File size: 6,996 Bytes
dbf7cb0 33a4cab dbf7cb0 9fc4854 8ee1346 33a4cab 8ee1346 33a4cab 9fc4854 33a4cab 9fc4854 dbf7cb0 33a4cab 9fc4854 33a4cab 9fc4854 33a4cab 9fc4854 33a4cab 9fc4854 33a4cab dbf7cb0 33a4cab dbf7cb0 33a4cab dbf7cb0 33a4cab 9fc4854 33a4cab 9fc4854 33a4cab dbf7cb0 33a4cab 1fbfac0 33a4cab 9fc4854 33a4cab 9fc4854 33a4cab 9fc4854 33a4cab 9fc4854 33a4cab dbf7cb0 8ee1346 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 |
import gradio as gr
from apscheduler.schedulers.background import BackgroundScheduler
from src.css_html_js import custom_css
from src.envs import API, REPO_ID
from src.process_leaderboard_data import Leaderboard
def restart_space():
API.restart_space(repo_id=REPO_ID)
CITATION_TEXT = r"""@misc{cloneval,
author={Christop, Iwona and Kuczyński, Tomasz and Kubis, Marek},
title={{ClonEval: An Open Voice Cloning Benchmark}},
year={2025},
}"""
app = gr.Blocks(css=custom_css)
with app:
# Init class Leaderboard
leaderboard = Leaderboard()
# Custom CSS styling for tab-item components
app.css = """
.tab-item {
font-size: 10px;
padding: 10px 20px;
}
"""
# Title and Description of the Leaderboard
gr.HTML(open("pages/description.html", "r").read())
# LEADERBOARD
with gr.Tabs(elem_classes="tab-buttons") as tabs:
with gr.TabItem("🏅 Leaderboard", elem_id="Leaderboard", id=0, elem_classes="tab-item"):
# OVERALL
with gr.TabItem("Overall", elem_id="Overall", id=1, elem_classes="tab-item"):
gr.Markdown(open("pages/overall.md", "r").read())
# Create and display leaderboard table
leaderboard_dataframe = leaderboard.create_leaderboard_data('All', 'wavlm', 'emotion')
leaderboard_table = gr.DataFrame(leaderboard_dataframe,
datatype= ["markdown" if col == "Model" else "str" for col in leaderboard_dataframe.columns],
interactive=False,
)
# EMOTIONS
with gr.TabItem("Emotions", elem_id="Emotions", id=2, elem_classes="tab-item"):
gr.Markdown(open("pages/emotions.md", "r").read())
# UI for selecting dataset and emotion options
with gr.Row():
with gr.Column():
dataset = gr.Radio(
choices = leaderboard.get_emotional_dataset_list(),
value = None,
label = "Dataset",
)
with gr.Column(min_width=750):
emotion = gr.Radio(
choices = leaderboard.get_emotion_list(),
value = "Anger",
label = "Emotion",
)
# Create and display leaderboard table
leaderboard_dataframe = leaderboard.create_leaderboard_data(emotion.value, 'wavlm', 'emotion')
leaderboard_table = gr.DataFrame(leaderboard_dataframe,
datatype= ["markdown" if col == "Model" else "str" for col in leaderboard_dataframe.columns],
interactive=False,
)
# Update leaderboard table based on user selection changes in emotion or dataset options.
dataset.change(leaderboard.update_leaderboard_data_in_emotion_section,
[dataset, gr.State('dataset'), leaderboard_table],
[emotion, leaderboard_table]
)
emotion.change(leaderboard.update_leaderboard_data_in_emotion_section,
[emotion, gr.State('emotion'), leaderboard_table],
[dataset, leaderboard_table]
)
# FEATURES
with gr.TabItem("Features", elem_id="Features", id=3, elem_classes="tab-item"):
gr.Markdown(open("pages/features.md", "r").read())
# UI for selecting dataset, emotion, and feature options
with gr.Row():
with gr.Column():
dataset = gr.Radio(
choices = leaderboard.get_emotional_dataset_list(),
value = None,
elem_id = "dataset_features",
label = "Dataset",
)
with gr.Column(min_width=750):
emotion = gr.Radio(
choices = leaderboard.get_emotion_list(),
value = "Anger",
elem_id = "emotion_features",
label = "Emotion",
)
with gr.Row():
hidden_columns = ['model', 'dataset', 'emotion', 'wavlm', "Unnamed: 0"]
feature = gr.Radio(
choices = [kol for kol in list(leaderboard.get_models_performance_dataframe_column()) if kol not in hidden_columns],
value = "pitch",
label = "Feature",
)
# Create and display leaderboard table
leaderboard_dataframe = leaderboard.create_leaderboard_data(emotion.value, feature.value, 'emotion')
leaderboard_table = gr.DataFrame(leaderboard_dataframe,
datatype= ["markdown" if col == "Model" else "str" for col in leaderboard_dataframe.columns],
interactive=False,
)
# Update leaderboard table based on user selection changes in emotion, dataset, or feature options.
emotion.change(leaderboard.update_leaderboard_data_in_feature_section,
[emotion, feature, gr.State('emotion'), leaderboard_table],
[dataset, leaderboard_table]
)
dataset.change(leaderboard.update_leaderboard_data_in_feature_section,
[dataset, feature, gr.State('dataset'), leaderboard_table],
[emotion, leaderboard_table]
)
feature.change(leaderboard.update_leaderboard_data_by_feature,
[emotion, dataset, feature],
[leaderboard_table]
)
# ABOUT
with gr.TabItem("📝 About", elem_id="About", id=4):
gr.Markdown(open("pages/about.md", "r").read())
# SUBMIT HERE
with gr.TabItem("🚀 Submit here! ", elem_id="Submit", id=5):
gr.Markdown(open("pages/submit.md", "r").read())
with gr.Column():
with gr.Accordion("📙 Citation", open=False):
citation_button = gr.Textbox(
label="",
value=CITATION_TEXT,
lines=5,
elem_id="citation-button",
show_copy_button=True,
)
scheduler = BackgroundScheduler()
scheduler.add_job(restart_space, "interval", seconds=1800)
scheduler.start()
app.queue(default_concurrency_limit=40).launch() |