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import gradio as gr |
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import pandas as pd |
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from langdetect import detect |
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from datasets import load_dataset |
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import threading, time, uuid, sqlite3, shutil, os, random, asyncio, threading |
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from pathlib import Path |
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from huggingface_hub import CommitScheduler, delete_file, hf_hub_download |
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from gradio_client import Client |
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import pyloudnorm as pyln |
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import soundfile as sf |
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import librosa |
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from detoxify import Detoxify |
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import os |
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import tempfile |
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from pydub import AudioSegment |
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def match_target_amplitude(sound, target_dBFS): |
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change_in_dBFS = target_dBFS - sound.dBFS |
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return sound.apply_gain(change_in_dBFS) |
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toxicity = Detoxify('original') |
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with open('harvard_sentences.txt') as f: |
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sents = f.read().strip().splitlines() |
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AVAILABLE_MODELS = { |
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'XTTSv2': 'xtts', |
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'ElevenLabs': 'eleven', |
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'OpenVoice V2': 'openvoicev2', |
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'Play.HT 2.0': 'playht', |
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'MetaVoice': 'metavoice', |
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'MeloTTS': 'melo', |
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'StyleTTS 2': 'styletts2', |
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'GPT-SoVITS': 'sovits', |
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'VoiceCraft 2.0': 'voicecraft', |
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'Parler TTS': 'parler' |
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} |
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SPACE_ID = os.getenv('SPACE_ID') |
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MAX_SAMPLE_TXT_LENGTH = 300 |
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MIN_SAMPLE_TXT_LENGTH = 10 |
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DB_DATASET_ID = os.getenv('DATASET_ID') |
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DB_NAME = "database.db" |
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DB_PATH = f"/data/{DB_NAME}" if os.path.isdir("/data") else DB_NAME |
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print(f"Using {DB_PATH}") |
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CITATION_TEXT = """@misc{tts-arena, |
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title = {Text to Speech Arena}, |
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author = {mrfakename and Srivastav, Vaibhav and Fourrier, Clémentine and Pouget, Lucain and Lacombe, Yoach and main and Gandhi, Sanchit}, |
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year = 2024, |
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publisher = {Hugging Face}, |
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howpublished = "\\url{https://huggingface.co/spaces/TTS-AGI/TTS-Arena}" |
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}""" |
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def create_db_if_missing(): |
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conn = get_db() |
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cursor = conn.cursor() |
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cursor.execute(''' |
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CREATE TABLE IF NOT EXISTS model ( |
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name TEXT UNIQUE, |
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upvote INTEGER, |
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downvote INTEGER |
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); |
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''') |
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cursor.execute(''' |
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CREATE TABLE IF NOT EXISTS vote ( |
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id INTEGER PRIMARY KEY AUTOINCREMENT, |
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username TEXT, |
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model TEXT, |
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vote INTEGER, |
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timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP |
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); |
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''') |
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cursor.execute(''' |
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CREATE TABLE IF NOT EXISTS votelog ( |
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id INTEGER PRIMARY KEY AUTOINCREMENT, |
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username TEXT, |
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chosen TEXT, |
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rejected TEXT, |
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timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP |
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); |
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''') |
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cursor.execute(''' |
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CREATE TABLE IF NOT EXISTS spokentext ( |
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id INTEGER PRIMARY KEY AUTOINCREMENT, |
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spokentext TEXT, |
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timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP |
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); |
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''') |
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def get_db(): |
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return sqlite3.connect(DB_PATH) |
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if not os.path.isfile(DB_PATH): |
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print("Downloading DB...") |
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try: |
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cache_path = hf_hub_download(repo_id=DB_DATASET_ID, repo_type='dataset', filename=DB_NAME) |
