egs

egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/finetune2.py

@@ -1,37 +0,0 @@
-import os
-
-from modelscope.metainfo import Trainers
-from modelscope.trainers import build_trainer
-
-from funasr.datasets.ms_dataset import MsDataset
-from funasr.utils.modelscope_param import modelscope_args
-
-
-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_path)
-    kwargs = dict(
-        model=params.model,
-        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 = modelscope_args(model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch", data_path="./data")
-    params.output_dir = "./checkpoint"              # m模型保存路径
-    params.data_path = "./example_data/"            # 数据路径
-    params.dataset_type = "small"                   # 小数据量设置small，若数据量大于1000小时，请使用large
-    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，
-    params.max_epoch = 50                           # 最大训练轮数
-    params.lr = 0.0005                              # 设置学习率
-    params.scheduler_conf = {"warmup_steps": 30000}
-    
-    modelscope_finetune(params)