Delete infer_after_finetune.py

egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-minnan-16k-common-vocab3825/infer_after_finetune.py

@@ -1,54 +0,0 @@
-import json
-import os
-import shutil
-
-from modelscope.pipelines import pipeline
-from modelscope.utils.constant import Tasks
-
-from funasr.utils.compute_wer import compute_wer
-
-
-def modelscope_infer_after_finetune(params):
-    # prepare for decoding
-    pretrained_model_path = os.path.join(os.environ["HOME"], ".cache/modelscope/hub", params["modelscope_model_name"])
-    for file_name in params["required_files"]:
-        if file_name == "configuration.json":
-            with open(os.path.join(pretrained_model_path, file_name)) as f:
-                config_dict = json.load(f)
-                config_dict["model"]["am_model_name"] = params["decoding_model_name"]
-            with open(os.path.join(params["output_dir"], "configuration.json"), "w") as f:
-                json.dump(config_dict, f, indent=4, separators=(',', ': '))
-        else:
-            shutil.copy(os.path.join(pretrained_model_path, file_name),
-                        os.path.join(params["output_dir"], file_name))
-    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=params["output_dir"],
-        output_dir=decoding_path,
-        batch_size=1
-    )
-    audio_in = os.path.join(params["data_dir"], "wav.scp")
-    inference_pipeline(audio_in=audio_in, param_dict={"decoding_model": "normal"})
-
-    # 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/token")
-        compute_wer(text_in, text_proc_file, os.path.join(decoding_path, "text.cer"))
-        os.system("tail -n 3 {}".format(os.path.join(decoding_path, "text.cer")))
-
-
-if __name__ == '__main__':
-    params = {}
-    params["modelscope_model_name"] = "damo/speech_UniASR_asr_2pass-minnan-16k-common-vocab3825"
-    params["required_files"] = ["am.mvn", "decoding.yaml", "configuration.json"]
-    params["output_dir"] = "./checkpoint"
-    params["data_dir"] = "./data/test"
-    params["decoding_model_name"] = "20epoch.pb"
-    modelscope_infer_after_finetune(params)