Merge branch 'structured'

LICENSE

@@ -1,5 +1,3 @@
-ZLIB/LIBPNG LICENSE
-
 Copyright (c) 2024 TTS-AGI Contributors
 
 This software is provided ‘as-is’, without any express or implied

app.py

@@ -1,1695 +1,4 @@
-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, handle_file
-import pyloudnorm as pyln
-import soundfile as sf
-import librosa
-from detoxify import Detoxify
-import os
-import tempfile
-from pydub import AudioSegment
-import itertools
-from typing import List, Tuple, Set, Dict
-from hashlib import md5, sha1
-import spaces
+from app.ui import app
 
-class User:
-    def __init__(self, user_id: str):
-        self.user_id = user_id
-        self.voted_pairs: Set[Tuple[str, str]] = set()
-
-class Sample:
-    def __init__(self, filename: str, transcript: str, modelName: str):
-        self.filename = filename
-        self.transcript = transcript
-        self.modelName = modelName
-
-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')
-sents = []
-with open('harvard_sentences.txt') as f:
-    sents += f.read().strip().splitlines()
-with open('llama3_command-r_sentences_1st_person.txt') as f:
-    sents += f.read().strip().splitlines()
-# With other punctuation marks
-# Exclamations - # conversational characters/animation entertainment/tv
-with open('llama3_command-r_sentences_excla.txt') as f:
-    sents += f.read().strip().splitlines()
-# Questions - # conversational characters/animation entertainment/tv
-with open('llama3_command-r_questions.txt') as f:
-    sents += f.read().strip().splitlines()
-
-# Credit: llama3_command-r sentences generated by user KingNish
-
-####################################
-# 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'
-
-    # HF Gradio Spaces: # <works with gradio version #>
-    # gravio version that works with most spaces: 4.29
-    'coqui/xtts': 'coqui/xtts', # 4.29 4.32
-    # 'collabora/WhisperSpeech': 'collabora/WhisperSpeech', # 4.32 4.36.1 DEAD SPACE
-    # 'myshell-ai/OpenVoice': 'myshell-ai/OpenVoice', # same devs as MeloTTS, which scores higher # 4.29
-    # 'myshell-ai/OpenVoiceV2': 'myshell-ai/OpenVoiceV2', # same devs as MeloTTS, which scores higher # 4.29
-    #'mrfakename/MetaVoice-1B-v0.1': 'mrfakename/MetaVoice-1B-v0.1', # 4.29 4.32 DEAD SPACE
-    'Pendrokar/xVASynth-TTS': 'Pendrokar/xVASynth-TTS', # 4.29 4.32 4.42.0
-    # 'coqui/CoquiTTS': 'coqui/CoquiTTS',
-    'mrfakename/MeloTTS': 'mrfakename/MeloTTS', # 4.29 4.32
-    'fishaudio/fish-speech-1': 'fishaudio/fish-speech-1', # 4.29 4.32 4.36.1
-
-    # E2 & F5 TTS
-    # F5 model
-    'mrfakename/E2-F5-TTS': 'mrfakename/E2-F5-TTS', # 5.0
-
-    # # Parler
-    # Parler Large model
-    # 'parler-tts/parler_tts': 'parler-tts/parler_tts', # 4.29 4.32 4.36.1 4.42.0
-    # Parler Mini model
-    'parler-tts/parler_tts': 'parler-tts/parler_tts', # 4.29 4.32 4.36.1 4.42.0
-    # 'parler-tts/parler_tts_mini': 'parler-tts/parler_tts_mini', # Mini is the default model of parler_tts
-    # 'parler-tts/parler-tts-expresso': 'parler-tts/parler-tts-expresso', # 4.29 4.32 4.36.1 4.42.0 # overlly jolly
-
-    # # Microsoft Edge TTS
-    'innoai/Edge-TTS-Text-to-Speech': 'innoai/Edge-TTS-Text-to-Speech', # 4.29
-
-    # IMS-Toucan
-    # 'Flux9665/MassivelyMultilingualTTS': 'Flux9665/MassivelyMultilingualTTS', # 5.1 # randomly changes pitch male<=>female
-
-    # IMS-Toucan English non-artificial
-    # 'Flux9665/EnglishToucan': 'Flux9665/EnglishToucan', # 5.1; poor ratings => saving ZeroGPU resources;
-
-    # StyleTTS v2
-    # 'Pendrokar/style-tts-2': 'Pendrokar/style-tts-2', #  more votes in OG arena; emotionless
-    # StyleTTS kokoro
-    'hexgrad/kokoro': 'hexgrad/kokoro',
-
-    # MaskGCT (by Amphion)
-    # DEMANDS 300 seconds of ZeroGPU
-    # 'amphion/maskgct': 'amphion/maskgct',
-    # default ZeroGPU borrow time
-    'Svngoku/maskgct-audio-lab': 'Svngoku/maskgct-audio-lab',
-
-    # HF TTS w issues
-    'LeeSangHoon/HierSpeech_TTS': 'LeeSangHoon/HierSpeech_TTS', # irresponsive to exclamation marks # 4.29
-    # 'PolyAI/pheme': '/predict#0', # sleepy HF Space
-    # 'amphion/Text-to-Speech': '/predict#0', # disabled also on original HF space due to poor ratings
-    # 'suno/bark': '3#0', # Hallucinates
-    # 'shivammehta25/Matcha-TTS': '5#0', # seems to require multiple requests for setup
-    # 'styletts2/styletts2': '0#0', # API disabled, awaiting approval of PR #15
-    # 'Manmay/tortoise-tts': '/predict#0', # Cannot retrieve streamed file; 403
-    # 'pytorch/Tacotron2': '0#0', # old gradio
-}
-
-HF_SPACES = {
-    # XTTS v2
-    'coqui/xtts': {
-        'name': 'XTTS v2',
-        'function': '1',
-        'text_param_index': 0,
-        'return_audio_index': 1,
-        'series': 'XTTS',
-    },
-    # WhisperSpeech
-    'collabora/WhisperSpeech': {
-        'name': 'WhisperSpeech',
-        'function': '/whisper_speech_demo',
-        'text_param_index': 0,
-        'return_audio_index': 0,
-        'series': 'WhisperSpeech',
-    },
-    # OpenVoice (MyShell.ai)
-    'myshell-ai/OpenVoice': {
-        'name':'OpenVoice',
-        'function': '1',
-        'text_param_index': 0,
-        'return_audio_index': 1,
-        'series': 'OpenVoice',
-    },
-    # OpenVoice v2 (MyShell.ai)
-    'myshell-ai/OpenVoiceV2': {
-        'name':'OpenVoice v2',
-        'function': '1',
-        'text_param_index': 0,
-        'return_audio_index': 1,
-        'series': 'OpenVoice',
-    },
-    # MetaVoice
-    'mrfakename/MetaVoice-1B-v0.1': {
-        'name':'MetaVoice-1B',
-        'function': '/tts',
-        'text_param_index': 0,
-        'return_audio_index': 0,
-        'series': 'MetaVoice-1B',
-    },
-    # xVASynth (CPU)
-    'Pendrokar/xVASynth-TTS': {
-        'name': 'xVASynth v3',
-        'function': '/predict',
-        'text_param_index': 0,
-        'return_audio_index': 0,
-        'series': 'xVASynth',
-    },
-    # CoquiTTS (CPU)
-    'coqui/CoquiTTS': {
-        'name': 'CoquiTTS',
-        'function': '0',
-        'text_param_index': 0,
-        'return_audio_index': 0,
-        'series': 'CoquiTTS',
-    },
-    # HierSpeech_TTS
-    'LeeSangHoon/HierSpeech_TTS': {
-        'name': 'HierSpeech++',
-        'function': '/predict',
-        'text_param_index': 0,
-        'return_audio_index': 0,
-        'series': 'HierSpeech++',
-    },
-    # MeloTTS (MyShell.ai)
-    'mrfakename/MeloTTS': {
-        'name': 'MeloTTS',
-        'function': '/synthesize',
-        'text_param_index': 0,
-        'return_audio_index': 0,
-        'series': 'MeloTTS',
-    },
-
-    # Parler
-    'parler-tts/parler_tts': {
-        'name': 'Parler Mini',
-        'function': '/gen_tts',
-        'text_param_index': 0,
-        'return_audio_index': 0,
-        'is_zero_gpu_space': True,
-        'series': 'Parler',
-    },
-    # Parler Mini
-    # 'parler-tts/parler_tts': {
-    #     'name': 'Parler Large',
-    #     'function': '/gen_tts',
-    #     'text_param_index': 0,
-    #     'return_audio_index': 0,
-    #     'is_zero_gpu_space': True,
-    #    'series': 'Parler',
-    # },
-    # Parler Mini which using Expresso dataset
-    'parler-tts/parler-tts-expresso': {
-        'name': 'Parler Mini Expresso',
-        'function': '/gen_tts',
-        'text_param_index': 0,
-        'return_audio_index': 0,
-        'is_zero_gpu_space': True,
-        'series': 'Parler',
-    },
-
-    # Microsoft Edge TTS
-    'innoai/Edge-TTS-Text-to-Speech': {
-        'name': 'Microsoft™ Edge TTS',
-        'function': '/predict',
-        'text_param_index': 0,
-        'return_audio_index': 0,
-        'is_closed_off': True,
-        'series': 'Edge TTS',
-    },
-
-    # Fish Speech
-    'fishaudio/fish-speech-1': {
-        'name': 'Fish Speech',
-        'function': '/inference_wrapper',
-        'text_param_index': 0,
-        'return_audio_index': 0,
-        'series': 'Fish Speech',
-    },
-
-    # E2/F5 TTS
-    'mrfakename/E2-F5-TTS': {
-        'name': 'F5 TTS',
-        'function': '/basic_tts',
-        'text_param_index': 2,
-        'return_audio_index': 0,
-        'is_zero_gpu_space': True,
-        'series': 'E2/F5 TTS',
-    },
-
-    # IMS-Toucan
-    'Flux9665/MassivelyMultilingualTTS': {
-        'name': 'IMS-Toucan',
-		'function': "/predict",
-        'text_param_index': 0,
-        'return_audio_index': 0,
-        'series': 'IMS-Toucan',
-    },
-
-    # IMS-Toucan English non-artificial
-    'Flux9665/EnglishToucan': {
-        'name': 'IMS-Toucan EN',
-		'function': "/predict",
-        'text_param_index': 0,
-        'return_audio_index': 0,
-        'is_zero_gpu_space': True,
-        'series': 'IMS-Toucan',
-    },
-
-    # StyleTTS v2
-    'Pendrokar/style-tts-2': {
-        'name': 'StyleTTS v2',
-        'function': '/synthesize',
-        'text_param_index': 0,
-        'return_audio_index': 0,
-        # 'is_zero_gpu_space': True,
-        'series': 'StyleTTS',
-    },
-
-    # StyleTTS v2 kokoro fine tune
-    'hexgrad/kokoro': {
-        'name': 'StyleTTS Kokoro',
-        'function': '/generate',
-        'text_param_index': 0,
-        'return_audio_index': 0,
-        'is_zero_gpu_space': True,
-        'series': 'StyleTTS',
-    },
-
-    # MaskGCT (by Amphion)
-    'amphion/maskgct': {
-        'name': 'MaskGCT',
-        'function': '/predict',
-        'text_param_index': 1,
-        'return_audio_index': 0,
-        'is_zero_gpu_space': True,
-        'series': 'MaskGCT',
-    },
-    'Svngoku/maskgct-audio-lab': {
-        'name': 'MaskGCT',
-        'function': '/predict',
-        'text_param_index': 1,
-        'return_audio_index': 0,
-        'is_zero_gpu_space': True,
-        'series': 'MaskGCT',
-    },
-
-    # TTS w issues
-    # 'PolyAI/pheme': '/predict#0', #sleepy HF Space
-    # 'amphion/Text-to-Speech': '/predict#0', #takes a whole minute to synthesize
-    # 'suno/bark': '3#0', # Hallucinates
-    # 'shivammehta25/Matcha-TTS': '5#0', #seems to require multiple requests for setup
-    # 'styletts2/styletts2': '0#0', #API disabled
-    # 'Manmay/tortoise-tts': '/predict#0', #Cannot skip text-from-file parameter
-    # 'pytorch/Tacotron2': '0#0', #old gradio
-    # 'fishaudio/fish-speech-1': '/inference_wrapper#0', heavy hallucinations
-}
-
-# for zero-shot TTS - voice sample used by XTTS (11 seconds)
-DEFAULT_VOICE_SAMPLE_STR = 'https://cdn-uploads.huggingface.co/production/uploads/63d52e0c4e5642795617f668/V6-rMmI-P59DA4leWDIcK.wav'
-DEFAULT_VOICE_SAMPLE = handle_file(DEFAULT_VOICE_SAMPLE_STR)
-DEFAULT_VOICE_TRANSCRIPT = "The Hispaniola was rolling scuppers under in the ocean swell. The booms were tearing at the blocks, the rudder was banging to and fro, and the whole ship creaking, groaning, and jumping like a manufactory."
-
-OVERRIDE_INPUTS = {
-    'coqui/xtts': {
-        1: 'en',
-        2: DEFAULT_VOICE_SAMPLE_STR, # voice sample
-        3: None, # mic voice sample
-        4: False, #use_mic
-        5: False, #cleanup_reference
-        6: False, #auto_detect
-    },
-    'collabora/WhisperSpeech': {
-        1: DEFAULT_VOICE_SAMPLE, # voice sample
-        2: DEFAULT_VOICE_SAMPLE, # voice sample URL
-        3: 14.0, #Tempo - Gradio Slider issue: takes min. rather than value
-    },
-    'myshell-ai/OpenVoice': {
-        1: 'default', # style
-        2: 'https://huggingface.co/spaces/myshell-ai/OpenVoiceV2/resolve/main/examples/speaker0.mp3', # voice sample
-    },
-    'myshell-ai/OpenVoiceV2': {
-        1: 'en_us', # style
-        2: 'https://huggingface.co/spaces/myshell-ai/OpenVoiceV2/resolve/main/examples/speaker0.mp3', # voice sample
-    },
-    'PolyAI/pheme': {
-        1: 'YOU1000000044_S0000798', # voice
-        2: 210,
-        3: 0.7, #Tempo - Gradio Slider issue: takes min. rather than value
-    },
-    'Pendrokar/xVASynth-TTS': {
-        1: 'x_ex04', #fine-tuned voice model name
-        3: 1.0, #pacing/duration - Gradio Slider issue: takes min. rather than value
-    },
-    'suno/bark': {
-        1: 'Speaker 3 (en)', # voice
-    },
-    'amphion/Text-to-Speech': {
-        1: 'LikeManyWaters', # voice
-    },
-    'LeeSangHoon/HierSpeech_TTS': {
-        1: handle_file('https://huggingface.co/spaces/LeeSangHoon/HierSpeech_TTS/resolve/main/example/female.wav'), # voice sample
-        2: 0.333,
-        3: 0.333,
-        4: 1,
-        5: 1,
-        6: 0,
-        7: 1111,
-    },
-    'Manmay/tortoise-tts': {
-        1: None, # text-from-file
-        2: 'angie', # voice
-        3: 'disabled', # second voice for a dialogue
-        4: 'No', # split by newline
-    },
-    'mrfakename/MeloTTS': {
-        1: 'EN-Default',	# speaker; DEFAULT_VOICE_SAMPLE=EN-Default
-        2: 1, # speed
-        3: 'EN',	# language
-    },
-    'mrfakename/MetaVoice-1B-v0.1': {
-		1: 5,	# float (numeric value between 0.0 and 10.0) in 'Speech Stability - improves text following for a challenging speaker' Slider component
-		2: 5,	# float (numeric value between 1.0 and 5.0) in 'Speaker similarity - How closely to match speaker identity and speech style.' Slider component
-		3: "Preset voices",	# Literal['Preset voices', 'Upload target voice']  in 'Choose voice' Radio component
-		4: "Bria",	# Literal['Bria', 'Alex', 'Jacob']  in 'Preset voices' Dropdown component
-		5: None,	# filepath  in 'Upload a clean sample to clone. Sample should contain 1 speaker, be between 30-90 seconds and not contain background noise.' Audio component
-    },
-    'parler-tts/parler_tts': {
-        1: 'Laura; Laura\'s female voice; very clear audio', # description/prompt
-    },
-    'parler-tts/parler-tts-expresso': {
-        1: 'Elisabeth; Elisabeth\'s female voice; very clear audio', # description/prompt
-    },
-    'innoai/Edge-TTS-Text-to-Speech': {
-        1: 'en-US-EmmaMultilingualNeural - en-US (Female)', # voice
-        2: 0, # pace rate
-        3: 0, # pitch
-    },
-
-    'fishaudio/fish-speech-1': {
-        1: False, # normalize
-		2: handle_file('https://huggingface.co/spaces/fishaudio/fish-speech-1/resolve/main/examples/English.wav'), # reference_audio
-		3: 'In the ancient land of Eldoria, where the skies were painted with shades of mystic hues and the forests whispered secrets of old, there existed a dragon named Zephyros. Unlike the fearsome tales of dragons that plagued human hearts with terror, Zephyros was a creature of wonder and wisdom, revered by all who knew of his existence.', # reference_text
-		4: 0, # max_new_tokens
-		5: 200, # chunk_length
-		6: 0.7, # top_p
-		7: 1.2, # repetition_penalty
-		8: 0.7, # temperature
-		9: 0, #seed
-		10: "never", #use_memory_cache
-    },
-
-    'mrfakename/E2-F5-TTS': {
-		0: DEFAULT_VOICE_SAMPLE, # voice sample
-		1: DEFAULT_VOICE_TRANSCRIPT, # transcript of sample (< 15 seconds required)
-		3: False, # cleanup silence
-        4: 0.15, #crossfade
-        5: 32, #nfe_slider
-        6: 1, #speed
-    },
-
-    # IMS-Toucan
-    'Flux9665/MassivelyMultilingualTTS': {
-		1: "English (eng)", #language
-		2: 0.6, #prosody_creativity
-		3: 1, #duration_scaling_factor
-		4: 41, #voice_seed
-		5: 7.5, #emb1
-		6: None, #reference_audio
-    },
-
-    # StyleTTS 2
-    'Pendrokar/style-tts-2': {
-		1: "f-us-2", #voice
-        2: 'en-us', # lang
-		3: 8, # lngsteps
-    },
-
-    # StyleTTS 2 kokoro
-    'hexgrad/kokoro': {
-		1: "af", #voice
-		2: None, #ps
-		3: 1, #speed
-		4: 3000, #trim
-		5: False, #use_gpu; fast enough with multithreaded with CPU
-        6: os.getenv('KOKORO'), #sk
-    },
-
-    # maskGCT (by amphion)
-    'amphion/maskgct': {
-        0: DEFAULT_VOICE_SAMPLE, #prompt_wav
-		2: -1, #target_len
-		3: 25, #n_timesteps
-    },
-    'Svngoku/maskgct-audio-lab': {
-        0: DEFAULT_VOICE_SAMPLE, #prompt_wav
-		2: -1, #target_len
-		3: 25, #n_timesteps
-    },
-}
-
-hf_clients: Tuple[Client] = {}
-# cache audio samples for quick voting
-cached_samples: List[Sample] = []
-voting_users = {
-    # userid as the key and USER() as the value
-}
-# top five models in order to always have one of them picked and scrutinized
-top_five = ['fishaudio/fish-speech-1'] # fish 1.5
-
-def generate_matching_pairs(samples: List[Sample]) -> List[Tuple[Sample, Sample]]:
-    transcript_groups: Dict[str, List[Sample]] = {}
-    samples = random.sample(samples, k=len(samples))
-    for sample in samples:
-        if sample.transcript not in transcript_groups:
-            transcript_groups[sample.transcript] = []
-        transcript_groups[sample.transcript].append(sample)
-
-    matching_pairs: List[Tuple[Sample, Sample]] = []
-    for group in transcript_groups.values():
-        matching_pairs.extend(list(itertools.combinations(group, 2)))
