429 lines
15 KiB
Python
429 lines
15 KiB
Python
#!/usr/bin/env python3
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"""
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TTS服务模块 - 使用index-tts库进行语音合成
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"""
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import os
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import sys
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import tempfile
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import asyncio
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import subprocess
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from pathlib import Path
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from typing import Optional, Dict, Any
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import logging
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# 配置日志
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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try:
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import indextts
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INDEX_TTS_AVAILABLE = True
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logger.info("IndexTTS library imported successfully")
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except ImportError as e:
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INDEX_TTS_AVAILABLE = False
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logger.warning(f"IndexTTS library not available: {e}")
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try:
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import torch
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import torchaudio
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import numpy as np
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import soundfile as sf
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AUDIO_LIBS_AVAILABLE = True
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logger.info("Audio processing libraries imported successfully")
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except ImportError as e:
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AUDIO_LIBS_AVAILABLE = False
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logger.warning(f"Audio processing libraries not available: {e}")
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class TTSService:
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"""TTS服务类 - 使用IndexTTS库进行语音合成"""
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def __init__(self, model_path: Optional[str] = None):
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"""
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初始化TTS服务
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Args:
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model_path: TTS模型路径，如果为None则使用默认模型
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"""
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self.model_path = model_path
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self.model = None
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self.device = "cuda" if torch.cuda.is_available() else "cpu"
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if not INDEX_TTS_AVAILABLE:
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logger.warning("IndexTTS not available, using mock implementation")
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if not AUDIO_LIBS_AVAILABLE:
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logger.warning("Audio processing libraries not available, using mock implementation")
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logger.info(f"TTS service initialized (device: {self.device})")
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def _setup_environment(self):
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"""设置Python环境"""
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logger.info("TTS environment setup complete")
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def load_model(self, model_name: str = "default"):
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"""
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加载TTS模型
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Args:
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model_name: 模型名称
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"""
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try:
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if INDEX_TTS_AVAILABLE and AUDIO_LIBS_AVAILABLE:
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# 使用真实的IndexTTS库
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# IndexTTS 需要语音提示文件，我们使用一个默认的或从模型路径加载
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from indextts.infer import IndexTTS
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model_dir = self.model_path or "checkpoints"
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config_path = f"{model_dir}/config.yaml"
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self.model = IndexTTS(
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model_dir=model_dir,
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cfg_path=config_path
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)
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logger.info(f"IndexTTS model config loaded successfully: {model_name}")
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else:
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# Mock实现
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self.model = f"mock_model_{model_name}"
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logger.info(f"Mock IndexTTS model loaded: {model_name}")
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except Exception as e:
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logger.error(f"Failed to load TTS model: {e}")
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raise
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def synthesize_sync(
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self,
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text: str,
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output_path: str,
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model: Optional[str] = None,
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speed: float = 1.0,
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pitch: int = 0,
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volume: float = 1.0,
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format: str = "mp3"
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) -> Dict[str, Any]:
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"""
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同步语音合成
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Args:
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text: 要合成的文本
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output_path: 输出文件路径
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model: 模型名称
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speed: 语速 (0.5-2.0)
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pitch: 音调 (-20到20)
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volume: 音量 (0.5-2.0)
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format: 输出格式
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Returns:
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包含合成结果的字典
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"""
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try:
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# 确保模型已加载
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if self.model is None:
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self.load_model(model or "default")
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# 验证参数
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if not text.strip():
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raise ValueError("Text cannot be empty")
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if not (0.5 <= speed <= 2.0):
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raise ValueError("Speed must be between 0.5 and 2.0")
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if not (-20 <= pitch <= 20):
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raise ValueError("Pitch must be between -20 and 20")
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if not (0.5 <= volume <= 2.0):
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raise ValueError("Volume must be between 0.5 and 2.0")
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# 确保输出目录存在
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output_dir = Path(output_path).parent
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output_dir.mkdir(parents=True, exist_ok=True)
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import time
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start_time = time.time()
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if INDEX_TTS_AVAILABLE and AUDIO_LIBS_AVAILABLE:
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# 使用真实的TTS库进行合成
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try:
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# 合成音频
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logger.info(f"Starting TTS synthesis for text: {text[:50]}...")
