| import librosa |
| import librosa.filters |
| import numpy as np |
| |
| from scipy import signal |
| from scipy.io import wavfile |
| from src.utils.hparams import hparams as hp |
|
|
| def load_wav(path, sr): |
| return librosa.core.load(path, sr=sr)[0] |
|
|
| def save_wav(wav, path, sr): |
| wav *= 32767 / max(0.01, np.max(np.abs(wav))) |
| |
| wavfile.write(path, sr, wav.astype(np.int16)) |
|
|
| def save_wavenet_wav(wav, path, sr): |
| librosa.output.write_wav(path, wav, 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 _lws_processor(): |
| import lws |
| return lws.lws(hp.n_fft, get_hop_size(), fftsize=hp.win_size, mode="speech") |
|
|
| def _stft(y): |
| if hp.use_lws: |
| return _lws_processor(hp).stft(y).T |
| else: |
| return librosa.stft(y=y, n_fft=hp.n_fft, hop_length=get_hop_size(), win_length=hp.win_size) |
|
|
| |
| |
| 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 |
| |
| |
| def librosa_pad_lr(x, fsize, fshift): |
| return 0, (x.shape[0] // fshift + 1) * fshift - x.shape[0] |
|
|
| |
| _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) |
|
|