-
-    return matching_pairs
-
-cached_audio = []
-
-# @spaces.GPU(duration=10)
-def asr_cached_for_dataset():
-
-    for caudio in cached_audio:
-        pass
-    return True
-
-# List[Tuple[Sample, Sample]]
-all_pairs = []
-
-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"
-
-SPACE_ID = 'TTS-AGI/TTS-Arena'
-
-# 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,
-            votelog_id INTEGER UNIQUE,
-            spokentext TEXT,
-            lang TEXT,
-            timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP
-        );
-    ''')
-    # foreign keys
-    cursor.execute('''
-        CREATE UNIQUE INDEX IF NOT EXISTS st_to_vl ON spokentext(votelog_id);
-    ''')
-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()
-
-hf_token = os.getenv('HF_TOKEN')
-# 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,
-)
-
-# Load audio dataset
-# audio_dataset = load_dataset(AUDIO_DATASET_ID)
-
-
-# prioritize low vote models
-sql = 'SELECT name FROM model WHERE (upvote + downvote) < 750 ORDER BY (upvote + downvote) ASC'
-conn = get_db()
-cursor = conn.cursor()
-cursor.execute(sql)
-data = cursor.fetchall()
-for model in data:
-    if (
-        len(top_five) >= 5
-    ):
-        break
-
-    if model[0] in AVAILABLE_MODELS.keys():
-        top_five.append(model[0])
-print(f"low vote top_five: {top_five}")
-
-####################################
-# Router API
-####################################
-# router = Client("TTS-AGI/tts-router", hf_token=hf_token)
-router = {}
-####################################
-# Gradio app
-####################################
-MUST_BE_LOGGEDIN = "Please login with Hugging Face to participate in the TTS Arena."
-DESCR = """
-# TTS Spaces Arena: Benchmarking Gradio hosted TTS Models in the Wild
-
-Vote to help the community find the best available text-to-speech model!
-""".strip()
-INSTR = """
-## 🗳️ Vote
-
-* Press ⚡ to get cached sample pairs you've yet to vote on. (Fast 🐇)
-* Or press 🎲 to randomly use a sentence from the list. (Slow 🐢)
-* Or input text (🇺🇸 English only) to synthesize audio. (Slowest 🐌 due to _Toxicity_ test)
-* Listen to the two audio clips, one after the other.
-* _Vote on which audio sounds more natural to you._
-* Model names are revealed after the vote is cast.
-
-⚠ Note: It **may take up to 30 seconds** to ***synthesize*** audio.
-""".strip()
-request = ''
-if SPACE_ID:
-    request = f"""
-### Request a model
-
-Clone the repo of this space, add your model by adding the parameters required of the HF Space. Then make a pull request to {SPACE_ID}.
-"""
-ABOUT = f"""
-## 📄 About
-
-The TTS Arena evaluates leading speech synthesis models. It is inspired by LMsys's [Chatbot Arena](https://chat.lmsys.org/).
-
-### Motivation
-
-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.
-
-### The Arena
-
-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.
-
-### Credits
-
-Thank you to the following individuals who helped make this* project possible:
-
-* VB ([Twitter](https://twitter.com/reach_vb) / [Hugging Face](https://huggingface.co/reach-vb))
-* Clémentine Fourrier ([Twitter](https://twitter.com/clefourrier) / [Hugging Face](https://huggingface.co/clefourrier))
-* Lucain Pouget ([Twitter](https://twitter.com/Wauplin) / [Hugging Face](https://huggingface.co/Wauplin))
-* Yoach Lacombe ([Twitter](https://twitter.com/yoachlacombe) / [Hugging Face](https://huggingface.co/ylacombe))
-* Main Horse ([Twitter](https://twitter.com/main_horse) / [Hugging Face](https://huggingface.co/main-horse))
-* Sanchit Gandhi ([Twitter](https://twitter.com/sanchitgandhi99) / [Hugging Face](https://huggingface.co/sanchit-gandhi))
-* Apolinário Passos ([Twitter](https://twitter.com/multimodalart) / [Hugging Face](https://huggingface.co/multimodalart))
-* Pedro Cuenca ([Twitter](https://twitter.com/pcuenq) / [Hugging Face](https://huggingface.co/pcuenq))
-
-\* ***You are currently in a cloned/forked space of TTS-AGI/TTS-Arena***
-
-{request}
-
-### Privacy statement
-
-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.
-
-### License
-
-Generated audio clips cannot be redistributed and may be used for personal, non-commercial use only.
-
-Random sentences are sourced from a filtered subset of the [Harvard Sentences](https://www.cs.columbia.edu/~hgs/audio/harvard.html) and also from KingNish's generated LLM sentences.
-""".strip()
-
-LDESC = f"""
-## 🏆 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. [This dataset is public](https://huggingface.co/datasets/{DB_DATASET_ID}) and only saves the hardcoded sentences while keeping the voters anonymous.
-""".strip()
-
-TTS_INFO = f"""
-## 🗣 Contenders
-
-### Open Source TTS capabilities table
-
-See [the below dataset itself](https://huggingface.co/datasets/Pendrokar/open_tts_tracker) for the legend and more in depth information of each model.
-""".strip()
-
-model_series = []
-for model in HF_SPACES.values():
-    model_series.append('%27'+ model['series'].replace('+', '%2B') +'%27')
-TTS_DATASET_IFRAME_ORDER = '%2C+'.join(model_series)
-TTS_DATASET_IFRAME = f"""
-<iframe
-    src="https://huggingface.co/datasets/Pendrokar/open_tts_tracker/embed/sql-console/default/train?sql_console=true&sql=--+The+SQL+console+is+powered+by+DuckDB+WASM+and+runs+entirely+in+the+browser.%0A--+Get+started+by+typing+a+query+or+selecting+a+view+from+the+options+below.%0ASELECT+*%2C+%22Name%22+IN+%28{TTS_DATASET_IFRAME_ORDER}%29+AS+%22In+arena%22+FROM+train+WHERE+%22Insta-clone+%F0%9F%91%A5%22+IS+NOT+NULL+ORDER+BY+%22In+arena%22+DESC+LIMIT+50%3B&views%5B%5D=train"
-    frameborder="0"
-    width="100%"
-    height="650px"
-></iframe>
-""".strip()
-
-# def reload_audio_dataset():
-#     global audio_dataset
-#     audio_dataset = load_dataset(AUDIO_DATASET_ID)
-#     return 'Reload Audio Dataset'
-
-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',
-    'Pendrokar/xVASynth': 'GPT3',
-}
-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, name AS orig_name 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) > 300'
-    cursor.execute(sql)
-    data = cursor.fetchall()
-    df = pd.DataFrame(data, columns=['name', 'upvote', 'downvote', 'orig_name'])
-    # df['license'] = df['name'].map(model_license)
-    df['name'] = df['name'].replace(model_names)
-    for i in range(len(df)):
-        df.loc[i, "name"] = make_link_to_space(df['name'][i], True)
-    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'] += round(32 * (actual_a - expected_a))
-                df.at[j, 'score'] += round(32 * (actual_b - expected_b))
-    df['score'] = round(df['score'])
-    ## ELO SCORE
-    df = df.sort_values(by='score', ascending=False)
-    # medals
-    def assign_medal(rank, assign):
-        rank = str(rank + 1)
-        if assign:
-            if rank == '1':
-                rank += '🥇'
-            elif rank == '2':
-                rank += '🥈'
-            elif rank == '3':
-                rank += '🥉'
-
-        return '#'+ rank
-
-    df['order'] = [assign_medal(i, not reveal_prelim and len(df) > 2) for i in range(len(df))]
-    # fetch top_five
-    for orig_name in df['orig_name']:
-        if (
-            reveal_prelim
-            and len(top_five) < 5
-            and orig_name in AVAILABLE_MODELS.keys()
-        ):
-            top_five.append(orig_name)
-
-    df = df[['order', 'name', 'score', 'votes']]
-    return df
-
-def make_link_to_space(model_name, for_leaderboard=False):
-    # create a anchor link if a HF space
-    style = 'text-decoration: underline;text-decoration-style: dotted;'
-    title = ''
-
-    if model_name in AVAILABLE_MODELS:
-        style += 'color: var(--link-text-color);'
-        title = model_name
-    else:
-        style += 'font-style: italic;'
-        title = 'Disabled for Arena (See AVAILABLE_MODELS within code for why)'
-
-    model_basename = model_name
-    if model_name in HF_SPACES:
-        model_basename = HF_SPACES[model_name]['name']
-
-    try:
-        if(
-            for_leaderboard
-            and HF_SPACES[model_name]['is_closed_off']
-        ):
-            model_basename += ' 🔐'
-            title += '; 🔐 = online only or proprietary'
-    except:
-        pass
-
-    if '/' in model_name:
-        return '🤗 <a target="_blank" style="'+ style +'" title="'+ title +'" href="'+ 'https://huggingface.co/spaces/'+ model_name +'">'+ model_basename +'</a>'
-
-    # otherwise just return the model name
-    return '<span style="'+ style +'" title="'+ title +'" href="'+ 'https://huggingface.co/spaces/'+ model_name +'">'+ model_name +'</span>'
-
-def markdown_link_to_space(model_name):
-    # create a anchor link if a HF space using markdown syntax
-    if '/' in model_name:
-        return '🤗 [' + model_name + '](https://huggingface.co/spaces/' + model_name + ')'
-    # otherwise just return the model name
-    return model_name
-
-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, voteid):
-    # log only hardcoded sentences
-    if (text not in sents):
-        return
-
-    conn = get_db()
-    cursor = conn.cursor()
-    # TODO: multilang
-    cursor.execute('INSERT INTO spokentext (spokentext, lang, votelog_id) VALUES (?,?,?)', (text,'en',voteid))
-    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, text):
-    return is_better(model1, model2, userid, text, True)
-def b_is_better(model1, model2, userid, text):
-    return is_better(model1, model2, userid, text, False)
-
-def is_better(model1, model2, userid, text, chose_a):
-    if(
-        (
-            not model1 in AVAILABLE_MODELS.keys()
-            and not model1 in AVAILABLE_MODELS.values()
-        )
-        or (
-            not model2 in AVAILABLE_MODELS.keys()
-            and not model2 in AVAILABLE_MODELS.values()
-        )
-    ):
-        raise gr.Error('Sorry, please try voting again.')
-
-    # userid is unique for each cast vote pair
-    userid = mkuuid(userid)
-    if model1 and model2:
-        conn = get_db()
-        cursor = conn.cursor()
-        sql_query = 'INSERT INTO votelog (username, chosen, rejected) VALUES (?, ?, ?)'
-        if chose_a:
-            cursor.execute(sql_query, (str(userid), model1, model2))
-        else:
-            cursor.execute(sql_query, (str(userid), model2, model1))
-
-        with scheduler.lock:
-            conn.commit()
-            # also retrieve primary key ID
-            cursor.execute('SELECT last_insert_rowid()')
-            votelogid = cursor.fetchone()[0]
-            cursor.close()
-
-        if chose_a:
-            upvote_model(model1, str(userid))
-            downvote_model(model2, str(userid))
-        else:
-            upvote_model(model2, str(userid))
-            downvote_model(model1, str(userid))
-        log_text(text, votelogid)
-
-    return reload(model1, model2, userid, chose_a=chose_a, chose_b=(not chose_a))
-
-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
-    chosenmodel1 = make_link_to_space(chosenmodel1)
-    chosenmodel2 = make_link_to_space(chosenmodel2)
-    out = [
-        gr.update(interactive=False, visible=False),
-        gr.update(interactive=False, visible=False)
-    ]
-    style = 'text-align: center; font-size: 1rem; margin-bottom: 0; padding: var(--input-padding)'
-    if chose_a == True:
-        out.append(gr.update(value=f'<p style="{style}">Your vote: {chosenmodel1}</p>', visible=True))
-        out.append(gr.update(value=f'<p style="{style}">{chosenmodel2}</p>', visible=True))
-    else:
-        out.append(gr.update(value=f'<p style="{style}">{chosenmodel1}</p>', visible=True))
-        out.append(gr.update(value=f'<p style="{style}">Your vote: {chosenmodel2}</p>', 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],
-        datatype=["str", "html", "number", "number"]
-    )
-    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, request: gr.Request):
-    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 (
-            text not in sents
-            and not detect(text) == "en"
-        ):
-            gr.Warning('Warning: The input text may not be in English')
-    except:
-        pass
-    # Get two random models
-
-    # forced model: your TTS model versus The World!!!
-    # mdl1 = 'Pendrokar/xVASynth'
-
-    # scrutinize the top five by always picking one of them
-    if (len(top_five) >= 5):
-        mdl1 = random.sample(top_five, 1)[0]
-        vsModels = dict(AVAILABLE_MODELS)
-        del vsModels[mdl1]
-        # randomize position of the forced model
-        mdl2 = random.sample(list(vsModels.keys()), 1)
-        # forced random
-        mdl1, mdl2 = random.sample(list([mdl1, mdl2[0]]), 2)
-    else:
-        # actual random
-        mdl1, mdl2 = random.sample(list(AVAILABLE_MODELS.keys()), 2)
-
-    print("[debug] Using", mdl1, mdl2)
-    def predict_and_update_result(text, model, result_storage, request:gr.Request):
-
-        hf_headers = {}
-        try:
-            if HF_SPACES[model]['is_zero_gpu_space']:
-                hf_headers = {"X-IP-Token": request.headers['x-ip-token']}
-        except:
-            pass
-
-        # re-attempt if necessary
-        attempt_count = 0
-        max_attempts = 1 # 3 =May cause 429 Too Many Request
-        while attempt_count < max_attempts:
-            try:
-                if model in AVAILABLE_MODELS:
-                    if '/' in model:
-                        # Use public HF Space
-                        # if (model not in hf_clients):
-                        #     hf_clients[model] = Client(model, hf_token=hf_token, headers=hf_headers)
-                        mdl_space = Client(model, hf_token=hf_token, headers=hf_headers)
-
-                        # print(f"{model}: Fetching endpoints of HF Space")
-                        # assume the index is one of the first 9 return params
-                        return_audio_index = int(HF_SPACES[model]['return_audio_index'])
-                        endpoints = mdl_space.view_api(all_endpoints=True, print_info=False, return_format='dict')
-
-                        api_name = None
-                        fn_index = None
-                        end_parameters = None
-                        # has named endpoint
-                        if '/' == HF_SPACES[model]['function'][0]:
-                            # audio sync function name
-                            api_name = HF_SPACES[model]['function']
-
-                            end_parameters = _get_param_examples(
-                                endpoints['named_endpoints'][api_name]['parameters']
-                            )
-                        # has unnamed endpoint
-                        else:
-                            # endpoint index is the first character
-                            fn_index = int(HF_SPACES[model]['function'])
-
-                            end_parameters = _get_param_examples(
-                                endpoints['unnamed_endpoints'][str(fn_index)]['parameters']
-                            )
-
-                        # override some or all default parameters
-                        space_inputs = _override_params(end_parameters, model)
-
-                        # force text
-                        space_inputs[HF_SPACES[model]['text_param_index']] = text
-
-                        print(f"{model}: Sending request to HF Space")
-                        results = mdl_space.predict(*space_inputs, api_name=api_name, fn_index=fn_index)
-
-                        # return path to audio
-                        result = results
-                        if (not isinstance(results, str)):
-                            # return_audio_index may be a filepath string
-                            result = results[return_audio_index]
-                        if (isinstance(result, dict)):
-                            # return_audio_index is a dictionary
-                            result = results[return_audio_index]['value']
-                    else:
-                        # Use the private HF Space
-                        result = router.predict(text, AVAILABLE_MODELS[model].lower(), api_name="/synthesize")
-                else:
-                    result = router.predict(text, model.lower(), api_name="/synthesize")
-                break
-            except Exception as e:
-                attempt_count += 1
-                raise gr.Error(f"{model}:"+ repr(e))
-                # print(f"{model}: Unable to call API (attempt: {attempt_count})")
-                # sleep for three seconds to avoid spamming the server with requests
-                # time.sleep(3)
-
-                # Fetch and store client again
