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139 lines
5.2 KiB
139 lines
5.2 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 paddle.io import DataLoader
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from deepspeech.frontend.utility import read_manifest
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from deepspeech.io.batchfy import make_batchset
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from deepspeech.io.converter import CustomConverter
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from deepspeech.io.dataset import TransformDataset
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from deepspeech.io.reader import LoadInputsAndTargets
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from deepspeech.utils.log import Log
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__all__ = ["BatchDataLoader"]
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logger = Log(__name__).getlog()
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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: bool=False,
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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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num_encs: int=1):
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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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# read json data
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self.data_json = read_manifest(json_file)
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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.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, dtype=np.float32)
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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 pytorch tensor
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# we used an empty collate function instead which returns list
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self.dataset = TransformDataset(
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self.minibaches,
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lambda data: self.converter([self.reader(data, return_uttid=True)]))
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self.dataloader = DataLoader(
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dataset=self.dataset,
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batch_size=1,
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shuffle=not use_sortagrad if train_mode else False,
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collate_fn=lambda x: x[0],
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num_workers=n_iter_processes, )
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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"file: {self.json_file}"
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return echo
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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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