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.gitattributes

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 *.h5 filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+title: DF Arena Test
+emoji: 📉
+colorFrom: yellow
+colorTo: purple
+sdk: gradio
+sdk_version: 5.14.0
+app_file: app.py
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+
+
+
+import pandas as pd
+import gradio as gr
+from ui.leaderboard import render_leader_board, render_info_html
+from ui.df_arena_tool import render_tool_info
+from ui.submission import render_submission_page
+import os
+from utils import load_leaderboard
+from huggingface_hub import snapshot_download
+import gradio as gr
+import os
+import json
+
+REPO_ID = os.getenv('REPO_ID')
+DB_ERR_PATH = f'./data/data/leaderboard_err.csv'
+DB_ACCURACY_PATH = f'./data/data/leaderboard_accuracy.csv'
+CITATIONS_PATH = f'./data/data/model_citations.json'
+
+if not os.path.exists('./data/data'):
+    snapshot_download(repo_id=REPO_ID, 
+                      repo_type="dataset", local_dir='./data/data')
+
+
+with open(CITATIONS_PATH, 'r') as f:
+    model_citations = json.load(f)
+
+# Load leaderboard data
+leaderboard_df_err = load_leaderboard(DB_ERR_PATH)
+leaderboard_df_accuracy = load_leaderboard(DB_ACCURACY_PATH)
+
+# Function to load leaderboard data
+
+custom_css = """
+h1,  {
+    font-size: 50px !important; /* Increase heading sizes */
+    line-height: 2.0 !important; /* Increase line spacing */
+    text-align: center !important; /* Center align headings */
+}
+
+.gradio-container {
+    padding: 30px !important;  /* Increase padding around the UI */
+}
+
+.markdown-body p {
+    font-size: 30px !important; /* Increase text size */
+    line-height: 2.0 !important; /* More space between lines */
+}
+
+.gradio-container .gr-block {
+    margin-bottom: 20px !important; /* Add more space between elements */
+}
+"""
+
+# Gradio Interface Configuration
+def create_ui():
+    with gr.Blocks(theme=gr.themes.Soft(text_size=gr.themes.sizes.text_lg), css=custom_css) as demo:
+        # gr.Markdown("# Speech Deep Fake Arena")
+        gr.Image('/data/code/DF_arena_leaderboard/leaderboard/data/df_arena.jpg')
+        
+        with gr.Tabs():
+            with gr.Tab("🏆 Leaderboard"):
+                with gr.Column():
+                    render_info_html()
+                    gr.Markdown("Table for Equal Error Rate (EER %) for different systems")
+                    render_leader_board(leaderboard_df_err, model_citations)  # Adjust this to work with Gradio components
+                    gr.Markdown("Table for Accuracy (EER %) for different systems")
+
+                    render_leader_board(leaderboard_df_accuracy, model_citations)
+                     
+            with gr.Tab("🛠 Evaluation"):
+                render_tool_info()  
+            with gr.Tab("📤 Submission"):
+                render_submission_page()  
+
+    return demo
+
+
+# Launch the app
+create_ui().launch()

ui/df_arena_tool.py

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+import gradio as gr
+
+def render_tool_info():
+    text = """
+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/hoanmyTran/deepfake_arena/tree/main.
+
+### Usage 							
+#### 1. Data Preparation 
+Create metadata.csv 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 
+                      --transforms pad 
+                      --num workers 4
+```
+
+
+
+
+"""
+    return gr.Markdown(text)