-                # hf_clients[model] = Client(model, hf_token=hf_token, headers=hf_headers)
-
-        if attempt_count >= max_attempts:
-            raise gr.Error(f"{model}: Failed to call model")
-        else:
-            print('Done with', model)
-
-        try:
-            with tempfile.NamedTemporaryFile(suffix='.wav', delete=False) as f:
-                audio = AudioSegment.from_file(result)
-                current_sr = audio.frame_rate
-                if current_sr > 24000:
-                    print(f"{model}: Resampling")
-                    audio = audio.set_frame_rate(24000)
-                try:
-                    print(f"{model}: Trying to normalize audio")
-                    audio = match_target_amplitude(audio, -20)
-                except:
-                    print(f"{model}: [WARN] Unable to normalize audio")
-                audio.export(f.name, format="wav")
-                os.unlink(result)
-                result = f.name
-                gr.Info('Audio from a TTS model received')
-        except:
-            print(f"{model}: [WARN] Unable to resample audio")
-            pass
-        if model in AVAILABLE_MODELS.keys(): model = AVAILABLE_MODELS[model]
-        result_storage[model] = result
-
-    def _get_param_examples(parameters):
-        example_inputs = []
-        for param_info in parameters:
-            if (
-                param_info['component'] == 'Radio'
-                or param_info['component'] == 'Dropdown'
-                or param_info['component'] == 'Audio'
-                or param_info['python_type']['type'] == 'str'
-            ):
-                example_inputs.append(str(param_info['example_input']))
-                continue
-            if param_info['python_type']['type'] == 'int':
-                example_inputs.append(int(param_info['example_input']))
-                continue
-            if param_info['python_type']['type'] == 'float':
-                example_inputs.append(float(param_info['example_input']))
-                continue
-            if param_info['python_type']['type'] == 'bool':
-                example_inputs.append(bool(param_info['example_input']))
-                continue
-
-        return example_inputs
-
-    def _override_params(inputs, modelname):
-        try:
-            for key,value in OVERRIDE_INPUTS[modelname].items():
-                inputs[key] = value
-            print(f"{modelname}: Default inputs overridden by Arena")
-        except:
-            pass
-
-        return inputs
-
-    def _cache_sample(text, model):
-        # skip caching if not hardcoded sentence
-        if (text not in sents):
-            return False
-
-        already_cached = False
-        # check if already cached
-        for cached_sample in cached_samples:
-            # TODO:replace cached with newer version
-            if (cached_sample.transcript == text and cached_sample.modelName == model):
-                already_cached = True
-                return True
-
-        if (already_cached):
-            return False
-
-        try:
-            cached_samples.append(Sample(results[model], text, model))
-        except:
-            print('Error when trying to cache sample')
-            return False
-
-    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")
-
-    # do not use multithreading when both spaces are ZeroGPU type
-    if (
-        # exists
-        'is_zero_gpu_space' in HF_SPACES[mdl1]
-        # is True
-        and HF_SPACES[mdl1]['is_zero_gpu_space']
-        and 'is_zero_gpu_space' in HF_SPACES[mdl2]
-        and HF_SPACES[mdl2]['is_zero_gpu_space']
-    ):
-        # run Zero-GPU spaces one at a time
-        predict_and_update_result(text, mdl1k, results, request)
-        _cache_sample(text, mdl1k)
-
-        predict_and_update_result(text, mdl2k, results, request)
-        _cache_sample(text, mdl2k)
-    else:
-        # use multithreading
-        thread1 = threading.Thread(target=predict_and_update_result, args=(text, mdl1k, results, request))
-        thread2 = threading.Thread(target=predict_and_update_result, args=(text, mdl2k, results, request))
-
-        thread1.start()
-        # wait 3 seconds to calm hf.space domain
-        time.sleep(3)
-        thread2.start()
-        # timeout in 2 minutes
-        thread1.join(120)
-        thread2.join(120)
-
-        # cache the result
-        for model in [mdl1k, mdl2k]:
-            _cache_sample(text, model)
-
-    #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]
-
-    # all_pairs = generate_matching_pairs(cached_samples)
-
-    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], interactive=False, autoplay=True), # aud1
-        gr.update(visible=True, value=results[mdl2k], interactive=False, autoplay=False), # 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
-        # reset gr.State aplayed & bplayed
-        False, #aplayed
-        False, #bplayed
-    )
-
-def unlock_vote(btn_index, aplayed, bplayed):
-    # sample played
-    if btn_index == 0:
-        aplayed = True
-    if btn_index == 1:
-        bplayed = 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), True, True]
-
-    return [gr.update(), gr.update(), aplayed, bplayed]
-
-def play_other(bplayed):
-    return bplayed
-
-def get_userid(session_hash: str, request):
-    # JS cookie
-    if (session_hash != ''):
-        # print('auth by session cookie')
-        return sha1(bytes(session_hash.encode('ascii')), usedforsecurity=False).hexdigest()
-
-    if request.username:
-        # print('auth by username')
-        # by HuggingFace username - requires `auth` to be enabled therefore denying access to anonymous users
-        return sha1(bytes(request.username.encode('ascii')), usedforsecurity=False).hexdigest()
-    else:
-        # print('auth by ip')
-        # by IP address - unreliable when gradio within HTML iframe
-        # return sha1(bytes(request.client.host.encode('ascii')), usedforsecurity=False).hexdigest()
-        # by browser session cookie - Gradio on HF is run in an HTML iframe, access to parent session required to reach session token
-        # return sha1(bytes(request.headers.encode('ascii'))).hexdigest()
-        # by browser session hash - Not a cookie, session hash changes on page reload
-        return sha1(bytes(request.session_hash.encode('ascii')), usedforsecurity=False).hexdigest()
-
-# Give user a cached audio sample pair they have yet to vote on
-def give_cached_sample(session_hash: str, request: gr.Request):
-    # add new userid to voting_users from Browser session hash
-    # stored only in RAM
-    userid = get_userid(session_hash, request)
-
-    if userid not in voting_users:
-        voting_users[userid] = User(userid)
-
-    def get_next_pair(user: User):
-        # FIXME: all_pairs var out of scope
-        # all_pairs = generate_matching_pairs(cached_samples)
-
-        # for pair in all_pairs:
-        for pair in generate_matching_pairs(cached_samples):
-            hash1 = md5(bytes((pair[0].modelName + pair[0].transcript).encode('ascii'))).hexdigest()
-            hash2 = md5(bytes((pair[1].modelName + pair[1].transcript).encode('ascii'))).hexdigest()
-            pair_key = (hash1, hash2)
-            if (
-                pair_key not in user.voted_pairs
-                # or in reversed order
-                and (pair_key[1], pair_key[0]) not in user.voted_pairs
-            ):
-                return pair
-        return None
-
-    pair = get_next_pair(voting_users[userid])
-    if pair is None:
-        return [
-            *clear_stuff(),
-            # disable get cached sample button
-            gr.update(interactive=False)
-        ]
-
-    return (
-        gr.update(visible=True, value=pair[0].transcript, elem_classes=['blurred-text']),
-        "Synthesize",
-        gr.update(visible=True), # r2
-        pair[0].modelName, # model1
-        pair[1].modelName, # model2
-        gr.update(visible=True, value=pair[0].filename, interactive=False, autoplay=True), # aud1
-        gr.update(visible=True, value=pair[1].filename, interactive=False, autoplay=False), # 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
-        # reset aplayed, bplayed audio playback events
-        False, #aplayed
-        False, #bplayed
-        # fetch cached btn
-        gr.update(interactive=True)
-    )
-
-# note the vote on cached sample pair
-def voted_on_cached(modelName1: str, modelName2: str, transcript: str, session_hash: str, request: gr.Request):
-    userid = get_userid(session_hash, request)
-    # print(f'userid voted on cached: {userid}')
-
-    if userid not in voting_users:
-        voting_users[userid] = User(userid)
-
-    hash1 = md5(bytes((modelName1 + transcript).encode('ascii'))).hexdigest()
-    hash2 = md5(bytes((modelName2 + transcript).encode('ascii'))).hexdigest()
-
-    voting_users[userid].voted_pairs.add((hash1, hash2))
-    return []
-
-def randomsent():
-    return '⚡', random.choice(sents), '🎲'
-def clear_stuff():
-    return [
-        gr.update(visible=True, value="", elem_classes=[]),
-        "Synthesize",
-        gr.update(visible=False), # r2
-        '', # model1
-        '', # model2
-        gr.update(visible=False, interactive=False, autoplay=False), # aud1
-        gr.update(visible=False, interactive=False, autoplay=False), # aud2
-        gr.update(visible=False, interactive=False), #abetter
-        gr.update(visible=False, interactive=False), #bbetter
-        gr.update(visible=False), #prevmodel1
-        gr.update(visible=False), #prevmodel2
-        gr.update(visible=False), #nxt round btn
-        False, #aplayed
-        False, #bplayed
-    ]
-
-def disable():
-    return [gr.update(interactive=False), 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), gr.update(interactive=True)]
-def unblur_text():
-    return gr.update(elem_classes=[])
-
-# JavaScript within HTML head
-head_js = ""
-unblur_js = 'document.getElementById("arena-text-input").classList.remove("blurred-text")'
-shortcut_js = """
-<script>
-function shortcuts(e) {
-    var event = document.all ? window.event : e;
-    switch (e.target.tagName.toLowerCase()) {
-        case "input":
-        case "textarea":
-            break;
-        default:
-            switch (e.key.toLowerCase()) {
-                case "a":
-                    document.getElementById("arena-a-better").click();
-                    break;
-                case "b":
-                    document.getElementById("arena-b-better").click();
-                    break;
-                case "n":
-                    document.getElementById("arena-next-round").click();
-                    break;
-            }
-    }
-}
-document.addEventListener('keypress', shortcuts, false);
-
-"""
-head_js += shortcut_js
-head_js += open("cookie.js").read()
-head_js += '</script>'
-
-with gr.Blocks() as vote:
-    session_hash = gr.Textbox(visible=False, value='')
-
-    # sample played, using Checkbox so that JS can fetch the value
-    aplayed = gr.Checkbox(visible=False, value=False)
-    bplayed = gr.Checkbox(visible=False, value=False)
-    # voter ID
-    useridstate = gr.State()
-    gr.Markdown(INSTR)
-    with gr.Group():
-        with gr.Row():
-            cachedt = gr.Button('⚡', scale=0, min_width=0, variant='tool', interactive=True)
-            text = gr.Textbox(
-                container=False,
-                show_label=False,
-                placeholder="Enter text to synthesize",
-                lines=1,
-                max_lines=1,
-                scale=9999999,
-                min_width=0,
-                elem_id="arena-text-input",
-            )
-            randomt = gr.Button('🎲', scale=0, min_width=0, variant='tool')
-        randomt\
-            .click(randomsent, outputs=[cachedt, text, randomt])\
-            .then(None, js="() => "+ unblur_js)
-        btn = gr.Button("Synthesize", variant='primary')
-    model1 = gr.Textbox(interactive=False, lines=1, max_lines=1, visible=False)
-    model2 = gr.Textbox(interactive=False, lines=1, max_lines=1, visible=False)
-    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': '#EF4444'},
-                    # var(--color-red-500)'}); gradio only accepts HEX and CSS color
-                )
-                abetter = gr.Button(
-                    "A is better [a]",
-                    elem_id='arena-a-better',
-                    variant='primary',
-                    interactive=False,
-                )
-                prevmodel1 = gr.HTML(show_label=False, value="Vote to reveal model A", 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'},
-                    # var(--secondary-500)'}); gradio only accepts HEX and CSS color
-                )
-                bbetter = gr.Button(
-                    "B is better [b]",
-                    elem_id='arena-b-better',
-                    variant='primary',
-                    interactive=False
-                )
-                prevmodel2 = gr.HTML(show_label=False, value="Vote to reveal model B", visible=False)
-    nxtroundbtn = gr.Button(
-        '⚡ Next round [n]',
-        elem_id='arena-next-round',
-        visible=False
-    )
-    outputs = [
-        text,
-        btn,
-        r2,
-        model1,
-        model2,
-        aud1,
-        aud2,
-        abetter,
-        bbetter,
-        prevmodel1,
-        prevmodel2,
-        nxtroundbtn,
-        aplayed,
-        bplayed,
-    ]
-    """
-    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, cachedt])\
-        .then(synthandreturn, inputs=[text], outputs=outputs)\
-        .then(enable, outputs=[btn, gr.State(), gr.State(), cachedt])\
-        .then(None, js="() => "+ unblur_js)
-    nxtroundbtn\
-        .click(clear_stuff, outputs=outputs)\
-        .then(disable, outputs=[btn, abetter, bbetter, cachedt])\
-        .then(give_cached_sample, inputs=[session_hash], outputs=[*outputs, cachedt])\
-        .then(enable, outputs=[btn, gr.State(), gr.State(), gr.State()])
-
-    # fetch a comparison pair from cache
-    cachedt\
-        .click(disable, outputs=[btn, abetter, bbetter, cachedt])\
-        .then(give_cached_sample, inputs=[session_hash], outputs=[*outputs, cachedt])\
-        .then(enable, outputs=[btn, gr.State(), gr.State(), gr.State()])
-    # TODO: await download of sample before allowing playback
-
-    # 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],
-        )\
-        .then(
-            None,
-            inputs=[bplayed],
-            js="(b) => b ? 0 : document.querySelector('.row .gap+.gap button.play-pause-button[aria-label=Play]').click()",
-        )
-    # autoplay if unplayed
-    aud2\
-        .stop(
-            unlock_vote,
-            outputs=[abetter, bbetter, aplayed, bplayed],
-            inputs=[gr.State(value=1), aplayed, bplayed],
-        )\
-        .then(None, js="() => "+ unblur_js)
-
-    nxt_outputs = [abetter, bbetter, prevmodel1, prevmodel2, nxtroundbtn]
-    abetter\
-        .click(a_is_better, outputs=nxt_outputs, inputs=[model1, model2, useridstate, text])\
-        .then(voted_on_cached, inputs=[model1, model2, text, session_hash], outputs=[])
-    bbetter\
-        .click(b_is_better, outputs=nxt_outputs, inputs=[model1, model2, useridstate, text])\
-        .then(voted_on_cached, inputs=[model1, model2, text, session_hash], outputs=[])
-    # 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])
-
-    # get session cookie
-    vote\
-        .load(
-            None,
-            None,
-            session_hash,
-            js="() => { return getArenaCookie('session') }",
-        )
-    # give a cached sample pair to voter; .then() did not work here
-    vote\
-        .load(give_cached_sample, inputs=[session_hash], outputs=[*outputs, cachedt])
-
-with gr.Blocks() as about:
-    gr.Markdown(ABOUT)
-with gr.Blocks() as tts_info:
-    gr.Markdown(TTS_INFO)
-    gr.HTML(TTS_DATASET_IFRAME)
-# 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)
-# Blur cached sample text so the voting user picks up mispronouncements
-with gr.Blocks(theme=theme, css="footer {visibility: hidden}textbox{resize:none} .blurred-text {filter: blur(0.15em);}", head=head_js, 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, tts_info], ['🗳️ Vote', '🏆 Leaderboard', '📄 About', '🗣 Contenders'])
-    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=4)\
-    .launch(show_api=False, show_error=True)
+if __name__ == "__main__":
+    app.queue(default_concurrency_limit=50, api_open=False).launch(show_api=False)

