Metadata-Version: 2.1
Name: whisper-evaluate
Version: 1.2.0
Summary: Evaluate the effectiveness of the Whisper model.
Home-page: https://git.xindong.com/fengyanglu/whisper-evaluate
Author: geb
Author-email: 853934146@qq.com
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: numpy
Requires-Dist: pandas

# whisper-evaluate

Evaluate the effectiveness of the Whisper model.

## Install
从pypi安装:
```shell script
pip install whisper-evaluate
```
从本地安装:
```shell script
git clone https://git.xindong.com/fengyanglu/whisper-evaluate.git
cd whisper-evaluate
pip install dist/whisper_evaluate-1.2.0-py3-none-any.whl
```

## How to use
参考[test.py](./test.py)
```shell script
from whisper_evaluate import Report
import pprint

if __name__ == '__main__':
    # 设置参数: 任务名称
    rep = Report("test")
    # 生成评估报告

    result = rep.report("test_data/test-data-standard-latest.csv",
                   "test_data/test-data-standard-latest-whisper1.1-res.txt",
                   "test_data/test_antispam_0727.txt")
    pprint.pprint(result)
    # 保存统计备查数据 和 计算结果
    rep.save()



    # 单独计算whisper结果
    result = rep.whisper_report("test_data/test-data-standard-latest.csv",
                   "test_data/test-data-standard-latest-whisper1.1-res.txt")
    pprint.pprint(result)

    # 单独计算netease结果
    result = rep.netease_report("test_data/test-data-standard-latest.csv",
                   "test_data/test_antispam_0727.txt")
    pprint.pprint(result)


```

