text: # -*- coding: utf-8 -*-
  #/usr/bin/python2
  '''
  By kyubyong park. kbpark.linguist@gmail.com. 
  https://www.github.com/kyubyong/dc_tts
  '''
  from __future__ import print_function, division
  
  import numpy as np
  import librosa
  import os, copy
  import matplotlib
  matplotlib.use('pdf')
  import matplotlib.pyplot as plt
  from scipy import signal
  
  from hyperparams import Hyperparams as hp
  import tensorflow as tf
  
  def get_spectrograms(fpath):
      '''Parse the wave file in `fpath` and
      Returns normalized melspectrogram and linear spectrogram.
  
      Args:
        fpath: A string. The full path of a sound file.
  
      Returns:
        mel: A 2d array of shape (T, n_mels) and dtype of float32.
        mag: A 2d array of shape (T, 1+n_fft/2) and dtype of float32.
      '''
      # 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 linear magnitude spectrogram
  
      Args:
        mag: A numpy array of (T, 1+n_fft//2)
  
      Returns:
        wav: A 1-D numpy array.
      '''
      # 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**hp.power)
  
      # 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):
      '''Applies inverse fft.
      Args:
        spectrogram: [1+n_fft//2, t]
      '''
      return librosa.istft(spectrogram, hp.hop_length, win_length=hp.win_length, window="hann")
  
  def plot_alignment(alignment, gs, dir=hp.logdir):
      """Plots the alignment.
  
      Args:
        alignment: A numpy array with shape of (encoder_steps, decoder_steps)
        gs: (int) global step.
        dir: Output path.
      """
      if not os.path.exists(dir): os.mkdir(dir)
  
      fig, ax = plt.subplots()
      im = ax.imshow(alignment)
  
      fig.colorbar(im)
      plt.title('{} Steps'.format(gs))
      plt.savefig('{}/alignment_{}.png'.format(dir, gs), format='png')
      plt.close(fig)
  
  def guided_attention(g=0.2):
      '''Guided attention. Refer to page 3 on the paper.'''
      W = np.zeros((hp.max_N, hp.max_T), dtype=np.float32)
      for n_pos in range(W.shape[0]):
          for t_pos in range(W.shape[1]):
              W[n_pos, t_pos] = 1 - np.exp(-(t_pos / float(hp.max_T) - n_pos / float(hp.max_N)) ** 2 / (2 * g * g))
      return W
  
  def learning_rate_decay(init_lr, global_step, warmup_steps = 4000.0):
      '''Noam scheme from tensor2tensor'''
      step = tf.to_float(global_step + 1)
      return init_lr * warmup_steps**0.5 * tf.minimum(step * warmup_steps**-1.5, step**-0.5)
  
  def load_spectrograms(fpath):
      '''Read the wave file in `fpath`
      and extracts spectrograms'''
  
      fname = os.path.basename(fpath)
      mel, mag = get_spectrograms(fpath)
      t = mel.shape[0]
  
      # Marginal padding for reduction shape sync.
      num_paddings = hp.r - (t % hp.r) if t % hp.r != 0 else 0
      mel = np.pad(mel, [[0, num_paddings], [0, 0]], mode="constant")
      mag = np.pad(mag, [[0, num_paddings], [0, 0]], mode="constant")
  
      # Reduction
      mel = mel[::hp.r, :]
      return fname, mel, mag
  
  