text: '''
  Source samples from WaveGlow, https://github.com/NVIDIA/waveglow
  Source samples from Tacotron2, https://github.com/NVIDIA/tacotron2
  '''
  
  
  import librosa
  import torch
  import numpy as np
  from scipy.signal import get_window
  import librosa.util as librosa_util
  
  #from audio_processing import window_sumsquare
  def window_sumsquare(window, n_frames, hop_length=200, win_length=800,
                       n_fft=800, dtype=np.float32, norm=None):
      """
      # from librosa 0.6
      Compute the sum-square envelope of a window function at a given hop length.
      This is used to estimate modulation effects induced by windowing
      observations in short-time fourier transforms.
      Parameters
      ----------
      window : string, tuple, number, callable, or list-like
          Window specification, as in `get_window`
      n_frames : int > 0
          The number of analysis frames
      hop_length : int > 0
          The number of samples to advance between frames
      win_length : [optional]
          The length of the window function.  By default, this matches `n_fft`.
      n_fft : int > 0
          The length of each analysis frame.
      dtype : np.dtype
          The data type of the output
      Returns
      -------
      wss : np.ndarray, shape=`(n_fft + hop_length * (n_frames - 1))`
          The sum-squared envelope of the window function
      """
      if win_length is None:
          win_length = n_fft
  
      n = n_fft + hop_length * (n_frames - 1)
      x = np.zeros(n, dtype=dtype)
  
      # Compute the squared window at the desired length
      win_sq = get_window(window, win_length, fftbins=True)
      win_sq = librosa_util.normalize(win_sq, norm=norm)**2
      win_sq = librosa_util.pad_center(win_sq, n_fft)
  
      # Fill the envelope
      for i in range(n_frames):
          sample = i * hop_length
          x[sample:min(n, sample + n_fft)] += win_sq[:max(0, min(n_fft, n - sample))]
      return x
  
  def dynamic_range_compression(x, C=1, clip_val=1e-5):
      """
      PARAMS
      ------
      C: compression factor
      """
      return torch.log(torch.clamp(x, min=clip_val) * C)
  
  
  def dynamic_range_decompression(x, C=1):
      """
      PARAMS
      ------
      C: compression factor used to compress
      """
      return torch.exp(x) / C
  
  """
  BSD 3-Clause License
  Copyright (c) 2017, Prem Seetharaman
  All rights reserved.
  * Redistribution and use in source and binary forms, with or without
    modification, are permitted provided that the following conditions are met:
  * Redistributions of source code must retain the above copyright notice,
    this list of conditions and the following disclaimer.
  * Redistributions in binary form must reproduce the above copyright notice, this
    list of conditions and the following disclaimer in the
    documentation and/or other materials provided with the distribution.
  * Neither the name of the copyright holder nor the names of its
    contributors may be used to endorse or promote products derived from this
    software without specific prior written permission.
  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
  ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
  WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
  DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR
  ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
  (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
  LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
  ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
  (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
  SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
  """
  
  import torch
  import numpy as np
  import torch.nn.functional as F
  from torch.autograd import Variable
  from scipy.signal import get_window
  from librosa.util import pad_center, tiny
  
  
  class STFT(torch.nn.Module):
      """adapted from Prem Seetharaman's https://github.com/pseeth/pytorch-stft"""
      def __init__(self, filter_length=800, hop_length=200, win_length=800,
                   window='hann'):
          super(STFT, self).__init__()
          self.filter_length = filter_length
          self.hop_length = hop_length
          self.win_length = win_length
          self.window = window
          self.forward_transform = None
          scale = self.filter_length / self.hop_length
          fourier_basis = np.fft.fft(np.eye(self.filter_length))
  
          cutoff = int((self.filter_length / 2 + 1))
          fourier_basis = np.vstack([np.real(fourier_basis[:cutoff, :]),
                                     np.imag(fourier_basis[:cutoff, :])])
  
          forward_basis = torch.FloatTensor(fourier_basis[:, None, :])
          inverse_basis = torch.FloatTensor(
              np.linalg.pinv(scale * fourier_basis).T[:, None, :])
  
          if window is not None:
              assert(filter_length >= win_length)
              # get window and zero center pad it to filter_length
              fft_window = get_window(window, win_length, fftbins=True)
              fft_window = pad_center(fft_window, filter_length)
              fft_window = torch.from_numpy(fft_window).float()
  
              # window the bases
              forward_basis *= fft_window
              inverse_basis *= fft_window
  
          self.register_buffer('forward_basis', forward_basis.float())
          self.register_buffer('inverse_basis', inverse_basis.float())
  
      def transform(self, input_data):
          num_batches = input_data.size(0)
          num_samples = input_data.size(1)
  
          self.num_samples = num_samples
  
          # similar to librosa, reflect-pad the input
          input_data = input_data.view(num_batches, 1, num_samples)
          input_data = F.pad(
              input_data.unsqueeze(1),
              (int(self.filter_length / 2), int(self.filter_length / 2), 0, 0),
              mode='reflect')
          input_data = input_data.squeeze(1)
  
          forward_transform = F.conv1d(
              input_data,
              Variable(self.forward_basis, requires_grad=False),
              stride=self.hop_length,
              padding=0)
  
