import librosa import librosa.filters import numpy as np # import tensorflow as tf 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))) #proposed by @dsmiller 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) ########################################################## #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(hp.sample_rate, 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)