Source code for openspeech.data.data_loader

# MIT License
#
# Copyright (c) 2021 Soohwan Kim and Sangchun Ha and Soyoung Cho
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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import torch
import numpy as np
from torch.utils.data import DataLoader, Sampler


def _collate_fn(batch, pad_id: int = 0):
    r"""
    Functions that pad to the maximum sequence length

    Args:
        batch (tuple): tuple contains input and target tensors
        pad_id (int): identification of pad token

    Returns:
        seqs (torch.FloatTensor): tensor contains input sequences.
        target (torch.IntTensor): tensor contains target sequences.
        seq_lengths (torch.IntTensor): tensor contains input sequence lengths
        target_lengths (torch.IntTensor): tensor contains target sequence lengths
    """
    def seq_length_(p):
        return len(p[0])

    def target_length_(p):
        return len(p[1])

    # sort by sequence length for rnn.pack_padded_sequence()
    batch = sorted(batch, key=lambda sample: sample[0].size(0), reverse=True)

    seq_lengths = [len(s[0]) for s in batch]
    target_lengths = [len(s[1]) - 1 for s in batch]

    max_seq_sample = max(batch, key=seq_length_)[0]
    max_target_sample = max(batch, key=target_length_)[1]

    max_seq_size = max_seq_sample.size(0)
    max_target_size = len(max_target_sample)

    feat_size = max_seq_sample.size(1)
    batch_size = len(batch)

    seqs = torch.zeros(batch_size, max_seq_size, feat_size)

    targets = torch.zeros(batch_size, max_target_size).to(torch.long)
    targets.fill_(pad_id)

    for x in range(batch_size):
        sample = batch[x]
        tensor = sample[0]
        target = sample[1]
        seq_length = tensor.size(0)

        seqs[x].narrow(0, 0, seq_length).copy_(tensor)
        targets[x].narrow(0, 0, len(target)).copy_(torch.LongTensor(target))

    seq_lengths = torch.IntTensor(seq_lengths)
    target_lengths = torch.IntTensor(target_lengths)

    return seqs, targets, seq_lengths, target_lengths


[docs]class AudioDataLoader(DataLoader): r""" Audio Data Loader Args: dataset (torch.utils.data.Dataset): dataset from which to load the data. num_workers (int): how many subprocesses to use for data loading. batch_sampler (torch.utils.data.sampler.Sampler): defines the strategy to draw samples from the dataset. """ def __init__( self, dataset: torch.utils.data.Dataset, num_workers: int, batch_sampler: torch.utils.data.sampler.Sampler, **kwargs, ) -> None: super(AudioDataLoader, self).__init__( dataset=dataset, num_workers=num_workers, batch_sampler=batch_sampler, **kwargs, ) self.collate_fn = _collate_fn
[docs]class BucketingSampler(Sampler): r""" Samples batches assuming they are in order of size to batch similarly sized samples together. Args: data_source (torch.utils.data.Dataset): dataset to sample from batch_size (int): size of batch drop_last (bool): flat indication whether to drop last batch or not """ def __init__(self, data_source, batch_size: int = 32, drop_last: bool = False) -> None: super(BucketingSampler, self).__init__(data_source) self.batch_size = batch_size self.data_source = data_source ids = list(range(0, len(data_source))) self.bins = [ids[i:i + batch_size] for i in range(0, len(ids), batch_size)] self.drop_last = drop_last def __iter__(self): for ids in self.bins: np.random.shuffle(ids) yield ids def __len__(self): return len(self.bins) def shuffle(self, epoch): np.random.shuffle(self.bins)