app.py

import gradio as gr
import random
import os
import shutil
import pandas as pd
import sqlite3
from datasets import load_dataset
import threading
import time
from huggingface_hub import HfApi


DESCR = """
# TTS Arena

Vote on different speech synthesis models!
""".strip()
INSTR = """
## Instructions

* Listen to two anonymous models
* Vote on which one is more natural and realistic
* If there's a tie, click Skip

*IMPORTANT: Do not only rank the outputs based on naturalness. Also rank based on intelligibility (can you actually tell what they're saying?) and other factors (does it sound like a human?).*

**When you're ready to begin, click the Start button below!** The model names will be revealed once you vote.
""".strip()
request = ''
if os.getenv('HF_ID'):
    request = f"""
### Request Model

Please fill out [this form](https://huggingface.co/spaces/{os.getenv('HF_ID')}/discussions/new?title=%5BModel+Request%5D+&description=%23%23%20Model%20Request%0A%0A%2A%2AModel%20website%2Fpaper%20%28if%20applicable%29%2A%2A%3A%0A%2A%2AModel%20available%20on%2A%2A%3A%20%28coqui%7CHF%20pipeline%7Ccustom%20code%29%0A%2A%2AWhy%20do%20you%20want%20this%20model%20added%3F%2A%2A%0A%2A%2AComments%3A%2A%2A) to request a model.
"""
ABOUT = f"""
## About

TTS Arena is a project created to evaluate leading speech synthesis models. It is inspired by the [Chatbot Arena](https://chat.lmsys.org/) by LMSYS.

{request}
""".strip()
LDESC = """
## Leaderboard

A list of the models, based on how highly they are ranked!
""".strip()


dataset = load_dataset("ttseval/tts-arena", token=os.getenv('HF_TOKEN'))
theme = gr.themes.Base(
    font=[gr.themes.GoogleFont('Libre Franklin'), gr.themes.GoogleFont('Public Sans'), 'system-ui', 'sans-serif'],
)
model_names = {
    'styletts2': 'StyleTTS 2',
    'tacotron': 'Tacotron',
    'tacotronph': 'Tacotron Phoneme',
    'tacotrondca': 'Tacotron DCA',
    'speedyspeech': 'Speedy Speech',
    'overflow': 'Overflow TTS',
    'vits': 'VITS',
    'vitsneon': 'VITS Neon',
    'neuralhmm': 'Neural HMM',
    'glow': 'Glow TTS',
    'fastpitch': 'FastPitch',
    'jenny': 'Jenny',
    'tortoise': 'Tortoise TTS',
    'xtts2': 'XTTSv2',
    'xtts': 'XTTS',
    'elevenlabs': 'ElevenLabs',
    'speecht5': 'SpeechT5',
}
def get_random_split(existing_split=None):
    choice = random.choice(list(dataset.keys()))
    if existing_split and choice == existing_split:
        return get_random_split(choice)
    else:
        return choice
def get_db():
    return sqlite3.connect('database.db')
def create_db():
    conn = get_db()
    cursor = conn.cursor()
    cursor.execute('''
        CREATE TABLE IF NOT EXISTS model (
            name TEXT UNIQUE,
            upvote INTEGER,
            downvote INTEGER
        );
    ''')

def get_data():
    conn = get_db()
    cursor = conn.cursor()
    cursor.execute('SELECT name, upvote, downvote FROM model')
    data = cursor.fetchall()
    df = pd.DataFrame(data, columns=['name', 'upvote', 'downvote'])
    df['name'] = df['name'].replace(model_names)
    df['votes'] = df['upvote'] + df['downvote']
    # df['score'] = round((df['upvote'] / df['votes']) * 100, 2) # Percentage score