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shutil.copyfile(cache_path, DB_PATH) |
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print("Downloaded DB") |
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except Exception as e: |
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print("Error while downloading DB:", e) |
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create_db_if_missing() |
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scheduler = CommitScheduler( |
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repo_id=DB_DATASET_ID, |
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repo_type="dataset", |
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folder_path=Path(DB_PATH).parent, |
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every=5, |
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allow_patterns=DB_NAME, |
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) |
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router = Client("TTS-AGI/tts-router", hf_token=os.getenv('HF_TOKEN')) |
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MUST_BE_LOGGEDIN = "Please login with Hugging Face to participate in the TTS Arena." |
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DESCR = """ |
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# TTS Arena: Benchmarking TTS Models in the Wild |
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Vote to help the community find the best available text-to-speech model! |
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""".strip() |
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INSTR = """ |
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## 🗳️ Vote |
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* Input text (English only) to synthesize audio (or press 🎲 for random text). |
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* Listen to the two audio clips, one after the other. |
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* Vote on which audio sounds more natural to you. |
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* _Note: Model names are revealed after the vote is cast._ |
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Note: It may take up to 30 seconds to synthesize audio. |
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""".strip() |
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request = '' |
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if SPACE_ID: |
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request = f""" |
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### Request a model |
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Please [create a Discussion](https://huggingface.co/spaces/{SPACE_ID}/discussions/new) to request a model. |
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""" |
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ABOUT = f""" |
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## 📄 About |
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The TTS Arena evaluates leading speech synthesis models. It is inspired by LMsys's [Chatbot Arena](https://chat.lmsys.org/). |
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### Motivation |
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The field of speech synthesis has long lacked an accurate method to measure the quality of different models. Objective metrics like WER (word error rate) are unreliable measures of model quality, and subjective measures such as MOS (mean opinion score) are typically small-scale experiments conducted with few listeners. As a result, these measurements are generally not useful for comparing two models of roughly similar quality. To address these drawbacks, we are inviting the community to rank models in an easy-to-use interface, and opening it up to the public in order to make both the opportunity to rank models, as well as the results, more easily accessible to everyone. |
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### The Arena |
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The leaderboard allows a user to enter text, which will be synthesized by two models. After listening to each sample, the user can vote on which model sounds more natural. Due to the risks of human bias and abuse, model names are revealed only after a vote is submitted. |
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### Credits |
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Thank you to the following individuals who helped make this project possible: |
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* VB ([Twitter](https://twitter.com/reach_vb) / [Hugging Face](https://huggingface.co/reach-vb)) |
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* Clémentine Fourrier ([Twitter](https://twitter.com/clefourrier) / [Hugging Face](https://huggingface.co/clefourrier)) |
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* Lucain Pouget ([Twitter](https://twitter.com/Wauplin) / [Hugging Face](https://huggingface.co/Wauplin)) |
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* Yoach Lacombe ([Twitter](https://twitter.com/yoachlacombe) / [Hugging Face](https://huggingface.co/ylacombe)) |
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* Main Horse ([Twitter](https://twitter.com/main_horse) / [Hugging Face](https://huggingface.co/main-horse)) |
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* Sanchit Gandhi ([Twitter](https://twitter.com/sanchitgandhi99) / [Hugging Face](https://huggingface.co/sanchit-gandhi)) |
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* Apolinário Passos ([Twitter](https://twitter.com/multimodalart) / [Hugging Face](https://huggingface.co/multimodalart)) |
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* Pedro Cuenca ([Twitter](https://twitter.com/pcuenq) / [Hugging Face](https://huggingface.co/pcuenq)) |
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{request} |
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### Privacy statement |
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We may store text you enter and generated audio. We store a unique ID for each session. You agree that we may collect, share, and/or publish any data you input for research and/or commercial purposes. |
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### License |
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Generated audio clips cannot be redistributed and may be used for personal, non-commercial use only. |
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Random sentences are sourced from a filtered subset of the [Harvard Sentences](https://www.cs.columbia.edu/~hgs/audio/harvard.html). |
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""".strip() |
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LDESC = """ |
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## 🏆 Leaderboard |
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Vote to help the community determine the best text-to-speech (TTS) models. |
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The leaderboard displays models in descending order of how natural they sound (based on votes cast by the community). |
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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. |
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""".strip() |
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def del_db(txt): |
