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# Copyright (c) 2021 Soohwan Kim and Sangchun Ha and Soyoung Cho
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import torch
import logging
from torch.utils.data import Dataset
logger = logging.getLogger(__name__)
[docs]class TextDataset(Dataset):
"""
Dataset for language modeling.
Args:
transcripts (list): list of transcript
tokenizer (Tokenizer): tokenizer is in charge of preparing the inputs for a model.
"""
def __init__(self, transcripts: list, tokenizer):
super(TextDataset, self).__init__()
self.transcripts = transcripts
self.tokenizer = tokenizer
self.sos_id = tokenizer.sos_id
self.eos_id = tokenizer.eos_id
def _get_inputs(self, transcript):
tokens = transcript.split(' ')
transcript = [int(self.sos_id)]
for token in tokens:
transcript.append(int(token))
return transcript
def _get_targets(self, transcript):
tokens = transcript.split(' ')
transcript = list()
for token in tokens:
transcript.append(int(token))
transcript.append(int(self.eos_id))
return transcript
def __getitem__(self, idx):
transcript = self.tokenizer(self.transcripts[idx])
inputs = torch.LongTensor(self._get_inputs(transcript))
targets = torch.LongTensor(self._get_targets(transcript))
return inputs, targets
def __len__(self):
return len(self.transcripts)
def count(self):
return len(self.transcripts)