mirror of
https://github.com/jamiepine/voicebox.git
synced 2026-09-19 06:40:38 -07:00
ADDED MLX FOR SUPER FAST GENERATIONS ON APPLE SILICON
- Added support for MLX backend on Apple Silicon, enabling optimized performance for TTS and STT tasks. - Updated release workflow to include MLX-specific dependencies and configurations for macOS platforms. - Refactored backend code to dynamically select between MLX and PyTorch based on the runtime environment. - Enhanced model loading and inference logic to accommodate backend-specific requirements, including updated model IDs and hidden imports. - Improved health check and model status reporting to reflect the active backend type. - Streamlined caching mechanisms to support both backend types, ensuring compatibility and performance.
This commit is contained in:
Jamie Pine
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"""
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PyTorch backend implementation for TTS and STT.
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"""
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from typing import Optional, List, Tuple
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import asyncio
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import torch
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import numpy as np
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from pathlib import Path
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from . import TTSBackend, STTBackend
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from ..utils.cache import get_cache_key, get_cached_voice_prompt, cache_voice_prompt
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from ..utils.audio import normalize_audio, load_audio
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from ..utils.progress import get_progress_manager
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from ..utils.hf_progress import HFProgressTracker, create_hf_progress_callback
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from ..utils.tasks import get_task_manager
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class PyTorchTTSBackend:
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"""PyTorch-based TTS backend using Qwen3-TTS."""
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def __init__(self, model_size: str = "1.7B"):
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self.model = None
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self.model_size = model_size
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self.device = self._get_device()
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self._current_model_size = None
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def _get_device(self) -> str:
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"""Get the best available device."""
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if torch.cuda.is_available():
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return "cuda"
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elif hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
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# MPS can have issues, use CPU for stability
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return "cpu"
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return "cpu"
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def is_loaded(self) -> bool:
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"""Check if model is loaded."""
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return self.model is not None
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def _get_model_path(self, model_size: str) -> str:
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"""
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Get the HuggingFace Hub model ID.
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Args:
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model_size: Model size (1.7B or 0.6B)
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Returns:
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HuggingFace Hub model ID
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"""
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hf_model_map = {
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"1.7B": "Qwen/Qwen3-TTS-12Hz-1.7B-Base",
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"0.6B": "Qwen/Qwen3-TTS-12Hz-0.6B-Base",
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}
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if model_size not in hf_model_map:
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raise ValueError(f"Unknown model size: {model_size}")
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return hf_model_map[model_size]
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async def load_model_async(self, model_size: Optional[str] = None):
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"""
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Lazy load the TTS model with automatic downloading from HuggingFace Hub.
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Args:
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model_size: Model size to load (1.7B or 0.6B)
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"""
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if model_size is None:
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model_size = self.model_size
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# If already loaded with correct size, return
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if self.model is not None and self._current_model_size == model_size:
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return
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# Unload existing model if different size requested
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if self.model is not None and self._current_model_size != model_size:
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self.unload_model()
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# Run blocking load in thread pool
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await asyncio.to_thread(self._load_model_sync, model_size)
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# Alias for compatibility
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load_model = load_model_async
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def _load_model_sync(self, model_size: str):
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"""Synchronous model loading."""
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try:
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from qwen_tts import Qwen3TTSModel
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# Get model path (local or HuggingFace Hub ID)
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model_path = self._get_model_path(model_size)
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# Set up progress tracking
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progress_manager = get_progress_manager()
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model_name = f"qwen-tts-{model_size}"
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print(f"Loading TTS model {model_size} on {self.device}...")
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# Start tracking download task
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task_manager = get_task_manager()
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task_manager.start_download(model_name)
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# Initialize progress state to show download has started
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progress_manager.update_progress(
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model_name=model_name,
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current=0,
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total=1, # Set to 1 initially, will be updated by callback
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filename="",
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status="downloading",
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)
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# Set up progress callback
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progress_callback = create_hf_progress_callback(model_name, progress_manager)
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tracker = HFProgressTracker(progress_callback)
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# Use progress tracker during download
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with tracker.patch_download():
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# Load the model - downloads will happen automatically with progress tracking
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self.model = Qwen3TTSModel.from_pretrained(
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model_path,
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device_map=self.device,
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torch_dtype=torch.float32 if self.device == "cpu" else torch.bfloat16,
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)
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# Mark as complete
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progress_manager.mark_complete(model_name)
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task_manager.complete_download(model_name)
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self._current_model_size = model_size
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self.model_size = model_size
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print(f"TTS model {model_size} loaded successfully")
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except ImportError as e:
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print(f"Error: qwen_tts package not found. Install with: pip install git+https://github.com/QwenLM/Qwen3-TTS.git")
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progress_manager = get_progress_manager()
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task_manager = get_task_manager()
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model_name = f"qwen-tts-{model_size}"
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progress_manager.mark_error(model_name, str(e))
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task_manager.error_download(model_name, str(e))
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raise
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except Exception as e:
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print(f"Error loading TTS model: {e}")
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print(f"Tip: The model will be automatically downloaded from HuggingFace Hub on first use.")
