详解python的webrtc库实现语音端点检测

引言

语音端点检测最早应用于电话传输和检测系统当中,用于通信信道的时间分配,提高传输线路的利用效率.端点检测属于语音处理系统的前端操作,在语音检测领域意义重大.

但是目前的语音端点检测,尤其是检测 人声 开始和结束的端点始终是属于技术难点,各家公司始终处于 能判断,但是不敢保证 判别准确性 的阶段.

现在基于云端语义库的聊天机器人层出不穷,其中最著名的当属amazon的 Alexa/Echo 智能音箱.

国内如雨后春笋般出现了各种搭载语音聊天的智能音箱(如前几天在知乎上广告的若琪机器人)和各类智能机器人产品.国内语音服务提供商主要面对中文语音服务,由于语音不像图像有分辨率等等较为客观的指标,很多时候凭主观判断,所以较难判断各家语音识别和合成技术的好坏.但是我个人认为,国内的中文语音服务和国外的英文语音服务,在某些方面已经有超越的趋势.

通常搭建机器人聊天系统主要包括以下三个方面:

1. 语音转文字(ASR/STT)
2. 语义内容(NLU/NLP)
3. 文字转语音(TTS)

语音转文字(ASR/STT)

在将语音传给云端API之前,是本地前端的语音采集,这部分主要包括如下几个方面:

1. 麦克风降噪
2. 声源定位
3. 回声消除
4. 唤醒词
5. 语音端点检测
6. 音频格式压缩

python 端点检测

由于实际应用中,单纯依靠能量检测特征检测等方法很难判断人声说话的起始点,所以市面上大多数的语音产品都是使用唤醒词判断语音起始.另外加上声音回路,还可以做语音打断.这样的交互方式可能有些傻,每次必须喊一下 唤醒词 才能继续聊天.这种方式聊多了,个人感觉会嘴巴疼:-O .现在github上有snowboy唤醒词的开源库,大家可以登录snowboy官网训练自己的唤醒词模型.

1. Kitt-AI : Snowboy
2. Sensory : Sensory

考虑到用唤醒词嘴巴会累,所以大致调研了一下,Python拥有丰富的库,直接import就能食用.这种方式容易受强噪声干扰,适合一个人在家玩玩.

1. pyaudio: pip install pyaudio 可以从设备节点读取原始音频流数据,音频编码是PCM格式;
2. webrtcvad: pip install webrtcvad 检测判断一组语音数据是否为空语音;

当检测到持续时间长度 T1 vad检测都有语音活动,可以判定为语音起始;

当检测到持续时间长度 T2 vad检测都没有有语音活动,可以判定为语音结束;

完整程序代码可以从我的github下载

程序很简单,相信看一会儿就明白了

    '''
    Requirements:
    + pyaudio - `pip install pyaudio`
    + py-webrtcvad - `pip install webrtcvad`
    '''
    import webrtcvad
    import collections
    import sys
    import signal
    import pyaudio

    from array import array
    from struct import pack
    import wave
    import time

    FORMAT = pyaudio.paInt16
    CHANNELS = 1
    RATE = 16000
    CHUNK_DURATION_MS = 30    # supports 10, 20 and 30 (ms)
    PADDING_DURATION_MS = 1500  # 1 sec jugement
    CHUNK_SIZE = int(RATE CHUNK_DURATION_MS / 1000) # chunk to read
    CHUNK_BYTES = CHUNK_SIZE 2 # 16bit = 2 bytes, PCM
    NUM_PADDING_CHUNKS = int(PADDING_DURATION_MS / CHUNK_DURATION_MS)
    # NUM_WINDOW_CHUNKS = int(240 / CHUNK_DURATION_MS)
    NUM_WINDOW_CHUNKS = int(400 / CHUNK_DURATION_MS) # 400 ms/ 30ms ge
    NUM_WINDOW_CHUNKS_END = NUM_WINDOW_CHUNKS 2

