import os
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from modelscope.metainfo import Trainers
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from modelscope.trainers import build_trainer
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from funasr.datasets.ms_dataset import MsDataset
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def modelscope_finetune(params):
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if not os.path.exists(params["output_dir"]):
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os.makedirs(params["output_dir"], exist_ok=True)
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# dataset split ["train", "validation"]
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ds_dict = MsDataset.load(params["data_dir"])
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kwargs = dict(
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model=params["model"],
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model_revision=params["model_revision"],
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data_dir=ds_dict,
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dataset_type=params["dataset_type"],
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work_dir=params["output_dir"],
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batch_bins=params["batch_bins"],
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max_epoch=params["max_epoch"],
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lr=params["lr"])
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trainer = build_trainer(Trainers.speech_asr_trainer, default_args=kwargs)
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trainer.train()
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if __name__ == '__main__':
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params = {}
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params["output_dir"] = "./checkpoint"
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params["data_dir"] = "./data"
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params["batch_bins"] = 2000
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params["dataset_type"] = "small"
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params["max_epoch"] = 50
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params["lr"] = 0.00005
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params["model"] = "damo/speech_UniASR_asr_2pass-ko-16k-common-vocab6400-tensorflow1-offline"
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params["model_revision"] = None
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modelscope_finetune(params)
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