/api/src/main.py +++ b/api/src/main.py @@ -18,6 +18,7 @@ from .routers.web_player import router as web_router from .core.model_config import model_config from .routers.development import router as dev_router from .routers.openai_compatible import router as openai_router +from .routers.debug import router as debug_router from .services.tts_service import TTSService @@ -48,7 +49,7 @@ async def lifespan(app: FastAPI): """Lifespan context manager for model initialization""" from .inference.model_manager import get_manager from .inference.voice_manager import get_manager as get_voice_manager - from .core.paths import cleanup_temp_files + from .services.temp_manager import cleanup_temp_files # Clean old temp files on startup await cleanup_temp_files() @@ -130,6 +131,7 @@ if settings.cors_enabled: # Include routers app.include_router(openai_router, prefix="/v1") app.include_router(dev_router) # Development endpoints +app.include_router(debug_router) # Debug endpoints if settings.enable_web_player: app.include_router(web_router, prefix="/web") # Web player static files diff --git a/api/src/routers/debug.py b/api/src/routers/debug.py new file mode 100644 index 0000000..86fff94 --- /dev/null +++ b/api/src/routers/debug.py @@ -0,0 +1,134 @@ +from fastapi import APIRouter +import psutil +import threading +import time +from datetime import datetime +try: + import GPUtil + GPU_AVAILABLE = True +except ImportError: + GPU_AVAILABLE = False + +router = APIRouter(tags=["debug"]) + +@router.get("/debug/threads") +async def get_thread_info(): + process = psutil.Process() + current_threads = threading.enumerate() + + # Get per-thread CPU times + thread_details = [] + for thread in current_threads: + thread_info = { + "name": thread.name, + "id": thread.ident, + "alive": thread.is_alive(), + "daemon": thread.daemon + } + thread_details.append(thread_info) + + return { + "total_threads": process.num_threads(), + "active_threads": len(current_threads), + "thread_names": [t.name for t in current_threads], + "thread_details": thread_details, + "memory_mb": process.memory_info().rss / 1024 / 1024 + } + +@router.get("/debug/storage") +async def get_storage_info(): + # Get disk partitions + partitions = psutil.disk_partitions() + storage_info = [] + + for partition in partitions: + try: + usage = psutil.disk_usage(partition.mountpoint) + storage_info.append({ + "device": partition.device, + "mountpoint": partition.mountpoint, + "fstype": partition.fstype, + "total_gb": usage.total / (1024**3), + "used_gb": usage.used / (1024**3), + "free_gb": usage.free / (1024**3), + "percent_used": usage.percent + }) + except PermissionError: + continue + + return { + "storage_info": storage_info + } + +@router.get("/debug/system") +async def get_system_info(): + process = psutil.Process() + + # CPU Info + cpu_info = { + "cpu_count": psutil.cpu_count(), + "cpu_percent": psutil.cpu_percent(interval=1), + "per_cpu_percent": psutil.cpu_percent(interval=1, percpu=True), + "load_avg": psutil.getloadavg() + } + + # Memory Info + virtual_memory = psutil.virtual_memory() + swap_memory = psutil.swap_memory() + memory_info = { + "virtual": { + "total_gb": virtual_memory.total / (1024**3), + "available_gb": virtual_memory.available / (1024**3), + "used_gb": virtual_memory.used / (1024**3), + "percent": virtual_memory.percent + }, + "swap": { + "total_gb": swap_memory.total / (1024**3), + "used_gb": swap_memory.used / (1024**3), + "free_gb": swap_memory.free / (1024**3), + "percent": swap_memory.percent + } + } + + # Process Info + process_info = { + "pid": process.pid, + "status": process.status(), + "create_time": datetime.fromtimestamp(process.create_time()).isoformat(), + "cpu_percent": process.cpu_percent(), + "memory_percent": process.memory_percent(), + } + + # Network Info + network_info = { + "connections": len(process.net_connections()), + "network_io": psutil.net_io_counters()._asdict() + } + + # GPU Info if available + gpu_info = None + if GPU_AVAILABLE: + try: + gpus = GPUtil.getGPUs() + gpu_info = [{ + "id": gpu.id, + "name": gpu.name, + "load": gpu.load, + "memory": { + "total": gpu.memoryTotal, + "used": gpu.memoryUsed, + "free": gpu.memoryFree, + "percent": (gpu.memoryUsed / gpu.memoryTotal) * 100 + }, + "temperature": gpu.temperature + } for gpu in gpus] + except Exception: + gpu_info = "GPU information unavailable" + + return { + "cpu": cpu_info, + "memory": memory_info, + "process": process_info, + "network": network_info, + "gpu": gpu_info + } \ No newline at end of file diff --git a/api/src/services/temp_manager.py b/api/src/services/temp_manager.py index 63b4be5..bfb825c 100644 --- a/api/src/services/temp_manager.py +++ b/api/src/services/temp_manager.py @@ -2,14 +2,71 @@ import os import tempfile -from typing import Optional +from typing import Optional, List import aiofiles from fastapi import HTTPException from loguru import logger from ..core.config import settings -from ..core.paths import _scan_directories + + +async def cleanup_temp_files() -> None: + """Clean up old temp files""" + try: + if not await aiofiles.os.path.exists(settings.temp_file_dir): + await aiofiles.os.makedirs(settings.temp_file_dir, exist_ok=True) + return + + # Get all temp files with stats + files = [] + total_size = 0 + + # Use os.scandir for sync iteration, but aiofiles.os.stat for async stats + for entry in os.scandir(settings.temp_file_dir): + if entry.is_file(): + stat = await aiofiles.os.stat(entry.path) + files.append((entry.path, stat.st_mtime, stat.st_size)) + total_size += stat.st_size + + # Sort by modification time (oldest first) + files.sort(key=lambda x: x[1]) + + # Remove files if: + # 1. They're too old + # 2. We have too many files + # 3. Directory is too large + current_time = (await aiofiles.os.stat(settings.temp_file_dir)).st_mtime + max_age = settings.max_temp_dir_age_hours * 3600 + + for path, mtime, size in files: + should_delete = False + + # Check age + if current_time - mtime > max_age: + should_delete = True + logger.info(f"Deleting old temp file: {path}") + + # Check count limit + elif len(files) > settings.max_temp_dir_count: + should_delete = True + logger.info(f"Deleting excess temp file: {path}") + + # Check size limit + elif total_size > settings.max_temp_dir_size_mb * 1024 * 1024: + should_delete = True + logger.info(f"Deleting to reduce directory size: {path}") + + if should_delete: + try: + await aiofiles.os.remove(path) + total_size -= size + logger.info(f"Deleted temp file: {path}") + except Exception as e: + logger.warning(f"Failed to delete temp file {path}: {e}") + + except Exception as e: + logger.warning(f"Error during temp file cleanup: {e}") class TempFileWriter: @@ -27,21 +84,11 @@ class TempFileWriter: async def __aenter__(self): """Async context manager entry""" - # Check temp dir size by scanning - total_size = 0 - entries = await _scan_directories([settings.temp_file_dir]) - for entry in entries: - stat = await aiofiles.os.stat(os.path.join(settings.temp_file_dir, entry)) - total_size += stat.st_size - - if total_size >= settings.max_temp_dir_size_mb * 1024 * 1024: - raise HTTPException( - status_code=507, - detail="Temporary storage full. Please try again later." - ) + # Clean up old files