dekun
f36056d293
Strip Markdown and stage directions before ChatTTS synthesis with chunked long scripts; document model pre-download, server-update, and microphone HTTPS notes. Co-authored-by: Cursor <cursoragent@cursor.com>
603 lines
18 KiB
Python
603 lines
18 KiB
Python
"""
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ChatTTS 本地语音合成服务
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支持从参考人声提取 Speaker Embedding 并固定音色合成配音。
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"""
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from __future__ import annotations
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import inspect
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import logging
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import os
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import re
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import traceback
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import uuid
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import warnings
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from datetime import datetime
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from pathlib import Path
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from typing import Any, Dict, List, Optional, Tuple
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import numpy as np
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import torch
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from scipy.io import wavfile
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from config import (
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BASE_DIR,
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CHATTTS_MODEL_DIR,
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HF_ENDPOINT,
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HF_HOME,
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HF_HUB_DOWNLOAD_TIMEOUT,
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OUTPUT_DIR,
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SPEAKER_EMB_PATH,
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SPEAKER_SAMPLE_MAX_SEC,
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SPEAKER_SAMPLE_MIN_SEC,
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TTS_MAX_CHARS_PER_CHUNK,
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TTS_SAMPLE_RATE,
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TTS_SPEED_PROMPT,
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TTS_TEMPERATURE,
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TTS_TOP_K,
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TTS_TOP_P,
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)
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logger = logging.getLogger(__name__)
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# 全局 ChatTTS 实例
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_chat = None
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_chat_error: Optional[str] = None
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def _ensure_hf_env() -> None:
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"""配置 HuggingFace 镜像与下载超时，避免默认 3s 访问 GitHub 超时。"""
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os.environ.setdefault("HF_ENDPOINT", HF_ENDPOINT)
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os.environ.setdefault("HF_HUB_DOWNLOAD_TIMEOUT", str(HF_HUB_DOWNLOAD_TIMEOUT))
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os.environ.setdefault("HF_HOME", str(HF_HOME))
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HF_HOME.mkdir(parents=True, exist_ok=True)
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CHATTTS_MODEL_DIR.mkdir(parents=True, exist_ok=True)
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def _chattts_model_ready(model_dir: Path) -> bool:
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"""检查本地 ChatTTS 模型目录是否完整。"""
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if not model_dir.is_dir():
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return False
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if (model_dir / "config" / "path.yaml").is_file():
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return True
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asset_dir = model_dir / "asset"
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if asset_dir.is_dir() and any(asset_dir.rglob("*.pt")):
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return True
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if any(model_dir.glob("*.pt")):
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return True
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return False
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def _build_load_error(exc: BaseException) -> str:
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"""生成用户可读的 ChatTTS 加载失败说明。"""
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msg = str(exc)
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hints = [
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"ChatTTS 模型加载失败。",
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f"详情: {msg}",
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"",
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"常见原因：服务器无法访问 GitHub（read timeout=3）。",
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"解决办法（在服务器执行一次）：",
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f" cd {BASE_DIR}",
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" bash scripts/download_chattts_models.sh",
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" pm2 restart trading_studio",
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"",
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f"模型将下载到: {CHATTTS_MODEL_DIR}",
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f"HF 镜像: {HF_ENDPOINT}",
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]
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return "\n".join(hints)
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def _load_chat_model(chat) -> None:
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"""按优先级加载 ChatTTS：本地 custom → 镜像下载到 cache_dir。"""
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_ensure_hf_env()
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model_dir = CHATTTS_MODEL_DIR
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base_kwargs: Dict[str, Any] = {"compile": False}
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if not hasattr(chat, "load"):
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if hasattr(chat, "load_models"):
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chat.load_models(**base_kwargs)
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return
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raise RuntimeError("当前 ChatTTS 版本缺少 load / load_models 方法。")
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sig = inspect.signature(chat.load)
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params = sig.parameters
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# 1) 本地已预下载 → 完全离线，不访问 GitHub
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if _chattts_model_ready(model_dir):
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logger.info("ChatTTS 从本地目录加载 (source=custom): %s", model_dir)
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kwargs = dict(base_kwargs)
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if "source" in params:
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kwargs["source"] = "custom"
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if "custom_path" in params:
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kwargs["custom_path"] = str(model_dir)
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result = chat.load(**kwargs)
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if result is False:
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raise RuntimeError(f"ChatTTS 本地加载失败，请检查 {model_dir}")
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return
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# 2) 未预下载 → 通过 HF 镜像下载到指定目录（仍可能尝试网络）
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logger.warning(
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"未找到本地 ChatTTS 模型 (%s)，尝试通过 HF 镜像下载…",
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model_dir,
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)
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kwargs = dict(base_kwargs)
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if "source" in params:
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kwargs["source"] = "local"
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if "cache_dir" in params:
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kwargs["cache_dir"] = str(model_dir)
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elif "source" in params:
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kwargs["source"] = "huggingface"
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result = chat.load(**kwargs)
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if result is False:
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raise RuntimeError(
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"ChatTTS 在线下载失败。请执行: bash scripts/download_chattts_models.sh"
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)
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def reset_chattts_instance() -> None:
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"""释放 ChatTTS 实例（模型下载后重启前可调用）。"""
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global _chat, _chat_error
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_chat = None
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_chat_error = None
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def get_chattts_instance():
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"""
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获取或初始化 ChatTTS 模型。
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启用 GPU 加速，compile=False 以兼容 3060 Ti 8GB 显存。
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"""
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global _chat, _chat_error
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if _chat is not None:
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return _chat, None
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if _chat_error is not None:
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return None, _chat_error
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try:
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_ensure_hf_env()
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import ChatTTS
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logger.info("正在加载 ChatTTS 模型...")
