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| 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 | |