[Feature] Add /tests/modelsuse 测试实时VAD检测。
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172
src/audiochunk.py
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172
src/audiochunk.py
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""
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音频数据块管理类 - 用于存储和处理16KHz音频数据
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"""
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import numpy as np
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import logging
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from typing import List, Optional, Union
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# 配置日志
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
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)
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logger = logging.getLogger('AudioChunk')
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class AudioChunk:
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"""音频数据块管理类，用于存储和处理16KHz音频数据"""
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def __init__(self,
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max_duration_ms: int = 1000*60*60*10,
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sample_rate: int = 16000,
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sample_width: int = 2,
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channels: int = 1):
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"""
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初始化音频数据块管理器
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参数:
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max_duration_ms: 音频池最大留存时间(ms)，默认10小时
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sample_rate: 采样率，默认16KHz
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sample_width: 采样位宽，默认16bit
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channels: 通道数，默认1
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"""
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# 音频参数
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self.sample_rate = sample_rate # 采样率：16KHz
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self.sample_width = sample_width # 采样位宽：16bit
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self.channels = channels # 通道数：单声道
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# 数据存储
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self._max_duration_ms = max_duration_ms
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self._max_chunk_size = self._time2size(max_duration_ms) # 最大数据大小
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self._chunk = [] # 当前音频数据块列表
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self._chunk_size = 0 # 当前数据总大小
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self._offset = 0 # 当前偏移量
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logger.info(f"初始化AudioChunk: 最大时长={max_duration_ms}ms, 最大数据大小={self._max_chunk_size}字节")
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def add_chunk(self, chunk: Union[bytes, np.ndarray]) -> bool:
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"""
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添加音频数据块
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参数:
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chunk: 音频数据块，可以是bytes或numpy数组
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返回:
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bool: 是否添加成功
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"""
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try:
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# 检查数据格式
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if isinstance(chunk, np.ndarray):
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# 确保是16bit整数格式
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if chunk.dtype != np.int16:
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chunk = chunk.astype(np.int16)
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# 转换为bytes
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chunk = chunk.tobytes()
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# 检查数据大小
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if len(chunk) % (self.sample_width * self.channels) != 0:
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logger.warning(f"音频数据大小不是{self.sample_width * self.channels}的倍数: {len(chunk)}")
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return False
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# 检查是否超过最大限制
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if self._chunk_size + len(chunk) > self._max_chunk_size:
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logger.warning("音频数据超过最大限制，将自动清除旧数据")
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self.clear_chunk()
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# 添加数据
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self._chunk.append(chunk)
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self._chunk_size += len(chunk)
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return True
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except Exception as e:
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logger.error(f"添加音频数据块时出错: {e}")
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return False
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def get_chunk(self, start_ms: int = 0, end_ms: Optional[int] = None) -> Optional[bytes]:
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"""
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获取指定时间范围的音频数据
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参数:
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start_ms: 开始时间(ms)
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end_ms: 结束时间(ms)，None表示到末尾
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返回:
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Optional[bytes]: 音频数据，如果获取失败则返回None
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"""
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try:
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if not self._chunk:
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return None
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# 计算字节偏移
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start_byte = self._time2size(start_ms)
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end_byte = self._time2size(end_ms) if end_ms is not None else self._chunk_size
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# 检查范围是否有效
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if start_byte >= self._chunk_size or start_byte >= end_byte:
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return None
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# 获取数据
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data = b''.join(self._chunk)
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return data[start_byte:end_byte]
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except Exception as e:
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logger.error(f"获取音频数据块时出错: {e}")
