docs

egs/aishell/transformer/utils/prepare_checkpoint.py

@@ -5,35 +5,6 @@ from modelscope.pipelines import pipeline
 from modelscope.utils.constant import Tasks
 from modelscope.hub.snapshot_download import snapshot_download
 
-def modelscope_infer_after_finetune(params):
-    # prepare for decoding
-
-    try:
-        pretrained_model_path = snapshot_download(params["modelscope_model_name"], cache_dir=params["output_dir"])
-    except BaseException:
-        raise BaseException(f"Please download pretrain model from ModelScope firstly.")
-    shutil.copy(os.path.join(params["output_dir"], params["decoding_model_name"]), os.path.join(pretrained_model_path, "model.pb"))
-    decoding_path = os.path.join(params["output_dir"], "decode_results")
-    if os.path.exists(decoding_path):
-        shutil.rmtree(decoding_path)
-    os.mkdir(decoding_path)
-
-    # decoding
-    inference_pipeline = pipeline(
-        task=Tasks.auto_speech_recognition,
-        model=pretrained_model_path,
-        output_dir=decoding_path,
-        batch_size=params["batch_size"]
-    )
-    audio_in = os.path.join(params["data_dir"], "wav.scp")
-    inference_pipeline(audio_in=audio_in)
-
-    # computer CER if GT text is set
-    text_in = os.path.join(params["data_dir"], "text")
-    if os.path.exists(text_in):
-        text_proc_file = os.path.join(decoding_path, "1best_recog/text")
-        compute_wer(text_in, text_proc_file, os.path.join(decoding_path, "text.cer"))
-
 
 if __name__ == '__main__':
     import sys