WIP: CPU/GPU Functional, few straggling tests to fix and check.

api/src/main.py

@@ -21,8 +21,8 @@ async def lifespan(app: FastAPI):
     logger.info("Loading TTS model and voice packs...")
 
     # Initialize the main model with warm-up
-    voicepack_count = TTSModel.initialize()
+    voicepack_count = TTSModel.setup()
-    logger.info(f"Model loaded and warmed up on {TTSModel._device}")
+    logger.info(f"Model loaded and warmed up on {TTSModel.get_device()}")
     logger.info(f"{voicepack_count} voice packs loaded successfully")
     yield
 

api/src/services/__init__.py

@@ -1,4 +1,3 @@
-from .tts_model import TTSModel
 from .tts_service import TTSService
 
-__all__ = ["TTSService", "TTSModel"]
+__all__ = ["TTSService"]

api/src/services/tts_base.py

@@ -0,0 +1,110 @@
+import os
+import threading
+from abc import ABC, abstractmethod
+import torch
+import numpy as np
+from loguru import logger
+from kokoro import tokenize, phonemize
+from typing import Union, List
+
+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
+    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")
+                    cls._device = "cuda"
+                except Exception as e:
+                    logger.error(f"CUDA test failed: {e}")
+                    cls._device = "cpu"
+            else:
+                cls._device = "cpu"
+            logger.info(f"Initializing model on {cls._device}")
+
+            # Initialize model
+            if not cls.initialize(settings.model_dir, settings.model_path):
+                raise RuntimeError(f"Failed to initialize {cls._device.upper()} 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)}")
+
+            # Warm up with default voice
+            try:
+                dummy_text = "Hello"
+                voice_path = os.path.join(cls.VOICES_DIR, "af.pt")
+                dummy_voicepack = torch.load(voice_path, map_location=cls._device, weights_only=True)
+                
+                if cls._device == "cuda":
+                    cls.generate(dummy_text, dummy_voicepack, "a", 1.0)
+                else:
+                    ps = phonemize(dummy_text, "a")
+                    tokens = tokenize(ps)
+                    tokens = [0] + tokens + [0]
+                    cls.generate(tokens, dummy_voicepack, 1.0)
+                
+                logger.info("Model warm-up complete")
+            except Exception as e:
+                logger.warning(f"Model warm-up failed: {e}")
+
+            # 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 generate(cls, input_data: Union[str, List[int]], voicepack: torch.Tensor, *args) -> np.ndarray:
+        """Generate audio from input
+        
+        Args:
+            input_data: Either text string (GPU) or tokenized input (CPU)
+            voicepack: Voice tensor
+            *args: Additional args (lang+speed for GPU, speed for CPU)
+            
+        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

api/src/services/tts_cpu.py

@@ -4,17 +4,35 @@ import torch
 from onnxruntime import InferenceSession, SessionOptions, GraphOptimizationLevel, ExecutionMode
 from loguru import logger
 
-class TTSCPUModel:
+from .tts_base import TTSBaseModel
+
+class TTSCPUModel(TTSBaseModel):
     _instance = None
     _onnx_session = None
 
     @classmethod
-    def initialize(cls, model_dir: str):
+    def initialize(cls, model_dir: str, model_path: str = None):
         """Initialize ONNX model for CPU inference"""
         if cls._onnx_session is None:
             # Try loading ONNX model
-            onnx_path = os.path.join(model_dir, "kokoro-v0_19.onnx")
+            # First try the specified path if provided
-            if not os.path.exists(onnx_path):
+            if model_path and model_path.endswith('.onnx'):
+                onnx_path = os.path.join(model_dir, model_path)
+                if os.path.exists(onnx_path):
+                    logger.info(f"Loading specified ONNX model from {onnx_path}")
+                else:
+                    onnx_path = None
+            else:
+                # Look for any .onnx file in the directory as fallback
+                onnx_files = [f for f in os.listdir(model_dir) if f.endswith('.onnx')]
+                if onnx_files:
+                    onnx_path = os.path.join(model_dir, onnx_files[0])
+                    logger.info(f"Found ONNX model: {onnx_path}")
+                else:
+                    logger.error(f"No ONNX model found in {model_dir}")
+                    return None
+
+            if not onnx_path:
                 return None
 
             logger.info(f"Loading ONNX model from {onnx_path}")
@@ -44,22 +62,33 @@ class TTSCPUModel:
         return cls._onnx_session
 
