text: import math
  import numpy as np
  import librosa
  import vocoder.hparams as hp
  from scipy.signal import lfilter
  import soundfile as sf
  
  
  def label_2_float(x, bits) :
      return 2 * x / (2**bits - 1.) - 1.
  
  
  def float_2_label(x, bits) :
      assert abs(x).max() <= 1.0
      x = (x + 1.) * (2**bits - 1) / 2
      return x.clip(0, 2**bits - 1)
  
  
  def load_wav(path) :
      return librosa.load(str(path), sr=hp.sample_rate)[0]
  
  
  def save_wav(x, path) :
      sf.write(path, x.astype(np.float32), hp.sample_rate)
  
  
  def split_signal(x) :
      unsigned = x + 2**15
      coarse = unsigned // 256
      fine = unsigned % 256
      return coarse, fine
  
  
  def combine_signal(coarse, fine) :
      return coarse * 256 + fine - 2**15
  
  
  def encode_16bits(x) :
      return np.clip(x * 2**15, -2**15, 2**15 - 1).astype(np.int16)
  
  
  mel_basis = None
  
  
  def linear_to_mel(spectrogram):
      global mel_basis
      if mel_basis is None:
          mel_basis = build_mel_basis()
      return np.dot(mel_basis, spectrogram)
  
  
  def build_mel_basis():
      return librosa.filters.mel(hp.sample_rate, hp.n_fft, n_mels=hp.num_mels, fmin=hp.fmin)
  
  
  def normalize(S):
      return np.clip((S - hp.min_level_db) / -hp.min_level_db, 0, 1)
  
  
  def denormalize(S):
      return (np.clip(S, 0, 1) * -hp.min_level_db) + hp.min_level_db
  
  
  def amp_to_db(x):
      return 20 * np.log10(np.maximum(1e-5, x))
  
  
  def db_to_amp(x):
      return np.power(10.0, x * 0.05)
  
  
  def spectrogram(y):
      D = stft(y)
      S = amp_to_db(np.abs(D)) - hp.ref_level_db
      return normalize(S)
  
  
  def melspectrogram(y):
      D = stft(y)
      S = amp_to_db(linear_to_mel(np.abs(D)))
      return normalize(S)
  
  
  def stft(y):
      return librosa.stft(y=y, n_fft=hp.n_fft, hop_length=hp.hop_length, win_length=hp.win_length)
  
  
  def pre_emphasis(x):
      return lfilter([1, -hp.preemphasis], [1], x)
  
  
  def de_emphasis(x):
      return lfilter([1], [1, -hp.preemphasis], x)
  
  
  def encode_mu_law(x, mu) :
      mu = mu - 1
      fx = np.sign(x) * np.log(1 + mu * np.abs(x)) / np.log(1 + mu)
      return np.floor((fx + 1) / 2 * mu + 0.5)
  
  
  def decode_mu_law(y, mu, from_labels=True) :
      if from_labels: 
          y = label_2_float(y, math.log2(mu))
      mu = mu - 1
      x = np.sign(y) / mu * ((1 + mu) ** np.abs(y) - 1)
      return x
  
  