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return None
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def get_duration(self) -> int:
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"""
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获取当前音频总时长(ms)
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返回:
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int: 音频时长(ms)
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"""
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return self._size2time(self._chunk_size)
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def clear_chunk(self) -> None:
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"""清除所有音频数据"""
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self._chunk = []
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self._chunk_size = 0
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self._offset = 0
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logger.info("已清除所有音频数据")
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def _time2size(self, time_ms: int) -> int:
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"""
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将时间(ms)转换为数据大小(字节)
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参数:
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time_ms: 时间(ms)
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返回:
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int: 数据大小(字节)
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"""
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return int(time_ms * self.sample_rate * self.sample_width * self.channels / 1000)
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def _size2time(self, size: int) -> int:
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"""
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将数据大小(字节)转换为时间(ms)
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参数:
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size: 数据大小(字节)
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返回:
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int: 时间(ms)
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"""
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return int(size * 1000 / (self.sample_rate * self.sample_width * self.channels))
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# instance(start_ms, end_ms, use_offset=True)
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def __call__(self, start_ms: int = 0, end_ms: Optional[int] = None, use_offset: bool = True) -> Optional[bytes]:
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"""
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获取指定时间范围的音频数据
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"""
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if use_offset:
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start_ms += self._offset
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end_ms += self._offset
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return self.get_chunk(start_ms, end_ms)
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def __len__(self) -> int:
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"""
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获取当前音频数据块大小
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"""
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return self._chunk_size
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196
src/client.py
196
src/client.py
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""
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WebSocket客户端示例 - 用于测试语音识别服务
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"""
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import asyncio
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import json
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import websockets
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import argparse
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import numpy as np
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import wave
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import os
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def parse_args():
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"""解析命令行参数"""
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parser = argparse.ArgumentParser(description="FunASR WebSocket客户端")
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parser.add_argument(
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"--host",
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type=str,
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default="localhost",
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help="服务器主机地址"
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)
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parser.add_argument(
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"--port",
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type=int,
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default=10095,
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help="服务器端口"
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)
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parser.add_argument(
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"--audio_file",
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type=str,
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required=True,
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help="要识别的音频文件路径"
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)
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parser.add_argument(
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"--mode",
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type=str,
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default="2pass",
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choices=["2pass", "online", "offline"],
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help="识别模式: 2pass(默认), online, offline"
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)
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parser.add_argument(
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"--chunk_size",
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type=str,
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default="5,10",
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help="分块大小, 格式为'encoder_size,decoder_size'"
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)
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parser.add_argument(
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"--use_ssl",
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action="store_true",
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help="是否使用SSL连接"
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)
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return parser.parse_args()
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async def send_audio(websocket, audio_file, mode, chunk_size):
