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import pyaudio
import numpy as np
import webrtcvad
# Set up PyAudio
FORMAT = pyaudio.paInt16
CHANNELS = 1
RATE = 48000
CHUNK_SIZE = 960 # 20ms audio chunks
# p = pyaudio.PyAudio()
# wav = "/home/kevingeng/Disk2/laronix/Laronix_ASR_TTS_VC/wav/20221228_video_good_normed_5/take1_001_norm.wav"
wav = "/home/kevingeng/Disk2/laronix/Laronix_ASR_TTS_VC/wav/VAD_test.wav"
import wave
wf = wave.open(wav, "rb")
# import pdb
# stream = p.open(format=p.get_format_from_width(wf.getsampwidth()),
# channels=wf.getnchannels(),
# rate=wf.getframerate(),
# output=True)
# pdb.set_trace()
# Set up VAD
def streaming_VAD(wf):
vad = webrtcvad.Vad()
vad.set_mode(2) # Aggressive mode
# Start audio stream
# stream = p.open(format=FORMAT,
# channels=CHANNELS,
# rate=RATE,
# input=True,
# frames_per_buffer=CHUNK_SIZE)
# VAD constants
MIN_SILENCE_DURATION = 2000 # in ms
MAX_SILENCE_DURATION = 4000 # in ms
BUFFER_SIZE = MAX_SILENCE_DURATION // CHUNK_SIZE
BUFFER_THRESHOLD = int(BUFFER_SIZE * 0.5)
# Initialize VAD buffer
vad_buffer = []
VAD_indicator = []
VAD_frame_indicator = []
data = wf.readframes(CHUNK_SIZE)
# Loop through audio stream
while data:
# Read audio chunk from stream
# pdb.set_trace()
# audio_chunk = np.frombuffer(stream.read(CHUNK_SIZE), dtype=np.int16)
audio_chunk = np.frombuffer(data, dtype=np.int16)
# Detect voice activity
# is_speech = vad.is_speech(audio_chunk.tobytes(), RATE)
try:
is_speech = vad.is_speech(audio_chunk, RATE)
except:
is_speech = False
vad_buffer.append(is_speech)
# If VAD buffer is full, check for silence and reset buffer
if len(vad_buffer) == BUFFER_SIZE:
# Check if buffer contains mostly silence
if vad_buffer.count(False) >= BUFFER_THRESHOLD:
# print("Slience")
# VAD_indicator.append(0)
# vad_buffer = []
return(False)
else:
# print("Voice detected!")
# VAD_indicator.append(1)
vad_buffer = vad_buffer[CHUNK_SIZE // BUFFER_SIZE:]
return(True)
data = wf.readframes(CHUNK_SIZE)
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