text: import random
  import numpy as np
  import torch
  import torch.utils.data
  
  import commons 
  from utils import load_wav_to_torch, load_filepaths_and_text
  from text import text_to_sequence, cmudict
  from text.symbols import symbols
  
  
  class TextMelLoader(torch.utils.data.Dataset):
      """
          1) loads audio,text pairs
          2) normalizes text and converts them to sequences of one-hot vectors
          3) computes mel-spectrograms from audio files.
      """
      def __init__(self, audiopaths_and_text, hparams):
          self.audiopaths_and_text = load_filepaths_and_text(audiopaths_and_text)
          self.text_cleaners = hparams.text_cleaners
          self.max_wav_value = hparams.max_wav_value
          self.sampling_rate = hparams.sampling_rate
          self.load_mel_from_disk = hparams.load_mel_from_disk
          self.add_noise = hparams.add_noise
          self.add_blank = getattr(hparams, "add_blank", False) # improved version
          if getattr(hparams, "cmudict_path", None) is not None:
            self.cmudict = cmudict.CMUDict(hparams.cmudict_path)
          self.stft = commons.TacotronSTFT(
              hparams.filter_length, hparams.hop_length, hparams.win_length,
              hparams.n_mel_channels, hparams.sampling_rate, hparams.mel_fmin,
              hparams.mel_fmax)
          random.seed(1234)
          random.shuffle(self.audiopaths_and_text)
  
      def get_mel_text_pair(self, audiopath_and_text):
          # separate filename and text
          audiopath, text = audiopath_and_text[0], audiopath_and_text[1]
          text = self.get_text(text)
          mel = self.get_mel(audiopath)
          return (text, mel)
  
      def get_mel(self, filename):
          if not self.load_mel_from_disk:
              audio, sampling_rate = load_wav_to_torch(filename)
              if sampling_rate != self.stft.sampling_rate:
                  raise ValueError("{} {} SR doesn't match target {} SR".format(
                      sampling_rate, self.stft.sampling_rate))
              if self.add_noise:
                  audio = audio + torch.rand_like(audio)
              audio_norm = audio / self.max_wav_value
              audio_norm = audio_norm.unsqueeze(0)
              melspec = self.stft.mel_spectrogram(audio_norm)
              melspec = torch.squeeze(melspec, 0)
          else:
              melspec = torch.from_numpy(np.load(filename))
              assert melspec.size(0) == self.stft.n_mel_channels, (
                  'Mel dimension mismatch: given {}, expected {}'.format(
                      melspec.size(0), self.stft.n_mel_channels))
  
          return melspec
  
      def get_text(self, text):
          text_norm = text_to_sequence(text, self.text_cleaners, getattr(self, "cmudict", None))
          if self.add_blank:
              text_norm = commons.intersperse(text_norm, len(symbols)) # add a blank token, whose id number is len(symbols)
          text_norm = torch.IntTensor(text_norm)
          return text_norm
  
      def __getitem__(self, index):
          return self.get_mel_text_pair(self.audiopaths_and_text[index])
  
      def __len__(self):
          return len(self.audiopaths_and_text)
  
  
  class TextMelCollate():
      """ Zero-pads model inputs and targets based on number of frames per step
      """
      def __init__(self, n_frames_per_step=1):
          self.n_frames_per_step = n_frames_per_step
  
      def __call__(self, batch):
          """Collate's training batch from normalized text and mel-spectrogram
          PARAMS
          ------
          batch: [text_normalized, mel_normalized]
          """
          # Right zero-pad all one-hot text sequences to max input length
          input_lengths, ids_sorted_decreasing = torch.sort(
              torch.LongTensor([len(x[0]) for x in batch]),
              dim=0, descending=True)
          max_input_len = input_lengths[0]
  
          text_padded = torch.LongTensor(len(batch), max_input_len)
          text_padded.zero_()
          for i in range(len(ids_sorted_decreasing)):
              text = batch[ids_sorted_decreasing[i]][0]
              text_padded[i, :text.size(0)] = text
  
          # Right zero-pad mel-spec
          num_mels = batch[0][1].size(0)
          max_target_len = max([x[1].size(1) for x in batch])
          if max_target_len % self.n_frames_per_step != 0:
              max_target_len += self.n_frames_per_step - max_target_len % self.n_frames_per_step
              assert max_target_len % self.n_frames_per_step == 0
  
          # include mel padded
          mel_padded = torch.FloatTensor(len(batch), num_mels, max_target_len)
          mel_padded.zero_()
          output_lengths = torch.LongTensor(len(batch))
          for i in range(len(ids_sorted_decreasing)):
              mel = batch[ids_sorted_decreasing[i]][1]
              mel_padded[i, :, :mel.size(1)] = mel
              output_lengths[i] = mel.size(1)
  
