| import gradio as gr | |
| def render_eval_info(): | |
| text = r""" | |
| ### How to compute your own EER score file ? | |
| In order to streamline the evaluation process across many models and datasets, we | |
| have developed df_arena_toolkit which can be used to compute score files for evaluation. | |
| The tool can be found at https://github.com/Speech-Arena/speech_df_arena. | |
| ### Usage | |
| #### 1. Data Preparation | |
| Create metadata.csv for your desired dataset with below format: | |
| ``` | |
| file_name,label | |
| /path/to/audio1,spoof | |
| /path/to/audio2,bonafide | |
| ... | |
| ``` | |
| NOTE : The labels should contain "spoof" for spoofed samples and "bonafide" for real samples. | |
| All the file_name paths should be absolute | |
| #### 2. Evaluation | |
| Example usage : | |
| ```py | |
| python evaluation.py --model_name wavlm_ecapa | |
| --batch_size 32 | |
| --protocol_file_path /path/to/metadata.csv | |
| --model_path /path/to/model.ckpt | |
| --out_score_file_name scores.txt | |
| --trim pad | |
| --num workers 4 | |
| ``` | |
| """ | |
| return gr.Markdown(text, latex_delimiters=[{ "left": "$", "right": "$", "display": True }]) | |