app.py

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()
LDESC = """
## Leaderboard

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

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):
    upvote_model(model1)
    downvote_model(model2)
    return reload(model1, model2)
def b_is_better(model1, model2):
    upvote_model(model2)
    downvote_model(model1)
    return reload(model1, model2)
def both_bad(model1, model2):
    downvote_model(model1)
    downvote_model(model2)
    return reload(model1, model2)
def both_good(model1, 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)
    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.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(theme=theme, css="footer {visibility: hidden}") as demo:
    gr.Markdown(DESCR)
    gr.TabbedInterface([vote, leaderboard], ['Vote', 'Leaderboard'])
def restart_space():
    api = HfApi(
        token=os.getenv('HF_TOKEN')
    )
    time.sleep(60 * 60) # Every hour
    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)