Python实现WebRTC语音端点检测：从原理到代码实战

'''
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()