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https://github.com/jamiepine/voicebox.git
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feat: add LuxTTS as second TTS engine with multi-engine support
Introduce LuxTTS (ZipVoice) alongside Qwen TTS, enabling users to choose between engines at generation time. LuxTTS offers fast, English-focused voice cloning at 48kHz with ~1GB VRAM. Backend: - Add LuxTTSBackend with encode_prompt/generate_speech integration - Multi-engine registry (get_tts_backend_for_engine) replacing singleton - Engine-prefixed voice prompt cache keys to avoid collisions - Engine field on GenerationRequest (default 'qwen' for backward compat) - Engine dispatch in /generate and /generate/stream endpoints - LuxTTS in model status, download, and delete maps Frontend: - TTS Engine selector dropdown in GenerationForm (Qwen TTS / LuxTTS) - Conditionally hide Model Size and Delivery Instructions for LuxTTS - Engine field added to TypeScript types and Zod schema - LuxTTS section in Model Management page
This commit is contained in:
James Pine
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"""
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LuxTTS backend implementation.
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Wraps the LuxTTS (ZipVoice) model for zero-shot voice cloning.
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~1GB VRAM, 48kHz output, 150x realtime on CPU.
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"""
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import asyncio
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import logging
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from pathlib import Path
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from typing import List, Optional, Tuple
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import numpy as np
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from . import TTSBackend
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from ..utils.audio import normalize_audio, load_audio
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from ..utils.cache import get_cache_key, get_cached_voice_prompt, cache_voice_prompt
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from ..utils.progress import get_progress_manager
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from ..utils.tasks import get_task_manager
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logger = logging.getLogger(__name__)
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# HuggingFace repo for model weight detection
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LUXTTS_HF_REPO = "YatharthS/LuxTTS"
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class LuxTTSBackend:
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"""LuxTTS backend for zero-shot voice cloning."""
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def __init__(self):
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self.model = None
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self.model_size = "default" # LuxTTS has only one model size
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self._device = None
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def _get_device(self) -> str:
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"""Get the best available device."""
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import torch
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if torch.cuda.is_available():
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return "cuda"
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if hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
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return "mps"
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return "cpu"
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def is_loaded(self) -> bool:
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return self.model is not None
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@property
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def device(self) -> str:
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if self._device is None:
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self._device = self._get_device()
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return self._device
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def _get_model_path(self, model_size: str) -> str:
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return LUXTTS_HF_REPO
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def _is_model_cached(self, model_size: str = "default") -> bool:
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"""Check if LuxTTS model weights are cached locally."""
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try:
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from huggingface_hub import constants as hf_constants
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repo_cache = (
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Path(hf_constants.HF_HUB_CACHE)
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/ ("models--" + LUXTTS_HF_REPO.replace("/", "--"))
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)
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if not repo_cache.exists():
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return False
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blobs_dir = repo_cache / "blobs"
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if blobs_dir.exists() and any(blobs_dir.glob("*.incomplete")):
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return False
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snapshots_dir = repo_cache / "snapshots"
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if snapshots_dir.exists():
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has_weights = any(snapshots_dir.rglob("*.pt")) or any(
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snapshots_dir.rglob("*.safetensors")
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) or any(snapshots_dir.rglob("*.onnx")) or any(
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snapshots_dir.rglob("*.bin")
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)
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return has_weights
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return False
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except Exception as e:
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logger.warning(f"Error checking LuxTTS cache: {e}")
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return False
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async def load_model(self, model_size: str = "default") -> None:
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"""Load the LuxTTS model."""
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if self.model is not None:
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return
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await asyncio.to_thread(self._load_model_sync)
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def _load_model_sync(self):
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"""Synchronous model loading."""
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progress_manager = get_progress_manager()
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task_manager = get_task_manager()
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model_name = "luxtts"
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is_cached = self._is_model_cached()
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if not is_cached:
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task_manager.start_download(model_name)
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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=0,
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filename="Downloading LuxTTS model...",
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status="downloading",
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)
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try:
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from zipvoice.luxvoice import LuxTTS
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device = self.device
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logger.info(f"Loading LuxTTS on {device}...")
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# LuxTTS constructor downloads model and loads everything
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if device == "cpu":
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import os
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threads = os.cpu_count() or 4
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self.model = LuxTTS(
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model_path=LUXTTS_HF_REPO,
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device="cpu",
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threads=min(threads, 8),
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)
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else:
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self.model = LuxTTS(
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model_path=LUXTTS_HF_REPO,
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device=device,
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)
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if not is_cached:
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progress_manager.mark_complete(model_name)
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task_manager.complete_download(model_name)
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logger.info("LuxTTS loaded successfully")
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except Exception as e:
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logger.error(f"Failed to load LuxTTS: {e}")
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if not is_cached:
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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) -> None:
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"""Unload 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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import torch
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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logger.info("LuxTTS 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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LuxTTS uses its own encode_prompt() which runs Whisper ASR internally
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to transcribe the reference. The reference_text parameter is not used
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by LuxTTS itself, but we include it in the cache key for consistency.
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"""
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await self.load_model()
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if use_cache:
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# Include "luxtts" in the cache key so it doesn't collide with Qwen prompts
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cache_key = "luxtts_" + get_cache_key(audio_path, reference_text)
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cached = get_cached_voice_prompt(cache_key)
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if cached is not None and isinstance(cached, dict):
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return cached, True
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def _encode_sync():
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return self.model.encode_prompt(
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prompt_audio=str(audio_path),
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duration=5,
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rms=0.01,
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)
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encoded = await asyncio.to_thread(_encode_sync)
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if use_cache:
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cache_key = "luxtts_" + get_cache_key(audio_path, reference_text)
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cache_voice_prompt(cache_key, encoded)
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return encoded, 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.
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LuxTTS doesn't have native multi-prompt support, so we concatenate
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the audio and let encode_prompt handle the combined clip.
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"""
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combined_audio = []
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for path in audio_paths:
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audio, sr = load_audio(path, sample_rate=24000)
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audio = normalize_audio(audio)
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combined_audio.append(audio)
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mixed = np.concatenate(combined_audio)
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mixed = normalize_audio(mixed)
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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 LuxTTS.
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Args:
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text: Text to synthesize
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voice_prompt: Encoded prompt dict from encode_prompt()
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language: Language code (LuxTTS is English-focused)
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seed: Random seed for reproducibility
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instruct: Not supported by LuxTTS (ignored)
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Returns:
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Tuple of (audio_array, sample_rate)
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"""
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await self.load_model()
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def _generate_sync():
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import torch
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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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wav = self.model.generate_speech(
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text=text,
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encode_dict=voice_prompt,
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num_steps=4,
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guidance_scale=3.0,
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t_shift=0.5,
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speed=1.0,
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return_smooth=False, # 48kHz output
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)
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# LuxTTS returns a tensor, convert to numpy
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audio = wav.numpy().squeeze()
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return audio, 48000
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return await asyncio.to_thread(_generate_sync)
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