| File was renamed from egs_modelscope/asr/TEMPLATE/infer_after_finetune.py |
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| | | import json |
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| | | import os |
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| | | import shutil |
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| | | from modelscope.pipelines import pipeline |
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| | | from modelscope.utils.constant import Tasks |
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| | | from modelscope.hub.snapshot_download import snapshot_download |
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| | | from funasr.utils.compute_wer import compute_wer |
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| | | def modelscope_infer_after_finetune(params): |
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| | | # prepare for decoding |
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| | | if __name__ == '__main__': |
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| | | params = {} |
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| | | params["modelscope_model_name"] = "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch" |
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| | | params["output_dir"] = "./checkpoint" |
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| | | params["data_dir"] = "./data/test" |
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| | | params["decoding_model_name"] = "valid.acc.ave_10best.pb" |
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| | | params["batch_size"] = 64 |
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| | | modelscope_infer_after_finetune(params) |
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| | | import sys |
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| | | model = sys.argv[1] |
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| | | checkpoint_dir = sys.argv[2] |
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| | | checkpoint_name = sys.argv[3] |
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| | | try: |
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| | | pretrained_model_path = snapshot_download(model, cache_dir=checkpoint_dir) |
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| | | except BaseException: |
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| | | raise BaseException(f"Please download pretrain model from ModelScope firstly.") |
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| | | shutil.copy(os.path.join(checkpoint_dir, checkpoint_name), os.path.join(pretrained_model_path, "model.pb")) |
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