from scipy.ndimage.morphology import binary_dilation from speaker_encoder.params_data import * from pathlib import Path from typing import Optional, Union import numpy as np import webrtcvad import librosa import struct int16_max = (2 ** 15) - 1 def preprocess_wav(fpath_or_wav: Union[str, Path, np.ndarray], source_sr: Optional[int] = None): """ Applies the preprocessing operations used in training the Speaker Encoder to a waveform either on disk or in memory. The waveform will be resampled to match the data hyperparameters. :param fpath_or_wav: either a filepath to an audio file (many extensions are supported, not just .wav), either the waveform as a numpy array of floats. :param source_sr: if passing an audio waveform, the sampling rate of the waveform before preprocessing. After preprocessing, the waveform's sampling rate will match the data hyperparameters. If passing a filepath, the sampling rate will be automatically detected and this argument will be ignored. """ # Load the wav from disk if needed if isinstance(fpath_or_wav, str) or isinstance(fpath_or_wav, Path): wav, source_sr = librosa.load(fpath_or_wav, sr=None) else: wav = fpath_or_wav # Resample the wav if needed if source_sr is not None and source_sr != sampling_rate: wav = librosa.resample(wav, source_sr, sampling_rate) # Apply the preprocessing: normalize volume and shorten long silences wav = normalize_volume(wav, audio_norm_target_dBFS, increase_only=True) wav = trim_long_silences(wav) return wav def wav_to_mel_spectrogram(wav): """ Derives a mel spectrogram ready to be used by the encoder from a preprocessed audio waveform. Note: this not a log-mel spectrogram. """ frames = librosa.feature.melspectrogram( y=wav, sr=sampling_rate, n_fft=int(sampling_rate * mel_window_length / 1000), hop_length=int(sampling_rate * mel_window_step / 1000), n_mels=mel_n_channels ) return frames.astype(np.float32).T def trim_long_silences(wav): """ 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 :return: the same waveform with silences trimmed away (length <= original wav length) """ # 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) return wav[audio_mask == True] def normalize_volume(wav, target_dBFS, increase_only=False, decrease_only=False): if increase_only and decrease_only: raise ValueError("Both increase only and decrease only are set") dBFS_change = target_dBFS - 10 * np.log10(np.mean(wav ** 2)) if (dBFS_change < 0 and increase_only) or (dBFS_change > 0 and decrease_only): return wav return wav * (10 ** (dBFS_change / 20))