安装使用webrtcvad

webrtc 的vad使用GMM(Gaussian Mixture Model)对语音和噪声建模，通过相应的概率来判断语音和噪声。这种算法是无监督的，不需要严格的训练。
参考：https://www.cnblogs.com/zhenyuyaodidiao/p/9288455.html
环境：linux 7

安装依赖

yum -y install epel-release
yum -y install python-pip
yum -y install python-devel
pip install webrtcvad

安装webrtcvad时出现以下报错：

unable to execute 'gcc': No such file or directory
error: command 'gcc' failed with exit status 1

我的解决方法如下,安装gcc：

yum install -y libffi-devel openssl-devel #安装其他依赖
yum -y install gcc#安装gcc

然后再次安装

pip install webrtcvad

成功了

使用webrtvad

代码是从上面的参考上摘抄下来的。

import collections
import contextlib
import sys
import wave
 
import webrtcvad
 
 
def read_wave(path):
    with contextlib.closing(wave.open(path, 'rb')) as wf:
        num_channels = wf.getnchannels()
        assert num_channels == 1
        sample_width = wf.getsampwidth()
        assert sample_width == 2
        sample_rate = wf.getframerate()
        assert sample_rate in (8000, 16000, 32000)
        pcm_data = wf.readframes(wf.getnframes())
        return pcm_data, sample_rate
 
 
def write_wave(path, audio, sample_rate):
    with contextlib.closing(wave.open(path, 'wb')) as wf:
        wf.setnchannels(1)
        wf.setsampwidth(2)
        wf.setframerate(sample_rate)
        wf.writeframes(audio)
 
 
class Frame(object):
    def __init__(self, bytes, timestamp, duration):
        self.bytes = bytes
        self.timestamp = timestamp
        self.duration = duration
 
 
def frame_generator(frame_duration_ms, audio, sample_rate):
    n = int(sample_rate * (frame_duration_ms / 1000.0) * 2)
    offset = 0
    timestamp = 0.0
    duration = (float(n) / sample_rate) / 2.0
    while offset + n < len(audio):
        yield Frame(audio[offset:offset + n], timestamp, duration)
        timestamp += duration
        offset += n
 
 
def vad_collector(sample_rate, frame_duration_ms,
                  padding_duration_ms, vad, frames):
    num_padding_frames = int(padding_duration_ms / frame_duration_ms)
    ring_buffer = collections.deque(maxlen=num_padding_frames)
    triggered = False
    voiced_frames = []
    for frame in frames:
        sys.stdout.write(
            '1' if vad.is_speech(frame.bytes, sample_rate) else '0')
        if not triggered:
            ring_buffer.append(frame)
            num_voiced = len([f for f in ring_buffer
                              if vad.is_speech(f.bytes, sample_rate)])
            if num_voiced > 0.9 * ring_buffer.maxlen:
                sys.stdout.write('+(%s)' % (ring_buffer[0].timestamp,))
                triggered = True
                voiced_frames.extend(ring_buffer)
                ring_buffer.clear()
        else:
            voiced_frames.append(frame)
            ring_buffer.append(frame)
            num_unvoiced = len([f for f in ring_buffer
                                if not vad.is_speech(f.bytes, sample_rate)])
            if num_unvoiced > 0.9 * ring_buffer.maxlen:
                sys.stdout.write('-(%s)' % (frame.timestamp + frame.duration))
                triggered = False
                yield b''.join([f.bytes for f in voiced_frames])
                ring_buffer.clear()
                voiced_frames = []
    if triggered:
        sys.stdout.write('-(%s)' % (frame.timestamp + frame.duration))
    sys.stdout.write('\n')
    if voiced_frames:
        yield b''.join([f.bytes for f in voiced_frames])
 
 
def main(args):
    if len(args) != 2:
        sys.stderr.write(
            'Usage: example.py <aggressiveness> <path to wav file>\n')
        sys.exit(1)
    audio, sample_rate = read_wave(args[1])
    vad = webrtcvad.Vad(int(args[0]))
    frames = frame_generator(30, audio, sample_rate)
    frames = list(frames)
    segments = vad_collector(sample_rate, 30, 300, vad, frames)
    for i, segment in enumerate(segments):
        #path = 'chunk-%002d.wav' % (i,)
        print('--end')
        #write_wave(path, segment, sample_rate)
 
 
if __name__ == '__main__':
    main(sys.argv[1:])

把上面的代码存为webrtc_vad.py文件，然后再linux下运转。下面代码中的2是敏感系数，vad检测的敏感系数共四种模式，用数字0~3来区分，激进程度与数值大小正相关。0: Normal，1：low Bitrate， 2：Aggressive；3：Very Aggressive 可以根据实际更改。；第二个参数为wav文件存放路径，目前仅支持8K，16K，32K的采样率。

python3 webrtc_vad.py  2 123456_1.wav 

转成功后。会有以下结果

1111111111+(0.0)111111111111111000011111111111111111111111111111111111111111111111111111111111111111111101111111111111111000011111111111111111111111111111111111111111111111-(4.979999999999997)
--end