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| import librosa | |
| import librosa.filters | |
| import numpy as np | |
| from scipy import signal | |
| from wav2mel_hparams import hparams as hp | |
| from librosa.core.audio import resample | |
| import soundfile as sf | |
| def load_wav(path, sr): | |
| return librosa.core.load(path, sr=sr) | |
| def preemphasis(wav, k, preemphasize=True): | |
| if preemphasize: | |
| return signal.lfilter([1, -k], [1], wav) | |
| return wav | |
| def inv_preemphasis(wav, k, inv_preemphasize=True): | |
| if inv_preemphasize: | |
| return signal.lfilter([1], [1, -k], wav) | |
| return wav | |
| def get_hop_size(): | |
| hop_size = hp.hop_size | |
| if hop_size is None: | |
| assert hp.frame_shift_ms is not None | |
| hop_size = int(hp.frame_shift_ms / 1000 * hp.sample_rate) | |
| return hop_size | |
| def linearspectrogram(wav): | |
| D = _stft(preemphasis(wav, hp.preemphasis, hp.preemphasize)) | |
| S = _amp_to_db(np.abs(D)) - hp.ref_level_db | |
| if hp.signal_normalization: | |
| return _normalize(S) | |
| return S | |
| def melspectrogram(wav): | |
| D = _stft(preemphasis(wav, hp.preemphasis, hp.preemphasize)) | |
| S = _amp_to_db(_linear_to_mel(np.abs(D))) - hp.ref_level_db | |
| if hp.signal_normalization: | |
| return _normalize(S) | |
| return S | |
| def _stft(y): | |
| return librosa.stft(y=y, n_fft=hp.n_fft, hop_length=get_hop_size(), win_length=hp.win_size) | |
| ########################################################## | |
| #Those are only correct when using lws!!! (This was messing with Wavenet quality for a long time!) | |
| def num_frames(length, fsize, fshift): | |
| """Compute number of time frames of spectrogram | |
| """ | |
| pad = (fsize - fshift) | |
| if length % fshift == 0: | |
| M = (length + pad * 2 - fsize) // fshift + 1 | |
| else: | |
| M = (length + pad * 2 - fsize) // fshift + 2 | |
| return M | |
| def pad_lr(x, fsize, fshift): | |
| """Compute left and right padding | |
| """ | |
| M = num_frames(len(x), fsize, fshift) | |
| pad = (fsize - fshift) | |
| T = len(x) + 2 * pad | |
| r = (M - 1) * fshift + fsize - T | |
| return pad, pad + r | |
| ########################################################## | |
| #Librosa correct padding | |
| def librosa_pad_lr(x, fsize, fshift): | |
| return 0, (x.shape[0] // fshift + 1) * fshift - x.shape[0] | |
| # Conversions | |
| _mel_basis = None | |
| def _linear_to_mel(spectogram): | |
| global _mel_basis | |
| if _mel_basis is None: | |
| _mel_basis = _build_mel_basis() | |
| return np.dot(_mel_basis, spectogram) | |
| def _build_mel_basis(): | |
| assert hp.fmax <= hp.sample_rate // 2 | |
| return librosa.filters.mel(sr=hp.sample_rate, n_fft=hp.n_fft, n_mels=hp.num_mels, | |
| fmin=hp.fmin, fmax=hp.fmax) | |
| def _amp_to_db(x): | |
| min_level = np.exp(hp.min_level_db / 20 * np.log(10)) | |
| return 20 * np.log10(np.maximum(min_level, x)) | |
| def _db_to_amp(x): | |
| return np.power(10.0, (x) * 0.05) | |
| def _normalize(S): | |
| if hp.allow_clipping_in_normalization: | |
| if hp.symmetric_mels: | |
| return np.clip((2 * hp.max_abs_value) * ((S - hp.min_level_db) / (-hp.min_level_db)) - hp.max_abs_value, | |
| -hp.max_abs_value, hp.max_abs_value) | |
| else: | |
| return np.clip(hp.max_abs_value * ((S - hp.min_level_db) / (-hp.min_level_db)), 0, hp.max_abs_value) | |
| assert S.max() <= 0 and S.min() - hp.min_level_db >= 0 | |
| if hp.symmetric_mels: | |
| return (2 * hp.max_abs_value) * ((S - hp.min_level_db) / (-hp.min_level_db)) - hp.max_abs_value | |
| else: | |
| return hp.max_abs_value * ((S - hp.min_level_db) / (-hp.min_level_db)) | |
| def _denormalize(D): | |
| if hp.allow_clipping_in_normalization: | |
| if hp.symmetric_mels: | |
| return (((np.clip(D, -hp.max_abs_value, | |
| hp.max_abs_value) + hp.max_abs_value) * -hp.min_level_db / (2 * hp.max_abs_value)) | |
| + hp.min_level_db) | |
| else: | |
| return ((np.clip(D, 0, hp.max_abs_value) * -hp.min_level_db / hp.max_abs_value) + hp.min_level_db) | |
| if hp.symmetric_mels: | |
| return (((D + hp.max_abs_value) * -hp.min_level_db / (2 * hp.max_abs_value)) + hp.min_level_db) | |
| else: | |
| return ((D * -hp.min_level_db / hp.max_abs_value) + hp.min_level_db) | |
| def wav2mel(wav, sr): | |
| wav16k = resample(wav, orig_sr=sr, target_sr=16000) | |
| # print('wav16k', wav16k.shape, wav16k.dtype) | |
| mel = melspectrogram(wav16k) | |
| # print('mel', mel.shape, mel.dtype) | |
| if np.isnan(mel.reshape(-1)).sum() > 0: | |
| raise ValueError( | |
| 'Mel contains nan! Using a TTS voice? Add a small epsilon noise to the wav file and try again') | |
| # mel.dtype = np.float32 | |
| mel_chunks = [] | |
| mel_idx_multiplier = 80. / 25 | |
| mel_step_size = 8 | |
| i = start_idx = 0 | |
| while start_idx < len(mel[0]): | |
| start_idx = int(i * mel_idx_multiplier) | |
| if start_idx + mel_step_size // 2 > len(mel[0]): | |
| mel_chunks.append(mel[:, len(mel[0]) - mel_step_size:]) | |
| elif start_idx - mel_step_size // 2 < 0: | |
| mel_chunks.append(mel[:, :mel_step_size]) | |
| else: | |
| mel_chunks.append(mel[:, start_idx - mel_step_size // 2 : start_idx + mel_step_size // 2]) | |
| i += 1 | |
| return mel_chunks | |
| if __name__ == '__main__': | |
| import argparse | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('--wav', type=str, default='') | |
| parser.add_argument('--save_feats', action='store_true') | |
| opt = parser.parse_args() | |
| wav, sr = librosa.core.load(opt.wav) | |
| mel_chunks = np.array(wav2mel(wav.T, sr)) | |
| print(mel_chunks.shape, mel_chunks.transpose(0,2,1).shape) | |
| if opt.save_feats: | |
| save_path = opt.wav.replace('.wav', '_mel.npy') | |
| np.save(save_path, mel_chunks.transpose(0,2,1)) | |
| print(f"[INFO] saved logits to {save_path}") |