     @classmethod
-    def generate(cls, tokens: list, voicepack: torch.Tensor, speed: float) -> np.ndarray:
+    def generate(cls, input_data: list[int], voicepack: torch.Tensor, *args) -> np.ndarray:
-        """Generate audio using ONNX model"""
+        """Generate audio using ONNX model
+        
+        Args:
+            input_data: list of token IDs
+            voicepack: Voice tensor
+            *args: (speed,) tuple
+            
+        Returns:
+            np.ndarray: Generated audio samples
+        """
         if cls._onnx_session is None:
             raise RuntimeError("ONNX model not initialized")
 
+        speed = args[0]
         # Pre-allocate and prepare inputs
-        tokens_input = np.array([tokens], dtype=np.int64)
+        tokens_input = np.array([input_data], dtype=np.int64)
-        style_input = voicepack[len(tokens)-2].numpy()  # Already has correct dimensions
+        style_input = voicepack[len(input_data)-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
-        return cls._onnx_session.run(
+        result = cls._onnx_session.run(
             None,
             {
                 'tokens': tokens_input,
                 'style': style_input,
                 'speed': speed_input
             }
-        )[0]
+        )
+        return result[0]

api/src/services/tts_gpu.py

@@ -1,10 +1,13 @@
 import os
+import numpy as np
 import torch
 from loguru import logger
 from models import build_model
 from kokoro import generate
 
-class TTSGPUModel:
+from .tts_base import TTSBaseModel
+
+class TTSGPUModel(TTSBaseModel):
     _instance = None
     _device = "cuda"
 
@@ -24,9 +27,26 @@ class TTSGPUModel:
         return cls._instance
 
     @classmethod
-    def generate(cls, text: str, voicepack: torch.Tensor, lang: str, speed: float) -> tuple[torch.Tensor, dict]:
+    def generate(cls, input_data: str, voicepack: torch.Tensor, *args) -> np.ndarray:
-        """Generate audio using PyTorch model on GPU"""
+        """Generate audio using PyTorch model on GPU
+        
+        Args:
+            input_data: Text string to generate audio from
+            voicepack: Voice tensor
+            *args: (lang, speed) tuple
+            
+        Returns:
+            np.ndarray: Generated audio samples
+        """
         if cls._instance is None:
             raise RuntimeError("GPU model not initialized")
             
-        return generate(cls._instance, text, voicepack, lang=lang, speed=speed)
+        lang, speed = args
+        result = generate(cls._instance, input_data, voicepack, lang=lang, speed=speed)
+        # kokoro.generate returns (audio, metadata, info), we only want audio
+        audio = result[0]
+        
+        # Convert to numpy array if needed
+        if isinstance(audio, torch.Tensor):
+            audio = audio.cpu().numpy()
+        return audio

api/src/services/tts_model.py

@@ -1,94 +1,8 @@
-import os
-import threading
 import torch
-from loguru import logger
-from kokoro import tokenize, phonemize
 
-from ..core.config import settings
+if torch.cuda.is_available():
-from .tts_cpu import TTSCPUModel
+    from .tts_gpu import TTSGPUModel as TTSModel
-from .tts_gpu import TTSGPUModel
-
-
-class TTSModel:
-    _device = None
-    _lock = threading.Lock()
-    VOICES_DIR = os.path.join(os.path.dirname(os.path.dirname(__file__)), "voices")
-
-    @classmethod
-    def initialize(cls):
-        """Initialize and warm up the model"""
-        with cls._lock:
-            # Set device and initialize model
-            cls._device = "cuda" if torch.cuda.is_available() else "cpu"
-            logger.info(f"Initializing model on {cls._device}")
-
-            # Initialize appropriate model based on device
-            if cls._device == "cuda":
-                if not TTSGPUModel.initialize(settings.model_dir, settings.model_path):
-                    raise RuntimeError("Failed to initialize GPU model")
 else:
-                # Try CPU ONNX first, fallback to CPU PyTorch if needed
+    from .tts_cpu import TTSCPUModel as TTSModel
-                if not TTSCPUModel.initialize(settings.model_dir):
-                    logger.warning("ONNX initialization failed, falling back to PyTorch CPU")
-                    if not TTSGPUModel.initialize(settings.model_dir, settings.model_path):
-                        raise RuntimeError("Failed to initialize CPU model")
 
