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469 lines
18 KiB
469 lines
18 KiB
# Copyright (c) 2021 PaddlePaddle Authors. 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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from typing import Any
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from typing import Dict
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from typing import List
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from typing import Text
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import jsonlines
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import numpy as np
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import paddle
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from paddle.io import BatchSampler
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from paddle.io import DataLoader
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from paddle.io import DistributedBatchSampler
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from yacs.config import CfgNode
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import paddlespeech.audio.streamdata as streamdata
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from paddlespeech.audio.text.text_featurizer import TextFeaturizer
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from paddlespeech.s2t.io.batchfy import make_batchset
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from paddlespeech.s2t.io.converter import CustomConverter
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from paddlespeech.s2t.io.dataset import TransformDataset
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from paddlespeech.s2t.io.reader import LoadInputsAndTargets
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from paddlespeech.s2t.utils.log import Log
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__all__ = ["BatchDataLoader", "StreamDataLoader"]
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logger = Log(__name__).getlog()
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def feat_dim_and_vocab_size(data_json: List[Dict[Text, Any]],
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mode: Text="asr",
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iaxis=0,
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oaxis=0):
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if mode == 'asr':
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feat_dim = data_json[0]['input'][oaxis]['shape'][1]
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vocab_size = data_json[0]['output'][oaxis]['shape'][1]
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else:
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raise ValueError(f"{mode} mode not support!")
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return feat_dim, vocab_size
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def batch_collate(x):
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"""de-minibatch, since user compose batch.
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Args:
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x (List[Tuple]): [(utts, xs, ilens, ys, olens)]
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Returns:
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Tuple: (utts, xs, ilens, ys, olens)
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"""
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return x[0]
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def read_preprocess_cfg(preprocess_conf_file):
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augment_conf = dict()
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preprocess_cfg = CfgNode(new_allowed=True)
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preprocess_cfg.merge_from_file(preprocess_conf_file)
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for idx, process in enumerate(preprocess_cfg["process"]):
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opts = dict(process)
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process_type = opts.pop("type")
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if process_type == 'time_warp':
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augment_conf['max_w'] = process['max_time_warp']
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augment_conf['w_inplace'] = process['inplace']
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augment_conf['w_mode'] = process['mode']
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if process_type == 'freq_mask':
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augment_conf['max_f'] = process['F']
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augment_conf['num_f_mask'] = process['n_mask']
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augment_conf['f_inplace'] = process['inplace']
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augment_conf['f_replace_with_zero'] = process['replace_with_zero']
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if process_type == 'time_mask':
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augment_conf['max_t'] = process['T']
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augment_conf['num_t_mask'] = process['n_mask']
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augment_conf['t_inplace'] = process['inplace']
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augment_conf['t_replace_with_zero'] = process['replace_with_zero']
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return augment_conf
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class StreamDataLoader():
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def __init__(self,
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manifest_file: str,
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train_mode: bool,
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unit_type: str='char',
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batch_size: int=0,
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preprocess_conf=None,
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num_mel_bins=80,
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frame_length=25,
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frame_shift=10,
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dither=0.0,
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minlen_in: float=0.0,
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maxlen_in: float=float('inf'),
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minlen_out: float=0.0,
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maxlen_out: float=float('inf'),
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resample_rate: int=16000,
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shuffle_size: int=10000,
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sort_size: int=1000,
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n_iter_processes: int=1,
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prefetch_factor: int=2,
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dist_sampler: bool=False,
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cmvn_file="data/mean_std.json",
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vocab_filepath='data/lang_char/vocab.txt'):
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self.manifest_file = manifest_file
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self.train_model = train_mode
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self.batch_size = batch_size
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self.prefetch_factor = prefetch_factor
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self.dist_sampler = dist_sampler
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self.n_iter_processes = n_iter_processes
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text_featurizer = TextFeaturizer(unit_type, vocab_filepath)
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symbol_table = text_featurizer.vocab_dict
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self.feat_dim = num_mel_bins
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self.vocab_size = text_featurizer.vocab_size
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augment_conf = read_preprocess_cfg(preprocess_conf)
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# The list of shard
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shardlist = []
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with open(manifest_file, "r") as f:
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for line in f.readlines():
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shardlist.append(line.strip())
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world_size = 1
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try:
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world_size = paddle.distributed.get_world_size()
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except Exception as e:
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logger.warninig(e)
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logger.warninig(
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"can not get world_size using paddle.distributed.get_world_size(), use world_size=1"
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)
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assert len(shardlist) >= world_size, \
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"the length of shard list should >= number of gpus/xpus/..."
