168 lines
5.2 KiB
Python
168 lines
5.2 KiB
Python
# Code based on https://github.com/keithito/tacotron/blob/master/util/audio.py
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import math
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import numpy as np
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import tensorflow as tf
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from scipy import signal
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from hparams import hparams
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import librosa
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import librosa.filters
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def load_audio(path, pre_silence_length=0, post_silence_length=0):
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audio = librosa.core.load(path, sr=hparams.sample_rate)[0]
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if pre_silence_length > 0 or post_silence_length > 0:
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audio = np.concatenate([
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get_silence(pre_silence_length),
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audio,
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get_silence(post_silence_length),
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])
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return audio
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def save_audio(audio, path, sample_rate=None):
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audio *= 32767 / max(0.01, np.max(np.abs(audio)))
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librosa.output.write_wav(path, audio.astype(np.int16),
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hparams.sample_rate if sample_rate is None else sample_rate)
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print(" [*] Audio saved: {}".format(path))
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def resample_audio(audio, target_sample_rate):
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return librosa.core.resample(
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audio, hparams.sample_rate, target_sample_rate)
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def get_duration(audio):
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return librosa.core.get_duration(audio, sr=hparams.sample_rate)
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def frames_to_hours(n_frames):
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return sum((n_frame for n_frame in n_frames)) * \
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hparams.frame_shift_ms / (3600 * 1000)
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def get_silence(sec):
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return np.zeros(hparams.sample_rate * sec)
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def spectrogram(y):
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D = _stft(_preemphasis(y))
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S = _amp_to_db(np.abs(D)) - hparams.ref_level_db
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return _normalize(S)
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def inv_spectrogram(spectrogram):
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S = _db_to_amp(_denormalize(spectrogram) + hparams.ref_level_db) # Convert back to linear
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return inv_preemphasis(_griffin_lim(S ** hparams.power)) # Reconstruct phase
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def inv_spectrogram_tensorflow(spectrogram):
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S = _db_to_amp_tensorflow(_denormalize_tensorflow(spectrogram) + hparams.ref_level_db)
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return _griffin_lim_tensorflow(tf.pow(S, hparams.power))
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def melspectrogram(y):
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D = _stft(_preemphasis(y))
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S = _amp_to_db(_linear_to_mel(np.abs(D)))
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return _normalize(S)
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def inv_melspectrogram(melspectrogram):
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S = _mel_to_linear(_db_to_amp(_denormalize(melspectrogram))) # Convert back to linear
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return inv_preemphasis(_griffin_lim(S ** hparams.power)) # Reconstruct phase
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# Based on https://github.com/librosa/librosa/issues/434
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def _griffin_lim(S):
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angles = np.exp(2j * np.pi * np.random.rand(*S.shape))
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S_complex = np.abs(S).astype(np.complex)
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y = _istft(S_complex * angles)
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for i in range(hparams.griffin_lim_iters):
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angles = np.exp(1j * np.angle(_stft(y)))
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y = _istft(S_complex * angles)
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return y
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def _griffin_lim_tensorflow(S):
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with tf.variable_scope('griffinlim'):
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S = tf.expand_dims(S, 0)
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S_complex = tf.identity(tf.cast(S, dtype=tf.complex64))
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y = _istft_tensorflow(S_complex)
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for i in range(hparams.griffin_lim_iters):
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est = _stft_tensorflow(y)
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angles = est / tf.cast(tf.maximum(1e-8, tf.abs(est)), tf.complex64)
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y = _istft_tensorflow(S_complex * angles)
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return tf.squeeze(y, 0)
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def _stft(y):
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n_fft, hop_length, win_length = _stft_parameters()
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return librosa.stft(y=y, n_fft=n_fft, hop_length=hop_length, win_length=win_length)
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def _istft(y):
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_, hop_length, win_length = _stft_parameters()
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return librosa.istft(y, hop_length=hop_length, win_length=win_length)
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def _stft_tensorflow(signals):
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n_fft, hop_length, win_length = _stft_parameters()
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return tf.contrib.signal.stft(signals, win_length, hop_length, n_fft, pad_end=False)
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def _istft_tensorflow(stfts):
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n_fft, hop_length, win_length = _stft_parameters()
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return tf.contrib.signal.inverse_stft(stfts, win_length, hop_length, n_fft)
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def _stft_parameters():
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n_fft = (hparams.num_freq - 1) * 2
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hop_length = int(hparams.frame_shift_ms / 1000 * hparams.sample_rate)
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win_length = int(hparams.frame_length_ms / 1000 * hparams.sample_rate)
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return n_fft, hop_length, win_length
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# Conversions:
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_mel_basis = None
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_inv_mel_basis = None
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def _linear_to_mel(spectrogram):
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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()
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return np.dot(_mel_basis, spectrogram)
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def _mel_to_linear(mel_spectrogram):
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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())
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return np.maximum(1e-10, np.dot(_inv_mel_basis, mel_spectrogram))
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def _build_mel_basis():
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n_fft = (hparams.num_freq - 1) * 2
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return librosa.filters.mel(hparams.sample_rate, n_fft, n_mels=hparams.num_mels)
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def _amp_to_db(x):
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return 20 * np.log10(np.maximum(1e-5, x))
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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 _db_to_amp_tensorflow(x):
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return tf.pow(tf.ones(tf.shape(x)) * 10.0, x * 0.05)
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def _preemphasis(x):
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return signal.lfilter([1, -hparams.preemphasis], [1], x)
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def inv_preemphasis(x):
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return signal.lfilter([1], [1, -hparams.preemphasis], x)
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def _normalize(S):
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return np.clip((S - hparams.min_level_db) / -hparams.min_level_db, 0, 1)
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def _denormalize(S):
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return (np.clip(S, 0, 1) * -hparams.min_level_db) + hparams.min_level_db
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def _denormalize_tensorflow(S):
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return (tf.clip_by_value(S, 0, 1) * -hparams.min_level_db) + hparams.min_level_db
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