-            # Setup voices directory
+__all__ = ["TTSModel"]
-            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)}"
-                                )
-
-            # Warm up with default voice
-            try:
-                dummy_text = "Hello"
-                voice_path = os.path.join(cls.VOICES_DIR, "af.pt")
-                dummy_voicepack = torch.load(
-                    voice_path, map_location=cls._device, weights_only=True
-                )
-                
-                if cls._device == "cuda":
-                    TTSGPUModel.generate(dummy_text, dummy_voicepack, "a", 1.0)
-                else:
-                    ps = phonemize(dummy_text, "a")
-                    tokens = tokenize(ps)
-                    tokens = [0] + tokens + [0]
-                    TTSCPUModel.generate(tokens, dummy_voicepack, 1.0)
-                
-                logger.info("Model warm-up complete")
-            except Exception as e:
-                logger.warning(f"Model warm-up failed: {e}")
-
-            # Count voices in directory
-            voice_count = len(
-                [f for f in os.listdir(cls.VOICES_DIR) if f.endswith(".pt")]
-            )
-            return voice_count
-
-    @classmethod
-    def get_device(cls):
-        """Get the current device or raise an error"""
-        if cls._device is None:
-            raise RuntimeError("Model not initialized. Call initialize() first.")
-        return cls._device

api/src/services/tts_service.py

@@ -12,8 +12,6 @@ from loguru import logger
 
 from ..core.config import settings
 from .tts_model import TTSModel
-from .tts_cpu import TTSCPUModel
-from .tts_gpu import TTSGPUModel
 
 
 class TTSService:
@@ -22,6 +20,8 @@ class TTSService:
 
     def _split_text(self, text: str) -> List[str]:
         """Split text into sentences"""
+        if not isinstance(text, str):
+            text = str(text) if text is not None else ""
         return [s.strip() for s in re.split(r"(?<=[.!?])\s+", text) if s.strip()]
 
     def _get_voice_path(self, voice_name: str) -> Optional[str]:
@@ -37,9 +37,12 @@ class TTSService:
 
         try:
             # Normalize text once at the start
-            text = normalize_text(text)
             if not text:
                 raise ValueError("Text is empty after preprocessing")
+            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)
@@ -61,12 +64,18 @@ class TTSService:
                     try:
                         # Process chunk
                         if TTSModel.get_device() == "cuda":
-                            chunk_audio, _ = TTSGPUModel.generate(chunk, voicepack, voice[0], speed)
+                            # GPU takes (text, voicepack, lang, speed)
+                            try:
+                                chunk_audio = TTSModel.generate(chunk, voicepack, voice[0], speed)
+                            except RuntimeError as e:
+                                logger.error(f"Failed to generate audio: {str(e)}")
+                                chunk_audio = None
                         else:
+                            # CPU takes (tokens, voicepack, speed)
                             ps = phonemize(chunk, voice[0])
                             tokens = tokenize(ps)
-                            tokens = [0] + tokens + [0]  # Add padding
+                            tokens = [0] + list(tokens) + [0]  # Add padding
-                            chunk_audio = TTSCPUModel.generate(tokens, voicepack, speed)
+                            chunk_audio = TTSModel.generate(tokens, voicepack, speed)
                             
