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 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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# 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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# 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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if __name__ == '__main__':
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params = {}
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params["modelscope_model_name"] = "damo/speech_data2vec_pretrain-zh-cn-aishell2-16k-pytorch"
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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.pb"
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modelscope_infer_after_finetune(params)
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