import gradio as gr import pandas as pd from langdetect import detect from datasets import load_dataset import threading, time, uuid, sqlite3, shutil, os, random, asyncio, threading from pathlib import Path from huggingface_hub import CommitScheduler, delete_file, hf_hub_download from gradio_client import Client import pyloudnorm as pyln import soundfile as sf import librosa from detoxify import Detoxify import os import tempfile from pydub import AudioSegment def match_target_amplitude(sound, target_dBFS): change_in_dBFS = target_dBFS - sound.dBFS return sound.apply_gain(change_in_dBFS) # from gradio_space_ci import enable_space_ci # enable_space_ci() toxicity = Detoxify('original') with open('bsc.txt') as f: sents = f.read().strip().splitlines() #################################### # Constants #################################### AVAILABLE_MODELS = { 'XTTSv2': 'xtts', # 'WhisperSpeech': 'whisperspeech', 'ElevenLabs': 'eleven', # 'OpenVoice': 'openvoice', 'OpenVoice V2': 'openvoicev2', 'Play.HT 2.0': 'playht', # 'MetaVoice': 'metavoice', 'MeloTTS': 'melo', 'StyleTTS 2': 'styletts2', 'GPT-SoVITS': 'sovits', # 'Vokan TTS': 'vokan', 'VoiceCraft 2.0': 'voicecraft', 'Parler TTS': 'parler' } SPACE_ID = os.getenv('SPACE_ID') MAX_SAMPLE_TXT_LENGTH = 300 MIN_SAMPLE_TXT_LENGTH = 10 DB_DATASET_ID = os.getenv('DATASET_ID') DB_NAME = "database.db" # If /data available => means local storage is enabled => let's use it! DB_PATH = f"/data/{DB_NAME}" if os.path.isdir("/data") else DB_NAME print(f"Using {DB_PATH}") # AUDIO_DATASET_ID = "ttseval/tts-arena-new" CITATION_TEXT = """@misc{tts-arena, title = {Text to Speech Arena}, author = {mrfakename and Srivastav, Vaibhav and Fourrier, Clémentine and Pouget, Lucain and Lacombe, Yoach and main and Gandhi, Sanchit}, year = 2024, publisher = {Hugging Face}, howpublished = "\\url{https://huggingface.co/spaces/TTS-AGI/TTS-Arena}" }""" #################################### # Functions #################################### def create_db_if_missing(): conn = get_db() cursor = conn.cursor() cursor.execute(''' CREATE TABLE IF NOT EXISTS model ( name TEXT UNIQUE, upvote INTEGER, downvote INTEGER ); ''') cursor.execute(''' CREATE TABLE IF NOT EXISTS vote ( id INTEGER PRIMARY KEY AUTOINCREMENT, username TEXT, model TEXT, vote INTEGER, timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP ); ''') cursor.execute(''' CREATE TABLE IF NOT EXISTS votelog ( id INTEGER PRIMARY KEY AUTOINCREMENT, username TEXT, chosen TEXT, rejected TEXT, timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP ); ''') cursor.execute(''' CREATE TABLE IF NOT EXISTS spokentext ( id INTEGER PRIMARY KEY AUTOINCREMENT, spokentext TEXT, timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP ); ''') def get_db(): return sqlite3.connect(DB_PATH) #################################### # Space initialization #################################### # Download existing DB if not os.path.isfile(DB_PATH): print("Downloading DB...") try: cache_path = hf_hub_download(repo_id=DB_DATASET_ID, repo_type='dataset', filename=DB_NAME) shutil.copyfile(cache_path, DB_PATH) print("Downloaded DB") except Exception as e: print("Error while downloading DB:", e) # Create DB table (if doesn't exist) create_db_if_missing() # Sync local DB with remote repo every 5 minute (only if a change is detected) scheduler = CommitScheduler( repo_id=DB_DATASET_ID, repo_type="dataset", folder_path=Path(DB_PATH).parent, every=5, allow_patterns=DB_NAME, ) ## 🏆 Leaderboard Vote to help the community