mirror of
https://github.com/babysor/MockingBird.git
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118 lines
4.6 KiB
Python
118 lines
4.6 KiB
Python
from scipy.ndimage.morphology import binary_dilation
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from models.encoder.params_data import *
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from pathlib import Path
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from typing import Optional, Union
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from warnings import warn
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import numpy as np
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import librosa
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import struct
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try:
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import webrtcvad
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except:
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warn("Unable to import 'webrtcvad'. This package enables noise removal and is recommended.")
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webrtcvad=None
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int16_max = (2 ** 15) - 1
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def preprocess_wav(fpath_or_wav: Union[str, Path, np.ndarray],
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source_sr: Optional[int] = None,
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normalize: Optional[bool] = True,
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trim_silence: Optional[bool] = True):
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"""
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Applies the preprocessing operations used in training the Speaker Encoder to a waveform
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either on disk or in memory. The waveform will be resampled to match the data hyperparameters.
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:param fpath_or_wav: either a filepath to an audio file (many extensions are supported, not
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just .wav), either the waveform as a numpy array of floats.
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:param source_sr: if passing an audio waveform, the sampling rate of the waveform before
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preprocessing. After preprocessing, the waveform's sampling rate will match the data
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hyperparameters. If passing a filepath, the sampling rate will be automatically detected and
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this argument will be ignored.
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"""
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# Load the wav from disk if needed
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if isinstance(fpath_or_wav, str) or isinstance(fpath_or_wav, Path):
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wav, source_sr = librosa.load(str(fpath_or_wav), sr=None)
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else:
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wav = fpath_or_wav
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# Resample the wav if needed
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if source_sr is not None and source_sr != sampling_rate:
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wav = librosa.resample(wav, source_sr, sampling_rate)
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# Apply the preprocessing: normalize volume and shorten long silences
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if normalize:
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wav = normalize_volume(wav, audio_norm_target_dBFS, increase_only=True)
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if webrtcvad and trim_silence:
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wav = trim_long_silences(wav)
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return wav
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def wav_to_mel_spectrogram(wav):
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"""
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Derives a mel spectrogram ready to be used by the encoder from a preprocessed audio waveform.
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Note: this not a log-mel spectrogram.
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"""
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frames = librosa.feature.melspectrogram(
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y=wav,
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sr=sampling_rate,
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n_fft=int(sampling_rate * mel_window_length / 1000),
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hop_length=int(sampling_rate * mel_window_step / 1000),
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n_mels=mel_n_channels
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)
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return frames.astype(np.float32).T
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def trim_long_silences(wav):
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"""
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Ensures that segments without voice in the waveform remain no longer than a
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threshold determined by the VAD parameters in params.py.
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:param wav: the raw waveform as a numpy array of floats
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:return: the same waveform with silences trimmed away (length <= original wav length)
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"""
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# Compute the voice detection window size
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samples_per_window = (vad_window_length * sampling_rate) // 1000
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# Trim the end of the audio to have a multiple of the window size
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wav = wav[:len(wav) - (len(wav) % samples_per_window)]
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# Convert the float waveform to 16-bit mono PCM
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pcm_wave = struct.pack("%dh" % len(wav), *(np.round(wav * int16_max)).astype(np.int16))
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# Perform voice activation detection
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voice_flags = []
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vad = webrtcvad.Vad(mode=3)
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for window_start in range(0, len(wav), samples_per_window):
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window_end = window_start + samples_per_window
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voice_flags.append(vad.is_speech(pcm_wave[window_start * 2:window_end * 2],
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sample_rate=sampling_rate))
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voice_flags = np.array(voice_flags)
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# Smooth the voice detection with a moving average
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def moving_average(array, width):
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array_padded = np.concatenate((np.zeros((width - 1) // 2), array, np.zeros(width // 2)))
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ret = np.cumsum(array_padded, dtype=float)
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ret[width:] = ret[width:] - ret[:-width]
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return ret[width - 1:] / width
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audio_mask = moving_average(voice_flags, vad_moving_average_width)
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audio_mask = np.round(audio_mask).astype(np.bool)
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# Dilate the voiced regions
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audio_mask = binary_dilation(audio_mask, np.ones(vad_max_silence_length + 1))
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audio_mask = np.repeat(audio_mask, samples_per_window)
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return wav[audio_mask == True]
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def normalize_volume(wav, target_dBFS, increase_only=False, decrease_only=False):
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if increase_only and decrease_only:
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raise ValueError("Both increase only and decrease only are set")
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dBFS_change = target_dBFS - 10 * np.log10(np.mean(wav ** 2))
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if (dBFS_change < 0 and increase_only) or (dBFS_change > 0 and decrease_only):
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return wav
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return wav * (10 ** (dBFS_change / 20))
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