def _preemphasis(x):
    return signal.lfilter([1, -hparams.preemphasis], [1], x) 

def _inv_preemphasis(x):
    return signal.lfilter([1], [1, -hparams.preemphasis], x) 

def _inv_preemphasis(x):
    N = tf.shape(x)[0]
    i = tf.constant(0)
    W = tf.zeros(shape=tf.shape(x), dtype=tf.float32)

    def condition(i, y):
        return tf.less(i, N)

    def body(i, y):
        tmp = tf.slice(x, [0], [i + 1])
        tmp = tf.concat([tf.zeros([N - i - 1]), tmp], -1)
        y = hparams.preemphasis * y + tmp
        i = tf.add(i, 1)
        return [i, y]

    final = tf.while_loop(condition, body, [i, W])

    y = final[1]

    return y 

def preemphasis(x):
  return scipy.signal.lfilter([1, -hparams.preemphasis], [1], x) 

def inv_preemphasis(x):
  return scipy.signal.lfilter([1], [1, -hparams.preemphasis], x) 

def spectrogram(y):
  D = _stft(preemphasis(y))
  S = _amp_to_db(np.abs(D)) - hparams.ref_level_db
  return _normalize(S) 

def inv_spectrogram_tensorflow(spectrogram):
  '''Builds computational graph to convert spectrogram to waveform using TensorFlow.

  Unlike inv_spectrogram, this does NOT invert the preemphasis. The caller should call
  inv_preemphasis on the output after running the graph.
  '''
  S = _db_to_amp_tensorflow(_denormalize_tensorflow(spectrogram) + hparams.ref_level_db)
  return _griffin_lim_tensorflow(tf.pow(S, hparams.power)) 

def melspectrogram(y):
  D = _stft(preemphasis(y))
  S = _amp_to_db(_linear_to_mel(np.abs(D))) - hparams.ref_level_db
  return _normalize(S) 

def preemphasis(x):
	return signal.lfilter([1, -hparams.preemphasis], [1], x) 

def inv_preemphasis(x):
	return signal.lfilter([1], [1, -hparams.preemphasis], x) 

def preemphasis(x):
	return signal.lfilter([1, -hparams.preemphasis], [1], x) 

def inv_preemphasis(x):
	return signal.lfilter([1], [1, -hparams.preemphasis], x) 

def preemphasis(x):
  return signal.lfilter([1, -hparams.preemphasis], [1], x) 

def inv_preemphasis(x):
  return signal.lfilter([1], [1, -hparams.preemphasis], x) 

def spectrogram(y):
  D = _stft(preemphasis(y))
  S = _amp_to_db(np.abs(D)) - hparams.ref_level_db
  return _normalize(S) 

def inv_spectrogram_tensorflow(spectrogram):
  '''Builds computational graph to convert spectrogram to waveform using TensorFlow.

  Unlike inv_spectrogram, this does NOT invert the preemphasis. The caller should call
  inv_preemphasis on the output after running the graph.
  '''
  S = _db_to_amp_tensorflow(_denormalize_tensorflow(spectrogram) + hparams.ref_level_db)
  return _griffin_lim_tensorflow(tf.pow(S, hparams.power)) 

def melspectrogram(y):
  D = _stft(preemphasis(y))
  S = _amp_to_db(_linear_to_mel(np.abs(D))) - hparams.ref_level_db
  return _normalize(S) 

def preemphasis(x):
  return scipy.signal.lfilter([1, -hparams.preemphasis], [1], x) 

def inv_preemphasis(x):
  return scipy.signal.lfilter([1], [1, -hparams.preemphasis], x) 

def spectrogram(y):
  D = _stft(preemphasis(y))
  S = _amp_to_db(np.abs(D)) - hparams.ref_level_db
  return _normalize(S) 

def inv_spectrogram_tensorflow(spectrogram):
  '''Builds computational graph to convert spectrogram to waveform using TensorFlow.

  Unlike inv_spectrogram, this does NOT invert the preemphasis. The caller should call
  inv_preemphasis on the output after running the graph.
  '''
  S = _db_to_amp_tensorflow(_denormalize_tensorflow(spectrogram) + hparams.ref_level_db)
  return _griffin_lim_tensorflow(tf.pow(S, hparams.power)) 

def melspectrogram(y):
  D = _stft(preemphasis(y))
  S = _amp_to_db(_linear_to_mel(np.abs(D))) - hparams.ref_level_db
  return _normalize(S) 

def preemphasis(x):
    from nnmnkwii.preprocessing import preemphasis
    return preemphasis(x, hparams.preemphasis) 

def inv_preemphasis(x):
    from nnmnkwii.preprocessing import inv_preemphasis
    return inv_preemphasis(x, hparams.preemphasis) 

def spectrogram(y):
    D = _lws_processor().stft(preemphasis(y)).T
    S = _amp_to_db(np.abs(D)) - hparams.ref_level_db
    return _normalize(S) 

def melspectrogram(y):
    D = _lws_processor().stft(preemphasis(y)).T
    S = _amp_to_db(_linear_to_mel(np.abs(D))) - hparams.ref_level_db
    if not hparams.allow_clipping_in_normalization:
        assert S.max() <= 0 and S.min() - hparams.min_level_db >= 0
    return _normalize(S) 

def preemphasis(x):
	return signal.lfilter([1, -hparams.preemphasis], [1], x) 

def inv_preemphasis(x):
	return signal.lfilter([1], [1, -hparams.preemphasis], x) 

def preemphasis(x):
	return signal.lfilter([1, -hparams.preemphasis], [1], x) 

def inv_preemphasis(x):
	return signal.lfilter([1], [1, -hparams.preemphasis], x)