code: import math
  import os
  import random
  import torch
  import torch.utils.data
  import numpy as np
  from librosa.core import load
  from librosa.util import normalize
  from librosa.filters import mel as librosa_mel_fn
  
  MAX_WAV_VALUE = 32768.0
  
  
  def load_wav(full_path, sampling_rate=None):
      if os.path.splitext(full_path)[1] != '.npy':
          data, sampling_rate = load(full_path, sr=sampling_rate)
      else:
          a = np.load(full_path, allow_pickle=True).item()
          assert sampling_rate == a['rate']
          data = a['array']
      return data, sampling_rate
  
  
  def dynamic_range_compression(x, C=1, clip_val=1e-5):
      return np.log(np.clip(x, a_min=clip_val, a_max=None) * C)
  
  
  def dynamic_range_decompression(x, C=1):
      return np.exp(x) / C
  
  
  def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
      return torch.log(torch.clamp(x, min=clip_val) * C)
  
  
  def dynamic_range_decompression_torch(x, C=1):
      return torch.exp(x) / C
  
  
  def spectral_normalize_torch(magnitudes):
      output = dynamic_range_compression_torch(magnitudes)
      return output
  
  
  def spectral_de_normalize_torch(magnitudes):
      output = dynamic_range_decompression_torch(magnitudes)
      return output
  
  
  mel_basis = {}
  hann_window = {}
  
  
  def mel_spectrogram(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
      if torch.min(y) < -1.:
          print('min value is ', torch.min(y))
      if torch.max(y) > 1.:
          print('max value is ', torch.max(y))
  
      global mel_basis, hann_window
      if fmax not in mel_basis:
          mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
          mel_basis[str(fmax)+'_'+str(y.device)] = torch.from_numpy(mel).float().to(y.device)
          hann_window[str(y.device)] = torch.hann_window(win_size).to(y.device)
  
      y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
      y = y.squeeze(1)
  
      spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[str(y.device)],
                        center=center, pad_mode='reflect', normalized=False, onesided=True)
  
      spec = torch.sqrt(spec.pow(2).sum(-1)+(1e-9))
  
      spec = torch.matmul(mel_basis[str(fmax)+'_'+str(y.device)], spec)
      spec = spectral_normalize_torch(spec)
  
      return spec
  
  
  def get_dataset_filelist(a):
      ext = '.wav' if not a.input_wavs_npy else '.npy'
      with open(a.input_training_file, 'r', encoding='utf-8') as fi:
          training_files = [os.path.join(a.input_wavs_dir, x.split('|')[0] + ext)
                            for x in fi.read().split('\n') if len(x) > 0]
  
      with open(a.input_validation_file, 'r', encoding='utf-8') as fi:
          validation_files = [os.path.join(a.input_wavs_dir, x.split('|')[0] + ext)
                              for x in fi.read().split('\n') if len(x) > 0]
      return training_files, validation_files
  
  
  class MelDataset(torch.utils.data.Dataset):
      def __init__(self, training_files, segment_size, n_fft, num_mels,
                   hop_size, win_size, sampling_rate,  fmin, fmax, split=True, shuffle=True, n_cache_reuse=1,
                   device=None, fmax_loss=None, fine_tuning=False, base_mels_path=None):
          self.audio_files = training_files
          random.seed(1234)
          if shuffle:
              random.shuffle(self.audio_files)
          self.segment_size = segment_size
          self.sampling_rate = sampling_rate
          self.split = split
          self.n_fft = n_fft
          self.num_mels = num_mels
          self.hop_size = hop_size
          self.win_size = win_size
          self.fmin = fmin
          self.fmax = fmax
          self.fmax_loss = fmax_loss
          self.cached_wav = None
          self.n_cache_reuse = n_cache_reuse
          self._cache_ref_count = 0
          self.device = device
          self.fine_tuning = fine_tuning
          self.base_mels_path = base_mels_path
  
      def __getitem__(self, index):
          filename = self.audio_files[index]
          if self._cache_ref_count == 0:
              audio, sampling_rate = load_wav(filename, self.sampling_rate)
              if not self.fine_tuning:
                  audio = normalize(audio) * 0.95
              self.cached_wav = audio
              if sampling_rate != self.sampling_rate:
                  raise ValueError("{} SR doesn't match target {} SR".format(
                      sampling_rate, self.sampling_rate))
              self._cache_ref_count = self.n_cache_reuse
          else:
              audio = self.cached_wav
              self._cache_ref_count -= 1
  
          audio = torch.FloatTensor(audio)
          audio = audio.unsqueeze(0)
  
          if not self.fine_tuning:
              if self.split:
                  if audio.size(1) >= self.segment_size:
                      max_audio_start = audio.size(1) - self.segment_size
                      audio_start = random.randint(0, max_audio_start)
                      audio = audio[:, audio_start:audio_start+self.segment_size]
                  else:
                      audio = torch.nn.functional.pad(audio, (0, self.segment_size - audio.size(1)), 'constant')
  
              mel = mel_spectrogram(audio, self.n_fft, self.num_mels,
                                    self.sampling_rate, self.hop_size, self.win_size, self.fmin, self.fmax,
                                    center=False)
          else:
              mel = np.load(
                  os.path.join(self.base_mels_path, os.path.splitext(os.path.split(filename)[-1])[0] + '.npy'))
              mel = torch.from_numpy(mel)
  
              if len(mel.shape) < 3:
                  mel = mel.unsqueeze(0)
  
              if self.split:
                  frames_per_seg = math.ceil(self.segment_size / self.hop_size)
  
                  if audio.size(1) >= self.segment_size:
                      mel_start = random.randint(0, mel.size(2) - frames_per_seg - 1)
                      mel = mel[:, :, mel_start:mel_start + frames_per_seg]
                      audio = audio[:, mel_start * self.hop_size:(mel_start + frames_per_seg) * self.hop_size]
                  else:
                      mel = torch.nn.functional.pad(mel, (0, frames_per_seg - mel.size(2)), 'constant')
                      audio = torch.nn.functional.pad(audio, (0, self.segment_size - audio.size(1)), 'constant')
  
          mel_loss = mel_spectrogram(audio, self.n_fft, self.num_mels,
                                     self.sampling_rate, self.hop_size, self.win_size, self.fmin, self.fmax_loss,
                                     center=False)
  
          return (mel.squeeze(), audio.squeeze(0), filename, mel_loss.squeeze())
  
      def __len__(self):
          return len(self.audio_files)
  
apis: numpy.load torch.FloatTensor torch.min torch.exp numpy.exp numpy.clip torch.from_numpy torch.max torch.hann_window torch.clamp