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@@ -1,37 +0,0 @@
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-import os
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-
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-from modelscope.metainfo import Trainers
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-from modelscope.trainers import build_trainer
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-
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-from funasr.datasets.ms_dataset import MsDataset
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-from funasr.utils.modelscope_param import modelscope_args
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-
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-
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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_path)
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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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-
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-
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-if __name__ == '__main__':
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- params = modelscope_args(model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch", data_path="./data")
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- params.output_dir = "./checkpoint" # m模型保存路径
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- params.data_path = "./example_data/" # 数据路径
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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 = 50 # 最大训练轮数
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- params.lr = 0.0005 # 设置学习率
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- params.scheduler_conf = {"warmup_steps": 30000}
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-
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- modelscope_finetune(params)
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游雁