                         if chunk_audio is not None:
                             audio_chunks.append(chunk_audio)
@@ -90,12 +99,18 @@ class TTSService:
             else:
                 # Process single chunk
                 if TTSModel.get_device() == "cuda":
-                    audio, _ = TTSGPUModel.generate(text, voicepack, voice[0], speed)
+                    # GPU takes (text, voicepack, lang, speed)
+                    try:
+                        audio = TTSModel.generate(text, voicepack, voice[0], speed)
+                    except RuntimeError as e:
+                        logger.error(f"Failed to generate audio: {str(e)}")
+                        raise ValueError("No audio chunks were generated successfully")
                 else:
+                    # CPU takes (tokens, voicepack, speed)
                     ps = phonemize(text, voice[0])
                     tokens = tokenize(ps)
-                    tokens = [0] + tokens + [0]  # Add padding
+                    tokens = [0] + list(tokens) + [0]  # Add padding
-                    audio = TTSCPUModel.generate(tokens, voicepack, speed)
+                    audio = TTSModel.generate(tokens, voicepack, speed)
 
             processing_time = time.time() - start_time
             return audio, processing_time

api/tests/conftest.py

@@ -36,7 +36,7 @@ sys.modules["kokoro.tokenize"] = Mock()
 @pytest.fixture(autouse=True)
 def mock_tts_model():
     """Mock TTSModel to avoid loading real models during tests"""
-    with patch("api.src.services.tts.TTSModel") as mock:
+    with patch("api.src.services.tts_model.TTSModel") as mock:
         model_instance = Mock()
         model_instance.get_instance.return_value = model_instance
         model_instance.get_voicepack.return_value = None

api/tests/test_main.py

@@ -26,13 +26,11 @@ def test_health_check(test_client):
 @patch("api.src.main.logger")
 async def test_lifespan_successful_warmup(mock_logger, mock_tts_model):
     """Test successful model warmup in lifespan"""
-    # Mock the model initialization with model info and voicepack count
-    mock_model = MagicMock()
     # Mock file system for voice counting
     mock_tts_model.VOICES_DIR = "/mock/voices"
     with patch("os.listdir", return_value=["voice1.pt", "voice2.pt", "voice3.pt"]):
-        mock_tts_model.initialize.return_value = (mock_model, 3)  # 3 voice files
+        mock_tts_model.setup.return_value = 3  # 3 voice files
-        mock_tts_model._device = "cuda"  # Set device class variable
+        mock_tts_model.get_device.return_value = "cuda"
 
     # Create an async generator from the lifespan context manager
     async_gen = lifespan(MagicMock())
@@ -44,8 +42,8 @@ async def test_lifespan_successful_warmup(mock_logger, mock_tts_model):
     mock_logger.info.assert_any_call("Model loaded and warmed up on cuda")
     mock_logger.info.assert_any_call("3 voice packs loaded successfully")
 
-    # Verify model initialization was called
+    # Verify model setup was called
-    mock_tts_model.initialize.assert_called_once()
+    mock_tts_model.setup.assert_called_once()
 
     # Clean up
     await async_gen.__aexit__(None, None, None)
@@ -56,14 +54,14 @@ async def test_lifespan_successful_warmup(mock_logger, mock_tts_model):
 @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 initialization to fail
+    # Mock the model setup to fail
-    mock_tts_model.initialize.side_effect = Exception("Failed to initialize model")
+    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(Exception, match="Failed to initialize model"):
+    with pytest.raises(RuntimeError, match="Failed to initialize model"):
         await async_gen.__aenter__()
 
     # Verify the expected logging sequence
@@ -77,20 +75,18 @@ async def test_lifespan_failed_warmup(mock_logger, mock_tts_model):
 @patch("api.src.main.TTSModel")
 async def test_lifespan_cuda_warmup(mock_tts_model):
     """Test model warmup specifically on CUDA"""
-    # Mock the model initialization with CUDA and voicepacks
-    mock_model = MagicMock()
     # Mock file system for voice counting
     mock_tts_model.VOICES_DIR = "/mock/voices"
     with patch("os.listdir", return_value=["voice1.pt", "voice2.pt"]):
-        mock_tts_model.initialize.return_value = (mock_model, 2)  # 2 voice files
+        mock_tts_model.setup.return_value = 2  # 2 voice files
-        mock_tts_model._device = "cuda"  # Set device class variable
+        mock_tts_model.get_device.return_value = "cuda"
 