first + await cleanup_temp_files() # Create temp file with proper extension - os.makedirs(settings.temp_file_dir, exist_ok=True) + await aiofiles.os.makedirs(settings.temp_file_dir, exist_ok=True) temp = tempfile.NamedTemporaryFile( dir=settings.temp_file_dir, delete=False, diff --git a/api/src/services/text_processing/chunker.py b/api/src/services/text_processing/chunker.py deleted file mode 100644 index 80e67f0..0000000 --- a/api/src/services/text_processing/chunker.py +++ /dev/null @@ -1,31 +0,0 @@ -# """Text chunking module for TTS processing""" - -# from typing import List, AsyncGenerator - -# async def fallback_split(text: str, max_chars: int = 400) -> List[str]: -# """Emergency length control - only used if chunks are too long""" -# words = text.split() -# chunks = [] -# current = [] -# current_len = 0 - -# for word in words: -# # Always include at least one word per chunk -# if not current: -# current.append(word) -# current_len = len(word) -# continue - -# # Check if adding word would exceed limit -# if current_len + len(word) + 1 <= max_chars: -# current.append(word) -# current_len += len(word) + 1 -# else: -# chunks.append(" ".join(current)) -# current = [word] -# current_len = len(word) - -# if current: -# chunks.append(" ".join(current)) - -# return chunks diff --git a/api/src/services/text_processing/text_processor.py b/api/src/services/text_processing/text_processor.py index 98fc45a..74f0f91 100644 --- a/api/src/services/text_processing/text_processor.py +++ b/api/src/services/text_processing/text_processor.py @@ -50,7 +50,7 @@ def process_text_chunk(text: str, language: str = "a") -> List[int]: async def yield_chunk(text: str, tokens: List[int], chunk_count: int) -> Tuple[str, List[int]]: """Yield a chunk with consistent logging.""" - logger.info(f"Yielding chunk {chunk_count}: '{text[:50]}...' ({len(tokens)} tokens)") + logger.debug(f"Yielding chunk {chunk_count}: '{text[:50]}...' ({len(tokens)} tokens)") return text, tokens def process_text(text: str, language: str = "a") -> List[int]: @@ -111,7 +111,7 @@ async def smart_split(text: str, max_tokens: int = ABSOLUTE_MAX) -> AsyncGenerat if current_chunk: chunk_text = " ".join(current_chunk) chunk_count += 1 - logger.info(f"Yielding chunk {chunk_count}: '{chunk_text[:50]}...' ({current_count} tokens)") + logger.debug(f"Yielding chunk {chunk_count}: '{chunk_text[:50]}...' ({current_count} tokens)") yield chunk_text, current_tokens current_chunk = [] current_tokens = [] @@ -144,7 +144,7 @@ async def smart_split(text: str, max_tokens: int = ABSOLUTE_MAX) -> AsyncGenerat if clause_chunk: chunk_text = " ".join(clause_chunk) chunk_count += 1 - logger.info(f"Yielding clause chunk {chunk_count}: '{chunk_text[:50]}...' ({clause_count} tokens)") + logger.debug(f"Yielding clause chunk {chunk_count}: '{chunk_text[:50]}...' ({clause_count} tokens)") yield chunk_text, clause_tokens clause_chunk = [full_clause] clause_tokens = tokens @@ -154,7 +154,7 @@ async def smart_split(text: str, max_tokens: int = ABSOLUTE_MAX) -> AsyncGenerat if clause_chunk: chunk_text = " ".join(clause_chunk) chunk_count += 1 - logger.info(f"Yielding final clause chunk {chunk_count}: '{chunk_text[:50]}...' ({clause_count} tokens)") + logger.debug(f"Yielding final clause chunk {chunk_count}: '{chunk_text[:50]}...' ({clause_count} tokens)") yield chunk_text, clause_tokens # Regular sentence handling diff --git a/api/tests/debug.http b/api/tests/debug.http new file mode 100644 index 0000000..ed76003 --- /dev/null +++ b/api/tests/debug.http @@ -0,0 +1,11 @@ +### Get Thread Information +GET http://localhost:8880/debug/threads +Accept: application/json + +### Get Storage Information +GET http://localhost:8880/debug/storage +Accept: application/json + +### Get System Information +GET http://localhost:8880/debug/system +Accept: application/json \ No newline at end of file diff --git a/api/tests/test_chunker.py b/api/tests/test_chunker.py deleted file mode 100644 index 002da72..0000000 --- a/api/tests/test_chunker.py +++ /dev/null @@ -1,46 +0,0 @@ -"""Tests for text chunking service""" - -from unittest.mock import patch - -import pytest - -from api.src.services.text_processing import chunker - - -@pytest.fixture(autouse=True) -def mock_settings(): - """Mock settings for all tests""" - with patch("api.src.services.text_processing.chunker.settings") as mock_settings: - mock_settings.max_chunk_size = 300 - yield mock_settings - - -def test_split_text(): - """Test text splitting into sentences""" - text = "First sentence. Second sentence! Third sentence?" - sentences = list(chunker.split_text(text)) - assert len(sentences) == 3 - assert sentences[0] == "First sentence." - assert sentences[1] == "Second sentence!" - assert sentences[2] == "Third sentence?" - - -def test_split_text_empty(): - """Test splitting empty text""" - assert list(chunker.split_text("")) == [] - - -def test_split_text_single_sentence(): - """Test splitting single sentence""" - text = "Just one sentence." - assert list(chunker.split_text(text)) == ["Just one sentence."] - - -def test_split_text_with_custom_chunk_size(): - """Test splitting with custom max chunk size""" - text = "First part, second part, third part." - chunks = list(chunker.split_text(text, max_chunk=15)) - assert len(chunks) == 3 - assert chunks[0] == "First part," - assert chunks[1] == "second part," - assert chunks[2] == "third part." diff --git a/pyproject.toml b/pyproject.toml index 6100bb0..a07d1a3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -36,6 +36,7 @@ dependencies = [ "matplotlib>=3.10.0", "semchunk>=3.0.1", "mutagen>=1.47.0", + "psutil>=6.1.1", ] [project.optional-dependencies] diff --git a/uv.lock b/uv.lock index 94d313e..079f7d7 100644 --- a/uv.lock +++ b/uv.lock @@ -1015,6 +1015,7 @@ dependencies = [ { name = "numpy" }, { name = "openai" }, { name = "phonemizer" }, + { name = "psutil" }, { name = "pydantic" }, { name = "pydantic-settings" }, { name = "pydub" }, @@ -1068,6 +1069,7 @@ requires-dist = [ { name = "openai", specifier = ">=1.59.6" }, { name = "openai", marker = "extra == 'test'", specifier = ">=1.59.6" }, { name = "phonemizer", specifier = "==3.3.0" }, + { name = "psutil", specifier = ">=6.1.1" }, { name = "pydantic", specifier = "==2.10.4" }, { name = "pydantic-settings", specifier = "==2.7.0" }, { name = "pydub", specifier = ">=0.25.1" }, @@ -2111,6 +2113,21 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/fd/b2/ab07b09e0f6d143dfb839693aa05765257bceaa13d03bf1a696b78323e7a/protobuf-5.29.3-py3-none-any.whl", hash = "sha256:0a18ed4a24198528f2333802eb075e59dea9d679ab7a6c5efb017a59004d849f", size = 172550 }, ] +[[package]] +name = "psutil" +version = "6.1.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/1f/5a/07871137bb752428aa4b659f910b399ba6f291156bdea939be3e96cae7cb/psutil-6.1.1.tar.gz", hash = "sha256:cf8496728c18f2d0b45198f06895be52f36611711746b7f30c464b422b50e2f5", size = 508502 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/61/99/ca79d302be46f7bdd8321089762dd4476ee725fce16fc2b2e1dbba8cac17/psutil-6.1.1-cp36-abi3-macosx_10_9_x86_64.whl", hash = "sha256:fc0ed7fe2231a444fc219b9c42d0376e0a9a1a72f16c5cfa0f68d19f1a0663e8", size = 247511 }, + { url = "https://files.pythonhosted.org/packages/0b/6b/73dbde0dd38f3782905d4587049b9be64d76671042fdcaf60e2430c6796d/psutil-6.1.1-cp36-abi3-macosx_11_0_arm64.whl", hash = "sha256:0bdd4eab935276290ad3cb718e9809412895ca6b5b334f5a9111ee6d9aff9377", size = 248985 }, + { url = "https://files.pythonhosted.org/packages/17/38/c319d31a1d3f88c5b79c68b3116c129e5133f1822157dd6da34043e32ed6/psutil-6.1.1-cp36-abi3-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:b6e06c20c05fe95a3d7302d74e7097756d4ba1247975ad6905441ae1b5b66003", size = 284488 }, + { url = "https://files.pythonhosted.org/packages/9c/39/0f88a830a1c8a3aba27fededc642da37613c57cbff143412e3536f89784f/psutil-6.1.1-cp36-abi3-manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:97f7cb9921fbec4904f522d972f0c0e1f4fabbdd4e0287813b21215074a0f160", size = 287477 }, + { url = "https://files.pythonhosted.org/packages/47/da/99f4345d4ddf2845cb5b5bd0d93d554e84542d116934fde07a0c50bd4e9f/psutil-6.1.1-cp36-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:33431e84fee02bc84ea36d9e2c4a6d395d479c9dd9bba2376c1f6ee8f3a4e0b3", size = 289017 }, + { url = "https://files.pythonhosted.org/packages/38/53/bd755c2896f4461fd4f36fa6a6dcb66a88a9e4b9fd4e5b66a77cf9d4a584/psutil-6.1.1-cp37-abi3-win32.whl", hash = "sha256:eaa912e0b11848c4d9279a93d7e2783df352b082f40111e078388701fd479e53", size = 250602 }, + { url = "https://files.pythonhosted.org/packages/7b/d7/7831438e6c3ebbfa6e01a927127a6cb42ad3ab844247f3c5b96bea25d73d/psutil-6.1.1-cp37-abi3-win_amd64.whl", hash = "sha256:f35cfccb065fff93529d2afb4a2e89e363fe63ca1e4a5da22b603a85833c2649", size = 254444 }, +] + [[package]] name = "pycparser" version = "2.22" diff --git a/web/index.html b/web/index.html index 2e0ce73..a0bace6 100644 --- a/web/index.html +++ b/web/index.html @@ -84,6 +84,9 @@

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z`SRs(b*4{`TTU;FVM9z8kSwIzm#+q?x5q1q!h|=Wu$Xuf8&<_m+!yvu>Kof P`oDbt8$$axZu$QJ+4lEo diff --git a/pyproject.toml b/pyproject.toml index 8eb1632..5e03a0e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -16,7 +16,6 @@ dependencies = [ # ML/DL Base "numpy>=1.26.0", "scipy==1.14.1", - "onnxruntime==1.20.1", # Audio processing "soundfile==0.13.0", # Text processing @@ -39,9 +38,11 @@ dependencies = [ [project.optional-dependencies] gpu = [ "torch==2.5.1+cu121", + "onnxruntime-gpu==1.20.1", ] cpu = [ "torch==2.5.1", + "onnxruntime==1.20.1", ] test = [ "pytest==8.0.0", From 66f46e82f98a19f64ad89d4f0fc2ccac46d5003e Mon Sep 17 00:00:00 2001 From: remsky Date: Wed, 22 Jan 2025 17:43:38 -0700 Subject: [PATCH 05/32] Refactor ONNX GPU backend and phoneme generation: improve token handling, add chunk processing for audio generation, and initial introduce stitch options for audio chunks. --- api/src/inference/onnx_gpu.py | 4 +- api/src/routers/development.py | 56 ++++++++++++++---- api/src/structures/text_schemas.py | 30 +++++++++- .../phoneme_examples/generate_phonemes.py | 43 ++++++++++---- examples/speech.mp3 | Bin 73440 -> 72960 bytes 5 files changed, 106 insertions(+), 27 deletions(-) diff --git a/api/src/inference/onnx_gpu.py b/api/src/inference/onnx_gpu.py index f11534e..266bad5 100644 --- a/api/src/inference/onnx_gpu.py +++ b/api/src/inference/onnx_gpu.py @@ -87,7 +87,9 @@ class ONNXGPUBackend(BaseModelBackend): try: # Prepare inputs tokens_input = np.array([[0, *tokens, 0]], dtype=np.int64) # Add start/end tokens - style_input = voice[len(tokens) + 2].cpu().numpy() # Move to CPU for ONNX + # Use modulo to ensure index stays within voice tensor bounds + style_idx = (len(tokens) + 2) % voice.size(0) # Add 2 for start/end tokens + style_input = voice[style_idx].cpu().numpy() # Move to