    START_OFFSET = int(NUM_WINDOW_CHUNKS CHUNK_DURATION_MS 0.5 RATE)

    vad = webrtcvad.Vad(1)

    pa = pyaudio.PyAudio()
    stream = pa.open(format=FORMAT,
             channels=CHANNELS,
             rate=RATE,
             input=True,
             start=False,
             # input_device_index=2,
             frames_per_buffer=CHUNK_SIZE)


    got_a_sentence = False
    leave = False


    def handle_int(sig, chunk):
      global leave, got_a_sentence
      leave = True
      got_a_sentence = True


    def record_to_file(path, data, sample_width):
      "Records from the microphone and outputs the resulting data to 'path'"
      # sample_width, data = record()
      data = pack('<' + ('h' len(data)), data)
      wf = wave.open(path, 'wb')
      wf.setnchannels(1)
      wf.setsampwidth(sample_width)
      wf.setframerate(RATE)
      wf.writeframes(data)
      wf.close()


    def normalize(snd_data):
      "Average the volume out"
      MAXIMUM = 32767 # 16384
      times = float(MAXIMUM) / max(abs(i) for i in snd_data)
      r = array('h')
      for i in snd_data:
        r.append(int(i times))
      return r

    signal.signal(signal.SIGINT, handle_int)

    while not leave:
      ring_buffer = collections.deque(maxlen=NUM_PADDING_CHUNKS)
      triggered = False
      voiced_frames = []
      ring_buffer_flags = [0] NUM_WINDOW_CHUNKS
      ring_buffer_index = 0

      ring_buffer_flags_end = [0] NUM_WINDOW_CHUNKS_END
      ring_buffer_index_end = 0
      buffer_in = ''
      # WangS
      raw_data = array('h')
      index = 0
      start_point = 0
      StartTime = time.time()
      print(" recording: ")
      stream.start_stream()

      while not got_a_sentence and not leave:
        chunk = stream.read(CHUNK_SIZE)
        # add WangS
        raw_data.extend(array('h', chunk))
        index += CHUNK_SIZE
        TimeUse = time.time() - StartTime

        active = vad.is_speech(chunk, RATE)

        sys.stdout.write('1' if active else '_')
        ring_buffer_flags[ring_buffer_index] = 1 if active else 0
        ring_buffer_index += 1
        ring_buffer_index %= NUM_WINDOW_CHUNKS

        ring_buffer_flags_end[ring_buffer_index_end] = 1 if active else 0
        ring_buffer_index_end += 1
        ring_buffer_index_end %= NUM_WINDOW_CHUNKS_END

        # start point detection
        if not triggered:
          ring_buffer.append(chunk)
          num_voiced = sum(ring_buffer_flags)
          if num_voiced > 0.8 NUM_WINDOW_CHUNKS:
            sys.stdout.write(' Open ')
            triggered = True
            start_point = index - CHUNK_SIZE 20 # start point
            # voiced_frames.extend(ring_buffer)
            ring_buffer.clear()
        # end point detection
        else:
          # voiced_frames.append(chunk)
          ring_buffer.append(chunk)
          num_unvoiced = NUM_WINDOW_CHUNKS_END - sum(ring_buffer_flags_end)
          if num_unvoiced > 0.90 NUM_WINDOW_CHUNKS_END or TimeUse > 10:
            sys.stdout.write(' Close ')
            triggered = False
            got_a_sentence = True

        sys.stdout.flush()

      sys.stdout.write('\n')
      # data = b''.join(voiced_frames)

      stream.stop_stream()
      print(" done recording")
      got_a_sentence = False

      # write to file
      raw_data.reverse()
      for index in range(start_point):
        raw_data.pop()
      raw_data.reverse()
      raw_data = normalize(raw_data)
      record_to_file("recording.wav", raw_data, 2)
      leave = True

    stream.close()

程序运行方式sudo python vad.py

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持脚本之家。