(self, batch)
return tuple(return_batch.values()), uttid_list
Function to load inputs, targets and uttid from list of dicts

    :param List[Tuple[str, dict]] batch: list of dict which is subset of
        loaded data.json
    :return: list of input token id sequences [(L_1), (L_2), ..., (L_B)]
    :return: list of input feature sequences
        [(T_1, D), (T_2, D), ..., (T_B, D)]
    :rtype: list of float ndarray
    :return: list of target token id sequences [(L_1), (L_2), ..., (L_B)]
    :rtype: list of int ndarray
    Reference: Espnet source code, /espnet/utils/io_utils.py
               https://github.com/espnet/espnet/blob/master/espnet/utils/io_utils.py
Function to load inputs, targets and uttid from list of dicts
Function to load inputs targets and uttid from list of dicts
def transform(self, batch):
    """Function to load inputs, targets and uttid from list of dicts

    :param List[Tuple[str, dict]] batch: list of dict which is subset of
        loaded data.json
    :return: list of input token id sequences [(L_1), (L_2), ..., (L_B)]
    :return: list of input feature sequences
        [(T_1, D), (T_2, D), ..., (T_B, D)]
    :rtype: list of float ndarray
    :return: list of target token id sequences [(L_1), (L_2), ..., (L_B)]
    :rtype: list of int ndarray
    Reference: Espnet source code, /espnet/utils/io_utils.py
               https://github.com/espnet/espnet/blob/master/espnet/utils/io_utils.py
    """
    x_feats_dict = OrderedDict()  # OrderedDict[str, List[np.ndarray]]
    y_feats_dict = OrderedDict()  # OrderedDict[str, List[np.ndarray]]
    uttid_list = []  # List[str]

    mode = self.load_inputs_and_targets.mode
    for uttid, info in batch:
      uttid_list.append(uttid)

      if self.load_inputs_and_targets.load_input:
        # Note(kamo): This for-loop is for multiple inputs
        for idx, inp in enumerate(info['input']):
          # {"input":
          #  [{"feat": "some/path.h5:F01_050C0101_PED_REAL",
          #    "filetype": "hdf5",
          #    "name": "input1", ...}], ...}

          #pylint: disable=protected-access
          x_data = self.load_inputs_and_targets._get_from_loader(
              filepath=inp['feat'], filetype=inp.get('filetype', 'mat'))
          x_feats_dict.setdefault(inp['name'], []).append(x_data)

      elif mode == 'tts' and self.load_inputs_and_targets.use_speaker_embedding:
        for idx, inp in enumerate(info['input']):
          if idx != 1 and len(info['input']) > 1:
            x_data = None
          else:
            x_data = self.load_inputs_and_targets._get_from_loader(  #pylint: disable=protected-access
                filepath=inp['feat'],
                filetype=inp.get('filetype', 'mat'))
          x_feats_dict.setdefault(inp['name'], []).append(x_data)

      if self.load_inputs_and_targets.load_output:
        for idx, inp in enumerate(info['output']):
          if 'tokenid' in inp:
            # ======= Legacy format for output =======
            # {"output": [{"tokenid": "1 2 3 4"}])
            x_data = np.fromiter(
                map(int, inp['tokenid'].split()), dtype=np.int64)
          else:
            # ======= New format =======
            # {"input":
            #  [{"feat": "some/path.h5:F01_050C0101_PED_REAL",
            #    "filetype": "hdf5",
            #    "name": "target1", ...}], ...}
            x_data = self.load_inputs_and_targets._get_from_loader(  #pylint: disable=protected-access
                filepath=inp['feat'],
                filetype=inp.get('filetype', 'mat'))

          y_feats_dict.setdefault(inp['name'], []).append(x_data)
    if self.load_inputs_and_targets.mode == 'asr':
      #pylint: disable=protected-access
      return_batch, uttid_list = self.load_inputs_and_targets._create_batch_asr(
          x_feats_dict, y_feats_dict, uttid_list)

    elif self.load_inputs_and_targets.mode == 'tts':
      _, info = batch[0]
      eos = int(info['output'][0]['shape'][1]) - 1
      #pylint: disable=protected-access
      return_batch, uttid_list = self.load_inputs_and_targets._create_batch_tts(
          x_feats_dict, y_feats_dict, uttid_list, eos)
    else:
      raise NotImplementedError

    if self.load_inputs_and_targets.preprocessing is not None:
      # Apply pre-processing only to input1 feature, now
      if 'input1' in return_batch:
        return_batch['input1'] = \
            self.load_inputs_and_targets.preprocessing(return_batch['input1'], uttid_list,
                               **self.load_inputs_and_targets.preprocess_args)