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progress_manager = get_progress_manager()
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task_manager = get_task_manager()
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model_name = f"qwen-tts-{model_size}"
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progress_manager.mark_error(model_name, str(e))
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task_manager.error_download(model_name, str(e))
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raise
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def unload_model(self):
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"""Unload the model to free memory."""
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if self.model is not None:
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del self.model
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self.model = None
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self._current_model_size = None
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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print("TTS model unloaded")
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async def create_voice_prompt(
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self,
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audio_path: str,
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reference_text: str,
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use_cache: bool = True,
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) -> Tuple[dict, bool]:
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"""
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Create voice prompt from reference audio.
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Args:
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audio_path: Path to reference audio file
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reference_text: Transcript of reference audio
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use_cache: Whether to use cached prompt if available
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Returns:
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Tuple of (voice_prompt_dict, was_cached)
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"""
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await self.load_model_async(None)
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# Check cache if enabled
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if use_cache:
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cache_key = get_cache_key(audio_path, reference_text)
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cached_prompt = get_cached_voice_prompt(cache_key)
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if cached_prompt is not None:
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# Cache stores as torch.Tensor but actual prompt is dict
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# Convert if needed
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if isinstance(cached_prompt, dict):
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return cached_prompt, True
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elif isinstance(cached_prompt, torch.Tensor):
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# Legacy cache format - convert to dict
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# This shouldn't happen in practice, but handle it
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return {"prompt": cached_prompt}, True
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def _create_prompt_sync():
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"""Run synchronous voice prompt creation in thread pool."""
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return self.model.create_voice_clone_prompt(
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ref_audio=str(audio_path),
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ref_text=reference_text,
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x_vector_only_mode=False,
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)
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# Run blocking operation in thread pool
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voice_prompt_items = await asyncio.to_thread(_create_prompt_sync)
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# Cache if enabled
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if use_cache:
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cache_key = get_cache_key(audio_path, reference_text)
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cache_voice_prompt(cache_key, voice_prompt_items)
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return voice_prompt_items, False
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async def combine_voice_prompts(
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self,
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audio_paths: List[str],
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reference_texts: List[str],
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) -> Tuple[np.ndarray, str]:
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"""
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Combine multiple reference samples for better quality.
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Args:
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audio_paths: List of audio file paths
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reference_texts: List of reference texts
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Returns:
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Tuple of (combined_audio, combined_text)
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"""
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combined_audio = []
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for audio_path in audio_paths:
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audio, sr = load_audio(audio_path)
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audio = normalize_audio(audio)
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combined_audio.append(audio)
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# Concatenate audio
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mixed = np.concatenate(combined_audio)
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mixed = normalize_audio(mixed)
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# Combine texts
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combined_text = " ".join(reference_texts)
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return mixed, combined_text
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async def generate(
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self,
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text: str,
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voice_prompt: dict,
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language: str = "en",
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seed: Optional[int] = None,
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instruct: Optional[str] = None,
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) -> Tuple[np.ndarray, int]:
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"""
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Generate audio from text using voice prompt.
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Args:
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text: Text to synthesize
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voice_prompt: Voice prompt dictionary from create_voice_prompt
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language: Language code (en or zh)
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seed: Random seed for reproducibility
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instruct: Natural language instruction for speech delivery control
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Returns:
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Tuple of (audio_array, sample_rate)
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"""
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# Load model
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await self.load_model_async(None)
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def _generate_sync():
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"""Run synchronous generation in thread pool."""
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# Set seed if provided
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if seed is not None:
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torch.manual_seed(seed)
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if torch.cuda.is_available():
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torch.cuda.manual_seed(seed)
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# Generate audio - this is the blocking operation
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wavs, sample_rate = self.model.generate_voice_clone(
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text=text,
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voice_clone_prompt=voice_prompt,
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instruct=instruct,
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)
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return wavs[0], sample_rate
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# Run blocking inference in thread pool to avoid blocking event loop
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audio, sample_rate = await asyncio.to_thread(_generate_sync)
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return audio, sample_rate
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class PyTorchSTTBackend:
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"""PyTorch-based STT backend using Whisper."""
