update uniasr infer finetune scripts

egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-ja-16k-common-vocab93-tensorflow1-offline/finetune.py

@@ -30,6 +30,6 @@ if __name__ == '__main__':
     params["dataset_type"] = "small"
     params["max_epoch"] = 50
     params["lr"] = 0.00005
-    params["model"] = "damo/speech_UniASR_asr_2pass-ja-16k-common-vocab93-tensorflow1-online"
+    params["model"] = "damo/speech_UniASR_asr_2pass-ja-16k-common-vocab93-tensorflow1-offline"
     params["model_revision"] = None
     modelscope_finetune(params)

egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-ja-16k-common-vocab93-tensorflow1-offline/infer.py

@@ -6,7 +6,7 @@ if __name__ == "__main__":
     output_dir = "./results"
     inference_pipline = pipeline(
         task=Tasks.auto_speech_recognition,
-        model="damo/speech_UniASR_asr_2pass-ja-16k-common-vocab93-tensorflow1-online",
+        model="damo/speech_UniASR_asr_2pass-ja-16k-common-vocab93-tensorflow1-offline",
         output_dir=output_dir,
     )
     rec_result = inference_pipline(audio_in=audio_in, param_dict={"decoding_model":"offline"})

egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-pt-16k-common-vocab1617-tensorflow1-offline/finetune.py

@@ -30,6 +30,6 @@ if __name__ == '__main__':
     params["dataset_type"] = "small"
     params["max_epoch"] = 50
     params["lr"] = 0.00005
-    params["model"] = "damo/speech_UniASR_asr_2pass-pt-16k-common-vocab1617-tensorflow1-online"
+    params["model"] = "damo/speech_UniASR_asr_2pass-pt-16k-common-vocab1617-tensorflow1-offline"
     params["model_revision"] = None
     modelscope_finetune(params)

egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-pt-16k-common-vocab1617-tensorflow1-offline/infer.py

@@ -6,7 +6,7 @@ if __name__ == "__main__":
     output_dir = "./results"
     inference_pipline = pipeline(
         task=Tasks.auto_speech_recognition,
-        model="damo/speech_UniASR_asr_2pass-pt-16k-common-vocab1617-tensorflow1-online",
+        model="damo/speech_UniASR_asr_2pass-pt-16k-common-vocab1617-tensorflow1-offline",
         output_dir=output_dir,
     )
     rec_result = inference_pipline(audio_in=audio_in, param_dict={"decoding_model":"offline"})