     # Create an async generator from the lifespan context manager
     async_gen = lifespan(MagicMock())
     await async_gen.__aenter__()
 
-    # Verify model was initialized
+    # Verify model setup was called
-    mock_tts_model.initialize.assert_called_once()
+    mock_tts_model.setup.assert_called_once()
 
     # Clean up
     await async_gen.__aexit__(None, None, None)
@@ -100,22 +96,20 @@ async def test_lifespan_cuda_warmup(mock_tts_model):
 @patch("api.src.main.TTSModel")
 async def test_lifespan_cpu_fallback(mock_tts_model):
     """Test model warmup falling back to CPU"""
-    # Mock the model initialization with CPU and voicepacks
-    mock_model = MagicMock()
     # Mock file system for voice counting
     mock_tts_model.VOICES_DIR = "/mock/voices"
     with patch(
         "os.listdir", return_value=["voice1.pt", "voice2.pt", "voice3.pt", "voice4.pt"]
     ):
-        mock_tts_model.initialize.return_value = (mock_model, 4)  # 4 voice files
+        mock_tts_model.setup.return_value = 4  # 4 voice files
-        mock_tts_model._device = "cpu"  # Set device class variable
+        mock_tts_model.get_device.return_value = "cpu"
 
     # Create an async generator from the lifespan context manager
     async_gen = lifespan(MagicMock())
     await async_gen.__aenter__()
 
-    # Verify model was initialized
+    # Verify model setup was called
-    mock_tts_model.initialize.assert_called_once()
+    mock_tts_model.setup.assert_called_once()
 
     # Clean up
     await async_gen.__aexit__(None, None, None)

api/tests/test_tts_service.py

@@ -7,6 +7,7 @@ import numpy as np
 import torch
 import pytest
 
+from api.src.core.config import settings
 from api.src.services.tts_model import TTSModel
 from api.src.services.tts_service import TTSService
 
@@ -14,7 +15,7 @@ from api.src.services.tts_service import TTSService
 @pytest.fixture
 def tts_service():
     """Create a TTSService instance for testing"""
-    return TTSService(start_worker=False)
+    return TTSService()
 
 
 @pytest.fixture
@@ -86,6 +87,7 @@ def test_generate_audio_empty_text(
 ):
     """Test generating audio with empty text"""
     mock_normalize.return_value = ""
+    mock_instance.return_value = (MagicMock(), "cpu")
 
     with pytest.raises(ValueError, match="Text is empty after preprocessing"):
         tts_service._generate_audio("", "af", 1.0)
@@ -111,7 +113,7 @@ def test_generate_audio_no_chunks(
     """Test generating audio with no successful chunks"""
     mock_normalize.return_value = "Test text"
     mock_phonemize.return_value = "Test text"
-    mock_tokenize.return_value = ["test", "text"]
+    mock_tokenize.return_value = [1, 2]  # Return integers instead of strings
     mock_generate.return_value = (None, None)
     mock_instance.return_value = (MagicMock(), "cpu")
     mock_exists.return_value = True
@@ -156,57 +158,23 @@ def test_combine_voices_invalid_input(tts_service):
         tts_service.combine_voices(["voice1"])
 