          return text_padded, input_lengths, mel_padded, output_lengths
  
  
  """Multi speaker version"""
  class TextMelSpeakerLoader(torch.utils.data.Dataset):
      """
          1) loads audio, speaker_id, text pairs
          2) normalizes text and converts them to sequences of one-hot vectors
          3) computes mel-spectrograms from audio files.
      """
      def __init__(self, audiopaths_sid_text, hparams):
          self.audiopaths_sid_text = load_filepaths_and_text(audiopaths_sid_text)
          self.text_cleaners = hparams.text_cleaners
          self.max_wav_value = hparams.max_wav_value
          self.sampling_rate = hparams.sampling_rate
          self.load_mel_from_disk = hparams.load_mel_from_disk
          self.add_noise = hparams.add_noise
          self.add_blank = getattr(hparams, "add_blank", False) # improved version
          self.min_text_len = getattr(hparams, "min_text_len", 1)
          self.max_text_len = getattr(hparams, "max_text_len", 190)
          if getattr(hparams, "cmudict_path", None) is not None:
            self.cmudict = cmudict.CMUDict(hparams.cmudict_path)
          self.stft = commons.TacotronSTFT(
              hparams.filter_length, hparams.hop_length, hparams.win_length,
              hparams.n_mel_channels, hparams.sampling_rate, hparams.mel_fmin,
              hparams.mel_fmax)
  
          self._filter_text_len()
          random.seed(1234)
          random.shuffle(self.audiopaths_sid_text)
  
      def _filter_text_len(self):
        audiopaths_sid_text_new = []
        for audiopath, sid, text in self.audiopaths_sid_text:
          if self.min_text_len <= len(text) and len(text) <= self.max_text_len:
            audiopaths_sid_text_new.append([audiopath, sid, text])
        self.audiopaths_sid_text = audiopaths_sid_text_new
  
      def get_mel_text_speaker_pair(self, audiopath_sid_text):
          # separate filename, speaker_id and text
          audiopath, sid, text = audiopath_sid_text[0], audiopath_sid_text[1], audiopath_sid_text[2]
          text = self.get_text(text)
          mel = self.get_mel(audiopath)
          sid = self.get_sid(sid)
          return (text, mel, sid)
  
      def get_mel(self, filename):
          if not self.load_mel_from_disk:
              audio, sampling_rate = load_wav_to_torch(filename)
              if sampling_rate != self.stft.sampling_rate:
                  raise ValueError("{} {} SR doesn't match target {} SR".format(
                      sampling_rate, self.stft.sampling_rate))
              if self.add_noise:
                  audio = audio + torch.rand_like(audio)
              audio_norm = audio / self.max_wav_value
              audio_norm = audio_norm.unsqueeze(0)
              melspec = self.stft.mel_spectrogram(audio_norm)
              melspec = torch.squeeze(melspec, 0)
          else:
              melspec = torch.from_numpy(np.load(filename))
              assert melspec.size(0) == self.stft.n_mel_channels, (
                  'Mel dimension mismatch: given {}, expected {}'.format(
                      melspec.size(0), self.stft.n_mel_channels))
  
          return melspec
  
      def get_text(self, text):
          text_norm = text_to_sequence(text, self.text_cleaners, getattr(self, "cmudict", None))
          if self.add_blank:
              text_norm = commons.intersperse(text_norm, len(symbols)) # add a blank token, whose id number is len(symbols)
          text_norm = torch.IntTensor(text_norm)
          return text_norm
  
      def get_sid(self, sid):
          sid = torch.IntTensor([int(sid)])
          return sid
  
      def __getitem__(self, index):
          return self.get_mel_text_speaker_pair(self.audiopaths_sid_text[index])
  
      def __len__(self):
          return len(self.audiopaths_sid_text)
  
  
  class TextMelSpeakerCollate():
      """ Zero-pads model inputs and targets based on number of frames per step
      """
      def __init__(self, n_frames_per_step=1):
          self.n_frames_per_step = n_frames_per_step
  
      def __call__(self, batch):
          """Collate's training batch from normalized text and mel-spectrogram
          PARAMS
          ------
          batch: [text_normalized, mel_normalized, speaker_id]
          """
          # Right zero-pad all one-hot text sequences to max input length
          input_lengths, ids_sorted_decreasing = torch.sort(
              torch.LongTensor([len(x[0]) for x in batch]),
              dim=0, descending=True)
          max_input_len = input_lengths[0]
  
          text_padded = torch.LongTensor(len(batch), max_input_len)
          text_padded.zero_()
          for i in range(len(ids_sorted_decreasing)):
              text = batch[ids_sorted_decreasing[i]][0]
              text_padded[i, :text.size(0)] = text
  
          # Right zero-pad mel-spec
          num_mels = batch[0][1].size(0)
          max_target_len = max([x[1].size(1) for x in batch])
          if max_target_len % self.n_frames_per_step != 0:
              max_target_len += self.n_frames_per_step - max_target_len % self.n_frames_per_step
              assert max_target_len % self.n_frames_per_step == 0
  
          # include mel padded & sid
          mel_padded = torch.FloatTensor(len(batch), num_mels, max_target_len)
          mel_padded.zero_()
          output_lengths = torch.LongTensor(len(batch))
          sid = torch.LongTensor(len(batch))
          for i in range(len(ids_sorted_decreasing)):
              mel = batch[ids_sorted_decreasing[i]][1]
              mel_padded[i, :, :mel.size(1)] = mel
              output_lengths[i] = mel.size(1)
              sid[i] = batch[ids_sorted_decreasing[i]][2]
  
          return text_padded, input_lengths, mel_padded, output_lengths, sid
  