app/config.py

@@ -0,0 +1,51 @@
+import os
+
+# NOTE: Configure models in `models.py`
+
+#########################
+# General Configuration #
+#########################
+
+DB_NAME = "database.db"
+
+TOXICITY_CHECK = False
+
+MAX_SAMPLE_TXT_LENGTH = 300 # Maximum text length (characters)
+MIN_SAMPLE_TXT_LENGTH = 10 # Minimum text length (characters)
+
+DB_PATH = f"/data/{DB_NAME}" if os.path.isdir("/data") else DB_NAME # If /data available => means local storage is enabled => let's use it!
+
+ROUTER_ID = "Pendrokar/xVASynth-TTS" # You should use a router space to route TTS models to avoid exposing your API keys!
+# ROUTER_ID = "TTS-AGI/tts-router" # You should use a router space to route TTS models to avoid exposing your API keys!
+
+SYNC_DB = True # Sync DB to HF dataset?
+DB_DATASET_ID = os.getenv('DATASET_ID') # HF dataset ID, can be None if not syncing
+
+SPACE_ID = os.getenv('SPACE_ID') # Don't change this! It detects if we're running in a HF Space
+
+sents = []
+with open(os.path.dirname(__file__) + '/../harvard_sentences.txt') as f:
+    sents += f.read().strip().splitlines()
+with open(os.path.dirname(__file__) + '/../llama3_command-r_sentences_1st_person.txt') as f:
+    sents += f.read().strip().splitlines()
+# With other punctuation marks
+# Exclamations - # conversational characters/animation entertainment/tv
+with open(os.path.dirname(__file__) + '/../llama3_command-r_sentences_excla.txt') as f:
+    sents += f.read().strip().splitlines()
+# Questions - # conversational characters/animation entertainment/tv
+with open(os.path.dirname(__file__) + '/../llama3_command-r_questions.txt') as f:
+    sents += f.read().strip().splitlines()
+
+# Credit: llama3_command-r sentences generated by user KingNish
+
+######################
+# TTS Arena Settings #
+######################
+
+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}"
+}"""

app/cookie.js

@@ -0,0 +1,30 @@
+window.getArenaCookie = function getArenaCookie(cname) {
+    let name = cname + "=";
+    let decodedCookie = decodeURIComponent(window.document.cookie);
+    let ca = decodedCookie.split(';');
+    for (let i = 0; i < ca.length; i++) {
+        let c = ca[i];
+        while (c.charAt(0) == ' ') {
+        c = c.substring(1);
+        }
+        if (c.indexOf(name) == 0) {
+        return c.substring(name.length, c.length);
+        }
+    }
+    return "";
+}
+
+window.setArenaCookie = function setArenaCookie(cname, cvalue, exdays) {
+    const d = new Date();
+    d.setTime(d.getTime() + (exdays * 24 * 60 * 60 * 1000));
+    let expires = "expires=" + d.toUTCString();
+    window.document.cookie = cname + "=" + cvalue + ";" + expires + "; path=/; SameSite=None; Secure";
+}
+
+if (window.getArenaCookie('session').length == 0)
+{
+    const d = new Date();
+    // store cookie for 90 days, about the time the the cached audio should be deleted
+    window.setArenaCookie('session', d.getTime().toString(), 90);
+    console.log('Session cookie created')
+}

app/db.py

@@ -0,0 +1,54 @@
+import sqlite3
+from .config import *
+import os
+import shutil
+from huggingface_hub import hf_hub_download
+
+def download_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)
+
+def get_db():
+    return sqlite3.connect(DB_PATH)
+
+def create_db():
+    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
+        );
+    ''')

app/init.py

@@ -0,0 +1,30 @@
+from .config import *
+from .db import *
+from huggingface_hub import CommitScheduler
+from pathlib import Path
+from gradio_client import Client
+import os
+
+
+scheduler = None
+
+if SYNC_DB:
+    download_db()
+    # 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,
+    )
+
+create_db()
+
+# Load TTS Router
+router = Client(ROUTER_ID, hf_token=os.getenv('HF_TOKEN'))
+
+if TOXICITY_CHECK:
+    # Load toxicity model
+    from detoxify import Detoxify
+    toxicity = Detoxify('original')