 
-@patch("os.makedirs")
+
+@patch("api.src.services.tts_model.TTSModel.get_instance")
+@patch("api.src.services.tts_model.TTSModel.get_device")
+@patch("api.src.services.tts_model.TTSModel.generate")
 @patch("os.path.exists")
-@patch("os.listdir")
+@patch("kokoro.normalize_text")
-@patch("torch.load")
+@patch("kokoro.phonemize")
-@patch("torch.save")
+@patch("kokoro.tokenize")
-@patch("os.path.join")
-def test_ensure_voices(
-    mock_join,
-    mock_save,
-    mock_load,
-    mock_listdir,
-    mock_exists,
-    mock_makedirs,
-    tts_service,
-):
-    """Test voice directory initialization"""
-    # Setup mocks
-    mock_exists.side_effect = [
-        True,
-        False,
-        False,
-    ]  # base_dir exists, voice files don't exist
-    mock_listdir.return_value = ["voice1.pt", "voice2.pt"]
-    mock_load.return_value = MagicMock()
-    mock_join.return_value = "/fake/path"
-
-    # Test voice directory initialization
-    tts_service._ensure_voices()
-
-    # Verify directory was created
-    mock_makedirs.assert_called_once()
-
-    # Verify voices were loaded and saved
-    assert mock_load.call_count == len(mock_listdir.return_value)
-    assert mock_save.call_count == len(mock_listdir.return_value)
-
-
-@patch("api.src.services.tts.TTSModel.get_instance")
-@patch("os.path.exists")
-@patch("api.src.services.tts.normalize_text")
-@patch("api.src.services.tts.phonemize")
-@patch("api.src.services.tts.tokenize")
-@patch("api.src.services.tts.generate")
 @patch("torch.load")
 def test_generate_audio_success(
     mock_torch_load,
-    mock_generate,
     mock_tokenize,
     mock_phonemize,
     mock_normalize,
     mock_exists,
+    mock_model_generate,
+    mock_get_device,
     mock_instance,
     tts_service,
     sample_audio,
@@ -214,12 +182,17 @@ def test_generate_audio_success(
     """Test successful audio generation"""
     mock_normalize.return_value = "Test text"
     mock_phonemize.return_value = "Test text"
-    mock_tokenize.return_value = ["test", "text"]
+    mock_tokenize.return_value = [1, 2]  # Return integers instead of strings
-    mock_generate.return_value = (sample_audio, None)
+    mock_model_generate.return_value = sample_audio
     mock_instance.return_value = (MagicMock(), "cpu")
+    mock_get_device.return_value = "cpu"
     mock_exists.return_value = True
     mock_torch_load.return_value = MagicMock()
 
+    # Initialize model
+    TTSModel._instance = None
+    TTSModel._device = "cpu"
+
     audio, processing_time = tts_service._generate_audio("Test text", "af", 1.0)
     assert isinstance(audio, np.ndarray)
     assert isinstance(processing_time, float)
@@ -227,35 +200,94 @@ def test_generate_audio_success(
 
 
 @patch("torch.cuda.is_available")
-@patch("models.build_model")
+@patch("api.src.services.tts_gpu.TTSGPUModel.initialize")
-def test_model_initialization_cuda(mock_build_model, mock_cuda_available):
+@patch("os.makedirs")
+@patch("os.path.exists")
+@patch("os.listdir")
+@patch("torch.load")
+@patch("torch.save")
+@patch("api.src.core.config.settings")
+@patch("torch.zeros")
+def test_model_initialization_cuda(
+    mock_zeros,
+    mock_settings,
+    mock_save,
+    mock_load,
+    mock_listdir,
+    mock_exists,
+    mock_makedirs,
+    mock_initialize,
+    mock_cuda_available,
+):
     """Test model initialization with CUDA"""
+    # Setup mocks
     mock_cuda_available.return_value = True
-    mock_model = MagicMock()
+    mock_initialize.return_value = True
-    mock_build_model.return_value = mock_model
+    mock_exists.return_value = True
+    mock_listdir.return_value = ["voice1.pt", "voice2.pt"]
+    mock_load.return_value = torch.zeros(1)
+    mock_settings.model_dir = "test_dir"
+    mock_settings.model_path = "test_path"
+    mock_settings.voices_dir = "voices"
+    mock_zeros.return_value = torch.zeros(1)
 
-    TTSModel._instance = None  # Reset singleton
+    # Reset singleton and device
-    model, voice_count = TTSModel.initialize()
+    TTSModel._instance = None
+    TTSModel._device = None
     
-    assert TTSModel._device == "cuda"  # Check the class variable instead
+    # Mock settings to prevent actual file operations
-    assert model == mock_model
+    with patch.object(settings, 'model_dir', 'test_dir'), \
-    mock_build_model.assert_called_once()
+         patch.object(settings, 'model_path', 'test_path'):
+        voice_count = TTSModel.setup()
+
+        assert TTSModel.get_device() == "cuda"
+        assert voice_count == 2
+        mock_initialize.assert_called_once_with("test_dir", "test_path")
 