          cutoff = int((self.filter_length / 2) + 1)
          real_part = forward_transform[:, :cutoff, :]
          imag_part = forward_transform[:, cutoff:, :]
  
          magnitude = torch.sqrt(real_part**2 + imag_part**2)
          phase = torch.autograd.Variable(
              torch.atan2(imag_part.data, real_part.data))
  
          return magnitude, phase
  
      def inverse(self, magnitude, phase):
          recombine_magnitude_phase = torch.cat(
              [magnitude*torch.cos(phase), magnitude*torch.sin(phase)], dim=1)
  
          inverse_transform = F.conv_transpose1d(
              recombine_magnitude_phase,
              Variable(self.inverse_basis, requires_grad=False),
              stride=self.hop_length,
              padding=0)
  
          if self.window is not None:
              window_sum = window_sumsquare(
                  self.window, magnitude.size(-1), hop_length=self.hop_length,
                  win_length=self.win_length, n_fft=self.filter_length,
                  dtype=np.float32)
              # remove modulation effects
              approx_nonzero_indices = torch.from_numpy(
                  np.where(window_sum > tiny(window_sum))[0])
              window_sum = torch.autograd.Variable(
                  torch.from_numpy(window_sum), requires_grad=False)
              window_sum = window_sum.cuda() if magnitude.is_cuda else window_sum
              inverse_transform[:, :, approx_nonzero_indices] /= window_sum[approx_nonzero_indices]
  
              # scale by hop ratio
              inverse_transform *= float(self.filter_length) / self.hop_length
  
          inverse_transform = inverse_transform[:, :, int(self.filter_length/2):]
          inverse_transform = inverse_transform[:, :, :-int(self.filter_length/2):]
  
          return inverse_transform
  
      def forward(self, input_data):
          self.magnitude, self.phase = self.transform(input_data)
          reconstruction = self.inverse(self.magnitude, self.phase)
          return reconstruction
  
  from librosa.filters import mel as librosa_mel_fn
  import librosa.util as librosa_util
  class TacotronSTFT(torch.nn.Module):
      def __init__(self, filter_length=1024, hop_length=256, win_length=1024,
                   n_mel_channels=80, sampling_rate=22050, mel_fmin=0.0,
                   mel_fmax=8000.0):
          super(TacotronSTFT, self).__init__()
          self.n_mel_channels = n_mel_channels
          self.sampling_rate = sampling_rate
          self.stft_fn = STFT(filter_length, hop_length, win_length)
          mel_basis = librosa_mel_fn(
              sampling_rate, filter_length, n_mel_channels, mel_fmin, mel_fmax)
          mel_basis = torch.from_numpy(mel_basis).float()
          self.register_buffer('mel_basis', mel_basis)
  
      def spectral_normalize(self, magnitudes):
          output = dynamic_range_compression(magnitudes)
          return output
  
      def spectral_de_normalize(self, magnitudes):
          output = dynamic_range_decompression(magnitudes)
          return output
  
      def mel_spectrogram(self, y):
          """Computes mel-spectrograms from a batch of waves
          PARAMS
          ------
          y: Variable(torch.FloatTensor) with shape (B, T) in range [-1, 1]
  
          RETURNS
          -------
          mel_output: torch.FloatTensor of shape (B, n_mel_channels, T)
          """
          assert(torch.min(y.data) >= -1)
          assert(torch.max(y.data) <= 1)
  
          magnitudes, phases = self.stft_fn.transform(y)
          magnitudes = magnitudes.data
          mel_output = torch.matmul(self.mel_basis, magnitudes)
          mel_output = self.spectral_normalize(mel_output)
          return mel_output
  
  def load_wav_to_torch(full_path):
      #sampling_rate, data = read(full_path)
      data,sampling_rate = librosa.load(full_path,sr=22050,mono=True)
      return torch.FloatTensor(data.astype(np.float32)), sampling_rate
  
  def get_mel(filename,stft):
              audio, sampling_rate = load_wav_to_torch(filename)
              if sampling_rate != stft.sampling_rate:
                  raise ValueError("{} {} SR doesn't match target {} SR".format(
                      sampling_rate, stft.sampling_rate))
              audio_norm = audio / 1.0
              audio_norm = audio_norm.unsqueeze(0)
              audio_norm = torch.autograd.Variable(audio_norm, requires_grad=False)
              melspec = stft.mel_spectrogram(audio_norm)
              melspec = torch.squeeze(melspec, 0)
              return melspec
  
  '''
  import matplotlib.pyplot as plt
  
  # Sample usage
  stft = TacotronSTFT(
              1024, 256,1024 ,
               80, 16000,0,8000)
  
  mel_data = get_mel("sample.wav", stft)
  
  fig, ax = plt.subplots(figsize=(12, 3))
  im = ax.imshow(mel_data, aspect="auto", origin="lower",
                 interpolation='none')
  plt.colorbar(im, ax=ax)
  plt.xlabel("Frames")
  plt.ylabel("Channels")
  plt.tight_layout()
  
  fig.canvas.draw()
  plt.savefig('sample_spectrogram.png')
  '''
  