app/leaderboard.py

@@ -0,0 +1,91 @@
+from .config import *
+from .db import *
+from .models import *
+from .synth import top_five
+
+import pandas as pd
+
+# for diff
+leaderboard_df = {}
+def get_leaderboard(reveal_prelim = False):
+    global leaderboard_df
+
+    conn = get_db()
+    cursor = conn.cursor()
+    sql = 'SELECT name, upvote, downvote, name AS orig_name FROM model'
+    if not reveal_prelim: sql += ' WHERE (upvote + downvote) > 300'
+    cursor.execute(sql)
+    data = cursor.fetchall()
+    df = pd.DataFrame(data, columns=['name', 'upvote', 'downvote', 'orig_name'])
+    # df['license'] = df['name'].map(model_license)
+    df['name'] = df['name'].replace(model_names)
+    for i in range(len(df)):
+        df.loc[i, "name"] = make_link_to_space(df['name'][i], True)
+    df['votes'] = df['upvote'] + df['downvote']
+    # df['score'] = round((df['upvote'] / df['votes']) * 100, 2) # Percentage score
+
+    ## ELO SCORE
+    df['score'] = 1200
+    df['score_diff'] = ""
+    for i in range(len(df)):
+        for j in range(len(df)):
+            if i != j:
+                try:
+                    expected_a = 1 / (1 + 10 ** ((df['score'].iloc[j] - df['score'].iloc[i]) / 400))
+                    expected_b = 1 / (1 + 10 ** ((df['score'].iloc[i] - df['score'].iloc[j]) / 400))
+                    actual_a = df['upvote'].iloc[i] / df['votes'].iloc[i] if df['votes'].iloc[i] > 0 else 0.5
+                    actual_b = df['upvote'].iloc[j] / df['votes'].iloc[j] if df['votes'].iloc[j] > 0 else 0.5
+                    df.at[i, 'score'] += round(32 * (actual_a - expected_a))
+                    df.at[j, 'score'] += round(32 * (actual_b - expected_b))
+                except Exception as e:
+                    print(f"Error in ELO calculation for rows {i} and {j}: {str(e)}")
+                    continue
+    df['score'] = round(df['score'])
+    df['score_diff'] = df['score']
+
+    if (
+        reveal_prelim == False
+        and len(leaderboard_df) == 0
+    ):
+        leaderboard_df = df
+
+    if (reveal_prelim == False):
+        for i in range(len(df)):
+            score_diff = (df['score'].iloc[i] - leaderboard_df['score'].iloc[i])
+            if (score_diff == 0):
+                continue
+            if (score_diff > 0):
+                plus = '<em style="color: green; font-family: monospace">+'
+            else:
+                plus = '<em style="color: red; font-family: monospace">'
+
+            df.at[i, 'score_diff'] = str(df['score'].iloc[i]) + plus + str(score_diff) +'</em>'
+
+    ## ELO SCORE
+    df = df.sort_values(by='score', ascending=False)
+    # medals
+    def assign_medal(rank, assign):
+        rank = str(rank + 1)
+        if assign:
+            if rank == '1':
+                rank += '🥇'
+            elif rank == '2':
+                rank += '🥈'
+            elif rank == '3':
+                rank += '🥉'
+
+        return '#'+ rank
+
+    df['order'] = [assign_medal(i, not reveal_prelim and len(df) > 2) for i in range(len(df))]
+    # fetch top_five
+    for orig_name in df['orig_name']:
+        if (
+            reveal_prelim
+            and len(top_five) < 5
+            and orig_name in AVAILABLE_MODELS.keys()
+        ):
+            top_five.append(orig_name)
+
+    df['score'] = df['score_diff']
+    df = df[['order', 'name', 'score', 'votes']]
+    return df

app/messages.py

@@ -0,0 +1,98 @@
+from .config import *
+from .models import *
+
+############
+# Messages #
+############
+
+MUST_BE_LOGGEDIN = "Please login with Hugging Face to participate in the TTS Arena."
+DESCR = """
+# TTS Arena: Benchmarking TTS Models in the Wild
+Vote to help the community find the best available text-to-speech model!
+""".strip()
+BATTLE_INSTR = """
+## Battle
+Choose 2 candidates and vote on which one is better! Currently in beta.
+* Input text (English only) to synthesize audio (or press 🎲 for random text).
+* Listen to the two audio clips, one after the other.
+* Vote on which audio sounds more natural to you.
+"""
+INSTR = """
+* Press ⚡ to quickly get cached sample pairs you've yet to vote on. (Fast 🐇)
+* Or press 🎲 to randomly use a sentence from the list. (Slow 🐢)
+* Or input text (🇺🇸 English only) to synthesize audio. (Slowest 🐌)
+* _Listen to the two audio clips, one after the other and then vote on which audio sounds more natural to you._
+* Model names are revealed after the vote is cast.
+⚠ Note: It **may take up to 30 seconds** to ***synthesize*** audio.
+""".strip()
+request = ""
+if SPACE_ID:
+    request = f"""
+### Request a model
+Please [create a Discussion](https://huggingface.co/spaces/{SPACE_ID}/discussions/new) to request a model.
+"""
+ABOUT = f"""
+## About
+The TTS Arena evaluates leading speech synthesis models. It is inspired by LMsys's [Chatbot Arena](https://chat.lmsys.org/).
+### Motivation
+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.
+### The Arena
+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.
+### Credits
+Thank you to the following individuals who helped make this project possible:
+* VB ([Twitter](https://twitter.com/reach_vb) / [Hugging Face](https://huggingface.co/reach-vb))
+* Clémentine Fourrier ([Twitter](https://twitter.com/clefourrier) / [Hugging Face](https://huggingface.co/clefourrier))
+* Lucain Pouget ([Twitter](https://twitter.com/Wauplin) / [Hugging Face](https://huggingface.co/Wauplin))
+* Yoach Lacombe ([Twitter](https://twitter.com/yoachlacombe) / [Hugging Face](https://huggingface.co/ylacombe))
+* Main Horse ([Twitter](https://twitter.com/main_horse) / [Hugging Face](https://huggingface.co/main-horse))
+* Sanchit Gandhi ([Twitter](https://twitter.com/sanchitgandhi99) / [Hugging Face](https://huggingface.co/sanchit-gandhi))
+* Apolinário Passos ([Twitter](https://twitter.com/multimodalart) / [Hugging Face](https://huggingface.co/multimodalart))
+* Pedro Cuenca ([Twitter](https://twitter.com/pcuenq) / [Hugging Face](https://huggingface.co/pcuenq))
+
+\* ***You are currently in a cloned/forked space of TTS-AGI/TTS-Arena***
+
+{request}
+### Privacy statement
+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.
+### License
+Generated audio clips cannot be redistributed and may be used for personal, non-commercial use only.
+Random sentences are sourced from a filtered subset of the [Harvard Sentences](https://www.cs.columbia.edu/~hgs/audio/harvard.html).
+""".strip()
+LDESC = """
+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. [This dataset is public](https://huggingface.co/datasets/{DB_DATASET_ID}) and only saves the hardcoded sentences while keeping the voters anonymous.
+""".strip()
+
+
+model_series = []
+for model in AVAILABLE_MODELS.keys():
+    # name up to first whitespace
+    model = model.split()[0]
+    model_series.append('%27'+ model +'%27')
+try:
+    for model in HF_SPACES.values():
+        # url encode pluses +
+        model_series.append('%27'+ model['series'].replace('+', '%2B') +'%27')
+except:
+    pass
+
+TTS_INFO = f"""
+## 🗣 Contenders
+
+### 🔐 Closed Source TTS
+* Microsoft _Edge TTS_
+
+### 🔓 Open Source TTS capabilities table
+
+See [the full dataset itself](https://huggingface.co/datasets/Pendrokar/open_tts_tracker) for the legend and more in depth information of each model.
+""".strip()
+TTS_DATASET_IFRAME_ORDER = '%2C+'.join(model_series)
+TTS_DATASET_IFRAME = f"""
+<iframe
+    src="https://huggingface.co/datasets/Pendrokar/open_tts_tracker/embed/sql-console/default/train?sql_console=true&sql=--+The+SQL+console+is+powered+by+DuckDB+WASM+and+runs+entirely+in+the+browser.%0A--+Get+started+by+typing+a+query+or+selecting+a+view+from+the+options+below.%0ASELECT+*%2C+%22Name%22+IN+%28{TTS_DATASET_IFRAME_ORDER}%29+AS+%22In+arena%22+FROM+train+WHERE+%22Insta-clone+%F0%9F%91%A5%22+IS+NOT+NULL+ORDER+BY+%22In+arena%22+DESC+LIMIT+50%3B&views%5B%5D=train"
+    frameborder="0"
+    width="100%"
+    height="650px"
+></iframe>
+""".strip()