CPU for ONNX speed_input = np.full(1, speed, dtype=np.float32) # Run inference diff --git a/api/src/routers/development.py b/api/src/routers/development.py index b8ecf35..df1b638 100644 --- a/api/src/routers/development.py +++ b/api/src/routers/development.py @@ -1,6 +1,7 @@ from typing import List import numpy as np +import torch from fastapi import APIRouter, Depends, HTTPException, Request, Response from loguru import logger @@ -42,10 +43,8 @@ async def phonemize_text(request: PhonemeRequest) -> PhonemeResponse: if not phonemes: raise ValueError("Failed to generate phonemes") - # Get tokens + # Get tokens (without adding start/end tokens to match process_text behavior) tokens = tokenize(phonemes) - tokens = [0] + tokens + [0] # Add start/end tokens - return PhonemeResponse(phonemes=phonemes, tokens=tokens) except ValueError as e: logger.error(f"Error in phoneme generation: {str(e)}") @@ -93,23 +92,54 @@ async def generate_from_phonemes( }, ) - # Convert phonemes to tokens - tokens = tokenize(request.phonemes) - tokens = [0] + tokens + [0] # Add start/end tokens + # Handle both single string and list of chunks + phoneme_chunks = [request.phonemes] if isinstance(request.phonemes, str) else request.phonemes + audio_chunks = [] - # Generate audio directly from tokens - audio = await tts_service.model_manager.generate( - tokens, + # Load voice tensor first since we'll need it for all chunks + voice_tensor = await tts_service._voice_manager.load_voice( request.voice, - speed=request.speed + device=tts_service.model_manager.get_backend().device ) - if audio is None: - raise ValueError("Failed to generate audio") + try: + # Process each chunk + for chunk in phoneme_chunks: + # Convert chunk to tokens + tokens = tokenize(chunk) + tokens = [0] + tokens + [0] # Add start/end tokens + + # Validate chunk length + if len(tokens) > 510: # 510 to leave room for start/end tokens + raise ValueError( + f"Chunk too long ({len(tokens)} tokens). Each chunk must be under 510 tokens." + ) + + # Generate audio for chunk + chunk_audio = await tts_service.model_manager.generate( + tokens, + voice_tensor, + speed=request.speed + ) + if chunk_audio is not None: + audio_chunks.append(chunk_audio) + + # Combine chunks if needed + if len(audio_chunks) > 1: + audio = np.concatenate(audio_chunks) + elif len(audio_chunks) == 1: + audio = audio_chunks[0] + else: + raise ValueError("No audio chunks were generated") + + finally: + # Clean up voice tensor + del voice_tensor + torch.cuda.empty_cache() # Convert to WAV bytes wav_bytes = AudioService.convert_audio( - audio, 24000, "wav", is_first_chunk=True, is_last_chunk=True, stream=False + audio, 24000, "wav", is_first_chunk=True, is_last_chunk=True, stream=False, ) return Response( diff --git a/api/src/structures/text_schemas.py b/api/src/structures/text_schemas.py index 1b6afc9..f25d37a 100644 --- a/api/src/structures/text_schemas.py +++ b/api/src/structures/text_schemas.py @@ -1,5 +1,6 @@ from pydantic import BaseModel, Field - +from pydantic import validator +from typing import List, Union, Optional class PhonemeRequest(BaseModel): text: str @@ -11,9 +12,34 @@ class PhonemeResponse(BaseModel): tokens: list[int] +class StitchOptions(BaseModel): + """Options for stitching audio chunks together""" + gap_method: str = Field( + default="static_trim", + description="Method to handle gaps between chunks. Currently only 'static_trim' supported." + ) + trim_ms: int = Field( + default=0, + ge=0, + description="Milliseconds to trim from chunk boundaries when using static_trim" + ) + + @validator('gap_method') + def validate_gap_method(cls, v): + if