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update_n_iter_processes = int(
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max(min(len(shardlist) / world_size - 1, self.n_iter_processes), 0))
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logger.info(f"update_n_iter_processes {update_n_iter_processes}")
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if update_n_iter_processes != self.n_iter_processes:
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self.n_iter_processes = update_n_iter_processes
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logger.info(f"change nun_workers to {self.n_iter_processes}")
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if self.dist_sampler:
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base_dataset = streamdata.DataPipeline(
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streamdata.SimpleShardList(shardlist), streamdata.split_by_node
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if train_mode else streamdata.placeholder(),
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streamdata.split_by_worker,
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streamdata.tarfile_to_samples(streamdata.reraise_exception))
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else:
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base_dataset = streamdata.DataPipeline(
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streamdata.SimpleShardList(shardlist),
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streamdata.split_by_worker,
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streamdata.tarfile_to_samples(streamdata.reraise_exception))
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self.dataset = base_dataset.append_list(
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streamdata.audio_tokenize(symbol_table),
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streamdata.audio_data_filter(
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frame_shift=frame_shift,
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max_length=maxlen_in,
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min_length=minlen_in,
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token_max_length=maxlen_out,
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token_min_length=minlen_out),
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streamdata.audio_resample(resample_rate=resample_rate),
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streamdata.audio_compute_fbank(
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num_mel_bins=num_mel_bins,
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frame_length=frame_length,
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frame_shift=frame_shift,
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dither=dither),
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streamdata.audio_spec_aug(**augment_conf)
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if train_mode else streamdata.placeholder(
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), # num_t_mask=2, num_f_mask=2, max_t=40, max_f=30, max_w=80)
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streamdata.shuffle(shuffle_size),
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streamdata.sort(sort_size=sort_size),
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streamdata.batched(batch_size),
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streamdata.audio_padding(),
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streamdata.audio_cmvn(cmvn_file))
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if paddle.__version__ >= '2.3.2':
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self.loader = streamdata.WebLoader(
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self.dataset,
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num_workers=self.n_iter_processes,
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prefetch_factor=self.prefetch_factor,
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batch_size=None)
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else:
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self.loader = streamdata.WebLoader(
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self.dataset,
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num_workers=self.n_iter_processes,
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batch_size=None)
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def __iter__(self):
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return self.loader.__iter__()
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def __call__(self):
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return self.__iter__()
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def __len__(self):
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logger.info(
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"Stream dataloader does not support calculate the length of the dataset"
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)
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return -1
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class BatchDataLoader():
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def __init__(self,
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json_file: str,
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train_mode: bool,
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sortagrad: int=0,
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batch_size: int=0,
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maxlen_in: float=float('inf'),
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maxlen_out: float=float('inf'),
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minibatches: int=0,
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mini_batch_size: int=1,
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batch_count: str='auto',
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batch_bins: int=0,
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batch_frames_in: int=0,
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batch_frames_out: int=0,
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batch_frames_inout: int=0,
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preprocess_conf=None,
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n_iter_processes: int=1,
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subsampling_factor: int=1,
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load_aux_input: bool=False,
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load_aux_output: bool=False,
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num_encs: int=1,
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dist_sampler: bool=False,
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shortest_first: bool=False):
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self.json_file = json_file
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self.train_mode = train_mode
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self.use_sortagrad = sortagrad == -1 or sortagrad > 0
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self.batch_size = batch_size
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self.maxlen_in = maxlen_in
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self.maxlen_out = maxlen_out
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self.batch_count = batch_count
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self.batch_bins = batch_bins
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self.batch_frames_in = batch_frames_in
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self.batch_frames_out = batch_frames_out
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self.batch_frames_inout = batch_frames_inout
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self.subsampling_factor = subsampling_factor
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self.num_encs = num_encs
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self.preprocess_conf = preprocess_conf
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self.n_iter_processes = n_iter_processes
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self.load_aux_input = load_aux_input
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self.load_aux_output = load_aux_output
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self.dist_sampler = dist_sampler
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self.shortest_first = shortest_first
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# read json data