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chat = ChatTTS.Chat()
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_load_chat_model(chat)
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_chat = chat
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logger.info("ChatTTS 模型加载成功。")
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return _chat, None
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except ImportError as exc:
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_chat_error = (
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"未安装 ChatTTS，请参考 DEPLOY.md 安装。\n"
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f"原始错误: {exc}"
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)
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logger.exception("ChatTTS 导入失败")
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return None, _chat_error
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except Exception as exc:
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_chat_error = _build_load_error(exc)
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logger.exception("ChatTTS 初始化异常")
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return None, _chat_error
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def _load_audio_via_ffmpeg(audio_path: str, sample_rate: int) -> np.ndarray:
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"""通过 ffmpeg 转码为 wav 再读取，兼容手机 webm/m4a 等格式。"""
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import subprocess
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import tempfile
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import soundfile as sf
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tmp_path = tempfile.mktemp(suffix=".wav")
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try:
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cmd = [
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"ffmpeg",
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"-y",
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"-i",
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audio_path,
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"-ac",
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"1",
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"-ar",
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str(sample_rate),
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"-f",
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"wav",
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tmp_path,
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]
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result = subprocess.run(cmd, capture_output=True, text=True, timeout=120)
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if result.returncode != 0:
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raise RuntimeError(result.stderr[-500:] if result.stderr else "ffmpeg 转码失败")
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audio, _ = sf.read(tmp_path, dtype="float32", always_2d=False)
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if isinstance(audio, np.ndarray) and audio.ndim > 1:
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audio = audio.mean(axis=1)
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return np.asarray(audio, dtype=np.float32)
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finally:
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Path(tmp_path).unlink(missing_ok=True)
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def _load_audio_for_chattts(audio_path: str, sample_rate: int = TTS_SAMPLE_RATE) -> np.ndarray:
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"""
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加载音频并重采样到 ChatTTS 所需采样率。
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优先 ChatTTS 工具 → ffmpeg 转码 → librosa 兜底。
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"""
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errors: list[str] = []
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try:
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from ChatTTS.utils import load_audio
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return load_audio(audio_path, sample_rate)
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except Exception as exc:
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errors.append(f"ChatTTS.utils: {exc}")
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try:
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from tools.audio import load_audio
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return load_audio(audio_path, sample_rate)
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except Exception as exc:
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errors.append(f"tools.audio: {exc}")
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try:
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return _load_audio_via_ffmpeg(audio_path, sample_rate)
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except Exception as exc:
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errors.append(f"ffmpeg: {exc}")
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try:
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import librosa
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with warnings.catch_warnings():
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warnings.filterwarnings("ignore", category=FutureWarning)
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warnings.filterwarnings("ignore", message="PySoundFile failed")
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audio, _ = librosa.load(audio_path, sr=sample_rate, mono=True)
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return audio
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except Exception as exc:
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errors.append(f"librosa: {exc}")
