text: import librosa
  import librosa.filters
  import numpy as np
  import tensorflow as tf
  from scipy import signal
  from scipy.io import wavfile
  
  
  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
  
  #From https://github.com/r9y9/wavenet_vocoder/blob/master/audio.py
  def start_and_end_indices(quantized, silence_threshold=2):
      for start in range(quantized.size):
          if abs(quantized[start] - 127) > silence_threshold:
              break
      for end in range(quantized.size - 1, 1, -1):
          if abs(quantized[end] - 127) > silence_threshold:
              break
      
      assert abs(quantized[start] - 127) > silence_threshold
      assert abs(quantized[end] - 127) > silence_threshold
      
      return start, end
  
  def get_hop_size(hparams):
      hop_size = hparams.hop_size
      if hop_size is None:
          assert hparams.frame_shift_ms is not None
          hop_size = int(hparams.frame_shift_ms / 1000 * hparams.sample_rate)
      return hop_size
  
  def linearspectrogram(wav, hparams):
      D = _stft(preemphasis(wav, hparams.preemphasis, hparams.preemphasize), hparams)
      S = _amp_to_db(np.abs(D), hparams) - hparams.ref_level_db
      
      if hparams.signal_normalization:
          return _normalize(S, hparams)
      return S
  
  def melspectrogram(wav, hparams):
      D = _stft(preemphasis(wav, hparams.preemphasis, hparams.preemphasize), hparams)
      S = _amp_to_db(_linear_to_mel(np.abs(D), hparams), hparams) - hparams.ref_level_db
      
      if hparams.signal_normalization:
          return _normalize(S, hparams)
      return S
  
  def inv_linear_spectrogram(linear_spectrogram, hparams):
      """Converts linear spectrogram to waveform using librosa"""
      if hparams.signal_normalization:
          D = _denormalize(linear_spectrogram, hparams)
      else:
          D = linear_spectrogram
      
      S = _db_to_amp(D + hparams.ref_level_db) #Convert back to linear
      
      if hparams.use_lws:
          processor = _lws_processor(hparams)
          D = processor.run_lws(S.astype(np.float64).T ** hparams.power)
          y = processor.istft(D).astype(np.float32)
          return inv_preemphasis(y, hparams.preemphasis, hparams.preemphasize)
      else:
          return inv_preemphasis(_griffin_lim(S ** hparams.power, hparams), hparams.preemphasis, hparams.preemphasize)
  
  def inv_mel_spectrogram(mel_spectrogram, hparams):
      """Converts mel spectrogram to waveform using librosa"""
      if hparams.signal_normalization:
          D = _denormalize(mel_spectrogram, hparams)
      else:
          D = mel_spectrogram
      
      S = _mel_to_linear(_db_to_amp(D + hparams.ref_level_db), hparams)  # Convert back to linear
      
      if hparams.use_lws:
          processor = _lws_processor(hparams)
          D = processor.run_lws(S.astype(np.float64).T ** hparams.power)
          y = processor.istft(D).astype(np.float32)
          return inv_preemphasis(y, hparams.preemphasis, hparams.preemphasize)
      else:
          return inv_preemphasis(_griffin_lim(S ** hparams.power, hparams), hparams.preemphasis, hparams.preemphasize)
  
  def _lws_processor(hparams):
      import lws
      return lws.lws(hparams.n_fft, get_hop_size(hparams), fftsize=hparams.win_size, mode="speech")
  
  def _griffin_lim(S, hparams):
      """librosa implementation of Griffin-Lim
      Based on https://github.com/librosa/librosa/issues/434
      """
      angles = np.exp(2j * np.pi * np.random.rand(*S.shape))
      S_complex = np.abs(S).astype(np.complex)
      y = _istft(S_complex * angles, hparams)
      for i in range(hparams.griffin_lim_iters):
          angles = np.exp(1j * np.angle(_stft(y, hparams)))
          y = _istft(S_complex * angles, hparams)
      return y
  
  def _stft(y, hparams):
      if hparams.use_lws:
          return _lws_processor(hparams).stft(y).T
      else:
          return librosa.stft(y=y, n_fft=hparams.n_fft, hop_length=get_hop_size(hparams), win_length=hparams.win_size)
  
  def _istft(y, hparams):
      return librosa.istft(y, hop_length=get_hop_size(hparams), win_length=hparams.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
  _inv_mel_basis = None
  
  def _linear_to_mel(spectogram, hparams):
      global _mel_basis
      if _mel_basis is None:
          _mel_basis = _build_mel_basis(hparams)
      return np.dot(_mel_basis, spectogram)
  
  def _mel_to_linear(mel_spectrogram, hparams):
      global _inv_mel_basis
      if _inv_mel_basis is None:
          _inv_mel_basis = np.linalg.pinv(_build_mel_basis(hparams))
      return np.maximum(1e-10, np.dot(_inv_mel_basis, mel_spectrogram))
  
  def _build_mel_basis(hparams):
      assert hparams.fmax <= hparams.sample_rate // 2
      return librosa.filters.mel(hparams.sample_rate, hparams.n_fft, n_mels=hparams.num_mels,
                                 fmin=hparams.fmin, fmax=hparams.fmax)
  
  def _amp_to_db(x, hparams):
      min_level = np.exp(hparams.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, hparams):
      if hparams.allow_clipping_in_normalization:
          if hparams.symmetric_mels:
              return np.clip((2 * hparams.max_abs_value) * ((S - hparams.min_level_db) / (-hparams.min_level_db)) - hparams.max_abs_value,
                             -hparams.max_abs_value, hparams.max_abs_value)
          else:
              return np.clip(hparams.max_abs_value * ((S - hparams.min_level_db) / (-hparams.min_level_db)), 0, hparams.max_abs_value)
      
      assert S.max() <= 0 and S.min() - hparams.min_level_db >= 0
      if hparams.symmetric_mels:
          return (2 * hparams.max_abs_value) * ((S - hparams.min_level_db) / (-hparams.min_level_db)) - hparams.max_abs_value
      else:
          return hparams.max_abs_value * ((S - hparams.min_level_db) / (-hparams.min_level_db))
  
  def _denormalize(D, hparams):
      if hparams.allow_clipping_in_normalization:
          if hparams.symmetric_mels:
              return (((np.clip(D, -hparams.max_abs_value,
                                hparams.max_abs_value) + hparams.max_abs_value) * -hparams.min_level_db / (2 * hparams.max_abs_value))
                      + hparams.min_level_db)
          else:
              return ((np.clip(D, 0, hparams.max_abs_value) * -hparams.min_level_db / hparams.max_abs_value) + hparams.min_level_db)
      
      if hparams.symmetric_mels:
          return (((D + hparams.max_abs_value) * -hparams.min_level_db / (2 * hparams.max_abs_value)) + hparams.min_level_db)
      else:
          return ((D * -hparams.min_level_db / hparams.max_abs_value) + hparams.min_level_db)
  
  
  def trim_silence(wav, hparams):
      '''Trim leading and trailing silence
      Useful for M-AILABS dataset if we choose to trim the extra 0.5 silence at beginning and end.
      '''
      # Thanks @begeekmyfriend and @lautjy for pointing out the params contradiction. These params are separate and tunable per dataset.
      return librosa.effects.trim(wav, top_db=hparams.trim_top_db, frame_length=hparams.trim_fft_size,
                                  hop_length=hparams.trim_hop_size)[0]
  