| New file |
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| | | import json |
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| | | import os |
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| | | import shutil |
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| | | |
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| | | from modelscope.pipelines import pipeline |
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| | | from modelscope.utils.constant import Tasks |
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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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| | | pretrained_model_path = os.path.join(os.environ["HOME"], ".cache/modelscope/hub", params["modelscope_model_name"]) |
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| | | for file_name in params["required_files"]: |
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| | | if file_name == "configuration.json": |
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| | | with open(os.path.join(pretrained_model_path, file_name)) as f: |
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| | | config_dict = json.load(f) |
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| | | config_dict["model"]["am_model_name"] = params["decoding_model_name"] |
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| | | with open(os.path.join(params["output_dir"], "configuration.json"), "w") as f: |
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| | | json.dump(config_dict, f, indent=4, separators=(',', ': ')) |
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| | | else: |
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| | | shutil.copy(os.path.join(pretrained_model_path, file_name), |
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| | | os.path.join(params["output_dir"], file_name)) |
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| | | decoding_path = os.path.join(params["output_dir"], "decode_results") |
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| | | if os.path.exists(decoding_path): |
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| | | shutil.rmtree(decoding_path) |
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| | | os.mkdir(decoding_path) |
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| | | |
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| | | # decoding |
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| | | inference_pipeline = pipeline( |
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| | | task=Tasks.auto_speech_recognition, |
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| | | model=params["output_dir"], |
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| | | output_dir=decoding_path, |
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| | | ) |
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| | | audio_in = os.path.join(params["data_dir"], "wav.scp") |
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| | | inference_pipeline(audio_in=audio_in) |
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| | | |
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| | | # computer CER if GT text is set |
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| | | text_in = os.path.join(params["data_dir"], "text") |
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| | | if os.path.exists(text_in): |
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| | | text_proc_file = os.path.join(decoding_path, "1best_recog/token") |
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| | | compute_wer(text_in, text_proc_file, os.path.join(decoding_path, "text.cer")) |
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| | | |
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| | | if __name__ == '__main__': |
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| | | params = {} |
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| | | params["modelscope_model_name"] = "damo/speech_data2vec_pretrain-paraformer-zh-cn-aishell2-16k" |
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| | | params["required_files"] = ["am.mvn", "decoding.yaml", "configuration.json"] |
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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.cer_ctc.ave.pth" |
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| | | modelscope_infer_after_finetune(params) |
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