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, inv_preemphasize, k):
  	# wav = inv_preemphasis(wav, k, inv_preemphasize)
  	wav *= 32767 / max(0.01, np.max(np.abs(wav)))
  	wavfile.write(path, sr, wav.astype(np.int16))
  
  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 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]
  
  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)
  	D = _stft(wav, hparams)
  	S = _amp_to_db(np.abs(D)**hparams.magnitude_power, 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)
  	D = _stft(wav, hparams)
  	S = _amp_to_db(_linear_to_mel(np.abs(D)**hparams.magnitude_power, 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)**(1/hparams.magnitude_power) #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)**(1/hparams.magnitude_power), 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)
  
  ###########################################################################################
  # tensorflow Griffin-Lim
  # Thanks to @begeekmyfriend: https://github.com/begeekmyfriend/Tacotron-2/blob/mandarin-new/datasets/audio.py
  
  def inv_linear_spectrogram_tensorflow(spectrogram, hparams):
  	'''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.
  	'''
  	if hparams.signal_normalization:
  		D = _denormalize_tensorflow(spectrogram, hparams)
  	else:
  		D = linear_spectrogram
  
  	S = tf.pow(_db_to_amp_tensorflow(D + hparams.ref_level_db), (1/hparams.magnitude_power))
  	return _griffin_lim_tensorflow(tf.pow(S, hparams.power), hparams)
  
  def inv_mel_spectrogram_tensorflow(mel_spectrogram, hparams):
  	'''Builds computational graph to convert mel spectrogram to waveform using TensorFlow.
  	Unlike inv_mel_spectrogram, this does NOT invert the preemphasis. The caller should call
  	inv_preemphasis on the output after running the graph.
  	'''
  	if hparams.signal_normalization:
  		D = _denormalize_tensorflow(mel_spectrogram, hparams)
  	else:
  		D = mel_spectrogram
  
  	S = tf.pow(_db_to_amp_tensorflow(D + hparams.ref_level_db), (1/hparams.magnitude_power))
  	S = _mel_to_linear_tensorflow(S, hparams)  # Convert back to linear
  	return _griffin_lim_tensorflow(tf.pow(S, hparams.power), hparams)
  
  ###########################################################################################
  
  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 _griffin_lim_tensorflow(S, hparams):
  	'''TensorFlow implementation of Griffin-Lim
  	Based on https://github.com/Kyubyong/tensorflow-exercises/blob/master/Audio_Processing.ipynb
  	'''
  	with tf.variable_scope('griffinlim'):
  		# TensorFlow's stft and istft operate on a batch of spectrograms; create batch of size 1
  		S = tf.expand_dims(S, 0)
  		S_complex = tf.identity(tf.cast(S, dtype=tf.complex64))
  		y = tf.contrib.signal.inverse_stft(S_complex, hparams.win_size, get_hop_size(hparams), hparams.n_fft)
  		for i in range(hparams.griffin_lim_iters):
  			est = tf.contrib.signal.stft(y, hparams.win_size, get_hop_size(hparams), hparams.n_fft)
  			angles = est / tf.cast(tf.maximum(1e-8, tf.abs(est)), tf.complex64)
  			y = tf.contrib.signal.inverse_stft(S_complex * angles, hparams.win_size, get_hop_size(hparams), hparams.n_fft)
  	return tf.squeeze(y, 0)
  
  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, pad_mode='constant')
  
  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, pad_sides=1):
  	'''compute right padding (final frame) or both sides padding (first and final frames)
  	'''
  	assert pad_sides in (1, 2)
  	# return int(fsize // 2)
  	pad = (x.shape[0] // fshift + 1) * fshift - x.shape[0]
  	if pad_sides == 1:
  		return 0, pad
  	else:
  		return pad // 2, pad // 2 + pad % 2
  
  # 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 _mel_to_linear_tensorflow(mel_spectrogram, hparams):
  	global _inv_mel_basis
  	if _inv_mel_basis is None:
  		_inv_mel_basis = np.linalg.pinv(_build_mel_basis(hparams))
  	return tf.transpose(tf.maximum(1e-10, tf.matmul(tf.cast(_inv_mel_basis, tf.float32), tf.transpose(mel_spectrogram, [1, 0]))), [1, 0])
  
  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 _db_to_amp_tensorflow(x):
  	return tf.pow(tf.ones(tf.shape(x)) * 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 _denormalize_tensorflow(D, hparams):
  	if hparams.allow_clipping_in_normalization:
  		if hparams.symmetric_mels:
  			return (((tf.clip_by_value(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 ((tf.clip_by_value(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)
  