determine the best text-to-speech (TTS) models. The leaderboard displays models in descending order of how natural they sound (based on votes cast by the community). Important: In order to help keep results fair, the leaderboard hides results by default until the number of votes passes a threshold. Tick the `Reveal preliminary results` to show models without sufficient votes. Please note that preliminary results may be inaccurate. """.strip() def del_db(txt): if not txt.lower() == 'delete db': raise gr.Error('You did not enter "delete db"') # Delete local + remote os.remove(DB_PATH) delete_file(path_in_repo=DB_NAME, repo_id=DB_DATASET_ID, repo_type='dataset') # Recreate create_db_if_missing() return 'Delete DB' 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': 'Coqui XTTSv2', 'xtts': 'Coqui XTTS', 'openvoice': 'MyShell OpenVoice', 'elevenlabs': 'ElevenLabs', 'openai': 'OpenAI', 'hierspeech': 'HierSpeech++', 'pheme': 'PolyAI Pheme', 'speecht5': 'SpeechT5', 'metavoice': 'MetaVoice-1B', } model_licenses = { 'styletts2': 'MIT', 'tacotron': 'BSD-3', 'tacotronph': 'BSD-3', 'tacotrondca': 'BSD-3', 'speedyspeech': 'BSD-3', 'overflow': 'MIT', 'vits': 'MIT', 'openvoice': 'MIT', 'vitsneon': 'BSD-3', 'neuralhmm': 'MIT', 'glow': 'MIT', 'fastpitch': 'Apache 2.0', 'jenny': 'Jenny License', 'tortoise': 'Apache 2.0', 'xtts2': 'CPML (NC)', 'xtts': 'CPML (NC)', 'elevenlabs': 'Proprietary', 'eleven': 'Proprietary', 'openai': 'Proprietary', 'hierspeech': 'MIT', 'pheme': 'CC-BY', 'speecht5': 'MIT', 'metavoice': 'Apache 2.0', 'elevenlabs': 'Proprietary', 'whisperspeech': 'MIT', } model_links = { 'styletts2': 'https://github.com/yl4579/StyleTTS2', 'tacotron': 'https://github.com/NVIDIA/tacotron2', 'speedyspeech': 'https://github.com/janvainer/speedyspeech', 'overflow': 'https://github.com/shivammehta25/OverFlow', 'vits': 'https://github.com/jaywalnut310/vits', 'openvoice': 'https://github.com/myshell-ai/OpenVoice', 'neuralhmm': 'https://github.com/ketranm/neuralHMM', 'glow': 'https://github.com/jaywalnut310/glow-tts', 'fastpitch': 'https://fastpitch.github.io/', 'tortoise': 'https://github.com/neonbjb/tortoise-tts', 'xtts2': 'https://huggingface.co/coqui/XTTS-v2', 'xtts': 'https://huggingface.co/coqui/XTTS-v1', 'elevenlabs': 'https://elevenlabs.io/', 'openai': 'https://help.openai.com/en/articles/8555505-tts-api', 'hierspeech': 'https://github.com/sh-lee-prml/HierSpeechpp', 'pheme': 'https://github.com/PolyAI-LDN/pheme', 'speecht5': 'https://github.com/microsoft/SpeechT5', 'metavoice': 'https://github.com/metavoiceio/metavoice-src', } def model_license(name): print(name) for k, v in AVAILABLE_MODELS.items(): if k == name: if v in model_licenses: return model_licenses[v] print('---') return 'Unknown' def get_leaderboard(reveal_prelim = False): conn = get_db() cursor = conn.cursor() sql = 'SELECT name, upvote, downvote FROM model' # if not reveal_prelim: sql += ' WHERE EXISTS (SELECT 1 FROM model WHERE (upvote + downvote) > 750)' if not reveal_prelim: sql += ' WHERE (upvote + downvote) > 500' cursor.execute(sql) data = cursor.fetchall() df = pd.DataFrame(data, columns=['name', 'upvote', 'downvote']) # df['license'] = df['name'].map(model_license) 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) df['score'] = round(df['score']) ## ELO SCORE df = df.sort_values(by='score', ascending=False) df['order'] = ['#' + str(i + 1) for i in range(len(df))] # df = df[['name', 'score', 'upvote', 'votes']] # df = df[['order', 'name', 'score', 'license', 'votes']] df = df[['order', 