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if not txt.lower() == 'delete db': |
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raise gr.Error('You did not enter "delete db"') |
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os.remove(DB_PATH) |
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delete_file(path_in_repo=DB_NAME, repo_id=DB_DATASET_ID, repo_type='dataset') |
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create_db_if_missing() |
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return 'Delete DB' |
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theme = gr.themes.Base( |
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font=[gr.themes.GoogleFont('Libre Franklin'), gr.themes.GoogleFont('Public Sans'), 'system-ui', 'sans-serif'], |
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) |
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model_names = { |
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'styletts2': 'StyleTTS 2', |
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'tacotron': 'Tacotron', |
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'tacotronph': 'Tacotron Phoneme', |
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'tacotrondca': 'Tacotron DCA', |
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'speedyspeech': 'Speedy Speech', |
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'overflow': 'Overflow TTS', |
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'vits': 'VITS', |
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'vitsneon': 'VITS Neon', |
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'neuralhmm': 'Neural HMM', |
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'glow': 'Glow TTS', |
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'fastpitch': 'FastPitch', |
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'jenny': 'Jenny', |
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'tortoise': 'Tortoise TTS', |
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'xtts2': 'Coqui XTTSv2', |
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'xtts': 'Coqui XTTS', |
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'openvoice': 'MyShell OpenVoice', |
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'elevenlabs': 'ElevenLabs', |
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'openai': 'OpenAI', |
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'hierspeech': 'HierSpeech++', |
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'pheme': 'PolyAI Pheme', |
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'speecht5': 'SpeechT5', |
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'metavoice': 'MetaVoice-1B', |
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} |
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model_licenses = { |
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'styletts2': 'MIT', |
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'tacotron': 'BSD-3', |
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'tacotronph': 'BSD-3', |
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'tacotrondca': 'BSD-3', |
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'speedyspeech': 'BSD-3', |
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'overflow': 'MIT', |
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'vits': 'MIT', |
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'openvoice': 'MIT', |
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'vitsneon': 'BSD-3', |
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'neuralhmm': 'MIT', |
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'glow': 'MIT', |
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'fastpitch': 'Apache 2.0', |
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'jenny': 'Jenny License', |
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'tortoise': 'Apache 2.0', |
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'xtts2': 'CPML (NC)', |
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'xtts': 'CPML (NC)', |
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'elevenlabs': 'Proprietary', |
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'eleven': 'Proprietary', |
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'openai': 'Proprietary', |
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'hierspeech': 'MIT', |
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'pheme': 'CC-BY', |
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'speecht5': 'MIT', |
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'metavoice': 'Apache 2.0', |
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'elevenlabs': 'Proprietary', |
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'whisperspeech': 'MIT', |
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} |
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model_links = { |
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'styletts2': 'https://github.com/yl4579/StyleTTS2', |
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'tacotron': 'https://github.com/NVIDIA/tacotron2', |
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'speedyspeech': 'https://github.com/janvainer/speedyspeech', |
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'overflow': 'https://github.com/shivammehta25/OverFlow', |
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'vits': 'https://github.com/jaywalnut310/vits', |
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'openvoice': 'https://github.com/myshell-ai/OpenVoice', |
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'neuralhmm': 'https://github.com/ketranm/neuralHMM', |
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'glow': 'https://github.com/jaywalnut310/glow-tts', |
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'fastpitch': 'https://fastpitch.github.io/', |
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'tortoise': 'https://github.com/neonbjb/tortoise-tts', |
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'xtts2': 'https://huggingface.co/coqui/XTTS-v2', |
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'xtts': 'https://huggingface.co/coqui/XTTS-v1', |
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'elevenlabs': 'https://elevenlabs.io/', |
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'openai': 'https://help.openai.com/en/articles/8555505-tts-api', |
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'hierspeech': 'https://github.com/sh-lee-prml/HierSpeechpp', |
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'pheme': 'https://github.com/PolyAI-LDN/pheme', |
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'speecht5': 'https://github.com/microsoft/SpeechT5', |
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'metavoice': 'https://github.com/metavoiceio/metavoice-src', |
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} |
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def model_license(name): |
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print(name) |
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for k, v in AVAILABLE_MODELS.items(): |
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if k == name: |
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if v in model_licenses: |
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return model_licenses[v] |
