Spaces:
Runtime error
Runtime error
File size: 3,289 Bytes
9206300 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 |
from skimage.transform import resize
import struct
import webrtcvad
from scipy.ndimage.morphology import binary_dilation
import librosa
import numpy as np
import pyloudnorm as pyln
import warnings
warnings.filterwarnings("ignore", message="Possible clipped samples in output")
int16_max = (2 ** 15) - 1
def trim_long_silences(path, sr=None, return_raw_wav=False, norm=True, vad_max_silence_length=12):
"""
Ensures that segments without voice in the waveform remain no longer than a
threshold determined by the VAD parameters in params.py.
:param wav: the raw waveform as a numpy array of floats
:param vad_max_silence_length: Maximum number of consecutive silent frames a segment can have.
:return: the same waveform with silences trimmed away (length <= original wav length)
"""
## Voice Activation Detection
# Window size of the VAD. Must be either 10, 20 or 30 milliseconds.
# This sets the granularity of the VAD. Should not need to be changed.
sampling_rate = 16000
wav_raw, sr = librosa.core.load(path, sr=sr)
if norm:
meter = pyln.Meter(sr) # create BS.1770 meter
loudness = meter.integrated_loudness(wav_raw)
wav_raw = pyln.normalize.loudness(wav_raw, loudness, -20.0)
if np.abs(wav_raw).max() > 1.0:
wav_raw = wav_raw / np.abs(wav_raw).max()
wav = librosa.resample(wav_raw, sr, sampling_rate, res_type='kaiser_best')
vad_window_length = 30 # In milliseconds
# Number of frames to average together when performing the moving average smoothing.
# The larger this value, the larger the VAD variations must be to not get smoothed out.
vad_moving_average_width = 8
# Compute the voice detection window size
samples_per_window = (vad_window_length * sampling_rate) // 1000
# Trim the end of the audio to have a multiple of the window size
wav = wav[:len(wav) - (len(wav) % samples_per_window)]
# Convert the float waveform to 16-bit mono PCM
pcm_wave = struct.pack("%dh" % len(wav), *(np.round(wav * int16_max)).astype(np.int16))
# Perform voice activation detection
voice_flags = []
vad = webrtcvad.Vad(mode=3)
for window_start in range(0, len(wav), samples_per_window):
window_end = window_start + samples_per_window
voice_flags.append(vad.is_speech(pcm_wave[window_start * 2:window_end * 2],
sample_rate=sampling_rate))
voice_flags = np.array(voice_flags)
# Smooth the voice detection with a moving average
def moving_average(array, width):
array_padded = np.concatenate((np.zeros((width - 1) // 2), array, np.zeros(width // 2)))
ret = np.cumsum(array_padded, dtype=float)
ret[width:] = ret[width:] - ret[:-width]
return ret[width - 1:] / width
audio_mask = moving_average(voice_flags, vad_moving_average_width)
audio_mask = np.round(audio_mask).astype(np.bool)
# Dilate the voiced regions
audio_mask = binary_dilation(audio_mask, np.ones(vad_max_silence_length + 1))
audio_mask = np.repeat(audio_mask, samples_per_window)
audio_mask = resize(audio_mask, (len(wav_raw),)) > 0
if return_raw_wav:
return wav_raw, audio_mask, sr
return wav_raw[audio_mask], audio_mask, sr
|