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raise RuntimeError(
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"无法读取音频文件，请上传 wav/mp3/m4a 或确认已安装 ffmpeg。\n"
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+ "\n".join(errors[-3:])
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)
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def _get_audio_duration_sec(audio: np.ndarray, sample_rate: int) -> float:
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"""计算音频时长（秒）。"""
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if audio is None or len(audio) == 0:
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return 0.0
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return len(audio) / float(sample_rate)
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def _encode_spk_emb(chat, tensor_or_str: Any) -> str:
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"""将 Speaker Embedding 编码为 ChatTTS 可用的字符串格式。"""
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if isinstance(tensor_or_str, str):
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return tensor_or_str
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if hasattr(chat, "_encode_spk_emb"):
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return chat._encode_spk_emb(tensor_or_str)
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return tensor_or_str
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def save_fixed_speaker(
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audio_sample_path: str,
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sample_transcript: str = "",
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) -> Tuple[bool, str]:
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"""
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从 10-30 秒干净人声中提取 Speaker Embedding 并序列化保存。
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Args:
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audio_sample_path: 参考人声 wav/mp3 等路径
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sample_transcript: 参考音频的精确转写（可选，有助于 zero-shot 音色还原）
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Returns:
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(success, message)
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"""
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if not audio_sample_path:
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return False, "未提供音色参考音频。"
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chat, init_err = get_chattts_instance()
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if chat is None:
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return False, init_err or "ChatTTS 不可用。"
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try:
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audio = _load_audio_for_chattts(audio_sample_path, TTS_SAMPLE_RATE)
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duration = _get_audio_duration_sec(audio, TTS_SAMPLE_RATE)
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if duration < SPEAKER_SAMPLE_MIN_SEC:
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return False, (
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f"参考音频过短（{duration:.1f}s），建议 {SPEAKER_SAMPLE_MIN_SEC}-"
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f"{SPEAKER_SAMPLE_MAX_SEC} 秒干净人声。"
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)
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if duration > SPEAKER_SAMPLE_MAX_SEC + 5:
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logger.warning("参考音频超过建议时长 %.1fs，将截取前 %ds", duration, SPEAKER_SAMPLE_MAX_SEC)
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max_samples = SPEAKER_SAMPLE_MAX_SEC * TTS_SAMPLE_RATE
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audio = audio[:max_samples]
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spk_smp = chat.sample_audio_speaker(audio)
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spk_emb = _encode_spk_emb(chat, spk_smp)
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payload: Dict[str, Any] = {
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"spk_emb": spk_emb,
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"spk_smp": spk_smp,
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"txt_smp": sample_transcript.strip(),
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"created_at": datetime.now().isoformat(),
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"source_audio": str(audio_sample_path),
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}
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torch.save(payload, SPEAKER_EMB_PATH)
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msg = (
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f"音色已锁定并保存至 {SPEAKER_EMB_PATH}\n"
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f"参考时长: {duration:.1f}s"
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)
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if not sample_transcript.strip():
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msg += "\n提示：填写参考音频精确转写可进一步提升音色还原度。"
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logger.info("Speaker Embedding 保存成功: %s", SPEAKER_EMB_PATH)
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return True, msg
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except Exception as exc:
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err = f"音色提取失败: {exc}\n{traceback.format_exc()}"
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logger.exception("save_fixed_speaker 失败")
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return False, err
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def _load_speaker_payload() -> Tuple[Optional[Dict[str, Any]], Optional[str]]:
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"""加载本地 speaker_emb.pt。"""