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print('---') |
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return 'Unknown' |
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def get_leaderboard(reveal_prelim = False): |
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conn = get_db() |
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cursor = conn.cursor() |
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sql = 'SELECT name, upvote, downvote FROM model' |
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if not reveal_prelim: sql += ' WHERE (upvote + downvote) > 500' |
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cursor.execute(sql) |
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data = cursor.fetchall() |
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df = pd.DataFrame(data, columns=['name', 'upvote', 'downvote']) |
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df['name'] = df['name'].replace(model_names) |
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df['votes'] = df['upvote'] + df['downvote'] |
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df['score'] = 1200 |
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for i in range(len(df)): |
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for j in range(len(df)): |
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if i != j: |
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expected_a = 1 / (1 + 10 ** ((df['score'][j] - df['score'][i]) / 400)) |
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expected_b = 1 / (1 + 10 ** ((df['score'][i] - df['score'][j]) / 400)) |
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actual_a = df['upvote'][i] / df['votes'][i] |
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actual_b = df['upvote'][j] / df['votes'][j] |
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df.at[i, 'score'] += 32 * (actual_a - expected_a) |
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df.at[j, 'score'] += 32 * (actual_b - expected_b) |
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df['score'] = round(df['score']) |
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df = df.sort_values(by='score', ascending=False) |
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df['order'] = ['#' + str(i + 1) for i in range(len(df))] |
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df = df[['order', 'name', 'score', 'votes']] |
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return df |
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def mkuuid(uid): |
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if not uid: |
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uid = uuid.uuid4() |
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return uid |
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def upvote_model(model, uname): |
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conn = get_db() |
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cursor = conn.cursor() |
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cursor.execute('UPDATE model SET upvote = upvote + 1 WHERE name = ?', (model,)) |
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if cursor.rowcount == 0: |
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cursor.execute('INSERT OR REPLACE INTO model (name, upvote, downvote) VALUES (?, 1, 0)', (model,)) |
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cursor.execute('INSERT INTO vote (username, model, vote) VALUES (?, ?, ?)', (uname, model, 1,)) |
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with scheduler.lock: |
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conn.commit() |
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cursor.close() |
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def log_text(text): |
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conn = get_db() |
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cursor = conn.cursor() |
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cursor.execute('INSERT INTO spokentext (spokentext) VALUES (?)', (text,)) |
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with scheduler.lock: |
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conn.commit() |
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cursor.close() |
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def downvote_model(model, uname): |
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conn = get_db() |
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cursor = conn.cursor() |
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cursor.execute('UPDATE model SET downvote = downvote + 1 WHERE name = ?', (model,)) |
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if cursor.rowcount == 0: |
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cursor.execute('INSERT OR REPLACE INTO model (name, upvote, downvote) VALUES (?, 0, 1)', (model,)) |
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cursor.execute('INSERT INTO vote (username, model, vote) VALUES (?, ?, ?)', (uname, model, -1,)) |
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with scheduler.lock: |
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conn.commit() |
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cursor.close() |
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def a_is_better(model1, model2, userid): |
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print("A is better", model1, model2) |
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if not model1 in AVAILABLE_MODELS.keys() and not model1 in AVAILABLE_MODELS.values(): |
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raise gr.Error('Sorry, please try voting again.') |
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userid = mkuuid(userid) |
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if model1 and model2: |
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conn = get_db() |
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cursor = conn.cursor() |
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cursor.execute('INSERT INTO votelog (username, chosen, rejected) VALUES (?, ?, ?)', (str(userid), model1, model2,)) |
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with scheduler.lock: |
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conn.commit() |
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cursor.close() |
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upvote_model(model1, str(userid)) |
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downvote_model(model2, str(userid)) |
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return reload(model1, model2, userid, chose_a=True) |
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def b_is_better(model1, model2, userid): |
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print("B is better", model1, model2) |
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if not model1 in AVAILABLE_MODELS.keys() and not model1 in AVAILABLE_MODELS.values(): |
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raise gr.Error('Sorry, please try voting again.') |
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userid = mkuuid(userid) |
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if model1 and model2: |
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conn = get_db() |
