import asyncio import base64 import logging import os import tempfile from typing import Optional # 设置 Hugging Face 镜像源为国内镜像 os.environ.setdefault("HF_ENDPOINT", "https://hf-mirror.com") import numpy as np import torch from fastapi import APIRouter, HTTPException from pydantic import BaseModel logger = logging.getLogger(__name__) # New TTS model import try: from qwen_tts import Qwen3TTSModel # type: ignore except Exception: # pragma: no cover Qwen3TTSModel = None # type: ignore router = APIRouter() # Global TTS model instance _tts_model: Optional["Qwen3TTSModel"] = None # Model paths for loading MODEL_ID_HF = "Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign" MODEL_ID_MS = "Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign" def _get_device_map() -> str: """设备检测逻辑：优先 CUDA，其次 MPS，最后 CPU""" if torch.cuda.is_available(): return "cuda:0" try: if hasattr(torch.backends, "mps") and torch.backends.mps.is_available(): return "mps" except Exception as e: logger.debug("MPS check failed: %s", e) return "cpu" def _download_model_from_modelscope() -> Optional[str]: """从 ModelScope 下载模型到本地临时目录""" try: from modelscope import snapshot_download cache_dir = os.path.join(os.path.dirname(__file__), "models") os.makedirs(cache_dir, exist_ok=True) model_dir = snapshot_download( MODEL_ID_MS, cache_dir=cache_dir, revision="master" ) logger.info("ModelScope 模型下载完成: %s", model_dir) return model_dir except Exception as e: logger.warning("ModelScope 下载失败: %s", e) return None async def _warmup_tts(): """预热 TTS 模型""" await asyncio.to_thread(_load_tts_model_with_retry) async def _warmup_all(): """预热所有模型（TTS 和 ASR）""" logger.info("[Warmup] 开始预热 TTS 模型...") await _warmup_tts() logger.info("[Warmup] TTS 模型预热完成") def _load_tts_model_with_retry(max_retries: int = 3) -> "Qwen3TTSModel": """加载 TTS 模型，支持多个镜像源""" global _tts_model if _tts_model is not None: return _tts_model if Qwen3TTSModel is None: raise RuntimeError("qwen_tts 库未安装，无法加载 TTS 模型") device_map = _get_device_map() last_err = None # 策略1: 尝试从 ModelScope 下载后加载 for attempt in range(max_retries): try: logger.info("尝试从 ModelScope 下载模型...") model_path = _download_model_from_modelscope() if model_path and os.path.isdir(model_path): _tts_model = Qwen3TTSModel.from_pretrained( model_path, device_map=device_map, dtype=torch.float16, ) logger.info("ModelScope 模型加载成功: %s", model_path) return _tts_model except Exception as e: logger.warning("ModelScope 加载失败 (尝试 %d/%d): %s", attempt + 1, max_retries, e) last_err = e # 策略2: 尝试从 HuggingFace 镜像加载 for attempt in range(max_retries): try: logger.info("尝试从 HuggingFace 镜像加载模型...") _tts_model = Qwen3TTSModel.from_pretrained( MODEL_ID_HF, device_map=device_map, dtype=torch.float16, ) logger.info("HuggingFace 模型加载成功") return _tts_model except Exception as e: logger.warning("HuggingFace 加载失败 (尝试 %d/%d): %s", attempt + 1, max_retries, e) last_err = e raise RuntimeError(f"无法加载 TTS 模型: {last_err}") from last_err class TTSRequest(BaseModel): text: str instruct: str = "" speaker: str = "Vivian" format: str = "wav" class TTSResponse(BaseModel): audio_base64: str format: str duration_ms: int class ModelStatus(BaseModel): tts_loaded: bool asr_loaded: bool = False device: str tts_last_used: Optional[float] = None asr_last_used: Optional[float] = None def _ensure_model() -> "Qwen3TTSModel": """确保模型已加载""" global _tts_model if _tts_model is None: _tts_model = _load_tts_model_with_retry() return _tts_model @router.get("/status", response_model=ModelStatus) async def get_status(): """获取模型状态""" return ModelStatus( tts_loaded=_tts_model is not None, asr_loaded=False, device=_get_device_map(), ) @router.get("/config") async def get_config(): """获取配置信息""" return { "model": { "tts": "Qwen3-TTS-12Hz-1.7B-VoiceDesign", "asr": None, }, "device": _get_device_map(), "status": { "tts_loaded": _tts_model is not None, "asr_loaded": False, } } @router.post("/warmup") async def warmup_models(): """手动触发模型预热""" await _warmup_tts() return { "tts_warmup": _tts_model is not None, "device": _get_device_map(), } @router.post("/tts", response_model=TTSResponse) async def tts_endpoint(req: TTSRequest): """TTS 文字转语音端点""" try: model = _ensure_model() except Exception as e: raise HTTPException(status_code=500, detail=str(e)) text = req.text instruct = req.instruct or "" try: # VoiceDesign 模型使用 generate_voice_design 方法 # 返回 (wavs, sr)，其中 wavs 是列表，wavs[0] 是第一个音频数据 wavs, sr = model.generate_voice_design( text=text, language="Chinese", instruct=instruct, ) except Exception as e: logger.exception("TTS 推理失败") raise HTTPException(status_code=500, detail=f"TTS 推理失败: {e}") # 获取第一个音频数据 wav_data = wavs[0] if isinstance(wavs, (list, tuple)) else wavs # 转换为 numpy 数组 if hasattr(wav_data, 'numpy'): wav_data = wav_data.cpu().numpy() wav_data = np.asarray(wav_data, dtype=np.float32) logger.debug("wav_data shape: %s, dtype: %s, sr: %s", wav_data.shape, wav_data.dtype, sr) # 编码 WAV 到内存 tmp_path = None try: import soundfile as sf # 创建临时文件 fd, tmp_path = tempfile.mkstemp(suffix=".wav") os.close(fd) sf.write(tmp_path, wav_data, sr) with open(tmp_path, "rb") as f: audio_bytes = f.read() except Exception as e: logger.exception("音频编码失败") raise HTTPException(status_code=500, detail=f"音频编码失败: {e}") finally: # 清理临时文件 if tmp_path and os.path.exists(tmp_path): try: os.unlink(tmp_path) except Exception: pass # 计算时长（毫秒） duration_ms = int(len(wav_data) / sr * 1000) if sr > 0 else 0 # 返回 JSON 格式，包含 base64 编码的音频 audio_base64 = base64.b64encode(audio_bytes).decode("utf-8") return TTSResponse( audio_base64=audio_base64, format="wav", duration_ms=duration_ms, ) def register_tts_asr_routes(app): """注册 TTS/ASR 路由到 FastAPI 应用""" app.include_router(router, prefix="/v1/tts-asr")