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| from scipy.ndimage.morphology import binary_dilation | |
| from encoder.params_data import * | |
| from pathlib import Path | |
| from typing import Optional, Union | |
| from warnings import warn | |
| import numpy as np | |
| import librosa | |
| import struct | |
| import os | |
| from pydub import AudioSegment | |
| import noisereduce | |
| try: | |
| import webrtcvad | |
| except: | |
| warn("Unable to import 'webrtcvad'. This package enables noise removal and is recommended.") | |
| webrtcvad=None | |
| int16_max = (2 ** 15) - 1 | |
| def preprocess_wav(fpath_or_wav: Union[str, Path, np.ndarray], | |
| source_sr: Optional[int] = None, | |
| normalize: Optional[bool] = True, | |
| trim_silence: Optional[bool] = True): | |
| """ | |
| 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): | |
| # if str(fpath_or_wav).endswith(".m4a"): | |
| # try: | |
| # track = AudioSegment.from_file(fpath_or_wav, format="m4a") | |
| # except: | |
| # return [] | |
| # fpath = os.path.splitext(str(fpath_or_wav))[0] | |
| # path_components = os.path.normpath(fpath).split(os.sep) | |
| # wav_dir = Path("D:\\liuhaozhe").joinpath(f"VoxCeleb2_wav") # local path | |
| # wav_dir.mkdir(exist_ok=True) | |
| # wav_name = "_".join(path_components[-6: ]) | |
| # wav_path = wav_dir.joinpath(f"{wav_name}.wav") | |
| # track.export(wav_path, format="wav") | |
| # wav, source_sr = librosa.load(str(wav_path), sr=None) | |
| # else: | |
| wav, source_sr = librosa.load(str(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 | |
| if normalize: | |
| wav = normalize_volume(wav, audio_norm_target_dBFS, increase_only=True) | |
| if webrtcvad and trim_silence: | |
| 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( | |
| wav, | |
| 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)) | |