267 lines
12 KiB
Python
267 lines
12 KiB
Python
# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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This file contains classes and functions related to data loading
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"""
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import torch
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import numpy as np
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import math
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from torch.utils.data import Dataset, Sampler
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import torch.distributed as dist
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from parts.manifest import Manifest
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from parts.features import WaveformFeaturizer
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class DistributedBucketBatchSampler(Sampler):
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def __init__(self, dataset, batch_size, num_replicas=None, rank=None):
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"""Distributed sampler that buckets samples with similar length to minimize padding,
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similar concept as pytorch BucketBatchSampler https://pytorchnlp.readthedocs.io/en/latest/source/torchnlp.samplers.html#torchnlp.samplers.BucketBatchSampler
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Args:
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dataset: Dataset used for sampling.
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batch_size: data batch size
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num_replicas (optional): Number of processes participating in
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distributed training.
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rank (optional): Rank of the current process within num_replicas.
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"""
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if num_replicas is None:
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if not dist.is_available():
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raise RuntimeError("Requires distributed package to be available")
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num_replicas = dist.get_world_size()
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if rank is None:
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if not dist.is_available():
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raise RuntimeError("Requires distributed package to be available")
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rank = dist.get_rank()
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self.dataset = dataset
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self.dataset_size = len(dataset)
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self.num_replicas = num_replicas
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self.rank = rank
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self.epoch = 0
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self.batch_size = batch_size
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self.tile_size = batch_size * self.num_replicas
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self.num_buckets = 6
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self.bucket_size = self.round_up_to(math.ceil(self.dataset_size / self.num_buckets), self.tile_size)
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self.index_count = self.round_up_to(self.dataset_size, self.tile_size)
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self.num_samples = self.index_count // self.num_replicas
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def round_up_to(self, x, mod):
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return (x + mod - 1) // mod * mod
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def __iter__(self):
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g = torch.Generator()
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g.manual_seed(self.epoch)
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indices = np.arange(self.index_count) % self.dataset_size
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for bucket in range(self.num_buckets):
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bucket_start = self.bucket_size * bucket
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bucket_end = min(bucket_start + self.bucket_size, self.index_count)
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indices[bucket_start:bucket_end] = indices[bucket_start:bucket_end][torch.randperm(bucket_end - bucket_start, generator=g)]
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tile_indices = torch.randperm(self.index_count // self.tile_size, generator=g)
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for tile_index in tile_indices:
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start_index = self.tile_size * tile_index + self.batch_size * self.rank
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end_index = start_index + self.batch_size
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yield indices[start_index:end_index]
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def __len__(self):
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return self.num_samples
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def set_epoch(self, epoch):
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self.epoch = epoch
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class data_prefetcher():
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def __init__(self, loader):
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self.loader = iter(loader)
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self.stream = torch.cuda.Stream()
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self.preload()
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def preload(self):
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try:
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self.next_input = next(self.loader)
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except StopIteration:
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self.next_input = None
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return
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with torch.cuda.stream(self.stream):
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self.next_input = [ x.cuda(non_blocking=True) for x in self.next_input]
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def __next__(self):
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torch.cuda.current_stream().wait_stream(self.stream)
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input = self.next_input
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self.preload()
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return input
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def next(self):
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return self.__next__()
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def __iter__(self):
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return self
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def seq_collate_fn(batch):
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"""batches samples and returns as tensors
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Args:
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batch : list of samples
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Returns
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batches of tensors
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"""
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batch_size = len(batch)
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def _find_max_len(lst, ind):
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max_len = -1
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for item in lst:
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if item[ind].size(0) > max_len:
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max_len = item[ind].size(0)
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return max_len
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max_audio_len = _find_max_len(batch, 0)
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max_transcript_len = _find_max_len(batch, 2)
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batched_audio_signal = torch.zeros(batch_size, max_audio_len)
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batched_transcript = torch.zeros(batch_size, max_transcript_len)
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audio_lengths = []
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transcript_lengths = []
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for ind, sample in enumerate(batch):
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batched_audio_signal[ind].narrow(0, 0, sample[0].size(0)).copy_(sample[0])
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audio_lengths.append(sample[1])
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batched_transcript[ind].narrow(0, 0, sample[2].size(0)).copy_(sample[2])
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transcript_lengths.append(sample[3])
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return batched_audio_signal, torch.stack(audio_lengths), batched_transcript, \
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torch.stack(transcript_lengths)
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class AudioToTextDataLayer:
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"""Data layer with data loader
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"""
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def __init__(self, **kwargs):
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self._device = torch.device("cuda")
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featurizer_config = kwargs['featurizer_config']
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pad_to_max = kwargs.get('pad_to_max', False)
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perturb_config = kwargs.get('perturb_config', None)
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manifest_filepath = kwargs['manifest_filepath']
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dataset_dir = kwargs['dataset_dir']
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labels = kwargs['labels']
