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服务器之家。

原文链接：http://blog.csdn.net/u012123989/article/details/72771667