update 8k uniasr recipe

egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-offline/finetune.py

@@ -25,7 +25,7 @@ def modelscope_finetune(params):
 
 
 if __name__ == '__main__':
-    params = modelscope_args(model="damo/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-online", data_path="./data")
+    params = modelscope_args(model="damo/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-offline", 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

egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-offline/infer.py

@@ -18,7 +18,7 @@ def modelscope_infer_core(output_dir, split_dir, njob, idx):
         os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_id)
     inference_pipline = pipeline(
         task=Tasks.auto_speech_recognition,
-        model="damo/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-online",
+        model="damo/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-offline",
         output_dir=output_dir_job,
         batch_size=1
     )

egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-offline/infer_after_finetune.py

@@ -45,7 +45,7 @@ def modelscope_infer_after_finetune(params):
 
 if __name__ == '__main__':
     params = {}
-    params["modelscope_model_name"] = "damo/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-online"
+    params["modelscope_model_name"] = "damo/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-offline"
     params["required_files"] = ["am.mvn", "decoding.yaml", "configuration.json"]
     params["output_dir"] = "./checkpoint"
     params["data_dir"] = "./data/test"

egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-online/finetune.py

@@ -25,7 +25,7 @@ def modelscope_finetune(params):
 
 
 if __name__ == '__main__':
-    params = modelscope_args(model="damo/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-offline", data_path="./data")
+    params = modelscope_args(model="damo/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-online", 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

egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-online/infer.py

@@ -18,7 +18,7 @@ def modelscope_infer_core(output_dir, split_dir, njob, idx):
         os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_id)
     inference_pipline = pipeline(
         task=Tasks.auto_speech_recognition,
-        model="damo/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-offline",
+        model="damo/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-online",
         output_dir=output_dir_job,
         batch_size=1
     )

egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-online/infer_after_finetune.py

@@ -45,7 +45,7 @@ def modelscope_infer_after_finetune(params):
 
 if __name__ == '__main__':
     params = {}
-    params["modelscope_model_name"] = "damo/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-offline"
+    params["modelscope_model_name"] = "damo/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-online"
     params["required_files"] = ["am.mvn", "decoding.yaml", "configuration.json"]
     params["output_dir"] = "./checkpoint"
     params["data_dir"] = "./data/test"