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Build error
| import numpy as np | |
| from numpy import linalg as LA | |
| import librosa | |
| from scipy.io import wavfile | |
| import soundfile as sf | |
| import librosa.filters | |
| def load_wav(wav_path, raw_sr, target_sr=16000, win_size=800, hop_size=200): | |
| audio = librosa.core.load(wav_path, sr=raw_sr)[0] | |
| if raw_sr != target_sr: | |
| audio = librosa.core.resample(audio, | |
| raw_sr, | |
| target_sr, | |
| res_type='kaiser_best') | |
| target_length = (audio.size // hop_size + | |
| win_size // hop_size) * hop_size | |
| pad_len = (target_length - audio.size) // 2 | |
| if audio.size % 2 == 0: | |
| audio = np.pad(audio, (pad_len, pad_len), mode='reflect') | |
| else: | |
| audio = np.pad(audio, (pad_len, pad_len + 1), mode='reflect') | |
| return audio | |
| def save_wav(wav, path, sample_rate, norm=False): | |
| if norm: | |
| wav *= 32767 / max(0.01, np.max(np.abs(wav))) | |
| wavfile.write(path, sample_rate, wav.astype(np.int16)) | |
| else: | |
| sf.write(path, wav, sample_rate) | |
| _mel_basis = None | |
| _inv_mel_basis = None | |
| def _build_mel_basis(hparams): | |
| assert hparams.fmax <= hparams.sample_rate // 2 | |
| return librosa.filters.mel(hparams.sample_rate, | |
| hparams.n_fft, | |
| n_mels=hparams.acoustic_dim, | |
| fmin=hparams.fmin, | |
| fmax=hparams.fmax) | |
| def _linear_to_mel(spectogram, hparams): | |
| global _mel_basis | |
| if _mel_basis is None: | |
| _mel_basis = _build_mel_basis(hparams) | |
| return np.dot(_mel_basis, spectogram) | |
| def _mel_to_linear(mel_spectrogram, hparams): | |
| global _inv_mel_basis | |
| if _inv_mel_basis is None: | |
| _inv_mel_basis = np.linalg.pinv(_build_mel_basis(hparams)) | |
| return np.maximum(1e-10, np.dot(_inv_mel_basis, mel_spectrogram)) | |
| def _stft(y, hparams): | |
| return librosa.stft(y=y, | |
| n_fft=hparams.n_fft, | |
| hop_length=hparams.hop_size, | |
| win_length=hparams.win_size) | |
| def _amp_to_db(x, hparams): | |
| min_level = np.exp(hparams.min_level_db / 20 * np.log(10)) | |
| return 20 * np.log10(np.maximum(min_level, x)) | |
| def _normalize(S, hparams): | |
| return hparams.max_abs_value * np.clip(((S - hparams.min_db) / | |
| (-hparams.min_db)), 0, 1) | |
| def _db_to_amp(x): | |
| return np.power(10.0, (x) * 0.05) | |
| def _stft(y, hparams): | |
| return librosa.stft(y=y, | |
| n_fft=hparams.n_fft, | |
| hop_length=hparams.hop_size, | |
| win_length=hparams.win_size) | |
| def _istft(y, hparams): | |
| return librosa.istft(y, | |
| hop_length=hparams.hop_size, | |
| win_length=hparams.win_size) | |
| def melspectrogram(wav, hparams): | |
| D = _stft(wav, hparams) | |
| S = _amp_to_db(_linear_to_mel(np.abs(D), hparams), | |
| hparams) - hparams.ref_level_db | |
| return _normalize(S, hparams) | |