"""TTS service using model and voice managers.""" import os import time import tempfile from typing import List, Tuple, Optional, AsyncGenerator, Union import asyncio import numpy as np 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.text_processor import process_text_chunk, smart_split from .text_processing import tokenize from ..inference.kokoro_v1 import KokoroV1 class TTSService: """Text-to-speech service.""" # Limit concurrent chunk processing _chunk_semaphore = asyncio.Semaphore(4) def __init__(self, output_dir: str = None): """Initialize service.""" self.output_dir = output_dir self.model_manager = None self._voice_manager = None @classmethod async def create(cls, output_dir: str = None) -> 'TTSService': """Create and initialize TTSService instance.""" service = cls(output_dir) service.model_manager = await get_model_manager() service._voice_manager = await get_voice_manager() return service async def _process_chunk( self, chunk_text: str, tokens: List[int], voice_name: str, voice_path: str, speed: float, output_format: Optional[str] = None, is_first: bool = False, is_last: bool = False, normalizer: Optional[AudioNormalizer] = None, ) -> AsyncGenerator[Union[np.ndarray, bytes], None]: """Process tokens into audio.""" async with self._chunk_semaphore: try: # Handle stream finalization if is_last: # Skip format conversion for raw audio mode if not output_format: yield np.array([], dtype=np.float32) return result = await AudioService.convert_audio( np.array([0], dtype=np.float32), # Dummy data for type checking 24000, output_format, is_first_chunk=False, normalizer=normalizer, is_last_chunk=True ) yield result return # Skip empty chunks if not tokens and not chunk_text: return # Get backend backend = self.model_manager.get_backend() # Generate audio using pre-warmed model if isinstance(backend, KokoroV1): # For Kokoro V1, pass text and voice info async for chunk_audio in self.model_manager.generate( chunk_text, (voice_name, voice_path), speed=speed ): # For streaming, convert to bytes if output_format: try: converted = await AudioService.convert_audio( chunk_audio, 24000, output_format, is_first_chunk=is_first, normalizer=normalizer, is_last_chunk=is_last ) yield converted except Exception as e: logger.error(f"Failed to convert audio: {str(e)}") else: yield chunk_audio else: # For legacy backends, load voice tensor voice_tensor = await self._voice_manager.load_voice(voice_name, device=backend.device) chunk_audio = await self.model_manager.generate( tokens, voice_tensor, speed=speed ) if chunk_audio is None: logger.error("Model generated None for audio chunk") return if len(chunk_audio) == 0: logger.error("Model generated empty audio chunk") return # For streaming, convert to bytes if output_format: try: converted = await AudioService.convert_audio( chunk_audio, 24000, output_format, is_first_chunk=is_first, normalizer=normalizer, is_last_chunk=is_last ) yield converted except Exception as e: logger.error(f"Failed to convert audio: {str(e)}") else: yield chunk_audio except Exception as e: logger.error(f"Failed to process tokens: {str(e)}") async def _get_voice_path(self, voice: str) -> Tuple[str, str]: """Get voice path, handling combined voices. Args: voice: Voice name or combined voice names (e.g., 'af_jadzia+af_jessica') Returns: Tuple of (voice name to use, voice path to use) Raises: RuntimeError: If voice not found """ try: # Check if it's a combined voice if "+" in voice: voices = [v.strip() for v in voice.split("+") if v.strip()] if len(voices) < 2: raise RuntimeError(f"Invalid combined voice name: {voice}") # Load and combine voices voice_tensors = [] for v in voices: path = self._voice_manager.get_voice_path(v) if not path: raise RuntimeError(f"Voice not found: {v}") logger.debug(f"Loading voice tensor from: {path}") voice_tensor = torch.load(path, map_location="cpu") voice_tensors.append(voice_tensor) # Average the voice tensors logger.debug(f"Combining {len(voice_tensors)} voice tensors") combined = torch.mean(torch.stack(voice_tensors), dim=0) # Save combined tensor temp_dir = tempfile.gettempdir() combined_path = os.path.join(temp_dir, f"{voice}.pt") logger.debug(f"Saving combined voice to: {combined_path}") torch.save(combined, combined_path) return voice, combined_path else: # Single voice path = self._voice_manager.get_voice_path(voice) if not path: raise RuntimeError(f"Voice not found: {voice}") logger.debug(f"Using single voice path: {path}") return voice, path except Exception as e: logger.error(f"Failed to get voice path: {e}") raise async def generate_audio_stream( self, text: str, voice: str, speed: float = 1.0, output_format: str = "wav", ) -> AsyncGenerator[bytes, None]: """Generate and stream audio chunks.""" stream_normalizer = AudioNormalizer() chunk_index = 0 try: # Get backend backend = self.model_manager.get_backend() # Get voice path, handling combined voices voice_name, voice_path = await self._get_voice_path(voice) logger.debug(f"Using voice path: {voice_path}") # Process text in chunks with smart splitting async for chunk_text, tokens in smart_split(text): try: # Process audio for chunk async for result in self._process_chunk( chunk_text, # Pass text for Kokoro V1 tokens, # Pass tokens for legacy backends voice_name, # Pass voice name voice_path, # Pass voice path speed, output_format, is_first=(chunk_index == 0), is_last=False, # We'll update the last chunk later normalizer=stream_normalizer ): if result is not None: yield result chunk_index += 1 else: logger.warning(f"No audio generated for chunk: '{chunk_text[:100]}...'") except Exception as e: logger.error(f"Failed to process audio for chunk: '{chunk_text[:100]}...'. Error: {str(e)}") continue # Only finalize if we successfully processed at least one chunk if chunk_index > 0: try: # Empty tokens list to finalize audio async for result in self._process_chunk( "", # Empty text [], # Empty tokens voice_name, voice_path, speed, output_format, is_first=False, is_last=True, # Signal this is the last chunk normalizer=stream_normalizer ): if result is not None: yield result except Exception as e: logger.error(f"Failed to finalize audio stream: {str(e)}") except Exception as e: logger.error(f"Error in phoneme audio generation: {str(e)}") raise async def generate_audio( self, text: str, voice: str, speed: float = 1.0 ) -> Tuple[np.ndarray, float]: """Generate complete audio for text using streaming internally.""" start_time = time.time() chunks = [] try: # Use streaming generator but collect all valid chunks async for chunk in self.generate_audio_stream( text, voice, speed, # Default to WAV for raw audio ): if chunk is not None: chunks.append(chunk) if not chunks: raise ValueError("No audio chunks were generated successfully") # Combine chunks, ensuring we have valid arrays if len(chunks) == 1: audio = chunks[0] else: # Filter out any zero-dimensional arrays valid_chunks = [c for c in chunks if c.ndim > 0] if not valid_chunks: raise ValueError("No valid audio chunks to concatenate") audio = np.concatenate(valid_chunks) processing_time = time.time() - start_time return audio, processing_time except Exception as e: logger.error(f"Error in audio generation: {str(e)}") raise async def combine_voices(self, voices: List[str]) -> str: """Combine multiple voices.""" return await self._voice_manager.combine_voices(voices) async def list_voices(self) -> List[str]: """List available voices.""" return await self._voice_manager.list_voices()