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with jsonlines.open(json_file, 'r') as reader:
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self.data_json = list(reader)
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self.feat_dim, self.vocab_size = feat_dim_and_vocab_size(
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self.data_json, mode='asr')
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# make minibatch list (variable length)
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self.minibaches = make_batchset(
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self.data_json,
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batch_size,
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maxlen_in,
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maxlen_out,
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minibatches, # for debug
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min_batch_size=mini_batch_size,
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shortest_first=self.shortest_first or self.use_sortagrad,
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count=batch_count,
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batch_bins=batch_bins,
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batch_frames_in=batch_frames_in,
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batch_frames_out=batch_frames_out,
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batch_frames_inout=batch_frames_inout,
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iaxis=0,
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oaxis=0, )
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# data reader
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self.reader = LoadInputsAndTargets(
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mode="asr",
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load_output=True,
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preprocess_conf=preprocess_conf,
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preprocess_args={"train":
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train_mode}, # Switch the mode of preprocessing
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)
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# Setup a converter
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if num_encs == 1:
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self.converter = CustomConverter(
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subsampling_factor=subsampling_factor,
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dtype=np.float32,
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load_aux_input=load_aux_input,
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load_aux_output=load_aux_output)
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else:
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assert NotImplementedError("not impl CustomConverterMulEnc.")
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# hack to make batchsize argument as 1
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# actual bathsize is included in a list
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# default collate function converts numpy array to paddle tensor
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# we used an empty collate function instead which returns list
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self.dataset = TransformDataset(self.minibaches, self.converter,
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self.reader)
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if self.dist_sampler:
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self.batch_sampler = DistributedBatchSampler(
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dataset=self.dataset,
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batch_size=1,
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shuffle=not self.use_sortagrad if self.train_mode else False,
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drop_last=False, )
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else:
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self.batch_sampler = BatchSampler(
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dataset=self.dataset,
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batch_size=1,
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shuffle=not self.use_sortagrad if self.train_mode else False,
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drop_last=False, )
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self.dataloader = DataLoader(
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dataset=self.dataset,
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batch_sampler=self.batch_sampler,
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collate_fn=batch_collate,
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num_workers=self.n_iter_processes, )
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def __len__(self):
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return len(self.dataloader)
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def __iter__(self):
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return self.dataloader.__iter__()
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def __call__(self):
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return self.__iter__()
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def __repr__(self):
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echo = f"<{self.__class__.__module__}.{self.__class__.__name__} object at {hex(id(self))}> "
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echo += f"train_mode: {self.train_mode}, "
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echo += f"sortagrad: {self.use_sortagrad}, "
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echo += f"batch_size: {self.batch_size}, "
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echo += f"maxlen_in: {self.maxlen_in}, "
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echo += f"maxlen_out: {self.maxlen_out}, "
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echo += f"batch_count: {self.batch_count}, "
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echo += f"batch_bins: {self.batch_bins}, "
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echo += f"batch_frames_in: {self.batch_frames_in}, "
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echo += f"batch_frames_out: {self.batch_frames_out}, "
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echo += f"batch_frames_inout: {self.batch_frames_inout}, "
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echo += f"subsampling_factor: {self.subsampling_factor}, "
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echo += f"num_encs: {self.num_encs}, "
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echo += f"num_workers: {self.n_iter_processes}, "
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echo += f"load_aux_input: {self.load_aux_input}, "
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echo += f"load_aux_output: {self.load_aux_output}, "
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echo += f"dist_sampler: {self.dist_sampler}, "
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echo += f"shortest_first: {self.shortest_first}, "
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echo += f"file: {self.json_file}"
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return echo
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class DataLoaderFactory():
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@staticmethod
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def get_dataloader(mode: str, config, args):
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config = config.clone()
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use_streamdata = config.get("use_stream_data", False)
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if use_streamdata:
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if mode == 'train':
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config['manifest'] = config.train_manifest
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config['train_mode'] = True
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elif mode == 'valid':
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config['manifest'] = config.dev_manifest
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config['train_mode'] = False
