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https://github.com/modelscope/FunASR
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38 lines
1.9 KiB
Python
38 lines
1.9 KiB
Python
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import logging
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import torch
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from funasr.register import tables
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@tables.register("dataloader_classes", "DataloaderMapStyle")
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def DataloaderMapStyle(frontend=None, tokenizer=None, **kwargs):
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# dataset
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logging.info("Build dataloader")
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dataset_class = tables.dataset_classes.get(kwargs.get("dataset", "AudioDataset"))
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dataset_tr = dataset_class(kwargs.get("train_data_set_list"), frontend=frontend, tokenizer=tokenizer, is_training=True, **kwargs.get("dataset_conf"))
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dataset_val = dataset_class(kwargs.get("valid_data_set_list"), frontend=frontend, tokenizer=tokenizer, is_training=False, **kwargs.get("dataset_conf"))
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# dataloader
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batch_sampler = kwargs["dataset_conf"].get("batch_sampler", "DynamicBatchLocalShuffleSampler")
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batch_sampler_val = None
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if batch_sampler is not None:
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batch_sampler_class = tables.batch_sampler_classes.get(batch_sampler)
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batch_sampler = batch_sampler_class(dataset_tr, **kwargs.get("dataset_conf"))
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batch_sampler_val = batch_sampler_class(dataset_val, is_training=False, **kwargs.get("dataset_conf"))
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dataloader_tr = torch.utils.data.DataLoader(dataset_tr, collate_fn=dataset_tr.collator, **batch_sampler)
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dataloader_val = torch.utils.data.DataLoader(dataset_val, collate_fn=dataset_val.collator, **batch_sampler_val)
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return dataloader_tr, dataloader_val
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@tables.register("dataloader_classes", "DataloaderIterable")
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def DataloaderIterable(frontend=None, tokenizer=None, **kwargs):
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logging.info("Build dataloader")
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dataset_class = tables.dataset_classes.get(kwargs.get("dataset", "LargeDataset"))
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dataset_tr = dataset_class(kwargs.get("train_data_set_list"), frontend=frontend, tokenizer=tokenizer,
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is_training=True, **kwargs.get("dataset_conf"))
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dataset_val = dataset_class(kwargs.get("valid_data_set_list"), frontend=frontend, tokenizer=tokenizer,
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is_training=False, **kwargs.get("dataset_conf"))
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return dataset_tr, dataset_val |