app/models.py

@@ -0,0 +1,469 @@
+import os
+from gradio_client import handle_file
+
+# Models to include in the leaderboard, only include models that users can vote on
+AVAILABLE_MODELS = {
+    # 'XTTSv2': 'xtts',
+    # 'WhisperSpeech': 'whisperspeech',
+    # 'ElevenLabs': 'eleven',
+    # 'OpenVoice': 'openvoice',
+    # 'OpenVoice V2': 'openvoicev2',
+    # 'Play.HT 2.0': 'playht',
+    # 'Play.HT 3.0 Mini': 'playht3',
+    # 'MetaVoice': 'metavoice',
+    # 'MeloTTS': 'melo',
+    # 'StyleTTS 2': 'styletts2',
+    # 'GPT-SoVITS': 'sovits',
+    # 'Vokan TTS': 'vokan',
+    # 'VoiceCraft 2.0': 'voicecraft',
+    # 'Parler TTS': 'parler',
+    # 'Parler TTS Large': 'parlerlarge',
+    # 'Fish Speech v1.4': 'fish',
+
+    # HF Gradio Spaces: # <works with gradio version #>
+    # gravio version that works with most spaces: 4.29
+    # 'coqui/xtts': 'coqui/xtts', # 4.29 4.32
+    # 'collabora/WhisperSpeech': 'collabora/WhisperSpeech', # 4.32 4.36.1
+    # 'myshell-ai/OpenVoice': 'myshell-ai/OpenVoice', # same devs as MeloTTS, which scores higher # 4.29
+    # 'myshell-ai/OpenVoiceV2': 'myshell-ai/OpenVoiceV2', # same devs as MeloTTS, which scores higher # 4.29
+    # 'mrfakename/MetaVoice-1B-v0.1': 'mrfakename/MetaVoice-1B-v0.1', # 4.29 4.32
+    'Pendrokar/xVASynth-TTS': 'Pendrokar/xVASynth-TTS', # 4.29 4.32 4.42.0
+    # 'coqui/CoquiTTS': 'coqui/CoquiTTS',
+    # 'mrfakename/MeloTTS': 'mrfakename/MeloTTS', # 4.29 4.32
+    # 'fishaudio/fish-speech-1': 'fishaudio/fish-speech-1', # 4.29 4.32 4.36.1
+
+    # E2 & F5 TTS
+    # F5 model
+    # 'mrfakename/E2-F5-TTS': 'mrfakename/E2-F5-TTS', # 5.0
+
+    # # Parler
+    # Parler Large model
+    # 'parler-tts/parler_tts': 'parler-tts/parler_tts', # 4.29 4.32 4.36.1 4.42.0
+    # Parler Mini model
+    # 'parler-tts/parler_tts': 'parler-tts/parler_tts', # 4.29 4.32 4.36.1 4.42.0
+    # 'parler-tts/parler_tts_mini': 'parler-tts/parler_tts_mini', # Mini is the default model of parler_tts
+    # 'parler-tts/parler-tts-expresso': 'parler-tts/parler-tts-expresso', # 4.29 4.32 4.36.1 4.42.0
+
+    # # Microsoft Edge TTS
+    # 'innoai/Edge-TTS-Text-to-Speech': 'innoai/Edge-TTS-Text-to-Speech', # 4.29
+
+    # IMS-Toucan
+    # 'Flux9665/MassivelyMultilingualTTS': 'Flux9665/MassivelyMultilingualTTS', # 5.1
+    # StyleTTS v2
+    # 'Pendrokar/style-tts-2': 'Pendrokar/style-tts-2', #  more votes in OG arena; emotionless
+    # StyleTTS kokoro
+    'hexgrad/kokoro': 'hexgrad/kokoro',
+
+    # MaskGCT (by Amphion)
+    # DEMANDS 300 seconds of ZeroGPU
+    # 'amphion/maskgct': 'amphion/maskgct',
+    # default ZeroGPU borrow time
+    'Svngoku/maskgct-audio-lab': 'Svngoku/maskgct-audio-lab',
+
+    # HF TTS w issues
+    'LeeSangHoon/HierSpeech_TTS': 'LeeSangHoon/HierSpeech_TTS', # irresponsive to exclamation marks # 4.29
+    # 'PolyAI/pheme': '/predict#0', # sleepy HF Space
+    # 'amphion/Text-to-Speech': '/predict#0', # disabled also on original HF space due to poor ratings
+    # 'suno/bark': '3#0', # Hallucinates
+    # 'shivammehta25/Matcha-TTS': '5#0', # seems to require multiple requests for setup
+    # 'styletts2/styletts2': '0#0', # API disabled, awaiting approval of PR #15
+    # 'Manmay/tortoise-tts': '/predict#0', # Cannot retrieve streamed file; 403
+    # 'pytorch/Tacotron2': '0#0', # old gradio
+}
+
+HF_SPACES = {
+    # XTTS v2
+    'coqui/xtts': {
+        'name': 'XTTS v2',
+        'function': '1',
+        'text_param_index': 0,
+        'return_audio_index': 1,
+        'series': 'XTTS',
+    },
+    # WhisperSpeech
+    'collabora/WhisperSpeech': {
+        'name': 'WhisperSpeech',
+        'function': '/whisper_speech_demo',
+        'text_param_index': 0,
+        'return_audio_index': 0,
+        'series': 'WhisperSpeech',
+    },
+    # OpenVoice (MyShell.ai)
+    'myshell-ai/OpenVoice': {
+        'name':'OpenVoice',
+        'function': '1',
+        'text_param_index': 0,
+        'return_audio_index': 1,
+        'series': 'OpenVoice',
+    },
+    # OpenVoice v2 (MyShell.ai)
+    'myshell-ai/OpenVoiceV2': {
+        'name':'OpenVoice v2',
+        'function': '1',
+        'text_param_index': 0,
+        'return_audio_index': 1,
+        'series': 'OpenVoice',
+    },
+    # MetaVoice
+    'mrfakename/MetaVoice-1B-v0.1': {
+        'name':'MetaVoice-1B',
+        'function': '/tts',
+        'text_param_index': 0,
+        'return_audio_index': 0,
+        'series': 'MetaVoice-1B',
+    },
+    # xVASynth (CPU)
+    'Pendrokar/xVASynth-TTS': {
+        'name': 'xVASynth v3',
+        'function': '/predict',
+        'text_param_index': 0,
+        'return_audio_index': 0,
+        'series': 'xVASynth',
+    },
+    # CoquiTTS (CPU)
+    'coqui/CoquiTTS': {
+        'name': 'CoquiTTS',
+        'function': '0',
+        'text_param_index': 0,
+        'return_audio_index': 0,
+        'series': 'CoquiTTS',
+    },
+    # HierSpeech_TTS
+    'LeeSangHoon/HierSpeech_TTS': {
+        'name': 'HierSpeech++',
+        'function': '/predict',
+        'text_param_index': 0,
+        'return_audio_index': 0,
+        'series': 'HierSpeech++',
+    },
+    # MeloTTS (MyShell.ai)
+    'mrfakename/MeloTTS': {
+        'name': 'MeloTTS',
+        'function': '/synthesize',
+        'text_param_index': 0,
+        'return_audio_index': 0,
+        'series': 'MeloTTS',
+    },
+
+    # Parler
+    'parler-tts/parler_tts': {
+        'name': 'Parler Mini',
+        'function': '/gen_tts',
+        'text_param_index': 0,
+        'return_audio_index': 0,
+        'is_zero_gpu_space': True,
+        'series': 'Parler',
+    },
+    # Parler Mini
+    # 'parler-tts/parler_tts': {
+    #     'name': 'Parler Large',
+    #     'function': '/gen_tts',
+    #     'text_param_index': 0,
+    #     'return_audio_index': 0,
+    #     'is_zero_gpu_space': True,
+    #    'series': 'Parler',
+    # },
+    # Parler Mini which using Expresso dataset
+    'parler-tts/parler-tts-expresso': {
+        'name': 'Parler Mini Expresso',
+        'function': '/gen_tts',
+        'text_param_index': 0,
+        'return_audio_index': 0,
+        'is_zero_gpu_space': True,
+        'series': 'Parler',
+    },
+
+    # Microsoft Edge TTS
+    'innoai/Edge-TTS-Text-to-Speech': {
+        'name': 'Edge TTS',
+        'function': '/predict',
+        'text_param_index': 0,
+        'return_audio_index': 0,
+        'is_closed_source': True,
+        'series': 'Edge TTS',
+    },
+
+    # Fish Speech
+    'fishaudio/fish-speech-1': {
+        'name': 'Fish Speech',
+        'function': '/inference_wrapper',
+        'text_param_index': 0,
+        'return_audio_index': 1,
+        'series': 'Fish Speech',
+    },
+
+    # E2/F5 TTS
+    'mrfakename/E2-F5-TTS': {
+        'name': 'F5 of E2 TTS',
+        'function': '/infer',
+        'text_param_index': 2,
+        'return_audio_index': 0,
+        'is_zero_gpu_space': True,
+        'series': 'E2/F5 TTS',
+    },
+
+    # IMS-Toucan
+    'Flux9665/MassivelyMultilingualTTS': {
+        'name': 'IMS-Toucan',
+		'function': "/predict",
+        'text_param_index': 0,
+        'return_audio_index': 0,
+        'series': 'IMS-Toucan',
+    },
+
+    # IMS-Toucan English non-artificial
+    'Flux9665/EnglishToucan': {
+        'name': 'IMS-Toucan EN',
+		'function': "/predict",
+        'text_param_index': 0,
+        'return_audio_index': 0,
+        'series': 'IMS-Toucan',
+    },
+
+    # StyleTTS v2
+    'Pendrokar/style-tts-2': {
+        'name': 'StyleTTS v2',
+        'function': '/synthesize',
+        'text_param_index': 0,
+        'return_audio_index': 0,
+        'is_zero_gpu_space': True,
+        'series': 'StyleTTS',
+    },
+
+    # StyleTTS v2 kokoro fine tune
+    'hexgrad/kokoro': {
+        'name': 'StyleTTS Kokoro',
+        'function': '/generate',
+        'text_param_index': 0,
+        'return_audio_index': 0,
+        'is_zero_gpu_space': True,
+        'series': 'StyleTTS',
+    },
+
+    # MaskGCT (by Amphion)
+    'amphion/maskgct': {
+        'name': 'MaskGCT',
+        'function': '/predict',
+        'text_param_index': 1,
+        'return_audio_index': 0,
+        'is_zero_gpu_space': True,
+        'series': 'MaskGCT',
+    },
+    'Svngoku/maskgct-audio-lab': {
+        'name': 'MaskGCT',
+        'function': '/predict',
+        'text_param_index': 1,
+        'return_audio_index': 0,
+        'is_zero_gpu_space': True,
+        'series': 'MaskGCT',
+    },
+}
+
+# for zero-shot TTS - voice sample used by XTTS (11 seconds)
+DEFAULT_VOICE_SAMPLE_STR = 'https://cdn-uploads.huggingface.co/production/uploads/63d52e0c4e5642795617f668/V6-rMmI-P59DA4leWDIcK.wav'
+DEFAULT_VOICE_SAMPLE = handle_file(DEFAULT_VOICE_SAMPLE_STR)
+DEFAULT_VOICE_TRANSCRIPT = "The Hispaniola was rolling scuppers under in the ocean swell. The booms were tearing at the blocks, the rudder was banging to and fro, and the whole ship creaking, groaning, and jumping like a manufactory."
+
+OVERRIDE_INPUTS = {
+    'coqui/xtts': {
+        1: 'en',
+        2: DEFAULT_VOICE_SAMPLE_STR, # voice sample
+        3: None, # mic voice sample
+        4: False, #use_mic
+        5: False, #cleanup_reference
+        6: False, #auto_detect
+    },
+    'collabora/WhisperSpeech': {
+        1: DEFAULT_VOICE_SAMPLE, # voice sample
+        2: DEFAULT_VOICE_SAMPLE, # voice sample URL
+        3: 14.0, #Tempo - Gradio Slider issue: takes min. rather than value
+    },
+    'myshell-ai/OpenVoice': {
+        1: 'default', # style
+        2: 'https://huggingface.co/spaces/myshell-ai/OpenVoiceV2/resolve/main/examples/speaker0.mp3', # voice sample
+    },
+    'myshell-ai/OpenVoiceV2': {
+        1: 'en_us', # style
+        2: 'https://huggingface.co/spaces/myshell-ai/OpenVoiceV2/resolve/main/examples/speaker0.mp3', # voice sample
+    },
+    'PolyAI/pheme': {
+        1: 'YOU1000000044_S0000798', # voice
+        2: 210,
+        3: 0.7, #Tempo - Gradio Slider issue: takes min. rather than value
+    },
+    'Pendrokar/xVASynth-TTS': {
+        1: 'x_ex04', #fine-tuned voice model name
+        3: 1.0, #pacing/duration - Gradio Slider issue: takes min. rather than value
+    },
+    'suno/bark': {
+        1: 'Speaker 3 (en)', # voice
+    },
+    'amphion/Text-to-Speech': {
+        1: 'LikeManyWaters', # voice
+    },
+    'LeeSangHoon/HierSpeech_TTS': {
+        1: handle_file('https://huggingface.co/spaces/LeeSangHoon/HierSpeech_TTS/resolve/main/example/female.wav'), # voice sample
+        2: 0.333,
+        3: 0.333,
+        4: 1,
+        5: 1,
+        6: 0,
+        7: 1111,
+    },
+    'Manmay/tortoise-tts': {
+        1: None, # text-from-file
+        2: 'angie', # voice
+        3: 'disabled', # second voice for a dialogue
+        4: 'No', # split by newline
+    },
+    'mrfakename/MeloTTS': {
+        1: 'EN-Default',	# speaker; DEFAULT_VOICE_SAMPLE=EN-Default
+        2: 1, # speed
+        3: 'EN',	# language
+    },
+    'mrfakename/MetaVoice-1B-v0.1': {
+		1: 5,	# float (numeric value between 0.0 and 10.0) in 'Speech Stability - improves text following for a challenging speaker' Slider component
+		2: 5,	# float (numeric value between 1.0 and 5.0) in 'Speaker similarity - How closely to match speaker identity and speech style.' Slider component
+		3: "Preset voices",	# Literal['Preset voices', 'Upload target voice']  in 'Choose voice' Radio component
+		4: "Bria",	# Literal['Bria', 'Alex', 'Jacob']  in 'Preset voices' Dropdown component
+		5: None,	# filepath  in 'Upload a clean sample to clone. Sample should contain 1 speaker, be between 30-90 seconds and not contain background noise.' Audio component
+    },
+    'parler-tts/parler_tts': {
+        1: 'Laura; Laura\'s female voice; very clear audio', # description/prompt
+    },
+    'parler-tts/parler-tts-expresso': {
+        1: 'Elisabeth; Elisabeth\'s female voice; very clear audio', # description/prompt
+    },
+    'innoai/Edge-TTS-Text-to-Speech': {
+        1: 'en-US-EmmaMultilingualNeural - en-US (Female)', # voice
+        2: 0, # pace rate
+        3: 0, # pitch
+    },
+
+    'fishaudio/fish-speech-1': {
+		1: True, # enable_reference_audio
+		2: handle_file('https://huggingface.co/spaces/fishaudio/fish-speech-1/resolve/main/examples/English.wav'), # reference_audio
+		3: 'In the ancient land of Eldoria, where the skies were painted with shades of mystic hues and the forests whispered secrets of old, there existed a dragon named Zephyros. Unlike the fearsome tales of dragons that plagued human hearts with terror, Zephyros was a creature of wonder and wisdom, revered by all who knew of his existence.', # reference_text
+		4: 0, # max_new_tokens
+		5: 200, # chunk_length
+		6: 0.7, # top_p
+		7: 1.2, # repetition_penalty
+		8: 0.7, # temperature
+		9: 1, # batch_infer_num
+		10: False, # if_load_asr_model
+    },
+
+    'mrfakename/E2-F5-TTS': {
+		0: DEFAULT_VOICE_SAMPLE, # voice sample
+		1: DEFAULT_VOICE_TRANSCRIPT, # transcript of sample (< 15 seconds required)
+		3: False, # cleanup silence
+        4: 0.15, #crossfade
+        5: 32, #nfe_slider
+        6: 1, #speed
+    },
+
+    # IMS-Toucan
+    'Flux9665/MassivelyMultilingualTTS': {
+		1: "English (eng)", #language
+		2: 0.6, #prosody_creativity
+		3: 1, #duration_scaling_factor
+		4: 41, #voice_seed
+		5: -7.5, #emb1
+		6: None, #reference_audio
+    },
+
+    # StyleTTS 2
+    'Pendrokar/style-tts-2': {
+		1: "f-us-2", #voice
+        2: 'en-us', # lang
+		3: 8, # lngsteps
+    },
+
+    # StyleTTS 2 kokoro
+    'hexgrad/kokoro': {
+		1: "af", #voice
+		2: None, #ps
+		3: 1, #speed
+		4: 3000, #trim
+		5: False, #use_gpu; fast enough with multithreaded with CPU
+        6: os.getenv('KOKORO'), #sk
+    },
+
+    # maskGCT (by amphion)
+    'amphion/maskgct': {
+        0: DEFAULT_VOICE_SAMPLE, #prompt_wav
+		2: -1, #target_len
+		3: 25, #n_timesteps
+    },
+    'Svngoku/maskgct-audio-lab': {
+        0: DEFAULT_VOICE_SAMPLE, #prompt_wav
+		2: -1, #target_len
+		3: 25, #n_timesteps
+    },
+}
+
+
+# Model name mapping, can include models that users cannot vote on
+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',
+}
+
+def make_link_to_space(model_name, for_leaderboard=False):
+    # create a anchor link if a HF space
+    style = 'text-decoration: underline;text-decoration-style: dotted;'
+    title = ''
+
+    if model_name in AVAILABLE_MODELS:
+        style += 'color: var(--link-text-color);'
+        title = model_name
+    else:
+        style += 'font-style: italic;'
+        title = 'Disabled for Arena (See AVAILABLE_MODELS within code for why)'
+
+    model_basename = model_name
+    if model_name in HF_SPACES:
+        model_basename = HF_SPACES[model_name]['name']
+
+    try:
+        if(
+            for_leaderboard
+            and HF_SPACES[model_name]['is_closed_source']
+        ):
+            model_basename += ' 🔐'
+            title += '; 🔐 = online only or proprietary'
+    except:
+        pass
+
+    if '/' in model_name:
+        return '🤗 <a target="_blank" style="'+ style +'" title="'+ title +'" href="'+ 'https://huggingface.co/spaces/'+ model_name +'">'+ model_basename +'</a>'
+
+    # otherwise just return the model name
+    return '<span style="'+ style +'" title="'+ title +'" href="'+ 'https://huggingface.co/spaces/'+ model_name +'">'+ model_name +'</span>'
+
+def markdown_link_to_space(model_name):
+    # create a anchor link if a HF space using markdown syntax
+    if '/' in model_name:
+        return '🤗 [' + model_name + '](https://huggingface.co/spaces/' + model_name + ')'
+    # otherwise just return the model name
+    return model_name