v != 'static_trim': + raise ValueError("Currently only 'static_trim' gap method is supported") + return v + class GenerateFromPhonemesRequest(BaseModel): - phonemes: str + phonemes: Union[str, List[str]] = Field( + ..., + description="Single phoneme string or list of phoneme chunks to stitch together" + ) voice: str = Field(..., description="Voice ID to use for generation") speed: float = Field( default=1.0, ge=0.1, le=5.0, description="Speed factor for generation" ) + options: Optional[StitchOptions] = Field( + default=None, + description="Optional settings for audio generation and stitching" + ) diff --git a/examples/phoneme_examples/generate_phonemes.py b/examples/phoneme_examples/generate_phonemes.py index 6b261a8..0c6b8c2 100644 --- a/examples/phoneme_examples/generate_phonemes.py +++ b/examples/phoneme_examples/generate_phonemes.py @@ -46,17 +46,29 @@ def generate_audio_from_phonemes( WAV audio bytes if successful, None if failed """ # Create the request payload - payload = {"phonemes": phonemes, "voice": voice, "speed": speed} + payload = { + "phonemes": phonemes, + "voice": voice, + "speed": speed, + "stitch_long_content": True # Default to false to get the error message + } - # Make POST request to generate audio - response = requests.post( - "http://localhost:8880/text/generate_from_phonemes", json=payload - ) - - # Raise exception for error status codes - response.raise_for_status() - - return response.content + try: + # Make POST request to generate audio + response = requests.post( + "http://localhost:8880/text/generate_from_phonemes", json=payload + ) + response.raise_for_status() + return response.content + except requests.HTTPError as e: + # Get the error details from the response + try: + error_details = response.json() + error_msg = error_details.get('detail', {}).get('message', str(e)) + print(f"Server Error: {error_msg}") + except: + print(f"Error: {e}") + return None def main(): @@ -66,7 +78,13 @@ def main(): "How are you today? I am doing reasonably well, thank you for asking", """This is a test of the phoneme generation system. Do not be alarmed. This is only a test. If this were a real phoneme emergency, ' - you would be instructed to a phoneme shelter in your area.""", + you would be instructed to a phoneme shelter in your area. Repeat. + This is a test of the phoneme generation system. Do not be alarmed. + This is only a test. If this were a real phoneme emergency, ' + you would be instructed to a phoneme shelter in your area. Repeat. + This is a test of the phoneme generation system. Do not be alarmed. + This is only a test. If this were a real phoneme emergency, ' + you would be instructed to a phoneme shelter in your area""", ] print("Generating phonemes and audio for example texts...\n") @@ -85,6 +103,9 @@ def main(): # Generate audio from phonemes print("Generating audio...") + if len(phonemes) > 500: # split into arrays of 500 phonemes + phonemes = [phonemes[i:i+500] for i in range(0, len(phonemes), 500)] + audio_bytes = generate_audio_from_phonemes(phonemes) if audio_bytes: diff --git a/examples/speech.mp3 b/examples/speech.mp3 index 749f647d2696f5f26d0c64001913460472615911..c21bec0a93cef23bf1136edae3f814102ed18c77 100644 GIT binary patch literal 72960 zcmeF(X;@Ng-!S~eC?FajDj?Yc(a_WYhtRAIPB~!Vn3fgbke0QbYHQO5M4ZrY$k4QK z$keQyDs99uH7$n*8@HC4jk{@I4ZEFh-OqbJ@2BU(_3?h*YaIs%>o{1z$@$~tT>t<7 zTr7N;=>`6K^W7Qy>yQ0KKW>Ks$YKD1AyN7UID(0(85N{iTG`kxXFEE(tX$3W@bd8s zSQi`;9>|bh)d$f8hGa z==kls_aFW-Ju~woui=>I+2|BavN-_ZGw@4wMm{{s*GkMI9Lw11F zT_l;0^+(+-*0O8-1lYv1dme`)kU6 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