From 37174dcf035897b2d8a29a948256d9bd16da4089 Mon Sep 17 00:00:00 2001
From: hnluo <haoneng.lhn@alibaba-inc.com>
Date: 星期二, 23 五月 2023 16:25:25 +0800
Subject: [PATCH] Delete infer_after_finetune.py
---
/dev/null | 54 ------------------------------------------------------
1 files changed, 0 insertions(+), 54 deletions(-)
diff --git a/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-minnan-16k-common-vocab3825/infer_after_finetune.py b/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-minnan-16k-common-vocab3825/infer_after_finetune.py
deleted file mode 100644
index d4df29e..0000000
--- a/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-minnan-16k-common-vocab3825/infer_after_finetune.py
+++ /dev/null
@@ -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)
--
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