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if not SPEAKER_EMB_PATH.exists():
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return None, (
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f"未找到固定音色文件 `{SPEAKER_EMB_PATH.name}`。"
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"请先在【音色锁定】模块上传 10-30 秒参考人声。"
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)
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try:
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payload = torch.load(SPEAKER_EMB_PATH, map_location="cpu", weights_only=False)
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if isinstance(payload, torch.Tensor):
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chat, err = get_chattts_instance()
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if chat is None:
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return None, err
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return {
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"spk_emb": _encode_spk_emb(chat, payload),
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"spk_smp": None,
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"txt_smp": "",
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}, None
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if not isinstance(payload, dict):
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return None, "speaker_emb.pt 格式无效，请重新锁定音色。"
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return payload, None
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except Exception as exc:
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return None, f"读取 speaker_emb.pt 失败: {exc}"
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def speaker_is_ready() -> Tuple[bool, str]:
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"""检查固定音色是否已配置。"""
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payload, err = _load_speaker_payload()
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if payload is None:
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return False, err or "音色未配置。"
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return True, f"已加载固定音色: {SPEAKER_EMB_PATH}"
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_EMOJI_RE = re.compile(
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"["
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"\U0001F300-\U0001FAFF"
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"\U00002700-\U000027BF"
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"\U00002600-\U000026FF"
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"]+",
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flags=re.UNICODE,
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)
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_TTS_NOTE_MARKERS = (
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"💡",
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"量化交易员的修改笔记",
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"修改笔记（供你参考）",
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"修改笔记",
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"供你参考",
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)
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_STAGE_DIRECTION_RE = re.compile(
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r"[（(][^）)]{0,80}(?:前奏|转场|语气|背景|BGM|配乐|节奏|环节)[^）)]{0,80}[）)]"
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)
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def prepare_text_for_tts(text: str) -> str:
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"""
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将 LLM 润色稿转为 ChatTTS 可朗读的纯文本。
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去除 Markdown、emoji、舞台提示、修改笔记等非朗读内容。
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"""
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if not text:
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return ""
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cleaned = text.replace("\r\n", "\n").strip()
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for marker in _TTS_NOTE_MARKERS:
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idx = cleaned.find(marker)
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if idx >= 0:
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cleaned = cleaned[:idx]
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# 去掉模型常见前言，从标题或正文起点开始
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for pattern in (
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r"^作为一名极其严谨的量化交易员.*?配音稿。\s*",
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r"^以下是为你润色后的文案[：:]*\s*",
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r"^以下(?:是|为).*?润色.*?文案[：:]*\s*",
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):
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cleaned = re.sub(pattern, "", cleaned, count=1, flags=re.DOTALL)
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cleaned = re.sub(r"^\*{3,}\s*$", "", cleaned, flags=re.MULTILINE)
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cleaned = re.sub(r"^-{3,}\s*$", "", cleaned, flags=re.MULTILINE)
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cleaned = re.sub(r"^#{1,6}\s*", "", cleaned, flags=re.MULTILINE)
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cleaned = re.sub(r"\*\*([^*\n]+)\*\*", r"\1", cleaned)
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cleaned = re.sub(r"\*([^*\n]+)\*", r"\1", cleaned)
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cleaned = re.sub(r"__([^_\n]+)__", r"\1", cleaned)
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cleaned = _STAGE_DIRECTION_RE.sub("", cleaned)
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cleaned = _EMOJI_RE.sub("", cleaned)
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cleaned = re.sub(r"^\d+\.\s*", "", cleaned, flags=re.MULTILINE)
