mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
37 lines
1.5 KiB
Python
37 lines
1.5 KiB
Python
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 = 0.00005 # 设置学习率
|
||
|
||
modelscope_finetune(params)
|