mirror of
https://github.com/jamiepine/voicebox.git
synced 2026-09-15 04:40:40 -07:00
Jamie Pine
0813a3d9d6
Phase 1 - delete dead code: - studio.py, migrate_add_instruct.py, utils/validation.py - duplicate _profile_to_response in main.py, duplicate asyncio import - pointless _get_profiles_dir/_get_generations_dir wrappers - duplicate LANGUAGE_CODE_TO_NAME and WHISPER_HF_REPOS constants Phase 2 - extract backends/base.py with shared utilities: - is_model_cached() replaces 7 copy-pasted HF cache checks - get_torch_device() replaces 5 device detection methods - combine_voice_prompts() replaces 5 identical implementations - model_load_progress() ctx manager replaces progress boilerplate in all backends - patch_chatterbox_f32() replaces identical monkey-patches in both chatterbox backends net -1078 lines across the backend
351 lines
12 KiB
Python
351 lines
12 KiB
Python
"""
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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 . import TTSBackend, STTBackend, LANGUAGE_CODE_TO_NAME, WHISPER_HF_REPOS
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from .base import is_model_cached, get_torch_device, combine_voice_prompts as _combine_voice_prompts, model_load_progress
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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 load_audio
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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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return get_torch_device(allow_xpu=True, allow_directml=True)
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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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def _is_model_cached(self, model_size: str) -> bool:
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return is_model_cached(self._get_model_path(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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model_name = f"qwen-tts-{model_size}"
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is_cached = self._is_model_cached(model_size)
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with model_load_progress(model_name, is_cached):
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from qwen_tts import Qwen3TTSModel
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model_path = self._get_model_path(model_size)
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print(f"Loading TTS model {model_size} on {self.device}...")
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if self.device == "cpu":
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self.model = Qwen3TTSModel.from_pretrained(
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model_path,
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torch_dtype=torch.float32,
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low_cpu_mem_usage=False,
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)
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else:
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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.bfloat16,
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)
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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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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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# For PyTorch backend, the dict should contain tensors, not file paths
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# So we can safely return it
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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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return await _combine_voice_prompts(audio_paths, reference_texts)
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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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language=LANGUAGE_CODE_TO_NAME.get(language, "auto"),
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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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return get_torch_device(allow_xpu=True, allow_directml=True)
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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 _is_model_cached(self, model_size: str) -> bool:
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hf_repo = WHISPER_HF_REPOS.get(model_size, f"openai/whisper-{model_size}")
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return is_model_cached(hf_repo)
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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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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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progress_model_name = f"whisper-{model_size}"
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is_cached = self._is_model_cached(model_size)
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with model_load_progress(progress_model_name, is_cached):
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from transformers import WhisperProcessor, WhisperForConditionalGeneration
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model_name = WHISPER_HF_REPOS.get(model_size, f"openai/whisper-{model_size}")
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print(f"Loading Whisper model {model_size} on {self.device}...")
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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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print(f"Whisper model {model_size} loaded successfully")
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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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# Generate transcription
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# If language is provided, force it; otherwise let Whisper auto-detect
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generate_kwargs = {}
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if language:
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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_kwargs["forced_decoder_ids"] = forced_decoder_ids
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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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**generate_kwargs,
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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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