 
-@patch("api.src.services.tts.torch.cuda.is_available")
+@patch("torch.cuda.is_available")
-@patch("api.src.services.tts.build_model")
+@patch("api.src.services.tts_base.TTSBaseModel.initialize")
-def test_model_initialization_cpu(mock_build_model, mock_cuda_available):
+@patch("os.makedirs")
+@patch("os.path.exists")
+@patch("os.listdir")
+@patch("torch.load")
+@patch("torch.save")
+@patch("api.src.core.config.settings")
+@patch("torch.zeros")
+def test_model_initialization_cpu(
+    mock_zeros,
+    mock_settings,
+    mock_save,
+    mock_load,
+    mock_listdir,
+    mock_exists,
+    mock_makedirs,
+    mock_initialize,
+    mock_cuda_available,
+):
     """Test model initialization with CPU"""
+    # Setup mocks
     mock_cuda_available.return_value = False
-    mock_model = MagicMock()
+    mock_initialize.return_value = False  # This will trigger the RuntimeError
-    mock_build_model.return_value = mock_model
+    mock_exists.return_value = True
+    mock_listdir.return_value = ["voice1.pt", "voice2.pt", "voice3.pt"]
+    mock_load.return_value = torch.zeros(1)
+    mock_settings.model_dir = "test_dir"
+    mock_settings.model_path = "test_path"
+    mock_settings.voices_dir = "voices"
+    mock_zeros.return_value = torch.zeros(1)
 
-    TTSModel._instance = None  # Reset singleton
+    # Reset singleton and device
-    model, voice_count = TTSModel.initialize()
+    TTSModel._instance = None
+    TTSModel._device = None
 
-    assert TTSModel._device == "cpu"  # Check the class variable instead
+    # Mock settings to prevent actual file operations
-    assert model == mock_model
+    with patch.object(settings, 'model_dir', 'test_dir'), \
-    mock_build_model.assert_called_once()
+         patch.object(settings, 'model_path', 'test_path'), \
+         pytest.raises(RuntimeError, match="Failed to initialize CPU model"):
+        TTSModel.setup()
+
+    mock_initialize.assert_called_once_with("test_dir", "test_path")
 
 
 @patch("api.src.services.tts_service.TTSService._get_voice_path")
@@ -267,7 +299,7 @@ def test_voicepack_loading_error(mock_get_instance, mock_get_voice_path):
 
     TTSModel._voicepacks = {}  # Reset voicepacks
 
-    service = TTSService(start_worker=False)
+    service = TTSService()
     with pytest.raises(ValueError, match="Voice not found: nonexistent_voice"):
         service._generate_audio("test", "nonexistent_voice", 1.0)
 
@@ -286,23 +318,32 @@ def test_save_audio(mock_tts_model, tts_service, sample_audio, tmp_path):
 
 
 @patch("api.src.services.tts_model.TTSModel.get_instance")
+@patch("api.src.services.tts_model.TTSModel.get_device")
+@patch("api.src.services.tts_model.TTSModel.generate")
 @patch("os.path.exists")
-@patch("api.src.services.tts.normalize_text")
+@patch("kokoro.normalize_text")
-@patch("api.src.services.tts.generate")
+@patch("kokoro.phonemize")
+@patch("kokoro.tokenize")
 @patch("torch.load")
 def test_generate_audio_without_stitching(
     mock_torch_load,
-    mock_generate,
+    mock_tokenize,
+    mock_phonemize,
     mock_normalize,
     mock_exists,
+    mock_model_generate,
+    mock_get_device,
     mock_instance,
     tts_service,
     sample_audio,
 ):
     """Test generating audio without text stitching"""
     mock_normalize.return_value = "Test text"
-    mock_generate.return_value = (sample_audio, None)
+    mock_phonemize.return_value = "Test text"
+    mock_tokenize.return_value = [1, 2]  # Return integers instead of strings
+    mock_model_generate.return_value = sample_audio
     mock_instance.return_value = (MagicMock(), "cpu")
+    mock_get_device.return_value = "cpu"
     mock_exists.return_value = True
     mock_torch_load.return_value = MagicMock()
 
