haoneng.lhn
2023-07-20 3e8159e5b0f6e7e96d4e6ca1f4b4d8ccb151ace5
egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/finetune.py
@@ -19,7 +19,8 @@
        work_dir=params.output_dir,
        batch_bins=params.batch_bins,
        max_epoch=params.max_epoch,
        lr=params.lr)
        lr=params.lr,
        mate_params=params.param_dict)
    trainer = build_trainer(Trainers.speech_asr_trainer, default_args=kwargs)
    trainer.train()
@@ -30,7 +31,18 @@
    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.max_epoch = 20                           # 最大训练轮数
    params.lr = 0.00005                             # 设置学习率
    init_param = []
    freeze_param = []
    ignore_init_mismatch = True
    use_lora = False
    params.param_dict = {"init_param":init_param, "freeze_param": freeze_param, "ignore_init_mismatch": ignore_init_mismatch}
    if use_lora:
        enable_lora = True
        lora_bias = "all"
        lora_params = {"lora_list":['q','v'], "lora_rank":8, "lora_alpha":16, "lora_dropout":0.1}
        lora_config = {"enable_lora": enable_lora, "lora_bias": lora_bias, "lora_params": lora_params}
        params.param_dict.update(lora_config)
    
    modelscope_finetune(params)