app/synth.py

@@ -0,0 +1,421 @@
+import time
+from .models import *
+from .utils import *
+from .config import *
+from .init import *
+from .sample_caching import *
+
+import gradio as gr
+from pydub import AudioSegment
+import random, os, threading, tempfile
+from langdetect import detect
+from .vote import log_text
+
+# top five models in order to always have one of them picked and scrutinized
+top_five = ['fishaudio/fish-speech-1'] # fish 1.5
+hf_token=os.getenv('HF_TOKEN')
+
+# prioritize low vote models
+sql = 'SELECT name FROM model WHERE (upvote + downvote) < 750 ORDER BY (upvote + downvote) ASC'
+conn = get_db()
+cursor = conn.cursor()
+cursor.execute(sql)
+data = cursor.fetchall()
+for model in data:
+    if (
+        len(top_five) >= 5
+    ):
+        break
+
+    if model[0] in AVAILABLE_MODELS.keys():
+        top_five.append(model[0])
+print(f"low vote top_five: {top_five}")
+
+def random_m():
+    return random.sample(list(set(AVAILABLE_MODELS.keys())), 2)
+
+def check_toxicity(text):
+    if not TOXICITY_CHECK:
+        return False
+    return toxicity.predict(text)['toxicity'] > 0.8
+
+def synthandreturn(text, autoplay, request: gr.Request):
+    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 check_toxicity(text)
+    ):
+        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 (
+            text not in sents
+            and not detect(text) == "en"
+        ):
+            gr.Warning('Warning: The input text may not be in English')
+    except:
+        pass
+    # Get two random models
+
+    # forced model: your TTS model versus The World!!!
+    # mdl1 = 'Pendrokar/xVASynth'
+
+    # scrutinize the top five by always picking one of them
+    if (len(top_five) >= 5):
+        mdl1 = random.sample(top_five, 1)[0]
+        vsModels = dict(AVAILABLE_MODELS)
+        del vsModels[mdl1]
+        # randomize position of the forced model
+        mdl2 = random.sample(list(vsModels.keys()), 1)
+        # forced random
+        mdl1, mdl2 = random.sample(list([mdl1, mdl2[0]]), 2)
+    else:
+        # actual random
+        mdl1, mdl2 = random.sample(list(AVAILABLE_MODELS.keys()), 2)
+
+    print("[debug] Using", mdl1, mdl2)
+    def predict_and_update_result(text, model, result_storage, request:gr.Request):
+
+        hf_headers = {}
+        try:
+            if HF_SPACES[model]['is_zero_gpu_space']:
+                hf_headers = {"X-IP-Token": request.headers['x-ip-token']}
+        except:
+            pass
+
+        # re-attempt if necessary
+        attempt_count = 0
+        max_attempts = 1 # 3 =May cause 429 Too Many Request
+        while attempt_count < max_attempts:
+            try:
+                if model in AVAILABLE_MODELS:
+                    if '/' in model:
+                        # Use public HF Space
+                        # if (model not in hf_clients):
+                        #     hf_clients[model] = Client(model, hf_token=hf_token, headers=hf_headers)
+                        mdl_space = Client(model, hf_token=hf_token, headers=hf_headers)
+
+                        # print(f"{model}: Fetching endpoints of HF Space")
+                        # assume the index is one of the first 9 return params
+                        return_audio_index = int(HF_SPACES[model]['return_audio_index'])
+                        endpoints = mdl_space.view_api(all_endpoints=True, print_info=False, return_format='dict')
+
+                        api_name = None
+                        fn_index = None
+                        end_parameters = None
+                        # has named endpoint
+                        if '/' == HF_SPACES[model]['function'][0]:
+                            # audio sync function name
+                            api_name = HF_SPACES[model]['function']
+
+                            end_parameters = _get_param_examples(
+                                endpoints['named_endpoints'][api_name]['parameters']
+                            )
+                        # has unnamed endpoint
+                        else:
+                            # endpoint index is the first character
+                            fn_index = int(HF_SPACES[model]['function'])
+
+                            end_parameters = _get_param_examples(
+                                endpoints['unnamed_endpoints'][str(fn_index)]['parameters']
+                            )
+
+                        # override some or all default parameters
+                        space_inputs = _override_params(end_parameters, model)
+
+                        # force text
+                        space_inputs[HF_SPACES[model]['text_param_index']] = text
+
+                        print(f"{model}: Sending request to HF Space")
+                        results = mdl_space.predict(*space_inputs, api_name=api_name, fn_index=fn_index)
+
+                        # return path to audio
+                        result = results
+                        if (not isinstance(results, str)):
+                            # return_audio_index may be a filepath string
+                            result = results[return_audio_index]
+                        if (isinstance(result, dict)):
+                            # return_audio_index is a dictionary
+                            result = results[return_audio_index]['value']
+                    else:
+                        # Use the private HF Space
+                        result = router.predict(text, AVAILABLE_MODELS[model].lower(), api_name="/synthesize")
+                else:
+                    result = router.predict(text, model.lower(), api_name="/synthesize")
+                break
+            except Exception as e:
+                attempt_count += 1
+                raise gr.Error(f"{model}:"+ repr(e))
+                # print(f"{model}: Unable to call API (attempt: {attempt_count})")
+                # sleep for three seconds to avoid spamming the server with requests
+                # time.sleep(3)
+
+                # Fetch and store client again
+                # hf_clients[model] = Client(model, hf_token=hf_token, headers=hf_headers)
+
+        if attempt_count >= max_attempts:
+            raise gr.Error(f"{model}: Failed to call model")
+        else:
+            print('Done with', model)
+
+        try:
+            with tempfile.NamedTemporaryFile(suffix='.wav', delete=False) as f:
+                audio = AudioSegment.from_file(result)
+                current_sr = audio.frame_rate
+                if current_sr > 24000:
+                    print(f"{model}: Resampling")
+                    audio = audio.set_frame_rate(24000)
+                try:
+                    print(f"{model}: Trying to normalize audio")
+                    audio = match_target_amplitude(audio, -20)
+                except:
+                    print(f"{model}: [WARN] Unable to normalize audio")
+                audio.export(f.name, format="wav")
+                os.unlink(result)
+                result = f.name
+                gr.Info('Audio from a TTS model received')
+        except:
+            print(f"{model}: [WARN] Unable to resample audio")
+            pass
+        if model in AVAILABLE_MODELS.keys(): model = AVAILABLE_MODELS[model]
+        result_storage[model] = result
+
+    def _get_param_examples(parameters):
+        example_inputs = []
+        for param_info in parameters:
+            if (
+                param_info['component'] == 'Radio'
+                or param_info['component'] == 'Dropdown'
+                or param_info['component'] == 'Audio'
+                or param_info['python_type']['type'] == 'str'
+            ):
+                example_inputs.append(str(param_info['example_input']))
+                continue
+            if param_info['python_type']['type'] == 'int':
+                example_inputs.append(int(param_info['example_input']))
+                continue
+            if param_info['python_type']['type'] == 'float':
+                example_inputs.append(float(param_info['example_input']))
+                continue
+            if param_info['python_type']['type'] == 'bool':
+                example_inputs.append(bool(param_info['example_input']))
+                continue
+
+        return example_inputs
+
+    def _override_params(inputs, modelname):
+        try:
+            for key,value in OVERRIDE_INPUTS[modelname].items():
+                inputs[key] = value
+            print(f"{modelname}: Default inputs overridden by Arena")
+        except:
+            pass
+
+        return inputs
+
+    def _cache_sample(text, model):
+        # skip caching if not hardcoded sentence
+        if (text not in sents):
+            return False
+
+        already_cached = False
+        # check if already cached
+        for cached_sample in cached_samples:
+            # TODO:replace cached with newer version
+            if (cached_sample.transcript == text and cached_sample.modelName == model):
+                already_cached = True
+                return True
+
+        if (already_cached):
+            return False
+
+        try:
+            cached_samples.append(Sample(results[model], text, model))
+        except:
+            print('Error when trying to cache sample')
+            return False
+
+    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")
+
+    # do not use multithreading when both spaces are ZeroGPU type
+    if (
+        # exists
+        'is_zero_gpu_space' in HF_SPACES[mdl1]
+        # is True
+        and HF_SPACES[mdl1]['is_zero_gpu_space']
+        and 'is_zero_gpu_space' in HF_SPACES[mdl2]
+        and HF_SPACES[mdl2]['is_zero_gpu_space']
+    ):
+        # run Zero-GPU spaces one at a time
+        predict_and_update_result(text, mdl1k, results, request)
+        _cache_sample(text, mdl1k)
+
+        predict_and_update_result(text, mdl2k, results, request)
+        _cache_sample(text, mdl2k)
+    else:
+        # use multithreading
+        thread1 = threading.Thread(target=predict_and_update_result, args=(text, mdl1k, results, request))
+        thread2 = threading.Thread(target=predict_and_update_result, args=(text, mdl2k, results, request))
+
+        thread1.start()
+        # wait 3 seconds to calm hf.space domain
+        time.sleep(3)
+        thread2.start()
+        # timeout in 2 minutes
+        thread1.join(120)
+        thread2.join(120)
+
+        # cache the result
+        for model in [mdl1k, mdl2k]:
+            _cache_sample(text, model)
+
+    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], autoplay=autoplay), # aud1
+        gr.update(visible=True, value=results[mdl2k], autoplay=False), # 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
+        # reset gr.State aplayed & bplayed
+        False, #aplayed
+        False, #bplayed
+    )
+
+# Battle Mode
+
+def synthandreturn_battle(text, mdl1, mdl2, autoplay):
+    if mdl1 == mdl2:
+        raise gr.Error('You can\'t pick two of the same models.')
+    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 check_toxicity(text)
+    ):
+        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
+    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()
+
+    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], autoplay=autoplay), # aud1
+        gr.update(visible=True, value=results[mdl2k], autoplay=False), # 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
+    )
+
+def randomsent():
+    return '⚡', random.choice(sents), '🎲'
+def randomsent_battle():
+    return tuple(randomsent()) + tuple(random_m())
+def clear_stuff():
+    return [
+        gr.update(visible=True, value="", elem_classes=[]),
+        "Synthesize",
+        gr.update(visible=False), # r2
+        '', # model1
+        '', # model2
+        gr.update(visible=False, interactive=False, autoplay=False), # aud1
+        gr.update(visible=False, interactive=False, autoplay=False), # aud2
+        gr.update(visible=False, interactive=False), #abetter
+        gr.update(visible=False, interactive=False), #bbetter
+        gr.update(visible=False), #prevmodel1
+        gr.update(visible=False), #prevmodel2
+        gr.update(visible=False), #nxt round btn
+        False, #aplayed
+        False, #bplayed
+    ]

app/ui.py

@@ -0,0 +1,52 @@
+import gradio as gr
+from .config import *
+from .messages import *
+from .ui_vote import *
+from .ui_leaderboard import *
+from .ui_contenders import *
+
+# JavaScript within HTML head
+head_js = ""
+shortcut_js = """
+<script>
+function shortcuts(e) {
+    var event = document.all ? window.event : e;
+    switch (e.target.tagName.toLowerCase()) {
+        case "input":
+        case "textarea":
+            break;
+        default:
+            switch (e.key.toLowerCase()) {
+                case "a":
+                    document.getElementById("arena-a-better").click();
+                    break;
+                case "b":
+                    document.getElementById("arena-b-better").click();
+                    break;
+                case "n":
+                    document.getElementById("arena-next-round").click();
+                    break;
+            }
+    }
+}
+document.addEventListener('keypress', shortcuts, false);
+
+"""
+head_js += shortcut_js
+head_js += open("app/cookie.js").read()
+head_js += '</script>'
+
+with gr.Blocks() as about:
+    gr.Markdown(ABOUT)
+
+with gr.Blocks(
+    css="footer {visibility: hidden}textbox{resize:none} .blurred-text {filter: blur(0.15em);}",
+    head=head_js,
+    title="TTS Arena"
+) as app:
+    gr.Markdown(DESCR)
+    gr.TabbedInterface([vote, leaderboard, about, tts_info], ['🗳️ Vote', '🏆 Leaderboard', '📄 About', '🗣 Contenders'])
+    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 note that all generated audio clips should be assumed unsuitable for redistribution or commercial use.")

