import os
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import sys
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from modelscope.metainfo import Trainers
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from modelscope.trainers import build_trainer
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from modelscope.msdatasets.audio.asr_dataset import ASRDataset
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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 = ASRDataset.load(params.data_path, namespace='speech_asr')
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kwargs = dict(
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model=params.model,
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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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from funasr.utils.modelscope_param import modelscope_args
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params = modelscope_args(model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch")
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params.output_dir = "./checkpoint" # 模型保存路径
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params.data_path = "./data" # 数据路径，可以为modelscope中已上传数据，也可以是本地数据
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params.dataset_type = "small" # 小数据量设置small，若数据量大于1000小时，请使用large
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params.batch_bins = 2000 # batch size，如果dataset_type="small"，batch_bins单位为fbank特征帧数，如果dataset_type="large"，batch_bins单位为毫秒，
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params.max_epoch = 20 # 最大训练轮数
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params.lr = 0.00005 # 设置学习率
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modelscope_finetune(params)
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