mirror of
https://github.com/xinnan-tech/xiaozhi-esp32-server.git
synced 2026-07-22 23:23:55 +08:00
65 lines
2.7 KiB
Python
65 lines
2.7 KiB
Python
import time
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import numpy as np
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import torch
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import opuslib_next
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from config.logger import setup_logging
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from core.providers.vad.base import VADProviderBase
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TAG = __name__
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logger = setup_logging()
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class VADProvider(VADProviderBase):
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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(
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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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)
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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 = float(config.get("threshold", 0.5))
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self.silence_threshold_ms = int(config.get("min_silence_duration_ms", 1000))
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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.extend(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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with torch.no_grad():
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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 = (
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time.time() * 1000 - conn.client_have_voice_last_time
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)
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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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