'name', 'score', 'votes']] return df def mkuuid(uid): if not uid: uid = uuid.uuid4() return uid def upvote_model(model, uname): 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,)) cursor.execute('INSERT INTO vote (username, model, vote) VALUES (?, ?, ?)', (uname, model, 1,)) with scheduler.lock: conn.commit() cursor.close() def log_text(text): conn = get_db() cursor = conn.cursor() cursor.execute('INSERT INTO spokentext (spokentext) VALUES (?)', (text,)) with scheduler.lock: conn.commit() cursor.close() def downvote_model(model, uname): 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,)) cursor.execute('INSERT INTO vote (username, model, vote) VALUES (?, ?, ?)', (uname, model, -1,)) with scheduler.lock: conn.commit() cursor.close() def a_is_better(model1, model2, userid): print("A is better", model1, model2) if not model1 in AVAILABLE_MODELS.keys() and not model1 in AVAILABLE_MODELS.values(): raise gr.Error('Sorry, please try voting again.') userid = mkuuid(userid) if model1 and model2: conn = get_db() cursor = conn.cursor() cursor.execute('INSERT INTO votelog (username, chosen, rejected) VALUES (?, ?, ?)', (str(userid), model1, model2,)) with scheduler.lock: conn.commit() cursor.close() upvote_model(model1, str(userid)) downvote_model(model2, str(userid)) return reload(model1, model2, userid, chose_a=True) def b_is_better(model1, model2, userid): print("B is better", model1, model2) if not model1 in AVAILABLE_MODELS.keys() and not model1 in AVAILABLE_MODELS.values(): raise gr.Error('Sorry, please try voting again.') userid = mkuuid(userid) if model1 and model2: conn = get_db() cursor = conn.cursor() cursor.execute('INSERT INTO votelog (username, chosen, rejected) VALUES (?, ?, ?)', (str(userid), model2, model1,)) with scheduler.lock: conn.commit() cursor.close() upvote_model(model2, str(userid)) downvote_model(model1, str(userid)) return reload(model1, model2, userid, chose_b=True) def both_bad(model1, model2, userid): userid = mkuuid(userid) if model1 and model2: downvote_model(model1, str(userid)) downvote_model(model2, str(userid)) return reload(model1, model2, userid) def both_good(model1, model2, userid): userid = mkuuid(userid) if model1 and model2: upvote_model(model1, str(userid)) upvote_model(model2, str(userid)) return reload(model1, model2, userid) def reload(chosenmodel1=None, chosenmodel2=None, userid=None, chose_a=False, chose_b=False): # Select random splits # row = random.choice(list(audio_dataset['train'])) # options = list(random.choice(list(audio_dataset['train'])).keys()) # split1, split2 = random.sample(options, 2) # choice1, choice2 = (row[split1], row[split2]) # 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 userid: out.append(userid) # if chosenmodel1: out.append(f'This model was {chosenmodel1}') # if chosenmodel2: out.append(f'This model was {chosenmodel2}') # return out # return (f'This model was {chosenmodel1}', f'This model was {chosenmodel2}', gr.update(visible=False), gr.update(visible=False)) # return (gr.update(variant='secondary', value=chosenmodel1, interactive=False), gr.update(variant='secondary', value=chosenmodel2, interactive=False)) out = [ gr.update(interactive=False, visible=False), gr.update(interactive=False, visible=False) ] if chose_a == True: out.append(gr.update(value=f'Your vote: {chosenmodel1}', interactive=False, visible=True)) out.append(gr.update(value=f'{chosenmodel2}', interactive=False, visible=True)) else: out.append(gr.update(value=f'{chosenmodel1}', interactive=False, visible=True)) out.append(gr.update(value=f'Your