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"""
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发送音频文件到服务器进行识别
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参数:
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websocket: WebSocket连接
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audio_file: 音频文件路径
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mode: 识别模式
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chunk_size: 分块大小
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"""
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# 打开并读取WAV文件
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with wave.open(audio_file, "rb") as wav_file:
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params = wav_file.getparams()
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frames = wav_file.readframes(wav_file.getnframes())
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# 音频文件信息
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print(f"音频文件: {os.path.basename(audio_file)}")
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print(f"采样率: {params.framerate}Hz, 通道数: {params.nchannels}")
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print(f"采样位深: {params.sampwidth * 8}位, 总帧数: {params.nframes}")
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# 设置配置参数
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config = {
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"mode": mode,
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"chunk_size": chunk_size,
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"wav_name": os.path.basename(audio_file),
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"is_speaking": True
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}
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# 发送配置
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await websocket.send(json.dumps(config))
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# 模拟实时发送音频数据
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chunk_size_bytes = 3200 # 每次发送100ms的16kHz音频
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total_chunks = len(frames) // chunk_size_bytes
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print(f"开始发送音频数据，共 {total_chunks} 个数据块...")
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try:
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for i in range(0, len(frames), chunk_size_bytes):
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chunk = frames[i:i+chunk_size_bytes]
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await websocket.send(chunk)
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# 模拟实时，每100ms发送一次
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await asyncio.sleep(0.1)
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# 显示进度
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if (i // chunk_size_bytes) % 10 == 0:
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print(f"已发送 {i // chunk_size_bytes}/{total_chunks} 数据块")
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# 发送结束信号
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await websocket.send(json.dumps({"is_speaking": False}))
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print("音频数据发送完成")
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except Exception as e:
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print(f"发送音频时出错: {e}")
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async def receive_results(websocket):
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"""
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接收并显示识别结果
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参数:
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websocket: WebSocket连接
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"""
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online_text = ""
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offline_text = ""
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try:
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async for message in websocket:
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# 解析服务器返回的JSON消息
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result = json.loads(message)
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mode = result.get("mode", "")
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text = result.get("text", "")
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is_final = result.get("is_final", False)
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# 根据模式更新文本
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if "online" in mode:
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online_text = text
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print(f"\r[在线识别] {online_text}", end="", flush=True)
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elif "offline" in mode:
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offline_text = text
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print(f"\n[离线识别] {offline_text}")
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# 如果是最终结果，打印完整信息
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if is_final and offline_text:
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print("\n最终识别结果:")
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print(f"[离线识别] {offline_text}")
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return
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except Exception as e:
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print(f"接收结果时出错: {e}")
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async def main():
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"""主函数"""
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args = parse_args()
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# WebSocket URI
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protocol = "wss" if args.use_ssl else "ws"
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uri = f"{protocol}://{args.host}:{args.port}"
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print(f"连接到服务器: {uri}")
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try:
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# 创建WebSocket连接
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async with websockets.connect(
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uri,
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subprotocols=["binary"]
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) as websocket:
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print("连接成功")
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# 创建两个任务: 发送音频和接收结果
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send_task = asyncio.create_task(
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send_audio(websocket, args.audio_file, args.mode, args.chunk_size)
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)
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receive_task = asyncio.create_task(
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receive_results(websocket)
