text: # -*- coding: utf-8 -*- #
  """*********************************************************************************************"""
  #   FileName     [ audio.py ]
  #   Synopsis     [ audio utility functions ]
  #   Author       [ Ting-Wei Liu (Andi611) ]
  #   Copyright    [ Copyleft(c), Speech Lab, NTU, Taiwan ]
  """*********************************************************************************************"""
  
  
  ###############
  # IMPORTATION #
  ###############
  import math
  import numpy as np
  import librosa
  import librosa.filters
  from scipy import signal
  from model.tacotron.config import config
  from scipy.io import wavfile
  
  
  #############
  # FUNCTIONS #
  #############
  
  
  def load_wav(path):
  	return librosa.core.load(path, sr=config.sample_rate)[0]
  
  
  def save_wav(wav, path):
  	wav *= 32767 / max(0.01, np.max(np.abs(wav)))
  	wavfile.write(path, config.sample_rate, wav.astype(np.int16))
  
  
  def preemphasis(x):
  	return signal.lfilter([1, -config.preemphasis], [1], x)
  
  
  def inv_preemphasis(x):
  	return signal.lfilter([1], [1, -config.preemphasis], x)
  
  
  def spectrogram(y):
  	D = _stft(preemphasis(y))
  	S = _amp_to_db(np.abs(D)) - config.ref_level_db
  	return _normalize(S)
  
  
  def inv_spectrogram(spectrogram):
  	"""
  		Converts spectrogram to waveform using librosa
  	"""
  	S = _db_to_amp(_denormalize(spectrogram) + config.ref_level_db)  # Convert back to linear
  	return inv_preemphasis(_griffin_lim(S ** config.power))          # Reconstruct phase
  
  
  def melspectrogram(y):
  	D = _stft(preemphasis(y))
  	S = _amp_to_db(_linear_to_mel(np.abs(D)))
  	return _normalize(S)
  
  
  def find_endpoint(wav, threshold_db=-40, min_silence_sec=0.8):
  	window_length = int(config.sample_rate * min_silence_sec)
  	hop_length = int(window_length / 4)
  	threshold = _db_to_amp(threshold_db)
  	for x in range(hop_length, len(wav) - window_length, hop_length):
  		if np.max(wav[x:x+window_length]) < threshold:
  			return x + hop_length
  	return len(wav)
  
  
  def _griffin_lim(S):
  	"""
  		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)
  	for i in range(config.griffin_lim_iters):
  		angles = np.exp(1j * np.angle(_stft(y)))
  		y = _istft(S_complex * angles)
  	return y
  
  
  def _stft(y):
  	n_fft, hop_length, win_length = _stft_parameters()
  	return librosa.stft(y=y, n_fft=n_fft, hop_length=hop_length, win_length=win_length)
  
  
  def _istft(y):
  	_, hop_length, win_length = _stft_parameters()
  	return librosa.istft(y, hop_length=hop_length, win_length=win_length)
  
  
  def _stft_parameters():
  	n_fft = (config.num_freq - 1) * 2
  	hop_length = int(config.frame_shift_ms / 1000 * config.sample_rate)
  	win_length = int(config.frame_length_ms / 1000 * config.sample_rate)
  	return n_fft, hop_length, win_length
  
  
  ########################
  # CONVERSION FUNCTIONS #
  ########################
  
  
  _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():
  	n_fft = (config.num_freq - 1) * 2
  	return librosa.filters.mel(config.sample_rate, n_fft, n_mels=config.num_mels)
  
  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 _normalize(S):
  	return np.clip((S - config.min_level_db) / -config.min_level_db, 0, 1)
  
  def _denormalize(S):
  	return (np.clip(S, 0, 1) * -config.min_level_db) + config.min_level_db
  