ui/leaderboard.py

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+
+import pandas as pd
+import gradio as gr
+from utils import load_leaderboard
+import numpy as np
+from huggingface_hub import snapshot_download
+
+
+def make_clickable(url, name):
+    return f'<a href="{url}" target="_blank">{name}</a>'
+
+def render_info_html():
+    info_text = "With the growing advent of machine-generated speech, the scientific community is responding with exciting resources " \
+                 "to detect deep fakes. With research moving at such a rapid pace, it becomes challenging to keep track of generalizability " \
+                 "of SOTA DF detection systems. This leaderboard thus presents a comprehensive benchmark of 10 SOTA speech antispoofing " \
+                 "systems across 13 popular speech deep fake detection datasets."
+    
+    # HTML formatted info text
+    return gr.Markdown(info_text)
+
+def highlight_min(s, props=''):
+    return np.where(s == np.nanmin(s.values), props, '')
+
+def render_leader_board(leaderboard_df, model_citations):
+
+    if not leaderboard_df.empty:
+        # Convert model names and dataset names to clickable links
+        # leaderboard_df['System'] = leaderboard_df['System'].apply(lambda x: make_clickable(model_citations.get(x, "#"), x))
+        # leaderboard_df['Average'] = leaderboard_df.loc[:, ['In-the-wild','ASVSpoof2019','ASVSpoof2021LA','ASVSpoof2021DF','ASVSpoof2024-Dev','ASVSpoof2024-Eval','FakeOrReal','codecfake3','ADD2022','ADD2023','DFADD','LibriVoc','SONAR']].mean(axis=1)
+        leaderboard_df.insert(1, 'Average', leaderboard_df.loc[:, ['In-the-wild','ASVSpoof2019','ASVSpoof2021LA','ASVSpoof2021DF','ASVSpoof2024-Dev','ASVSpoof2024-Eval','FakeOrReal','codecfake3','ADD2022','ADD2023','DFADD','LibriVoc','SONAR']].mean(axis=1))
+
+        leaderboard_df = leaderboard_df.sort_values(by="Average", ascending=True).reset_index(drop=True)
+
+        # Assign rank emojis 🥇🥈🥉
+        leaderboard_df["System"] = leaderboard_df["System"].apply(lambda x: f"[{x}]({model_citations.get(x, '#')})")
+
+        emojis = ["🥇", "🥈", "🥉"]
+
+        leaderboard_df.System[0] = f"{emojis[0]} {leaderboard_df.System[0]}"
+        leaderboard_df.System[1] = f"{emojis[1]} {leaderboard_df.System[1]}"
+        leaderboard_df.System[2] = f"{emojis[2]} {leaderboard_df.System[2]}"
+        temp = leaderboard_df.loc[:, ['System', 'Training Data', 'Num Parameters', 'Data Augmentation']]
+        # styler = leaderboard_df.drop(columns=['Training Data', 'Num Parameters', 'Data Augmentation'], axis=1).style.highlight_min(color = 'lightgreen', axis = 0).format(precision=2)
+        styler = (
+        leaderboard_df
+        .drop(columns=['Training Data', 'Num Parameters', 'Data Augmentation'], axis=1).style \
+        .format(precision=2))
+        styler.apply(highlight_min, props='color:green', axis=0)
+
+        # styler['System'] = temp['System']
+        # styler['Training Data'] = temp['Training Data']
+        # styler['Num Parameters'] = temp['Num Parameters'] 
+        # styler['Data Augmentation'] = temp['Data Augmentation']  
+        return gr.Dataframe(styler, datatype=['markdown'] * 1 + ['number']  * 14)
+    return gr.HTML(value="<p>No data available in the leaderboard.</p>")

ui/submission.py

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+import gradio as gr
+
+def render_submission_page():
+    text = """ Want to submit your own system to the leaderboard? Submit 
+    all the scores files for your system across evaluation sets of the supported datasets at
+    <speech.arena.eval@gmail.com> and we will handle the rest. Ensure the filenames are named as follows for each datasets:
+
+    - asvspoof_2019.txt
+    - asvspoof_2021_df_eval.txt
+    - asvspoof_2021_la_eval.txt
+    - asvspoof_2024_dev.txt
+    - asvspoof_2024_eval.txt
+    - codecfake.txt
+    - fake_or_real.txt
+    - in_the_wild.txt
+    - sonar.txt
+    - dfadd.txt
+    - libri_voc.txt
+    - add_2022.txt
+    - add_2023.txt
+"""
+    return gr.Markdown(text)
+
+
+

utils.py

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+import sqlite3
+import pandas as pd
+import streamlit as st
+
+def load_leaderboard(db_path):
+    df = pd.read_csv(db_path)  # Update table name if needed
+    
+    return df