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)
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# 等待任务完成
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await asyncio.gather(send_task, receive_task)
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except Exception as e:
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print(f"连接服务器失败: {e}")
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if __name__ == "__main__":
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# 运行主函数
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asyncio.run(main())
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164
src/logic_trager.py
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164
src/logic_trager.py
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""
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逻辑触发器类 - 用于处理音频数据并触发相应的处理逻辑
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"""
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import logging
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from typing import Any, Dict, Type
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# 配置日志
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
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)
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logger = logging.getLogger('LogicTrager')
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class AutoAfterMeta(type):
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"""
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自动调用__after__函数的元类
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实现单例模式
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"""
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_instances: Dict[Type, Any] = {} # 存储单例实例
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def __new__(cls, name, bases, attrs):
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# 遍历所有属性
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for attr_name, attr_value in attrs.items():
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# 如果是函数且不是以_开头
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if callable(attr_value) and not attr_name.startswith('__'):
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# 获取原函数
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original_func = attr_value
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# 创建包装函数
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def make_wrapper(func):
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def wrapper(self, *args, **kwargs):
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# 执行原函数
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result = func(self, *args, **kwargs)
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# 构建_after_函数名
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after_func_name = f"__after__{func.__name__}"
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# 检查是否存在对应的_after_函数
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if hasattr(self, after_func_name):
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after_func = getattr(self, after_func_name)
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if callable(after_func):
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try:
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# 调用_after_函数
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after_func()
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except Exception as e:
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logger.error(f"调用{after_func_name}时出错: {e}")
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return result
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return wrapper
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# 替换原函数
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attrs[attr_name] = make_wrapper(original_func)
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# 创建类
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new_class = super().__new__(cls, name, bases, attrs)
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return new_class
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def __call__(cls, *args, **kwargs):
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"""
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重写__call__方法实现单例模式
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当类被调用时（即创建实例时）执行
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"""
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if cls not in cls._instances:
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# 如果实例不存在，创建新实例
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cls._instances[cls] = super().__call__(*args, **kwargs)
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logger.info(f"创建{cls.__name__}的新实例")
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else:
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logger.debug(f"返回{cls.__name__}的现有实例")
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return cls._instances[cls]
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"""
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整体识别的处理逻辑：
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1.压入二进制音频信息
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2.不断检测VAD
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3.当检测到完整VAD时,将VAD的音频信息压入音频块,并清除对应二进制信息
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4.对音频块进行语音转文字offline,时间戳预测,说话人识别
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5.将识别结果整合压入结果队列
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6.结果队列被压入时调用回调函数
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1->2 __after__push_binary_data 外部压入二进制信息
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2,3->4 __after__push_audio_chunk 内部压入音频块
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4->5 push_result_queue 压入结果队列
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5->6 __after__push_result_queue 调用回调函数
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"""
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class LogicTrager(metaclass=AutoAfterMeta):
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"""逻辑触发器类"""
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def __init__(self,
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audio_chunk_max_size: int = 1024 * 1024 * 10,
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sample_rate: int = 16000,
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channels: int = 1,
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on_result_callback: Callable = None,
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):
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||||
"""初始化"""
|
||||
# 存储音频块
|
||||
self._audio_chunk = []
|
||||
# 存储二进制数据
|
||||
self._audio_chunk_binary = b''
|
||||
self._audio_chunk_max_size = audio_chunk_max_size
|
||||
# 音频参数
|
||||
self._sample_rate = sample_rate
|
||||
self._channels = channels
|
||||
# 结果队列
|
||||
self._result_queue = []
|
||||
# 回调函数
|
||||
self._on_result_callback = on_result_callback
|
||||
logger.info("初始化LogicTrager")
|
||||
|
||||