    # Doesn't return the names now.
    return tuple(return_batch.values()), uttid_list
def transform ( self , batch ) : x_feats_dict = OrderedDict ( ) # OrderedDict[str, List[np.ndarray]] y_feats_dict = OrderedDict ( ) # OrderedDict[str, List[np.ndarray]] uttid_list = [ ] # List[str] mode = self . load_inputs_and_targets . mode for uttid , info in batch : uttid_list . append ( uttid ) if self . load_inputs_and_targets . load_input : # Note(kamo): This for-loop is for multiple inputs for idx , inp in enumerate ( info [ 'input' ] ) : # {"input": #  [{"feat": "some/path.h5:F01_050C0101_PED_REAL", #    "filetype": "hdf5", #    "name": "input1", ...}], ...} #pylint: disable=protected-access x_data = self . load_inputs_and_targets . _get_from_loader ( filepath = inp [ 'feat' ] , filetype = inp . get ( 'filetype' , 'mat' ) ) x_feats_dict . setdefault ( inp [ 'name' ] , [ ] ) . append ( x_data ) elif mode == 'tts' and self . load_inputs_and_targets . use_speaker_embedding : for idx , inp in enumerate ( info [ 'input' ] ) : if idx != 1 and len ( info [ 'input' ] ) > 1 : x_data = None else : x_data = self . load_inputs_and_targets . _get_from_loader ( #pylint: disable=protected-access filepath = inp [ 'feat' ] , filetype = inp . get ( 'filetype' , 'mat' ) ) x_feats_dict . setdefault ( inp [ 'name' ] , [ ] ) . append ( x_data ) if self . load_inputs_and_targets . load_output : for idx , inp in enumerate ( info [ 'output' ] ) : if 'tokenid' in inp : # ======= Legacy format for output ======= # {"output": [{"tokenid": "1 2 3 4"}]) x_data = np . fromiter ( map ( int , inp [ 'tokenid' ] . split ( ) ) , dtype = np . int64 ) else : # ======= New format ======= # {"input": #  [{"feat": "some/path.h5:F01_050C0101_PED_REAL", #    "filetype": "hdf5", #    "name": "target1", ...}], ...} x_data = self . load_inputs_and_targets . _get_from_loader ( #pylint: disable=protected-access filepath = inp [ 'feat' ] , filetype = inp . get ( 'filetype' , 'mat' ) ) y_feats_dict . setdefault ( inp [ 'name' ] , [ ] ) . append ( x_data ) if self . load_inputs_and_targets . mode == 'asr' : #pylint: disable=protected-access return_batch , uttid_list = self . load_inputs_and_targets . _create_batch_asr ( x_feats_dict , y_feats_dict , uttid_list ) elif self . load_inputs_and_targets . mode == 'tts' : _ , info = batch [ 0 ] eos = int ( info [ 'output' ] [ 0 ] [ 'shape' ] [ 1 ] ) - 1 #pylint: disable=protected-access return_batch , uttid_list = self . load_inputs_and_targets . _create_batch_tts ( x_feats_dict , y_feats_dict , uttid_list , eos ) else : raise NotImplementedError if self . load_inputs_and_targets . preprocessing is not None : # Apply pre-processing only to input1 feature, now if 'input1' in return_batch : return_batch [ 'input1' ] = self . load_inputs_and_targets . preprocessing ( return_batch [ 'input1' ] , uttid_list , * * self . load_inputs_and_targets . preprocess_args ) # Doesn't return the names now. return tuple ( return_batch . values ( ) ) , uttid_list