    ## ELO SCORE
    df['score'] = 1200
    for i in range(len(df)):
        for j in range(len(df)):
            if i != j:
                expected_a = 1 / (1 + 10 ** ((df['score'][j] - df['score'][i]) / 400))
                expected_b = 1 / (1 + 10 ** ((df['score'][i] - df['score'][j]) / 400))
                actual_a = df['upvote'][i] / df['votes'][i]
                actual_b = df['upvote'][j] / df['votes'][j]
                df.at[i, 'score'] += 32 * (actual_a - expected_a)
                df.at[j, 'score'] += 32 * (actual_b - expected_b)
                if df['votes'][j] < 3:
                    df.at[j, 'score'] -= (3 - df['votes'][j]) * 5
    df['score'] = round(df['score'])
    ## ELO SCORE

    df = df.sort_values(by='score', ascending=False)
    # df = df[['name', 'score', 'upvote', 'votes']]
    df = df[['name', 'score', 'votes']]
    return df

def get_random_splits():
    choice1 = get_random_split()
    choice2 = get_random_split(choice1)
    return (choice1, choice2)
def upvote_model(model):
    conn = get_db()
    cursor = conn.cursor()
    cursor.execute('UPDATE model SET upvote = upvote + 1 WHERE name = ?', (model,))
    if cursor.rowcount == 0:
        cursor.execute('INSERT OR REPLACE INTO model (name, upvote, downvote) VALUES (?, 1, 0)', (model,))
    conn.commit()
    cursor.close()
def downvote_model(model):
    conn = get_db()
    cursor = conn.cursor()
    cursor.execute('UPDATE model SET downvote = downvote + 1 WHERE name = ?', (model,))
    if cursor.rowcount == 0:
        cursor.execute('INSERT OR REPLACE INTO model (name, upvote, downvote) VALUES (?, 0, 1)', (model,))
    conn.commit()
    cursor.close()
def a_is_better(model1, model2):
    if model1 and model2:
        upvote_model(model1)
        downvote_model(model2)
    return reload(model1, model2)
def b_is_better(model1, model2):
    if model1 and model2:
        upvote_model(model2)
        downvote_model(model1)
    return reload(model1, model2)
def both_bad(model1, model2):
    if model1 and model2:
        downvote_model(model1)
        downvote_model(model2)
    return reload(model1, model2)
def both_good(model1, model2):
    if model1 and model2:
        upvote_model(model1)
        upvote_model(model2)
    return reload(model1, model2)
def reload(chosenmodel1=None, chosenmodel2=None):
    # Select random splits
    split1, split2 = get_random_splits()
    d1, d2 = (dataset[split1], dataset[split2])
    choice1, choice2 = (d1.shuffle()[0]['audio'], d2.shuffle()[0]['audio'])
    if chosenmodel1 in model_names:
        chosenmodel1 = model_names[chosenmodel1]
    if chosenmodel2 in model_names:
        chosenmodel2 = model_names[chosenmodel2]
    out = [
        (choice1['sampling_rate'], choice1['array']),
        (choice2['sampling_rate'], choice2['array']),
        split1,
        split2
    ]
    if chosenmodel1: out.append(f'This model was {chosenmodel1}')
    if chosenmodel2: out.append(f'This model was {chosenmodel2}')
    return out

with gr.Blocks() as leaderboard:
    gr.Markdown(LDESC)
    # df = gr.Dataframe(interactive=False, value=get_data())
    df = gr.Dataframe(interactive=False, min_width=0, wrap=True, column_widths=[200, 50, 50])
    leaderboard.load(get_data, outputs=[df])