def push_binary_data(self, chunk: bytes) -> None:
|
||||
"""
|
||||
添加音频块
|
||||
|
||||
参数:
|
||||
chunk: 音频数据块
|
||||
"""
|
||||
if self._audio_chunk is None:
|
||||
logger.error("AudioChunk未初始化")
|
||||
return
|
||||
|
||||
self._audio_chunk_binary += chunk
|
||||
logger.debug(f"添加音频块，大小: {len(chunk)}字节")
|
||||
|
||||
def __after__push_binary_data(self) -> None:
|
||||
"""
|
||||
添加音频块后处理
|
||||
VAD检测，将检测到的VAD压入音频块
|
||||
"""
|
||||
# VAD检测
|
||||
pass
|
||||
# 压入音频块 push_audio_chunk
|
||||
|
||||
def push_audio_chunk(self, chunk: bytes) -> None:
|
||||
"""
|
||||
压入音频块
|
||||
"""
|
||||
self._audio_chunk.append(chunk)
|
||||
|
||||
def __after__push_audio_chunk(self) -> None:
|
||||
"""
|
||||
压入音频块后处理
|
||||
"""
|
||||
pass
|
||||
|
||||
def push_result_queue(self, result: Dict[str, Any]) -> None:
|
||||
"""
|
||||
压入结果队列
|
||||
"""
|
||||
self._result_queue.append(result)
|
||||
|
||||
def __after__push_result_queue(self) -> None:
|
||||
"""
|
||||
压入结果队列后处理
|
||||
"""
|
||||
pass
|
||||
|
||||
def __call__(self):
|
||||
"""调用函数"""
|
||||
pass
|
||||
@ -4,6 +4,8 @@
|
||||
模型加载模块 - 负责加载各种语音识别相关模型
|
||||
"""
|
||||
|
||||
from typing import List, Optional
|
||||
|
||||
def load_models(args):
|
||||
"""
|
||||
加载所有需要的模型
|
||||
|
||||
127
src/pydantic_models.py
Normal file
127
src/pydantic_models.py
Normal file
@ -0,0 +1,127 @@
|
||||
from pydantic import BaseModel, Field, validator
|
||||
from typing import List, Optional, Callable
|
||||
|
||||
class VADSegment(BaseModel):
|
||||
"""VAD片段"""
|
||||
start: int = Field(description="开始时间(ms)")
|
||||
end: int = Field(description="结束时间(ms)")
|
||||
|
||||
class VADResult(BaseModel):
|
||||
"""VAD结果"""
|
||||
key: str = Field(description="音频标识")
|
||||
value: List[VADSegment] = Field(description="VAD片段列表")
|
||||
|
||||
class VADResponse(BaseModel):
|
||||
"""VAD响应"""
|
||||
results: List[VADResult] = Field(description="VAD结果列表", default_factory=list)
|
||||
time_chunk: List[VADSegment] = Field(description="时间块", default_factory=list)
|
||||
time_chunk_index: int = Field(description="当前处理时间块索引", default=0)
|
||||
time_chunk_index_callback: Optional[Callable[[int], None]] = Field(
|
||||
description="时间块索引回调函数",
|
||||
default=None
|
||||
)
|
||||
|
||||
@validator('time_chunk')
|
||||
def validate_time_chunk(cls, v):
|
||||
"""验证时间块的有效性"""
|
||||
if not v:
|
||||
return v
|
||||
|
||||
# 检查时间顺序
|
||||
for i in range(len(v) - 1):
|
||||
if v[i].end >= v[i + 1].start:
|
||||
raise ValueError(f"时间块{i}的结束时间({v[i].end})大于等于下一个时间块的开始时间({v[i + 1].start})")
|
||||
return v
|
||||
|
||||
# 回调未处理的时间块
|
||||
def process_time_chunk(self, callback: Callable[[int], None] = None) -> None:
|
||||
"""处理时间块"""
|
||||
# print("Enter process_time_chunk", self.time_chunk_index, len(self.time_chunk))
|
||||
while self.time_chunk_index < len(self.time_chunk) - 1:
|
||||
if self.time_chunk[self.time_chunk_index].end != -1:
|
||||
if callback is not None:
|
||||
callback(self.time_chunk_index)
|
||||
elif self.time_chunk_index_callback is not None:
|
||||
self.time_chunk_index_callback(self.time_chunk_index)
|
||||
else:
|
||||
print("[Warning] No callback available")
|
||||
self.time_chunk_index += 1
|
||||
|
||||
def __add__(self, other: 'VADResponse') -> 'VADResponse':
|
||||
"""合并两个VADResponse"""
|
||||
if not self.results:
|
||||
self.results = other.results
|
||||
self.time_chunk = other.time_chunk
|
||||
return self
|
||||
|
||||
# 检查是否可以合并最后一个结果
|
||||
last_result = self.results[-1]
|
||||
first_other = other.results[0]
|
||||
|
||||
if last_result.value[-1].end == first_other.value[0].start:
|
||||
# 合并相邻的时间段
|
||||
last_result.value[-1].end = first_other.value[0].end
|
||||
first_other.value.pop(0)
|
||||
|
||||
# 更新time_chunk
|
||||
self.time_chunk[-1].end = other.time_chunk[0].end
|
||||
other.time_chunk.pop(0)
|
||||
|
||||
# 添加剩余的结果
|
||||
if first_other.value:
|
||||
self.results.extend(other.results)
|
||||
self.time_chunk.extend(other.time_chunk)
|
||||
else:
|
||||
# 直接添加所有结果
|
||||
self.results.extend(other.results)
|
||||
self.time_chunk.extend(other.time_chunk)
|
||||
|
||||
return self
|
||||
|
||||
@classmethod
|
||||
def from_raw(cls, raw_data: List[dict]) -> "VADResponse":
|
||||
"""
|
||||
从原始数据创建VADResponse
|
||||
|
||||
参数:
|
||||
raw_data: 原始数据，格式如 [{'key': 'xxx', 'value': [[-1, 59540], [59820, -1]]}]
|
||||
|
||||
返回:
|
||||
VADResponse: 解析后的VAD响应
|
||||
"""
|
||||
results = []
|
||||
time_chunk = []
|
||||
for item in raw_data:
|
||||
segments = [
|
||||
VADSegment(start=seg[0], end=seg[1])
|
||||
for seg in item['value']
|
||||
]
|
||||
results.append(VADResult(
|
||||
key=item['key'],
|
||||
value=segments
|
||||
))
|
||||
time_chunk.extend(segments)
|
||||
return cls(results=results, time_chunk=time_chunk)
|
||||
|
||||
def to_raw(self) -> List[dict]:
|
||||
"""
|
||||
转换为原始数据格式
|
||||
|
||||
返回:
|
||||
List[dict]: 原始数据格式
|
||||
"""
|
||||
return [
|
||||
{
|
||||
'key': result.key,
|
||||
'value': [[seg.start, seg.end] for seg in result.value]
|
||||
}
|
||||
for result in self.results
|
||||
]
|
||||
|
||||
def __str__(self):
|
||||
result_str = "VADResponse:\n"
|
||||
for result in self.results:
|
||||
for value_item in result.value:
|
||||
result_str += f"[{value_item.start}:{value_item.end}]\n"
|
||||
return result_str
|
||||
|
||||
4
test_main.py
Normal file
4
test_main.py
Normal file
@ -0,0 +1,4 @@
|
||||
from tests.modelsuse import vad_model_use_online
|
||||
|
||||
vad_result = vad_model_use_online("tests/vad_example.wav")
|
||||
print(vad_result)
|
||||
37
tests/modelsuse.py
Normal file
37
tests/modelsuse.py
Normal file
@ -0,0 +1,37 @@
|
||||
from funasr import AutoModel
|
||||
from typing import List, Dict, Any
|
||||
from src.pydantic_models import VADResponse
|
||||
import time
|
||||
|
||||
def vad_model_use_online(file_path: str) -> List[Dict[str, Any]]:
|
||||
chunk_size = 100 # ms
|
||||
model = AutoModel(model="fsmn-vad", model_revision="v2.0.4", disable_update=True)
|
||||
|
||||
vad_result = VADResponse()
|
||||
vad_result.time_chunk_index_callback = lambda index: print(f"回调: {index}")
|
||||
items = []
|
||||
import soundfile
|
||||
|
||||
speech, sample_rate = soundfile.read(file_path)
|
||||
chunk_stride = int(chunk_size * sample_rate / 1000)
|
||||
|
||||
cache = {}
|
||||
total_chunk_num = int(len((speech)-1)/chunk_stride+1)
|
||||
for i in range(total_chunk_num):
|
||||
time.sleep(0.1)
|
||||
speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
|
||||
is_final = i == total_chunk_num - 1
|
||||
res = model.generate(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size)
|
||||
if len(res[0]["value"]):
|
||||
vad_result += VADResponse.from_raw(res)
|
||||
for item in res[0]["value"]:
|
||||
items.append(item)
|
||||
vad_result.process_time_chunk()
|
||||
|
||||
# for item in items:
|
||||
# print(item)
|
||||
return vad_result
|
||||
|
||||
if __name__ == "__main__":
|
||||
vad_result = vad_model_use_online("tests/vad_example.wav")
|
||||
# print(vad_result)
|
||||
BIN
tests/vad_example.wav
Normal file
BIN
tests/vad_example.wav
Normal file
Binary file not shown.
Loading…
x
Reference in New Issue
Block a user