提交qiming-mcp-proxy
This commit is contained in:
Codex
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# Voice CLI Server Configuration - 单节点语音转录服务
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server:
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# Server binding address (0.0.0.0 for all interfaces)
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host: "0.0.0.0"
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# Server port
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port: 8080
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# Maximum file size for uploads (in bytes) - 200MB default
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max_file_size: 209715200
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# Enable CORS for web browser access
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cors_enabled: true
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whisper:
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# Default model to use for transcription
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default_model: "base"
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# Directory to store whisper models
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models_dir: "./models"
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# Automatically download models when needed
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auto_download: true
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# List of supported whisper models
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supported_models:
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- "tiny"
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- "tiny.en"
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- "base"
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- "base.en"
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- "small"
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- "small.en"
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- "medium"
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- "medium.en"
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- "large-v1"
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- "large-v2"
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- "large-v3"
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# Audio processing settings
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audio_processing:
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supported_formats: ["mp3", "wav", "flac", "m4a", "ogg", "aac", "opus", "amr", "wma", "aiff", "caf", "mp4", "mov", "avi", "mkv", "webm", "3gp", "flv", "wmv", "mpeg", "mxf"]
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auto_convert: true
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conversion_timeout: 60 # seconds
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temp_file_cleanup: true
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temp_file_retention: 300 # 5 minutes
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# Worker-based concurrency settings
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workers:
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transcription_workers: 3 # Number of worker threads for transcription
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channel_buffer_size: 100 # Channel buffer size for task queue
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worker_timeout: 3600 # Worker processing timeout in seconds
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logging:
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# Log level (trace, debug, info, warn, error)
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level: "info"
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# Directory for log files
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log_dir: "./logs"
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# Maximum size per log file
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max_file_size: "100MB"
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# Maximum number of log files to keep
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max_files: 30
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daemon:
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# PID file for daemon mode
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pid_file: "./voice-cli-server.pid"
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# Log file for daemon output
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log_file: "./logs/server-daemon.log"
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# Working directory for daemon
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work_dir: "./"
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# Async task management configuration
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task_management:
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# Apalis worker configuration
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max_concurrent_tasks: 4 # Max concurrent async tasks (should match or be less than transcription_workers)
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retry_attempts: 2 # Number of retry attempts for failed tasks
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task_timeout_seconds: 3600 # Task processing timeout in seconds (0 to disable)
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catch_panic: true # Catch panics in execution and pipe them as errors
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task_retention_minutes: 1440 # task retention in minutes (1440 minutes = 24 hours)
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# SQLite database configuration
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sqlite_db_path: "./data/tasks.db" # Path to SQLite database file
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429
qiming-mcp-proxy/voice-cli/templates/tts_service.py.template
Normal file
429
qiming-mcp-proxy/voice-cli/templates/tts_service.py.template
Normal file
@@ -0,0 +1,429 @@
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#!/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
|
||||
}
|
||||
}
|
||||
|
||||
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,
|
||||
pitch: int = 0,
|
||||
volume: float = 1.0,
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format: str = "mp3"
|
||||
) -> 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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||||
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duration = max(1.0, len(text) * 0.05) # 基础1秒 + 每字符0.05秒
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
"output_path": output_path,
|
||||
"file_size": Path(output_path).stat().st_size,
|
||||
"duration": duration,
|
||||
"text_length": len(text),
|
||||
"parameters": {
|
||||
"speed": speed,
|
||||
"pitch": pitch,
|
||||
"volume": volume,
|
||||
"format": format
|
||||
}
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
raise Exception(f"Simple mock TTS synthesis failed: {e}")
|
||||
|
||||
async def synthesize_async(
|
||||
self,
|
||||
text: str,
|
||||
output_path: str,
|
||||
model: Optional[str] = None,
|
||||
speed: float = 1.0,
|
||||
pitch: int = 0,
|
||||
volume: float = 1.0,
|
||||
format: str = "mp3"
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
异步语音合成
|
||||
|
||||
Args:
|
||||
text: 要合成的文本
|
||||
output_path: 输出文件路径
|
||||
model: 模型名称
|
||||
speed: 语速
|
||||
pitch: 音调
|
||||
volume: 音量
|
||||
format: 输出格式
|
||||
|
||||
Returns:
|
||||
包含合成结果的字典
|
||||
"""
|
||||
# 在线程池中执行同步合成
|
||||
loop = asyncio.get_event_loop()
|
||||
result = await loop.run_in_executor(
|
||||
None,
|
||||
self.synthesize_sync,
|
||||
text, output_path, model, speed, pitch, volume, format
|
||||
)
|
||||
return result
|
||||
|
||||
def main():
|
||||
"""命令行接口"""
|
||||
import argparse
|
||||
|
||||
parser = argparse.ArgumentParser(description="TTS Service CLI")
|
||||
parser.add_argument("text", help="Text to synthesize")
|
||||
parser.add_argument("--output", "-o", help="Output file path")
|
||||
parser.add_argument("--model", "-m", help="Model name")
|
||||
parser.add_argument("--speed", "-s", type=float, default=1.0, help="Speech speed (0.5-2.0)")
|
||||
parser.add_argument("--pitch", "-p", type=int, default=0, help="Pitch (-20 to 20)")
|
||||
parser.add_argument("--volume", "-v", type=float, default=1.0, help="Volume (0.5-2.0)")
|
||||
parser.add_argument("--format", "-f", default="mp3", help="Output format")
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
# 如果没有指定输出路径，使用临时文件
|
||||
if not args.output:
|
||||
with tempfile.NamedTemporaryFile(suffix=f".{args.format}", delete=False) as f:
|
||||
args.output = f.name
|
||||
|
||||
try:
|
||||
# 初始化TTS服务
|
||||
tts_service = TTSService()
|
||||
|
||||
# 执行合成
|
||||
result = tts_service.synthesize_sync(
|
||||
text=args.text,
|
||||
output_path=args.output,
|
||||
model=args.model,
|
||||
speed=args.speed,
|
||||
pitch=args.pitch,
|
||||
volume=args.volume,
|
||||
format=args.format
|
||||
)
|
||||
|
||||
if result["success"]:
|
||||
print(f"Synthesis completed successfully!")
|
||||
print(f"Output file: {result['output_path']}")
|
||||
print(f"File size: {result['file_size']} bytes")
|
||||
print(f"Duration: {result['duration']} seconds")
|
||||
else:
|
||||
print(f"Synthesis failed: {result['error']}")
|
||||
sys.exit(1)
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error: {e}")
|
||||
sys.exit(1)
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user