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elif mode == 'test' or mode == 'align':
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config['manifest'] = config.test_manifest
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config['train_mode'] = False
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config['dither'] = 0.0
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config['minlen_in'] = 0.0
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config['maxlen_in'] = float('inf')
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config['minlen_out'] = 0
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config['maxlen_out'] = float('inf')
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config['dist_sampler'] = False
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else:
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raise KeyError(
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"not valid mode type!!, please input one of 'train, valid, test, align'"
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)
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return StreamDataLoader(
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manifest_file=config.manifest,
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train_mode=config.train_mode,
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unit_type=config.unit_type,
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preprocess_conf=config.preprocess_config,
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batch_size=config.batch_size,
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num_mel_bins=config.feat_dim,
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frame_length=config.window_ms,
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frame_shift=config.stride_ms,
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dither=config.dither,
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minlen_in=config.minlen_in,
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maxlen_in=config.maxlen_in,
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minlen_out=config.minlen_out,
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maxlen_out=config.maxlen_out,
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resample_rate=config.resample_rate,
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shuffle_size=config.shuffle_size,
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sort_size=config.sort_size,
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n_iter_processes=config.num_workers,
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prefetch_factor=config.prefetch_factor,
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dist_sampler=config.dist_sampler,
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cmvn_file=config.cmvn_file,
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vocab_filepath=config.vocab_filepath, )
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else:
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if mode == 'train':
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config['manifest'] = config.train_manifest
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config['train_mode'] = True
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config['mini_batch_size'] = args.ngpu
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config['subsampling_factor'] = 1
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config['num_encs'] = 1
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config['shortest_first'] = False
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elif mode == 'valid':
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config['manifest'] = config.dev_manifest
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config['train_mode'] = False
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config['sortagrad'] = False
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config['maxlen_in'] = float('inf')
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config['maxlen_out'] = float('inf')
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config['minibatches'] = 0
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config['mini_batch_size'] = args.ngpu
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config['batch_count'] = 'auto'
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config['batch_bins'] = 0
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config['batch_frames_in'] = 0
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config['batch_frames_out'] = 0
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config['batch_frames_inout'] = 0
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config['subsampling_factor'] = 1
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config['num_encs'] = 1
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config['shortest_first'] = False
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elif mode == 'test' or mode == 'align':
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config['manifest'] = config.test_manifest
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config['train_mode'] = False
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config['sortagrad'] = False
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config['batch_size'] = config.get('decode', dict()).get(
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'decode_batch_size', 1)
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config['maxlen_in'] = float('inf')
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config['maxlen_out'] = float('inf')
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config['minibatches'] = 0
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config['mini_batch_size'] = 1
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config['batch_count'] = 'auto'
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config['batch_bins'] = 0
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config['batch_frames_in'] = 0
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config['batch_frames_out'] = 0
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config['batch_frames_inout'] = 0
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config['num_workers'] = 1
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config['subsampling_factor'] = 1
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config['num_encs'] = 1
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config['dist_sampler'] = False
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config['shortest_first'] = False
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else:
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raise KeyError(
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"not valid mode type!!, please input one of 'train, valid, test, align'"
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)
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return BatchDataLoader(
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json_file=config.manifest,
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train_mode=config.train_mode,
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sortagrad=config.sortagrad,
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batch_size=config.batch_size,
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maxlen_in=config.maxlen_in,
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maxlen_out=config.maxlen_out,
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minibatches=config.minibatches,
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mini_batch_size=config.mini_batch_size,
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batch_count=config.batch_count,
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batch_bins=config.batch_bins,
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batch_frames_in=config.batch_frames_in,
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batch_frames_out=config.batch_frames_out,
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batch_frames_inout=config.batch_frames_inout,
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preprocess_conf=config.preprocess_config,
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n_iter_processes=config.num_workers,
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subsampling_factor=config.subsampling_factor,
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load_aux_output=config.get('load_transcript', None),
|
|
num_encs=config.num_encs,
|
|
dist_sampler=config.dist_sampler,
|
|
shortest_first=config.shortest_first)
|