with gr.Blocks() as vote:
    gr.Markdown(INSTR)
    with gr.Row():
        gr.HTML('<div align="left"><h3>Model A</h3></div>')
        gr.HTML('<div align="right"><h3>Model B</h3></div>')
    model1 = gr.Textbox(interactive=False, visible=False)
    model2 = gr.Textbox(interactive=False, visible=False)
    # with gr.Group():
    #     with gr.Row():
    #         prevmodel1 = gr.Textbox(interactive=False, show_label=False, container=False, value="Vote to reveal model A")
    #         prevmodel2 = gr.Textbox(interactive=False, show_label=False, container=False, value="Vote to reveal model B", text_align="right")
    #     with gr.Row():
    #         aud1 = gr.Audio(interactive=False, show_label=False, show_download_button=False, show_share_button=False, waveform_options={'waveform_progress_color': '#3C82F6'})
    #         aud2 = gr.Audio(interactive=False, show_label=False, show_download_button=False, show_share_button=False, waveform_options={'waveform_progress_color': '#3C82F6'})
    with gr.Group():
        with gr.Row():
            with gr.Column():
                with gr.Group():
                    prevmodel1 = gr.Textbox(interactive=False, show_label=False, container=False, value="Vote to reveal model A")
                    aud1 = gr.Audio(interactive=False, show_label=False, show_download_button=False, show_share_button=False, waveform_options={'waveform_progress_color': '#3C82F6'})
            with gr.Column():
                with gr.Group():
                    prevmodel2 = gr.Textbox(interactive=False, show_label=False, container=False, value="Vote to reveal model B", text_align="right")
                    aud2 = gr.Audio(interactive=False, show_label=False, show_download_button=False, show_share_button=False, waveform_options={'waveform_progress_color': '#3C82F6'})


    with gr.Row():
        abetter = gr.Button("A is Better", variant='primary')
        bbetter = gr.Button("B is Better", variant='primary')
    with gr.Row():
        bothbad = gr.Button("Both are Bad", scale=2)
        skipbtn = gr.Button("Skip", scale=1)
        bothgood = gr.Button("Both are Good", scale=2)
    outputs = [aud1, aud2, model1, model2, prevmodel1, prevmodel2]
    abetter.click(a_is_better, outputs=outputs, inputs=[model1, model2])
    bbetter.click(b_is_better, outputs=outputs, inputs=[model1, model2])
    skipbtn.click(b_is_better, outputs=outputs, inputs=[model1, model2])

    bothbad.click(both_bad, outputs=outputs, inputs=[model1, model2])
    bothgood.click(both_good, outputs=outputs, inputs=[model1, model2])

    vote.load(reload, outputs=[aud1, aud2, model1, model2])
with gr.Blocks() as about:
    gr.Markdown(ABOUT)
    pass
with gr.Blocks(theme=theme, css="footer {visibility: hidden}", title="TTS Leaderboard") as demo:
    gr.Markdown(DESCR)
    gr.TabbedInterface([vote, leaderboard, about], ['Vote', 'Leaderboard', 'About'])
def restart_space():
    api = HfApi(
        token=os.getenv('HF_TOKEN')
    )
    time.sleep(60 * 60) # Every hour
    print("Syncing DB before restarting space")
    api.upload_file(
        path_or_fileobj='database.db',
        path_in_repo='database.db',
        repo_id=os.getenv('DATASET_ID'),
        repo_type='dataset'
    )
    print("Restarting space")
    api.restart_space(repo_id=os.getenv('HF_ID'))
def sync_db():
    api = HfApi(
        token=os.getenv('HF_TOKEN')
    )
    while True:
        time.sleep(60 * 5)
        print("Uploading DB")
        api.upload_file(
            path_or_fileobj='database.db',
            path_in_repo='database.db',
            repo_id=os.getenv('DATASET_ID'),
            repo_type='dataset'
        )
if os.getenv('HF_ID'):
    restart_thread = threading.Thread(target=restart_space)
    restart_thread.daemon = True
    restart_thread.start()
if os.getenv('DATASET_ID'):
    # Fetch DB
    api = HfApi(
        token=os.getenv('HF_TOKEN')
    )
    print("Downloading DB...")
    try:
        path = api.hf_hub_download(
            repo_id=os.getenv('DATASET_ID'),
            repo_type='dataset',
            filename='database.db',
            cache_dir='./'
        )
        shutil.copyfile(path, 'database.db')
        print("Downloaded DB")
    except:
        pass
    # Update DB
    db_thread = threading.Thread(target=sync_db)
    db_thread.daemon = True
    db_thread.start()
create_db()
demo.queue(api_open=False).launch(show_api=False)