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cleaned = re.sub(r"^[-*]\s+", "", cleaned, flags=re.MULTILINE)
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cleaned = re.sub(r"[ \t]+\n", "\n", cleaned)
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cleaned = re.sub(r"\n{3,}", "\n\n", cleaned)
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lines = [ln.strip() for ln in cleaned.split("\n")]
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lines = [ln for ln in lines if ln and not re.fullmatch(r"[*\-#]+", ln)]
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return "\n".join(lines).strip()
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def split_text_for_tts(text: str, max_chars: int = TTS_MAX_CHARS_PER_CHUNK) -> List[str]:
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"""按句号/换行切分长稿，避免 ChatTTS 单段过长失败。"""
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text = text.strip()
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if not text:
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return []
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if len(text) <= max_chars:
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return [text]
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parts = re.split(r"(?<=[。！？!?；;])\s*|\n+", text)
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chunks: List[str] = []
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buf = ""
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for part in parts:
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part = part.strip()
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if not part:
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continue
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candidate = f"{buf}{part}" if buf else part
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if len(candidate) <= max_chars:
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buf = candidate
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continue
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if buf:
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chunks.append(buf)
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buf = ""
|
||
if len(part) <= max_chars:
|
||
buf = part
|
||
continue
|
||
for i in range(0, len(part), max_chars):
|
||
chunks.append(part[i : i + max_chars])
|
||
|
||
if buf:
|
||
chunks.append(buf)
|
||
|
||
return [c.strip() for c in chunks if c.strip()]
|
||
|
||
|
||
def _concat_wavs(
|
||
wavs: List[np.ndarray],
|
||
sample_rate: int,
|
||
pause_sec: float = 0.35,
|
||
) -> np.ndarray:
|
||
if not wavs:
|
||
return np.array([], dtype=np.float32)
|
||
|
||
pause = np.zeros(int(sample_rate * pause_sec), dtype=np.float32)
|
||
segments: List[np.ndarray] = []
|
||
for i, wav in enumerate(wavs):
|
||
segments.append(np.asarray(wav, dtype=np.float32).flatten())
|
||
if i < len(wavs) - 1:
|
||
segments.append(pause)
|
||
return np.concatenate(segments)
|
||
|
||
|
||
def generate_voice(refined_text: str) -> Tuple[bool, str, Optional[str]]:
|
||
"""
|
||
使用 ChatTTS 将润色后的文稿合成为 wav 配音。
|
||
|
||
Args:
|
||
refined_text: LLM 润色后的配音稿
|
||
|
||
Returns:
|
||
(success, message, output_wav_path_or_none)
|
||
"""
|
||
if not refined_text or not refined_text.strip():
|
||
return False, "合成文本为空，请先完成润色。", None
|
||
|
||
chat, init_err = get_chattts_instance()
|
||
if chat is None:
|
||
return False, init_err or "ChatTTS 不可用。", None
|
||
|
||
payload, spk_err = _load_speaker_payload()
|
||
if payload is None:
|
||
return False, spk_err or "请先锁定音色。", None
|
||
|
||
try:
|
||
import ChatTTS
|
||
|
||
speak_text = prepare_text_for_tts(refined_text)
|
||
if not speak_text:
|
||
return (
|
||
False,
|
||
"清洗后无有效朗读文本。请删除 Markdown（#、**）、emoji、舞台提示和「修改笔记」，"
|
||
"只保留可念出的正文后再合成。",
|
||
None,
|
||
)
|
||
|
||
chunks = split_text_for_tts(speak_text)
|
||
if not chunks:
|
||
return False, "无法切分朗读文本，请检查润色稿内容。", None
|
||
|
||
spk_emb = payload.get("spk_emb")
|
||
spk_smp = payload.get("spk_smp")
|
||
txt_smp = payload.get("txt_smp", "")
|
||
|
||
params_infer_code = ChatTTS.Chat.InferCodeParams(
|
||
prompt=TTS_SPEED_PROMPT,
|
||
spk_emb=spk_emb,
|
||
spk_smp=spk_smp if spk_smp else None,
|
||
txt_smp=txt_smp if txt_smp else None,
|
||
temperature=TTS_TEMPERATURE,
|
||
top_P=TTS_TOP_P,
|
||
top_K=TTS_TOP_K,
|
||
)
|
||
|
||
params_refine_text = ChatTTS.Chat.RefineTextParams(
|
||
prompt="[oral_2][laugh_0][break_4]",
|
||
)
|
||
|
||
logger.info(
|
||
"TTS 合成: 原文 %d 字 → 清洗后 %d 字，分 %d 段",
|
||
len(refined_text),
|
||
len(speak_text),
|
||
len(chunks),
|
||
)
|
||
|
||
segment_wavs: List[np.ndarray] = []
|
||
for idx, chunk in enumerate(chunks, start=1):
|
||
wavs = chat.infer(
|
||
chunk,
|
||
skip_refine_text=False,
|
||
params_refine_text=params_refine_text,
|
||
params_infer_code=params_infer_code,
|
||
)
|
||
if not wavs or len(wavs) == 0:
|
||
return (
|
||
False,
|
||
f"ChatTTS 第 {idx}/{len(chunks)} 段未生成音频。"
|
||
f"（段内容前 40 字: {chunk[:40]}…）",
|
||
None,
|
||
)
|
||
segment_wavs.append(np.asarray(wavs[0], dtype=np.float32))
|
||
|
||
wav_array = (
|
||
segment_wavs[0]
|
||
if len(segment_wavs) == 1
|
||
else _concat_wavs(segment_wavs, TTS_SAMPLE_RATE)
|
||
)
|
||
|
||
peak = np.max(np.abs(wav_array)) or 1.0
|
||
wav_int16 = (wav_array / peak * 32767).astype(np.int16)
|
||
|
||
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
|
||
filename = f"voiceover_{timestamp}_{uuid.uuid4().hex[:6]}.wav"
|
||
output_path = OUTPUT_DIR / filename
|
||
|
||
wavfile.write(str(output_path), TTS_SAMPLE_RATE, wav_int16)
|
||
|
||
chunk_note = f"，共 {len(chunks)} 段拼接" if len(chunks) > 1 else ""
|
||
msg = (
|
||
f"配音合成成功: {output_path}"
|
||
f"（朗读 {len(speak_text)} 字{chunk_note}）"
|
||
)
|
||
logger.info(msg)
|
||
return True, msg, str(output_path)
|
||
|
||
except Exception as exc:
|
||
err = f"语音合成失败: {exc}\n{traceback.format_exc()}"
|
||
logger.exception("generate_voice 失败")
|
||
return False, err, None
|