@@ -311,7 +352,7 @@ def test_generate_audio_without_stitching(
     )
     assert isinstance(audio, np.ndarray)
     assert len(audio) > 0
-    mock_generate.assert_called_once()
+    mock_model_generate.assert_called_once()
 
 
 @patch("os.listdir")
@@ -323,12 +364,13 @@ def test_list_voices_error(mock_listdir, tts_service):
     assert voices == []
 
 
-@patch("api.src.services.tts.TTSModel.get_instance")
+@patch("api.src.services.tts_model.TTSModel.get_instance")
+@patch("api.src.services.tts_model.TTSModel.get_device")
 @patch("os.path.exists")
-@patch("api.src.services.tts.normalize_text")
+@patch("kokoro.normalize_text")
-@patch("api.src.services.tts.phonemize")
+@patch("kokoro.phonemize")
-@patch("api.src.services.tts.tokenize")
+@patch("kokoro.tokenize")
-@patch("api.src.services.tts.generate")
+@patch("kokoro.generate")
 @patch("torch.load")
 def test_generate_audio_phonemize_error(
     mock_torch_load,
@@ -337,6 +379,7 @@ def test_generate_audio_phonemize_error(
     mock_phonemize,
     mock_normalize,
     mock_exists,
+    mock_get_device,
     mock_instance,
     tts_service,
 ):
@@ -344,33 +387,51 @@ def test_generate_audio_phonemize_error(
     mock_normalize.return_value = "Test text"
     mock_phonemize.side_effect = Exception("Phonemization failed")
     mock_instance.return_value = (MagicMock(), "cpu")
+    mock_get_device.return_value = "cpu"
     mock_exists.return_value = True
     mock_torch_load.return_value = MagicMock()
     mock_generate.return_value = (None, None)
 
+    # Initialize model
+    TTSModel._instance = None
+    TTSModel._device = "cpu"
+
     with pytest.raises(ValueError, match="No audio chunks were generated successfully"):
         tts_service._generate_audio("Test text", "af", 1.0)
 
 
-@patch("api.src.services.tts.TTSModel.get_instance")
+@patch("api.src.services.tts_model.TTSModel.get_instance")
+@patch("api.src.services.tts_model.TTSModel.get_device")
 @patch("os.path.exists")
-@patch("api.src.services.tts.normalize_text")
+@patch("kokoro.normalize_text")
-@patch("api.src.services.tts.generate")
+@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 = (MagicMock(), "cpu")
+    mock_get_device.return_value = "cpu"
     mock_exists.return_value = True
     mock_torch_load.return_value = MagicMock()
 
+    # Initialize model
+    TTSModel._instance = None
+    TTSModel._device = "cpu"
+
     with pytest.raises(ValueError, match="No audio chunks were generated successfully"):
         tts_service._generate_audio("Test text", "af", 1.0)

examples/benchmarks/benchmark_tts_rtf.py

@@ -69,22 +69,13 @@ def get_gpu_memory():
 
 def get_system_metrics():
     """Get current system metrics"""
-    # Take multiple CPU measurements over a short period
+    # Get per-CPU percentages and calculate average
-    samples = []
+    cpu_percentages = psutil.cpu_percent(percpu=True)
-    for _ in range(3):  # Take 3 samples
+    avg_cpu = sum(cpu_percentages) / len(cpu_percentages)
-        # Get both overall and per-CPU percentages
-        overall_cpu = psutil.cpu_percent(interval=0.1)
-        per_cpu = psutil.cpu_percent(percpu=True)
-        avg_per_cpu = sum(per_cpu) / len(per_cpu)
-        # Use the maximum of overall and average per-CPU
-        samples.append(max(overall_cpu, avg_per_cpu))
-    
-    # Use the maximum CPU usage from all samples
-    cpu_usage = round(max(samples), 2)
     
     metrics = {
         "timestamp": datetime.now().isoformat(),
-        "cpu_percent": cpu_usage,
+        "cpu_percent": round(avg_cpu, 2),
         "ram_percent": psutil.virtual_memory().percent,
         "ram_used_gb": psutil.virtual_memory().used / (1024**3),
     }