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* 🐳 chore: 优化打包速度 使用国内镜像 * 🎈 perf: 优化打包速度 注释日志的时间信息 * 🐳 chore: 改用docker hub * 🐎 ci(ci): 自动打包docker * 🐳 chore: 更新文档 * 🎈 perf: 改用opuslib_next * 🌈 style: 统一log
78 lines
3.1 KiB
Python
78 lines
3.1 KiB
Python
from abc import ABC, abstractmethod
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from config.logger import setup_logging
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import opuslib_next
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import time
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import numpy as np
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import torch
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TAG = __name__
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logger = setup_logging()
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class VAD(ABC):
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@abstractmethod
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def is_vad(self, conn, data):
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"""检测音频数据中的语音活动"""
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pass
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class SileroVAD(VAD):
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def __init__(self, config):
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logger.bind(tag=TAG).info("SileroVAD", config)
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self.model, self.utils = torch.hub.load(repo_or_dir=config["model_dir"],
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source='local',
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model='silero_vad',
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force_reload=False)
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(get_speech_timestamps, _, _, _, _) = self.utils
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self.decoder = opuslib_next.Decoder(16000, 1)
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self.vad_threshold = config.get("threshold")
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self.silence_threshold_ms = config.get("min_silence_duration_ms")
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def is_vad(self, conn, opus_packet):
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try:
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pcm_frame = self.decoder.decode(opus_packet, 960)
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conn.client_audio_buffer += pcm_frame # 将新数据加入缓冲区
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# 处理缓冲区中的完整帧(每次处理512采样点)
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client_have_voice = False
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while len(conn.client_audio_buffer) >= 512 * 2:
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# 提取前512个采样点(1024字节)
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chunk = conn.client_audio_buffer[:512 * 2]
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conn.client_audio_buffer = conn.client_audio_buffer[512 * 2:]
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# 转换为模型需要的张量格式
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audio_int16 = np.frombuffer(chunk, dtype=np.int16)
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audio_float32 = audio_int16.astype(np.float32) / 32768.0
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audio_tensor = torch.from_numpy(audio_float32)
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# 检测语音活动
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speech_prob = self.model(audio_tensor, 16000).item()
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client_have_voice = speech_prob >= self.vad_threshold
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# 如果之前有声音,但本次没有声音,且与上次有声音的时间查已经超过了静默阈值,则认为已经说完一句话
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if conn.client_have_voice and not client_have_voice:
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stop_duration = time.time() * 1000 - conn.client_have_voice_last_time
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if stop_duration >= self.silence_threshold_ms:
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conn.client_voice_stop = True
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if client_have_voice:
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conn.client_have_voice = True
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conn.client_have_voice_last_time = time.time() * 1000
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return client_have_voice
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except opuslib_next.OpusError as e:
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logger.bind(tag=TAG).info(f"解码错误: {e}")
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except Exception as e:
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logger.bind(tag=TAG).error(f"Error processing audio packet: {e}")
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def create_instance(class_name, *args, **kwargs) -> VAD:
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# 获取类对象
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cls_map = {
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"SileroVAD": SileroVAD,
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# 可扩展其他SileroVAD实现
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}
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if cls := cls_map.get(class_name):
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return cls(*args, **kwargs)
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raise ValueError(f"不支持的SileroVAD类型: {class_name}")
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