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batch_size = kwargs['batch_size']
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drop_last = kwargs.get('drop_last', False)
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shuffle = kwargs.get('shuffle', True)
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min_duration = featurizer_config.get('min_duration', 0.1)
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max_duration = featurizer_config.get('max_duration', None)
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normalize_transcripts = kwargs.get('normalize_transcripts', True)
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trim_silence = kwargs.get('trim_silence', False)
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multi_gpu = kwargs.get('multi_gpu', False)
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sampler_type = kwargs.get('sampler', 'default')
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speed_perturbation = featurizer_config.get('speed_perturbation', False)
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sort_by_duration=sampler_type == 'bucket'
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self._featurizer = WaveformFeaturizer.from_config(featurizer_config, perturbation_configs=perturb_config)
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self._dataset = AudioDataset(
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dataset_dir=dataset_dir,
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manifest_filepath=manifest_filepath,
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labels=labels, blank_index=len(labels),
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sort_by_duration=sort_by_duration,
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pad_to_max=pad_to_max,
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featurizer=self._featurizer, max_duration=max_duration,
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min_duration=min_duration, normalize=normalize_transcripts,
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trim=trim_silence, speed_perturbation=speed_perturbation)
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print('sort_by_duration', sort_by_duration)
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if not multi_gpu:
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self.sampler = None
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self._dataloader = torch.utils.data.DataLoader(
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dataset=self._dataset,
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batch_size=batch_size,
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collate_fn=lambda b: seq_collate_fn(b),
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drop_last=drop_last,
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shuffle=shuffle if self.sampler is None else False,
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num_workers=4,
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pin_memory=True,
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sampler=self.sampler
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)
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elif sampler_type == 'bucket':
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self.sampler = DistributedBucketBatchSampler(self._dataset, batch_size=batch_size)
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print("DDBucketSampler")
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self._dataloader = torch.utils.data.DataLoader(
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dataset=self._dataset,
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collate_fn=lambda b: seq_collate_fn(b),
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num_workers=4,
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pin_memory=True,
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batch_sampler=self.sampler
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)
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elif sampler_type == 'default':
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self.sampler = torch.utils.data.distributed.DistributedSampler(self._dataset)
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print("DDSampler")
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self._dataloader = torch.utils.data.DataLoader(
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dataset=self._dataset,
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batch_size=batch_size,
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collate_fn=lambda b: seq_collate_fn(b),
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drop_last=drop_last,
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shuffle=shuffle if self.sampler is None else False,
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num_workers=4,
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pin_memory=True,
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sampler=self.sampler
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)
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else:
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raise RuntimeError("Sampler {} not supported".format(sampler_type))
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def __len__(self):
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return len(self._dataset)
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@property
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def data_iterator(self):
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return self._dataloader
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class AudioDataset(Dataset):
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def __init__(self, dataset_dir, manifest_filepath, labels, featurizer, max_duration=None, pad_to_max=False,
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min_duration=None, blank_index=0, max_utts=0, normalize=True, sort_by_duration=False,
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trim=False, speed_perturbation=False):
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"""Dataset that loads tensors via a json file containing paths to audio files, transcripts, and durations
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(in seconds). Each entry is a different audio sample.
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Args:
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dataset_dir: absolute path to dataset folder
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manifest_filepath: relative path from dataset folder to manifest json as described above. Can be coma-separated paths.
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labels: String containing all the possible characters to map to
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featurizer: Initialized featurizer class that converts paths of audio to feature tensors
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max_duration: If audio exceeds this length, do not include in dataset
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min_duration: If audio is less than this length, do not include in dataset
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pad_to_max: if specified input sequences into dnn model will be padded to max_duration
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blank_index: blank index for ctc loss / decoder
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max_utts: Limit number of utterances
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normalize: whether to normalize transcript text
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sort_by_duration: whether or not to sort sequences by increasing duration
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trim: if specified trims leading and trailing silence from an audio signal.
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speed_perturbation: specify if using data contains speed perburbation
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"""
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m_paths = manifest_filepath.split(',')
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self.manifest = Manifest(dataset_dir, m_paths, labels, blank_index, pad_to_max=pad_to_max,
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max_duration=max_duration,
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sort_by_duration=sort_by_duration,
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min_duration=min_duration, max_utts=max_utts,
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normalize=normalize, speed_perturbation=speed_perturbation)
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self.featurizer = featurizer
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self.blank_index = blank_index
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self.trim = trim
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print(
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"Dataset loaded with {0:.2f} hours. Filtered {1:.2f} hours.".format(
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self.manifest.duration / 3600,
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self.manifest.filtered_duration / 3600))
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def __getitem__(self, index):
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sample = self.manifest[index]
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rn_indx = np.random.randint(len(sample['audio_filepath']))
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duration = sample['audio_duration'][rn_indx] if 'audio_duration' in sample else 0
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offset = sample['offset'] if 'offset' in sample else 0
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features = self.featurizer.process(sample['audio_filepath'][rn_indx],
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offset=offset, duration=duration,
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trim=self.trim)
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return features, torch.tensor(features.shape[0]).int(), \
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torch.tensor(sample["transcript"]), torch.tensor(
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len(sample["transcript"])).int()
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def __len__(self):
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return len(self.manifest)
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