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