update speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch (#688)

* update speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch finetune & infer scripts

* update speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch

egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch/finetune.py

@@ -1,5 +1,4 @@
 import os
-<<<<<<< HEAD
 
 from modelscope.metainfo import Trainers
 from modelscope.trainers import build_trainer
@@ -21,50 +20,17 @@ def modelscope_finetune(params):
         batch_bins=params.batch_bins,
         max_epoch=params.max_epoch,
         lr=params.lr)
-=======
-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"])
->>>>>>> main
     trainer = build_trainer(Trainers.speech_asr_trainer, default_args=kwargs)
     trainer.train()
 
 
 if __name__ == '__main__':
-<<<<<<< HEAD
     params = modelscope_args(model="damo/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-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.max_epoch = 20                           # 最大训练轮数
     params.lr = 0.00005                             # 设置学习率
     
-=======
-    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-tr-16k-common-vocab1582-pytorch"
-    params["model_revision"] = None
->>>>>>> main
-    modelscope_finetune(params)
+    modelscope_finetune(params)

egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch/infer.py

@@ -1,33 +1,3 @@
-<<<<<<< HEAD
-import os
-import shutil
-import argparse
-from modelscope.pipelines import pipeline
-from modelscope.utils.constant import Tasks
-
-def modelscope_infer(args):
-    os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpuid)
-    inference_pipeline = pipeline(
-        task=Tasks.auto_speech_recognition,
-        model=args.model,
-        output_dir=args.output_dir,
-        batch_size=args.batch_size,
-        param_dict={"decoding_model": args.decoding_mode, "hotword": args.hotword_txt}
-    )
-    inference_pipeline(audio_in=args.audio_in)
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--model', type=str, default="damo/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch")
-    parser.add_argument('--audio_in', type=str, default="./data/test/wav.scp")
-    parser.add_argument('--output_dir', type=str, default="./results/")
-    parser.add_argument('--decoding_mode', type=str, default="normal")
-    parser.add_argument('--hotword_txt', type=str, default=None)
-    parser.add_argument('--batch_size', type=int, default=64)
-    parser.add_argument('--gpuid', type=str, default="0")
-    args = parser.parse_args()
-    modelscope_infer(args)
-=======
 from modelscope.pipelines import pipeline
 from modelscope.utils.constant import Tasks
 
@@ -40,5 +10,4 @@ if __name__ == "__main__":
         output_dir=output_dir,
     )
     rec_result = inference_pipeline(audio_in=audio_in, param_dict={"decoding_model":"offline"})
-    print(rec_result)
->>>>>>> main
+    print(rec_result)