| | 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) |
| |
|