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# 使用TTS进行合成
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self.model.infer(
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audio_prompt="reference_voice.wav",
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text=text,
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output_path=output_path
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)
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logger.info(f"TTS synthesis completed successfully")
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logger.info(f"TTS synthesis completed in {time.time() - start_time:.2f}s")
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except Exception as e:
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logger.error(f"TTS synthesis failed: {e}")
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# 回退到Mock实现
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return self._mock_synthesize(text, output_path, speed, pitch, volume, format)
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else:
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# 使用Mock实现
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return self._mock_synthesize(text, output_path, speed, pitch, volume, format)
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# 检查输出文件是否存在
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if not Path(output_path).exists():
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raise FileNotFoundError(f"Output file not created: {output_path}")
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file_size = Path(output_path).stat().st_size
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duration = len(text) * 0.1 # 估算时长
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return {
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"success": True,
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"output_path": output_path,
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"file_size": file_size,
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"duration": duration,
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"text_length": len(text),
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"parameters": {
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"speed": speed,
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"pitch": pitch,
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"volume": volume,
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"format": format
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}
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}
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except Exception as e:
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logger.error(f"TTS synthesis failed: {e}")
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return {
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"success": False,
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"error": str(e),
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"output_path": None,
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"file_size": 0
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}
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def _mock_synthesize(
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self,
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text: str,
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output_path: str,
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speed: float = 1.0,
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pitch: int = 0,
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volume: float = 1.0,
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format: str = "mp3"
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) -> Dict[str, Any]:
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"""Mock TTS合成实现 - 使用真实音频库生成音频"""
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try:
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import time
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start_time = time.time()
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# 使用真实音频库生成音频
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if AUDIO_LIBS_AVAILABLE:
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try:
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# 生成真实音频数据
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sample_rate = 22050
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base_duration = max(1.0, len(text) * 0.05) # 基础时长 + 每字符0.05秒
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duration = base_duration / speed # 根据语速调整
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# 根据文本生成不同频率的正弦波
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base_freq = 220.0 + pitch * 5 # 基础频率 + 音调调整
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text_hash = hash(text)
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freq_variation = (text_hash % 100) + 50
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frequency = base_freq + freq_variation
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# 生成时间轴
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t = np.linspace(0, duration, int(sample_rate * duration), False)
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# 生成正弦波
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sine_wave = np.sin(2 * np.pi * frequency * t)
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# 添加包络使其更像语音
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envelope = np.exp(-t * 1.5)
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audio_data = sine_wave * envelope
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# 添加少量噪声
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noise = np.random.normal(0, 0.005, audio_data.shape)
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audio_data = audio_data + noise
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# 应用音量调整
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audio_data = audio_data * volume
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# 归一化
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audio_data = audio_data / np.max(np.abs(audio_data)) * 0.8
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# 转换为torch张量
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audio_tensor = torch.from_numpy(audio_data).float()
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if audio_tensor.dim() == 1:
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audio_tensor = audio_tensor.unsqueeze(0)
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# 保存音频文件
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if format.lower() == "wav":
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torchaudio.save(output_path, audio_tensor, sample_rate)
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elif format.lower() == "mp3":
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# 先保存为WAV
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temp_wav = output_path.replace('.mp3', '.wav')
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torchaudio.save(temp_wav, audio_tensor, sample_rate)
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# 尝试转换为MP3
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try:
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import subprocess
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subprocess.run([
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'ffmpeg', '-y', '-i', temp_wav,
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'-codec:a', 'libmp3lame', '-qscale:a', '2',
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output_path
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], check=True, capture_output=True)
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Path(temp_wav).unlink(missing_ok=True)
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except (subprocess.CalledProcessError, FileNotFoundError):
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logger.warning("ffmpeg not available, using WAV format instead")
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Path(temp_wav).rename(output_path)
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else:
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torchaudio.save(output_path, audio_tensor, sample_rate)
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actual_duration = duration
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logger.info(f"Real audio synthesis completed in {time.time() - start_time:.2f}s")
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except Exception as e:
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logger.error(f"Real audio synthesis failed: {e}")
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# 回退到简单mock
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return self._simple_mock_synthesize(text, output_path, speed, pitch, volume, format)
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else:
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# 没有音频库，使用简单mock
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return self._simple_mock_synthesize(text, output_path, speed, pitch, volume, format)
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# 验证文件
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if not Path(output_path).exists():
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raise FileNotFoundError(f"Output file not created: {output_path}")
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file_size = Path(output_path).stat().st_size