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cursor = conn.cursor() |
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cursor.execute('INSERT INTO votelog (username, chosen, rejected) VALUES (?, ?, ?)', (str(userid), model2, model1,)) |
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with scheduler.lock: |
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conn.commit() |
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cursor.close() |
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upvote_model(model2, str(userid)) |
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downvote_model(model1, str(userid)) |
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return reload(model1, model2, userid, chose_b=True) |
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def both_bad(model1, model2, userid): |
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userid = mkuuid(userid) |
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if model1 and model2: |
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downvote_model(model1, str(userid)) |
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downvote_model(model2, str(userid)) |
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return reload(model1, model2, userid) |
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def both_good(model1, model2, userid): |
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userid = mkuuid(userid) |
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if model1 and model2: |
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upvote_model(model1, str(userid)) |
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upvote_model(model2, str(userid)) |
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return reload(model1, model2, userid) |
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def reload(chosenmodel1=None, chosenmodel2=None, userid=None, chose_a=False, chose_b=False): |
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out = [ |
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gr.update(interactive=False, visible=False), |
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gr.update(interactive=False, visible=False) |
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] |
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if chose_a == True: |
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out.append(gr.update(value=f'Your vote: {chosenmodel1}', interactive=False, visible=True)) |
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out.append(gr.update(value=f'{chosenmodel2}', interactive=False, visible=True)) |
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else: |
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out.append(gr.update(value=f'{chosenmodel1}', interactive=False, visible=True)) |
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out.append(gr.update(value=f'Your vote: {chosenmodel2}', interactive=False, visible=True)) |
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out.append(gr.update(visible=True)) |
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return out |
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with gr.Blocks() as leaderboard: |
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gr.Markdown(LDESC) |
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df = gr.Dataframe(interactive=False, min_width=0, wrap=True, column_widths=[30, 200, 50, 50]) |
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with gr.Row(): |
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reveal_prelim = gr.Checkbox(label="Reveal preliminary results", info="Show all models, including models with very few human ratings.", scale=1) |
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reloadbtn = gr.Button("Refresh", scale=3) |
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reveal_prelim.input(get_leaderboard, inputs=[reveal_prelim], outputs=[df]) |
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leaderboard.load(get_leaderboard, inputs=[reveal_prelim], outputs=[df]) |
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reloadbtn.click(get_leaderboard, inputs=[reveal_prelim], outputs=[df]) |
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def doloudnorm(path): |
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data, rate = sf.read(path) |
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meter = pyln.Meter(rate) |
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loudness = meter.integrated_loudness(data) |
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loudness_normalized_audio = pyln.normalize.loudness(data, loudness, -12.0) |
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sf.write(path, loudness_normalized_audio, rate) |
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def doresample(path_to_wav): |
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pass |
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def synthandreturn(text): |
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text = text.strip() |
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if len(text) > MAX_SAMPLE_TXT_LENGTH: |
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raise gr.Error(f'You exceeded the limit of {MAX_SAMPLE_TXT_LENGTH} characters') |
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if len(text) < MIN_SAMPLE_TXT_LENGTH: |
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raise gr.Error(f'Please input a text longer than {MIN_SAMPLE_TXT_LENGTH} characters') |
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if ( |
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text not in sents |
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and toxicity.predict(text)['toxicity'] > 0.8 |
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): |
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print(f'Detected toxic content! "{text}"') |
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raise gr.Error('Your text failed the toxicity test') |
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if not text: |
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raise gr.Error(f'You did not enter any text') |
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try: |
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if not detect(text) == "en": |
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gr.Warning('Warning: The input text may not be in English') |
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except: |
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pass |
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mdl1, mdl2 = random.sample(list(AVAILABLE_MODELS.keys()), 2) |
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log_text(text) |
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print("[debug] Using", mdl1, mdl2) |
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def predict_and_update_result(text, model, result_storage): |
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try: |
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if model in AVAILABLE_MODELS: |
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result = router.predict(text, AVAILABLE_MODELS[model].lower(), api_name="/synthesize") |
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else: |
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result = router.predict(text, model.lower(), api_name="/synthesize") |