app/ui_battle.py

@@ -0,0 +1,75 @@
+import gradio as gr
+from .config import *
+from .ui import *
+from .synth import *
+from .vote import *
+from .messages import *
+
+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 battle:
+    battle_useridstate = gr.State()
+
+    gr.Markdown(BATTLE_INSTR)
+    model1 = gr.Textbox(interactive=False, lines=1, max_lines=1, visible=False)
+    model2 = gr.Textbox(interactive=False, lines=1, max_lines=1, visible=False)
+    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_battle = gr.Button('🎲', scale=0, min_width=0, variant='tool')
+    with gr.Row():
+        with gr.Column(scale=10):
+            model1s = gr.Dropdown(label="Model 1", container=False, show_label=False, choices=AVAILABLE_MODELS.keys(), interactive=True, value=list(AVAILABLE_MODELS.keys())[0])
+        with gr.Column(scale=10):
+            model2s = gr.Dropdown(label="Model 2", container=False, show_label=False, choices=AVAILABLE_MODELS.keys(), interactive=True, value=list(AVAILABLE_MODELS.keys())[1])
+    randomt_battle.click(randomsent_battle, outputs=[text, randomt_battle, model1s, model2s])
+    btn = gr.Button("Synthesize", variant='primary')
+    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)
+                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)
+                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)
+    autoplay = gr.Checkbox(
+        label="Autoplay audio",
+        value=True
+    )
+
+    outputs = [
+        text,
+        btn,
+        r2,
+        model1,
+        model2,
+        aud1,
+        aud2,
+        abetter,
+        bbetter,
+        prevmodel1,
+        prevmodel2,
+    ]
+    btn\
+        .click(disable, outputs=[btn, abetter, bbetter])\
+        .then(synthandreturn_battle, inputs=[text, model1s, model2s, autoplay], outputs=outputs)\
+        .then(enable, outputs=[btn, abetter, bbetter])
+    nxt_outputs = [abetter, bbetter, prevmodel1, prevmodel2]
+    abetter.click(a_is_better_battle, outputs=nxt_outputs, inputs=[model1, model2, battle_useridstate])
+    bbetter.click(b_is_better_battle, outputs=nxt_outputs, inputs=[model1, model2, battle_useridstate])
+    battle.load(random_m, outputs=[model1s, model2s])
+
+    # Autoplay second audio using JS
+    aud1\
+        .stop(
+            None,
+            inputs=[autoplay],
+            js="(b) => b ? 0 : document.querySelector('.row .gap+.gap button.play-pause-button[aria-label=Play]').click()",
+        )

app/ui_leaderboard.py

@@ -0,0 +1,23 @@
+import gradio as gr
+from .config import *
+from .leaderboard import *
+from .messages import *
+
+with gr.Blocks() as leaderboard:
+    gr.Markdown(LDESC)
+    df = gr.Dataframe(
+        interactive=False,
+        min_width=0,
+        wrap=False,
+        column_widths=[30, 200, 50, 50],
+        datatype=["str", "html", "html", "number"]
+    )
+    reloadbtn = gr.Button("Refresh")
+    with gr.Row():
+        reveal_prelim = gr.Checkbox(label="Reveal preliminary results", info="Show all models, including models with very few human ratings.", scale=1)
+        # hide_battle_votes = gr.Checkbox(label="Hide Battle Mode votes", info="Exclude votes obtained through Battle Mode.", scale=1)
+    reveal_prelim.input(get_leaderboard, inputs=[reveal_prelim], outputs=[df])
+    # hide_battle_votes.input(get_leaderboard, inputs=[reveal_prelim, hide_battle_votes], 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.")

app/ui_vote.py

@@ -0,0 +1,194 @@
+import gradio as gr
+from .config import *
+from .synth import *
+from .vote import *
+from .messages import *
+from .sample_caching import *
+
+blur_text_js = 'document.getElementById("arena-text-input").classList.add("blurred-text")'
+unblur_text_js = 'document.getElementById("arena-text-input").classList.remove("blurred-text")'
+
+def disable():
+    return [gr.update(interactive=False), 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), gr.update(interactive=True)]
+def blur_text():
+    return gr.update(elem_classes=['blurred-text'])
+def unblur_text():
+    return gr.update(elem_classes=[])
+
+with gr.Blocks() as vote:
+    session_hash = gr.Textbox(visible=False, value='')
+
+    # sample played, using Checkbox so that JS can fetch the value
+    aplayed = gr.Checkbox(visible=False, value=False)
+    bplayed = gr.Checkbox(visible=False, value=False)
+    # voter ID
+    useridstate = gr.State()
+    gr.Markdown(INSTR)
+    with gr.Group():
+        with gr.Row():
+            cachedt = gr.Button('⚡', scale=0, min_width=0, variant='tool', interactive=True)
+            text = gr.Textbox(
+                container=False,
+                show_label=False,
+                placeholder="Enter text to synthesize",
+                lines=1,
+                max_lines=1,
+                scale=9999999,
+                min_width=0,
+                elem_id="arena-text-input",
+            )
+            randomt = gr.Button('🎲', scale=0, min_width=0, variant='tool')
+        randomt\
+            .click(randomsent, outputs=[cachedt, text, randomt])\
+            .then(None, js="() => "+ unblur_text_js)
+        btn = gr.Button("Synthesize", variant='primary')
+    model1 = gr.Textbox(interactive=False, lines=1, max_lines=1, visible=False)
+    model2 = gr.Textbox(interactive=False, lines=1, max_lines=1, visible=False)
+    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': '#EF4444'},
+                    # var(--color-red-500)'}); gradio only accepts HEX and CSS color
+                )
+                abetter = gr.Button(
+                    "[A] is better",
+                    elem_id='arena-a-better',
+                    variant='primary',
+                    interactive=False,
+                )
+                prevmodel1 = gr.HTML(
+                    show_label=False,
+                    value="Vote to reveal model A",
+                    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'},
+                    # var(--secondary-500)'}); gradio only accepts HEX and CSS color
+                )
+                bbetter = gr.Button(
+                    "[B] is better",
+                    elem_id='arena-b-better',
+                    variant='primary',
+                    interactive=False,
+                )
+                prevmodel2 = gr.HTML(
+                    show_label=False,
+                    value="Vote to reveal model B",
+                    visible=False,
+                )
+    nxtroundbtn = gr.Button(
+        '⚡ [N]ext round',
+        elem_id='arena-next-round',
+        visible=False,
+        variant='primary',
+    )
+    autoplay = gr.Checkbox(
+        label="Autoplay audio",
+        value=True
+    )
+
+    outputs = [
+        text,
+        btn,
+        r2,
+        model1,
+        model2,
+        aud1,
+        aud2,
+        abetter,
+        bbetter,
+        prevmodel1,
+        prevmodel2,
+        nxtroundbtn,
+        aplayed,
+        bplayed,
+    ]
+    """
+    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"""
+    # , concurrency_count=1, concurrency_id="sync_queue"
+    btn\
+        .click(disable, outputs=[btn, abetter, bbetter, cachedt])\
+        .then(synthandreturn, inputs=[text, autoplay], outputs=outputs)\
+        .then(enable, outputs=[btn, gr.State(), gr.State(), gr.State()])\
+        .then(None, js="() => "+ unblur_text_js)
+    # Next Round ; blur text
+    nxtroundbtn\
+        .click(clear_stuff, outputs=outputs)\
+        .then(disable, outputs=[btn, abetter, bbetter, cachedt])\
+        .then(give_cached_sample, inputs=[session_hash, autoplay], outputs=[*outputs, cachedt])\
+        .then(enable, outputs=[btn, gr.State(), gr.State(), gr.State()])
+    # blur text
+    nxtroundbtn.click(None, js="() => "+ blur_text_js)
+
+    # fetch a comparison pair from cache
+    cachedt\
+        .click(disable, outputs=[btn, abetter, bbetter, cachedt])\
+        .then(give_cached_sample, inputs=[session_hash, autoplay], outputs=[*outputs, cachedt])\
+        .then(enable, outputs=[btn, gr.State(), gr.State(), gr.State()])
+    # TODO: await download of sample before allowing playback
+
+    # Allow interaction with the vote buttons only when both audio samples have finished playing
+    aud1\
+        .stop(
+            unlock_vote,
+            inputs=[autoplay, gr.State(value=0), aplayed, bplayed],
+            outputs=[abetter, bbetter, aplayed, bplayed],
+        )\
+        .then(
+            None,
+            inputs=[bplayed if autoplay else True],
+            js="(b) => b ? 0 : document.querySelector('.row .gap+.gap button.play-pause-button[aria-label=Play]').click()",
+        )
+    # autoplay if unplayed
+    aud2\
+        .stop(
+            unlock_vote,
+            inputs=[autoplay, gr.State(value=1), aplayed, bplayed],
+            outputs=[abetter, bbetter, aplayed, bplayed],
+        )
+    # unblur text with JS; faster than sending output with elem_classes
+    aud2.stop(None, inputs=[aplayed], js="(a) => a ? "+ unblur_text_js +" : 0;")
+
+    nxt_outputs = [abetter, bbetter, prevmodel1, prevmodel2, nxtroundbtn]
+    abetter\
+        .click(a_is_better, outputs=nxt_outputs, inputs=[model1, model2, useridstate, text])\
+        .then(voted_on_cached, inputs=[model1, model2, text, session_hash], outputs=[])
+    bbetter\
+        .click(b_is_better, outputs=nxt_outputs, inputs=[model1, model2, useridstate, text])\
+        .then(voted_on_cached, inputs=[model1, model2, text, session_hash], outputs=[])
+
+    # get session cookie
+    vote\
+        .load(
+            None,
+            None,
+            session_hash,
+            js="() => { return getArenaCookie('session') }",
+        )
+    # give a cached sample pair to voter; .then() did not work here
+    vote\
+        .load(give_cached_sample, inputs=[session_hash, autoplay], outputs=[*outputs, cachedt])

app/utils.py

@@ -0,0 +1,20 @@
+import uuid
+import soundfile as sf
+import pydub
+import pyloudnorm as pyln
+
+def match_target_amplitude(sound, target_dBFS):
+    change_in_dBFS = target_dBFS - sound.dBFS
+    return sound.apply_gain(change_in_dBFS)
+
+def mkuuid(uid):
+    if not uid:
+        uid = uuid.uuid4()
+    return uid
+
+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)

app/vote.py

@@ -0,0 +1,143 @@
+from .utils import *
+from .config import *
+from .models import *
+from .db import *
+from .init import *
+
+import gradio as gr
+
+# Logging
+
+def log_text(text):
+    conn = get_db()
+    cursor = conn.cursor()
+    cursor.execute('INSERT INTO spokentext (spokentext) VALUES (?)', (text,))
+    if scheduler:
+        with scheduler.lock:
+            conn.commit()
+    else:
+        conn.commit()
+    cursor.close()
+
+# Vote
+
+def upvote_model(model, uname, battle=False):
+    conn = get_db()
+    cursor = conn.cursor()
+    if battle: uname = "unknown_battle"
+    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,))
+    if scheduler:
+        with scheduler.lock:
+            conn.commit()
+    else:
+        conn.commit()
+    cursor.close()
+
+def downvote_model(model, uname, battle=False):
+    conn = get_db()
+    cursor = conn.cursor()
+    if battle: uname = "unknown_battle"
+    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,))
+    if scheduler:
+        with scheduler.lock:
+            conn.commit()
+    else:
+        conn.commit()
+    cursor.close()
+
+# Battle Mode
+
+def a_is_better_battle(model1, model2, userid):
+    return a_is_better(model1, model2, 'unknown_battle', True)
+def b_is_better_battle(model1, model2, userid):
+    return b_is_better(model1, model2, 'unknown_battle', True)
+
+# A/B better
+
+def a_is_better(model1, model2, userid, text):
+    return is_better(model1, model2, userid, text, True)
+def b_is_better(model1, model2, userid, text):
+    return is_better(model1, model2, userid, text, False)
+
+def is_better(model1, model2, userid, text, chose_a):
+    if(
+        (
+            not model1 in AVAILABLE_MODELS.keys()
+            and not model1 in AVAILABLE_MODELS.values()
+        )
+        or (
+            not model2 in AVAILABLE_MODELS.keys()
+            and not model2 in AVAILABLE_MODELS.values()
+        )
+    ):
+        raise gr.Error('Sorry, please try voting again.')
+
+    # userid is unique for each cast vote pair
+    userid = mkuuid(userid)
+    if model1 and model2:
+        conn = get_db()
+        cursor = conn.cursor()
+        sql_query = 'INSERT INTO votelog (username, chosen, rejected) VALUES (?, ?, ?)'
+        if chose_a:
+            cursor.execute(sql_query, (str(userid), model1, model2))
+        else:
+            cursor.execute(sql_query, (str(userid), model2, model1))
+
+        with scheduler.lock:
+            conn.commit()
+            # also retrieve primary key ID
+            cursor.execute('SELECT last_insert_rowid()')
+            votelogid = cursor.fetchone()[0]
+            cursor.close()
+
+        if chose_a:
+            upvote_model(model1, str(userid))
+            downvote_model(model2, str(userid))
+        else:
+            upvote_model(model2, str(userid))
+            downvote_model(model1, str(userid))
+        log_text(text)
+        # log_text(text, votelogid)
+
+    return reload(model1, model2, userid, chose_a=chose_a, chose_b=(not chose_a))
+
+# Reload
+def reload(chosenmodel1=None, chosenmodel2=None, userid=None, chose_a=False, chose_b=False):
+    chosenmodel1 = make_link_to_space(chosenmodel1)
+    chosenmodel2 = make_link_to_space(chosenmodel2)
+    out = [
+        gr.update(interactive=False, visible=False),
+        gr.update(interactive=False, visible=False)
+    ]
+    style = 'text-align: center; font-size: 1rem; margin-bottom: 0; padding: var(--input-padding)'
+    if chose_a == True:
+        out.append(gr.HTML(value=f'<p style="{style}">Your vote: {chosenmodel1}</p>', visible=True))
+        out.append(gr.HTML(value=f'<p style="{style}">{chosenmodel2}</p>', visible=True))
+    else:
+        out.append(gr.HTML(value=f'<p style="{style}">{chosenmodel1}</p>', visible=True))
+        out.append(gr.HTML(value=f'<p style="{style}">Your vote: {chosenmodel2}</p>', visible=True))
+    out.append(gr.update(visible=True))
+    return out
+
+def unlock_vote(autoplay, btn_index, aplayed, bplayed):
+    if autoplay == False:
+        return [gr.update(), gr.update(), aplayed, bplayed]
+
+    # sample played
+    if btn_index == 0:
+        aplayed = True
+    if btn_index == 1:
+        bplayed = 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), True, True]
+
+    return [gr.update(), gr.update(), aplayed, bplayed]

requirements.txt

@@ -1,8 +1,8 @@
 datasets
 librosa
 soundfile
+gradio-client
 git+https://github.com/unitaryai/detoxify
 pyloudnorm
 langdetect
 pydub
-transformers