vote: {chosenmodel2}', interactive=False, visible=True)) out.append(gr.update(visible=True)) return out with gr.Blocks() as leaderboard: gr.Markdown(LDESC) # df = gr.Dataframe(interactive=False, value=get_leaderboard()) df = gr.Dataframe(interactive=False, min_width=0, wrap=True, column_widths=[30, 200, 50, 50]) with gr.Row(): reveal_prelim = gr.Checkbox(label="Reveal preliminary results", info="Show all models, including models with very few human ratings.", scale=1) reloadbtn = gr.Button("Refresh", scale=3) reveal_prelim.input(get_leaderboard, inputs=[reveal_prelim], outputs=[df]) leaderboard.load(get_leaderboard, inputs=[reveal_prelim], outputs=[df]) reloadbtn.click(get_leaderboard, inputs=[reveal_prelim], outputs=[df]) # gr.Markdown("DISCLAIMER: The licenses listed may not be accurate or up to date, you are responsible for checking the licenses before using the models. Also note that some models may have additional usage restrictions.") def doloudnorm(path): data, rate = sf.read(path) meter = pyln.Meter(rate) loudness = meter.integrated_loudness(data) loudness_normalized_audio = pyln.normalize.loudness(data, loudness, -12.0) sf.write(path, loudness_normalized_audio, rate) def doresample(path_to_wav): pass ########################## # 2x speedup (hopefully) # ########################## def synthandreturn(text): text = text.strip() if len(text) > MAX_SAMPLE_TXT_LENGTH: raise gr.Error(f'You exceeded the limit of {MAX_SAMPLE_TXT_LENGTH} characters') if len(text) < MIN_SAMPLE_TXT_LENGTH: raise gr.Error(f'Please input a text longer than {MIN_SAMPLE_TXT_LENGTH} characters') if ( # test toxicity if not prepared text text not in sents and toxicity.predict(text)['toxicity'] > 0.8 ): print(f'Detected toxic content! "{text}"') raise gr.Error('Your text failed the toxicity test') if not text: raise gr.Error(f'You did not enter any text') # Check language try: if not detect(text) == "en": gr.Warning('Warning: The input text may not be in English') except: pass # Get two random models mdl1, mdl2 = random.sample(list(AVAILABLE_MODELS.keys()), 2) log_text(text) print("[debug] Using", mdl1, mdl2) def predict_and_update_result(text, model, result_storage): try: if model in AVAILABLE_MODELS: result = router.predict(text, AVAILABLE_MODELS[model].lower(), api_name="/synthesize") else: result = router.predict(text, model.lower(), api_name="/synthesize") except: raise gr.Error('Unable to call API, please try again :)') print('Done with', model) # try: # doresample(result) # except: # pass try: with tempfile.NamedTemporaryFile(suffix='.wav', delete=False) as f: audio = AudioSegment.from_file(result) current_sr = audio.frame_rate if current_sr > 24000: audio = audio.set_frame_rate(24000) try: print('Trying to normalize audio') audio = match_target_amplitude(audio, -20) except: print('[WARN] Unable to normalize audio') audio.export(f.name, format="wav") os.unlink(result) result = f.name except: pass if model in AVAILABLE_MODELS.keys(): model = AVAILABLE_MODELS[model] print(model) print(f"Running model {model}") result_storage[model] = result # try: # doloudnorm(result) # except: # pass mdl1k = mdl1 mdl2k = mdl2 print(mdl1k, mdl2k) if mdl1 in AVAILABLE_MODELS.keys(): mdl1k=AVAILABLE_MODELS[mdl1] if mdl2 in AVAILABLE_MODELS.keys(): mdl2k=AVAILABLE_MODELS[mdl2] results = {} print(f"Sending models {mdl1k} and {mdl2k} to API") thread1 = threading.Thread(target=predict_and_update_result, args=(text, mdl1k, results)) thread2 = threading.Thread(target=predict_and_update_result, args=(text, mdl2k, results)) thread1.start() thread2.start() thread1.join() thread2.join() #debug # print(results) # print(list(results.keys())[0]) # y, sr = librosa.load(results[list(results.keys())[0]], sr=None) # print(sr) # print(list(results.keys())[1]) # y, sr = librosa.load(results[list(results.keys())[1]], sr=None) # print(sr) #debug # outputs = [text, btn, r2, model1, model2, aud1, aud2, abetter, bbetter, prevmodel1, prevmodel2, nxtroundbtn] print(f"Retrieving models {mdl1k} and {mdl2k} from API") return ( text, "Synthesize", gr.update(visible=True), # r2 mdl1, # model1 mdl2, # model2 gr.update(visible=True, value=results[mdl1k]), # aud1 gr.update(visible=True, value=results[mdl2k]), # aud2 gr.update(visible=True, interactive=False), #abetter gr.update(visible=True, interactive=False), #bbetter gr.update(visible=False), #prevmodel1 gr.update(visible=False), #prevmodel2 gr.update(visible=False), #nxt round btn ) # return ( # text, # "Synthesize", # gr.update(visible=True), # r2 # mdl1, # model1 # mdl2, # model2 # # 'Vote to reveal model A', # prevmodel1 # gr.update(visible=True, value=router.predict( # text, # AVAILABLE_MODELS[mdl1], # api_name="/synthesize" # )), # aud1 # # 'Vote to reveal model B', # prevmodel2 # gr.update(visible=True, value=router.predict( # text, # AVAILABLE_MODELS[mdl2], # api_name="/synthesize" # )), # aud2 # gr.update(visible=True, interactive=True), # gr.update(visible=True, interactive=True), # gr.update(visible=False), # gr.update(visible=False), # gr.update(visible=False), #nxt round btn # ) def unlock_vote(btn_index, aplayed, bplayed): # sample played if btn_index == 0: aplayed = gr.State(value=True) if btn_index == 1: bplayed = gr.State(value=True) # both audio samples played if bool(aplayed) and bool(bplayed): print('Both audio samples played, voting unlocked') return [gr.update(interactive=True), gr.update(interactive=True), gr.update(), gr.update()] return [gr.update(), gr.update(), aplayed, bplayed] def randomsent(): return random.choice(sents), '🎲' def clear_stuff(): return "", "Synthesize", gr.update(visible=False), '', '', gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False) def disable(): return [gr.update(interactive=False), gr.update(interactive=False), gr.update(interactive=False)] def enable(): return [gr.update(interactive=True), gr.update(interactive=True), gr.update(interactive=True)] with gr.Blocks() as vote: # sample played #aplayed = gr.State(value=False) #bplayed = gr.State(value=False) # voter ID useridstate = gr.State() gr.Markdown(INSTR) with gr.Group(): with gr.Row(): text = gr.Textbox(container=False, show_label=False, placeholder="Enter text to synthesize", lines=1, max_lines=1, scale=9999999, min_width=0) randomt = gr.Button('🎲', scale=0, min_width=0, variant='tool') randomt.click(randomsent, outputs=[text, randomt]) btn = gr.Button("Synthesize", variant='primary') model1 = gr.Textbox(interactive=False, lines=1, max_lines=1, visible=False) #model1 = gr.Textbox(interactive=False, lines=1, max_lines=1, visible=True) model2 = gr.Textbox(interactive=False, lines=1, max_lines=1, visible=False) #model2 = gr.Textbox(interactive=False, lines=1, max_lines=1, visible=True) with gr.Row(visible=False) as r2: with gr.Column(): with gr.Group(): aud1 = gr.Audio(interactive=False, show_label=False, show_download_button=False, show_share_button=False, waveform_options={'waveform_progress_color': '#3C82F6'}) abetter = gr.Button("A is better", variant='primary') prevmodel1 = gr.Textbox(interactive=False, show_label=False, container=False, value="Vote to reveal model A", text_align="center", lines=1, max_lines=1, visible=False) with gr.Column(): with gr.Group(): aud2 = gr.Audio(interactive=False, show_label=False, show_download_button=False, show_share_button=False, waveform_options={'waveform_progress_color': '#3C82F6'}) bbetter = gr.Button("B is better", variant='primary') prevmodel2 = gr.Textbox(interactive=False, show_label=False, container=False, value="Vote to reveal model B", text_align="center", lines=1, max_lines=1, visible=False) nxtroundbtn = gr.Button('Next round', visible=False) # outputs = [text, btn, r2, model1, model2, prevmodel1, aud1, prevmodel2, aud2, abetter, bbetter] outputs = [ text, btn, r2, model1, model2, aud1, aud2, abetter, bbetter, prevmodel1, prevmodel2, nxtroundbtn ] """ text, "Synthesize", gr.update(visible=True), # r2 mdl1, # model1 mdl2, # model2 gr.update(visible=True, value=results[mdl1]), # aud1 gr.update(visible=True, value=results[mdl2]), # aud2 gr.update(visible=True, interactive=False), #abetter gr.update(visible=True, interactive=False), #bbetter gr.update(visible=False), #prevmodel1 gr.update(visible=False), #prevmodel2 gr.update(visible=False), #nxt round btn""" btn.click(disable, outputs=[btn, abetter, bbetter]).then(synthandreturn, inputs=[text], outputs=outputs).then(enable, outputs=[btn, abetter, bbetter]) nxtroundbtn.click(clear_stuff, outputs=outputs) # Allow interaction with the vote buttons only when both audio samples have finished playing #aud1.stop(unlock_vote, outputs=[abetter, bbetter, aplayed, bplayed], inputs=[gr.State(value=0), aplayed, bplayed]) #aud2.stop(unlock_vote, outputs=[abetter, bbetter, aplayed, bplayed], inputs=[gr.State(value=1), aplayed, bplayed]) # nxt_outputs = [prevmodel1, prevmodel2, abetter, bbetter] nxt_outputs = [abetter, bbetter, prevmodel1, prevmodel2, nxtroundbtn] abetter.click(a_is_better, outputs=nxt_outputs, inputs=[model1, model2, useridstate]) bbetter.click(b_is_better, outputs=nxt_outputs, inputs=[model1, model2, useridstate]) # skipbtn.click(b_is_better, outputs=outputs, inputs=[model1, model2, useridstate]) # bothbad.click(both_bad, outputs=outputs, inputs=[model1, model2, useridstate]) # bothgood.click(both_good, outputs=outputs, inputs=[model1, model2, useridstate]) # vote.load(reload, outputs=[aud1, aud2, model1, model2]) with gr.Blocks() as about: gr.Markdown(ABOUT) # with gr.Blocks() as admin: # rdb = gr.Button("Reload Audio Dataset") # # rdb.click(reload_audio_dataset, outputs=rdb) # with gr.Group(): # dbtext = gr.Textbox(label="Type \"delete db\" to confirm", placeholder="delete db") # ddb = gr.Button("Delete DB") # ddb.click(del_db, inputs=dbtext, outputs=ddb) with gr.Blocks(theme=theme, css="footer {visibility: hidden}textbox{resize:none}", title="TTS Arena") as demo: gr.Markdown(DESCR) # gr.TabbedInterface([vote, leaderboard, about, admin], ['Vote', 'Leaderboard', 'About', 'Admin (ONLY IN BETA)']) gr.TabbedInterface([vote, leaderboard, about], ['🗳️ Vote', '🏆 Leaderboard', '📄 About']) if CITATION_TEXT: with gr.Row(): with gr.Accordion("Citation", open=False): gr.Markdown(f"If you use this data in your publication, please cite us!\n\nCopy the BibTeX citation to cite this source:\n\n```bibtext\n{CITATION_TEXT}\n```\n\nPlease remember that all generated audio clips should be assumed unsuitable for redistribution or commercial use.") demo.queue(api_open=False, default_concurrency_limit=40).launch(show_api=False)