from __future__ import print_function, division from Hyperparameters import Hyperparameters as hp import numpy as np # import tensorflow as tf import librosa import copy import matplotlib matplotlib.use('pdf') import matplotlib.pyplot as plt from scipy import signal import os def get_spectrograms(fpath): '''Returns normalized log(melspectrogram) and log(magnitude) from `sound_file`. Args: sound_file: A string. The full path of a sound file. Returns: mel: A 2d array of shape (T, n_mels) <- Transposed mag: A 2d array of shape (T, 1+n_fft/2) <- Transposed ''' # Loading sound file y, sr = librosa.load(fpath, sr=hp.sr) # Trimming y, _ = librosa.effects.trim(y) # Preemphasis y = np.append(y[0], y[1:] - hp.preemphasis * y[:-1]) # stft linear = librosa.stft(y=y, n_fft=hp.n_fft, hop_length=hp.hop_length, win_length=hp.win_length) # magnitude spectrogram mag = np.abs(linear) # (1+n_fft//2, T) # mel spectrogram mel_basis = librosa.filters.mel(hp.sr, hp.n_fft, hp.n_mels) # (n_mels, 1+n_fft//2) mel = np.dot(mel_basis, mag) # (n_mels, t) # to decibel mel = 20 * np.log10(np.maximum(1e-5, mel)) mag = 20 * np.log10(np.maximum(1e-5, mag)) # normalize mel = np.clip((mel - hp.ref_db + hp.max_db) / hp.max_db, 1e-8, 1) mag = np.clip((mag - hp.ref_db + hp.max_db) / hp.max_db, 1e-8, 1) # Transpose mel = mel.T.astype(np.float32) # (T, n_mels) mag = mag.T.astype(np.float32) # (T, 1+n_fft//2) return mel, mag def spectrogram2wav(mag): '''# Generate wave file from spectrogram''' # transpose mag = mag.T # de-noramlize mag = (np.clip(mag, 0, 1) * hp.max_db) - hp.max_db + hp.ref_db # to amplitude mag = np.power(10.0, mag * 0.05) # wav reconstruction wav = griffin_lim(mag) # de-preemphasis wav = signal.lfilter([1], [1, -hp.preemphasis], wav) # trim wav, _ = librosa.effects.trim(wav) return wav.astype(np.float32) def griffin_lim(spectrogram): '''Applies Griffin-Lim's raw. ''' X_best = copy.deepcopy(spectrogram) for i in range(hp.n_iter): X_t = invert_spectrogram(X_best) est = librosa.stft(X_t, hp.n_fft, hp.hop_length, win_length=hp.win_length) phase = est / np.maximum(1e-8, np.abs(est)) X_best = spectrogram * phase X_t = invert_spectrogram(X_best) y = np.real(X_t) return y def invert_spectrogram(spectrogram): ''' spectrogram: [f, t] ''' return librosa.istft(spectrogram, hp.hop_length, win_length=hp.win_length, window="hann") def plot_alignment(alignment, gs): """Plots the alignment alignments: A list of (numpy) matrix of shape (encoder_steps, decoder_steps) gs : (int) global step """ fig, ax = plt.subplots() im = ax.imshow(alignment) # cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7]) fig.colorbar(im) plt.title('{} Steps'.format(gs)) plt.savefig('{}/alignment_{}k.png'.format(hp.logdir, gs // 1000), format='png') def learning_rate_decay(init_lr, global_step, warmup_steps=4000.): '''Noam scheme from tensor2tensor''' step = tf.cast(global_step + 1, dtype=tf.float32) return init_lr * warmup_steps ** 0.5 * tf.minimum(step * warmup_steps ** -1.5, step ** -0.5) def load_spectrograms(fpath): fname = os.path.basename(fpath) mel, mag = get_spectrograms(fpath) t = mel.shape[0] num_paddings = hp.r - (t % hp.r) if t % hp.r != 0 else 0 # for reduction mel = np.pad(mel, [[0, num_paddings], [0, 0]], mode="constant") mag = np.pad(mag, [[0, num_paddings], [0, 0]], mode="constant") return fname, mel.reshape((-1, hp.n_mels * hp.r)), mag