| 1234567891011121314151617181920212223242526272829303132333435 |
- import os
- from modelscope.metainfo import Trainers
- from modelscope.trainers import build_trainer
- from funasr.datasets.ms_dataset import MsDataset
- def modelscope_finetune(params):
- if not os.path.exists(params["output_dir"]):
- os.makedirs(params["output_dir"], exist_ok=True)
- # dataset split ["train", "validation"]
- ds_dict = MsDataset.load(params["data_dir"])
- kwargs = dict(
- model=params["model"],
- model_revision=params["model_revision"],
- data_dir=ds_dict,
- dataset_type=params["dataset_type"],
- work_dir=params["output_dir"],
- batch_bins=params["batch_bins"],
- max_epoch=params["max_epoch"],
- lr=params["lr"])
- trainer = build_trainer(Trainers.speech_asr_trainer, default_args=kwargs)
- trainer.train()
- if __name__ == '__main__':
- params = {}
- params["output_dir"] = "./checkpoint"
- params["data_dir"] = "./data"
- params["batch_bins"] = 2000
- params["dataset_type"] = "small"
- params["max_epoch"] = 50
- params["lr"] = 0.00005
- params["model"] = "damo/speech_UniASR_asr_2pass-de-16k-common-vocab3690-tensorflow1-online"
- params["model_revision"] = None
- modelscope_finetune(params)
|