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def __init__(self, model_size: str = "base"):
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self.model = None
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self.processor = None
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self.model_size = model_size
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self.device = self._get_device()
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def _get_device(self) -> str:
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"""Get the best available device."""
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if torch.cuda.is_available():
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return "cuda"
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elif hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
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# MPS support for Whisper
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return "cpu" # Use CPU for stability
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return "cpu"
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def is_loaded(self) -> bool:
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"""Check if model is loaded."""
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return self.model is not None
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async def load_model_async(self, model_size: Optional[str] = None):
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"""
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Lazy load the Whisper model.
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Args:
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model_size: Model size (tiny, base, small, medium, large)
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"""
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if model_size is None:
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model_size = self.model_size
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if self.model is not None and self.model_size == model_size:
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return
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# Run blocking load in thread pool
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await asyncio.to_thread(self._load_model_sync, model_size)
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# Alias for compatibility
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load_model = load_model_async
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def _load_model_sync(self, model_size: str):
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"""Synchronous model loading."""
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try:
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from transformers import WhisperProcessor, WhisperForConditionalGeneration
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model_name = f"openai/whisper-{model_size}"
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# Set up progress tracking
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progress_manager = get_progress_manager()
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progress_model_name = f"whisper-{model_size}"
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# Start tracking download task
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task_manager = get_task_manager()
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task_manager.start_download(progress_model_name)
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print(f"Loading Whisper model {model_size} on {self.device}...")
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# Initialize progress state to show download has started
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progress_manager.update_progress(
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model_name=progress_model_name,
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current=0,
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total=1, # Set to 1 initially, will be updated by callback
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filename="",
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status="downloading",
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)
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# Set up progress callback
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progress_callback = create_hf_progress_callback(progress_model_name, progress_manager)
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tracker = HFProgressTracker(progress_callback)
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# Use progress tracker during download
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with tracker.patch_download():
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self.processor = WhisperProcessor.from_pretrained(model_name)
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self.model = WhisperForConditionalGeneration.from_pretrained(model_name)
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self.model.to(self.device)
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self.model_size = model_size
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# Mark as complete
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progress_manager.mark_complete(progress_model_name)
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task_manager.complete_download(progress_model_name)
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print(f"Whisper model {model_size} loaded successfully")
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except Exception as e:
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print(f"Error loading Whisper model: {e}")
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progress_manager = get_progress_manager()
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task_manager = get_task_manager()
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progress_model_name = f"whisper-{model_size}"
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progress_manager.mark_error(progress_model_name, str(e))
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task_manager.error_download(progress_model_name, str(e))
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raise
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def unload_model(self):
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"""Unload the model to free memory."""
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if self.model is not None:
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del self.model
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del self.processor
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self.model = None
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self.processor = None
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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print("Whisper model unloaded")
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async def transcribe(
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self,
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audio_path: str,
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language: Optional[str] = None,
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) -> str:
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"""
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Transcribe audio to text.
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Args:
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audio_path: Path to audio file
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language: Optional language hint (en or zh)
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Returns:
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Transcribed text
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"""
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await self.load_model_async(None)
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def _transcribe_sync():
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"""Run synchronous transcription in thread pool."""
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# Load audio
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audio, sr = load_audio(audio_path, sample_rate=16000)
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# Process audio
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inputs = self.processor(
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audio,
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sampling_rate=16000,
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return_tensors="pt",
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)
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inputs = inputs.to(self.device)
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# Set language if provided
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forced_decoder_ids = None
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if language:
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# Support all languages from frontend: en, zh, ja, ko, de, fr, ru, pt, es, it
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# Whisper supports these and many more
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forced_decoder_ids = self.processor.get_decoder_prompt_ids(
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language=language,
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task="transcribe",
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)
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# Generate transcription
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with torch.no_grad():
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predicted_ids = self.model.generate(
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inputs["input_features"],
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forced_decoder_ids=forced_decoder_ids,
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)
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# Decode
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transcription = self.processor.batch_decode(
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predicted_ids,
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skip_special_tokens=True,
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)[0]
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return transcription.strip()
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# Run blocking transcription in thread pool
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return await asyncio.to_thread(_transcribe_sync)
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