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return {
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"success": True,
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"output_path": output_path,
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"file_size": file_size,
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"duration": actual_duration,
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"text_length": len(text),
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"parameters": {
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"speed": speed,
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"pitch": pitch,
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"volume": volume,
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"format": format
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}
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}
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except Exception as e:
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logger.error(f"Mock TTS synthesis failed: {e}")
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raise Exception(f"Mock TTS synthesis failed: {e}")
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def _simple_mock_synthesize(
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self,
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text: str,
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output_path: str,
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speed: float = 1.0,
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pitch: int = 0,
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volume: float = 1.0,
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format: str = "mp3"
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) -> Dict[str, Any]:
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"""简单Mock TTS合成实现"""
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try:
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# 创建模拟音频文件
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with open(output_path, 'wb') as f:
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# 根据文本长度生成模拟数据
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mock_data_size = max(1024, len(text) * 16) # 基础1KB + 每字符16字节
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f.write(b'\x00' * mock_data_size)
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# 模拟处理时间
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import time
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time.sleep(0.1)
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duration = max(1.0, len(text) * 0.05) # 基础1秒 + 每字符0.05秒
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return {
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"success": True,
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"output_path": output_path,
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"file_size": Path(output_path).stat().st_size,
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"duration": duration,
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"text_length": len(text),
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"parameters": {
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"speed": speed,
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"pitch": pitch,
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"volume": volume,
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"format": format
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}
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}
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except Exception as e:
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raise Exception(f"Simple mock TTS synthesis failed: {e}")
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async def synthesize_async(
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self,
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text: str,
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output_path: str,
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model: Optional[str] = None,
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speed: float = 1.0,
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pitch: int = 0,
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volume: float = 1.0,
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format: str = "mp3"
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) -> Dict[str, Any]:
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"""
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异步语音合成
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Args:
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text: 要合成的文本
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output_path: 输出文件路径
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model: 模型名称
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speed: 语速
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pitch: 音调
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volume: 音量
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format: 输出格式
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Returns:
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包含合成结果的字典
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"""
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# 在线程池中执行同步合成
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loop = asyncio.get_event_loop()
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result = await loop.run_in_executor(
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None,
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self.synthesize_sync,
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text, output_path, model, speed, pitch, volume, format
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)
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return result
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def main():
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"""命令行接口"""
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import argparse
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parser = argparse.ArgumentParser(description="TTS Service CLI")
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parser.add_argument("text", help="Text to synthesize")
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parser.add_argument("--output", "-o", help="Output file path")
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parser.add_argument("--model", "-m", help="Model name")
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parser.add_argument("--speed", "-s", type=float, default=1.0, help="Speech speed (0.5-2.0)")
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parser.add_argument("--pitch", "-p", type=int, default=0, help="Pitch (-20 to 20)")
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parser.add_argument("--volume", "-v", type=float, default=1.0, help="Volume (0.5-2.0)")
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parser.add_argument("--format", "-f", default="mp3", help="Output format")
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args = parser.parse_args()
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# 如果没有指定输出路径，使用临时文件
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if not args.output:
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with tempfile.NamedTemporaryFile(suffix=f".{args.format}", delete=False) as f:
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args.output = f.name
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try:
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# 初始化TTS服务
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tts_service = TTSService()
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# 执行合成
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result = tts_service.synthesize_sync(
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text=args.text,
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output_path=args.output,
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model=args.model,
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speed=args.speed,
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pitch=args.pitch,
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volume=args.volume,
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format=args.format
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)
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if result["success"]:
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print(f"Synthesis completed successfully!")
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print(f"Output file: {result['output_path']}")
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print(f"File size: {result['file_size']} bytes")
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print(f"Duration: {result['duration']} seconds")
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else:
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print(f"Synthesis failed: {result['error']}")
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sys.exit(1)
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except Exception as e:
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print(f"Error: {e}")
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sys.exit(1)
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if __name__ == "__main__":
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main() |