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except: |
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raise gr.Error('Unable to call API, please try again :)') |
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print('Done with', model) |
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try: |
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with tempfile.NamedTemporaryFile(suffix='.wav', delete=False) as f: |
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audio = AudioSegment.from_file(result) |
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current_sr = audio.frame_rate |
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if current_sr > 24000: |
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audio = audio.set_frame_rate(24000) |
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try: |
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print('Trying to normalize audio') |
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audio = match_target_amplitude(audio, -20) |
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except: |
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print('[WARN] Unable to normalize audio') |
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audio.export(f.name, format="wav") |
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os.unlink(result) |
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result = f.name |
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except: |
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pass |
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if model in AVAILABLE_MODELS.keys(): model = AVAILABLE_MODELS[model] |
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print(model) |
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print(f"Running model {model}") |
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result_storage[model] = result |
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mdl1k = mdl1 |
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mdl2k = mdl2 |
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print(mdl1k, mdl2k) |
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if mdl1 in AVAILABLE_MODELS.keys(): mdl1k=AVAILABLE_MODELS[mdl1] |
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if mdl2 in AVAILABLE_MODELS.keys(): mdl2k=AVAILABLE_MODELS[mdl2] |
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results = {} |
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print(f"Sending models {mdl1k} and {mdl2k} to API") |
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thread1 = threading.Thread(target=predict_and_update_result, args=(text, mdl1k, results)) |
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thread2 = threading.Thread(target=predict_and_update_result, args=(text, mdl2k, results)) |
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thread1.start() |
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thread2.start() |
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thread1.join() |
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thread2.join() |
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print(f"Retrieving models {mdl1k} and {mdl2k} from API") |
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return ( |
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text, |
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"Synthesize", |
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gr.update(visible=True), |
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mdl1, |
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mdl2, |
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gr.update(visible=True, value=results[mdl1k]), |
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gr.update(visible=True, value=results[mdl2k]), |
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gr.update(visible=True, interactive=False), |
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gr.update(visible=True, interactive=False), |
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gr.update(visible=False), |
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gr.update(visible=False), |
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gr.update(visible=False), |
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) |
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def unlock_vote(btn_index, aplayed, bplayed): |
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if btn_index == 0: |
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aplayed = gr.State(value=True) |
|
|
if btn_index == 1: |
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|
bplayed = gr.State(value=True) |
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if bool(aplayed) and bool(bplayed): |
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print('Both audio samples played, voting unlocked') |
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|
return [gr.update(interactive=True), gr.update(interactive=True), gr.update(), gr.update()] |
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return [gr.update(), gr.update(), aplayed, bplayed] |
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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) |
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def disable(): |
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|
return [gr.update(interactive=False), gr.update(interactive=False), gr.update(interactive=False)] |
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def enable(): |
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|
return [gr.update(interactive=True), gr.update(interactive=True), gr.update(interactive=True)] |
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with gr.Blocks() as vote: |
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useridstate = gr.State() |
|
|
gr.Markdown(INSTR) |
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|
with gr.Group(): |
|
|
with gr.Row(): |
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text = gr.Textbox(container=False, show_label=False, placeholder="Enter text to synthesize", lines=1, max_lines=1, scale=9999999, min_width=0) |
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|
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) |
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|
model2 = gr.Textbox(interactive=False, lines=1, max_lines=1, visible=False) |
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|
|
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) |
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|
|
|
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) |
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|
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]) |
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|
with gr.Blocks() as about: |
|
|
gr.Markdown(ABOUT) |
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with gr.Blocks(theme=theme, css="footer {visibility: hidden}textbox{resize:none}", title="TTS Arena") as demo: |
|
|
gr.Markdown(DESCR) |
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|
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|
|
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.") |
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demo.queue(api_open=False, default_concurrency_limit=40).launch(show_api=False) |
