import argparse
import os
import sys
import tempfile
from pathlib import Path

import shutil
import glob

import gradio as gr
import librosa.display
import numpy as np

import torch
import torchaudio
import traceback
from utils.formatter import format_audio_list,find_latest_best_model, list_audios
from utils.gpt_train import train_gpt

from faster_whisper import WhisperModel

from TTS.tts.configs.xtts_config import XttsConfig
from TTS.tts.models.xtts import Xtts

import requests

def download_file(url, destination):
    try:
        response = requests.get(url, stream=True)
        response.raise_for_status()
        with open(destination, "wb") as f:
            for chunk in response.iter_content(chunk_size=8192):
                f.write(chunk)
        print(f"Downloaded file to {destination}")
        return destination
    except Exception as e:
        print(f"Failed to download the file: {e}")
        return None

# Clear logs
def remove_log_file(file_path):
     log_file = Path(file_path)

     if log_file.exists() and log_file.is_file():
         log_file.unlink()

# remove_log_file(str(Path.cwd() / "log.out"))

def clear_gpu_cache():
    # clear the GPU cache
    if torch.cuda.is_available():
        torch.cuda.empty_cache()

XTTS_MODEL = None

def create_zip(folder_path, zip_name):
    zip_path = os.path.join(tempfile.gettempdir(), f"{zip_name}.zip")
    shutil.make_archive(zip_path.replace('.zip', ''), 'zip', folder_path)
    return zip_path

def get_model_zip(out_path):
    ready_folder = os.path.join(out_path, "ready")
    if os.path.exists(ready_folder):
        return create_zip(ready_folder, "optimized_model")
    return None

def get_dataset_zip(out_path):
    dataset_folder = os.path.join(out_path, "dataset")
    if os.path.exists(dataset_folder):
        return create_zip(dataset_folder, "dataset")
    return None

def load_model(xtts_checkpoint, xtts_config, xtts_vocab,xtts_speaker):
    global XTTS_MODEL
    clear_gpu_cache()
    if not xtts_checkpoint or not xtts_config or not xtts_vocab:
        return "You need to run the previous steps or manually set the `XTTS checkpoint path`, `XTTS config path`, and `XTTS vocab path` fields !!"
    config = XttsConfig()
    config.load_json(xtts_config)
    XTTS_MODEL = Xtts.init_from_config(config)
    print("Loading XTTS model! ")
    XTTS_MODEL.load_checkpoint(config, checkpoint_path=xtts_checkpoint, vocab_path=xtts_vocab,speaker_file_path=xtts_speaker, use_deepspeed=False)
    if torch.cuda.is_available():
        XTTS_MODEL.cuda()

    print("Model Loaded!")
    return "Model Loaded!"

def run_tts0(selected_language, lang, tts_text, speaker_audio_file, temperature, length_penalty,repetition_penalty,top_k,top_p,sentence_split,use_config):
    if XTTS_MODEL is None or not speaker_audio_file:
        return "You need to run the previous step to load the model !!", None, None
#
    selected_speaker = speaker_audio_file
    selec_languaje = load_text_langs(selected_language)

    # Construct the file path
    speaker_audio_path = f"/tmp/Voice/{selec_languaje}/{selected_speaker}.mp3"

    gpt_cond_latent, speaker_embedding = XTTS_MODEL.get_conditioning_latents(audio_path=speaker_audio_path, gpt_cond_len=XTTS_MODEL.config.gpt_cond_len, max_ref_length=XTTS_MODEL.config.max_ref_len, sound_norm_refs=XTTS_MODEL.config.sound_norm_refs)
    
    if use_config:
        out = XTTS_MODEL.inference(
            text=tts_text,
            language=lang,
            gpt_cond_latent=gpt_cond_latent,
            speaker_embedding=speaker_embedding,
            temperature=XTTS_MODEL.config.temperature, # Add custom parameters here
            length_penalty=XTTS_MODEL.config.length_penalty,
            repetition_penalty=XTTS_MODEL.config.repetition_penalty,
            top_k=XTTS_MODEL.config.top_k,
            top_p=XTTS_MODEL.config.top_p,
            enable_text_splitting = True
        )
    else:
        out = XTTS_MODEL.inference(
            text=tts_text,
            language=lang,
            gpt_cond_latent=gpt_cond_latent,
            speaker_embedding=speaker_embedding,
            temperature=temperature, # Add custom parameters here
            length_penalty=length_penalty,
            repetition_penalty=float(repetition_penalty),
            top_k=top_k,
            top_p=top_p,
            enable_text_splitting = sentence_split
        )

    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp:
        out["wav"] = torch.tensor(out["wav"]).unsqueeze(0)
        out_path = fp.name
        torchaudio.save(out_path, out["wav"], 24000)

    return "Speech generated !", out_path, speaker_audio_path

def run_tts(lang, tts_text, speaker_audio_file, temperature, length_penalty,repetition_penalty,top_k,top_p,sentence_split,use_config):
    if XTTS_MODEL is None or not speaker_audio_file:
        return "You need to run the previous step to load the model !!", None, None

    gpt_cond_latent, speaker_embedding = XTTS_MODEL.get_conditioning_latents(audio_path=speaker_audio_file, gpt_cond_len=XTTS_MODEL.config.gpt_cond_len, max_ref_length=XTTS_MODEL.config.max_ref_len, sound_norm_refs=XTTS_MODEL.config.sound_norm_refs)
    
    if use_config:
        out = XTTS_MODEL.inference(
            text=tts_text,
            language=lang,
            gpt_cond_latent=gpt_cond_latent,
            speaker_embedding=speaker_embedding,
            temperature=XTTS_MODEL.config.temperature, # Add custom parameters here
            length_penalty=XTTS_MODEL.config.length_penalty,
            repetition_penalty=XTTS_MODEL.config.repetition_penalty,
            top_k=XTTS_MODEL.config.top_k,
            top_p=XTTS_MODEL.config.top_p,
            enable_text_splitting = True
        )
    else:
        out = XTTS_MODEL.inference(
            text=tts_text,
            language=lang,
            gpt_cond_latent=gpt_cond_latent,
            speaker_embedding=speaker_embedding,
            temperature=temperature, # Add custom parameters here
            length_penalty=length_penalty,
            repetition_penalty=float(repetition_penalty),
            top_k=top_k,
            top_p=top_p,
            enable_text_splitting = sentence_split
        )

    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp:
        out["wav"] = torch.tensor(out["wav"]).unsqueeze(0)
        out_path = fp.name
        torchaudio.save(out_path, out["wav"], 24000)

    return "Speech generated !", out_path, speaker_audio_file


# Diccionario de idiomas y sus códigos
leng_and_ids = {
    "Select language": "es",
    "Voices Legacy": "show_legacy",
    "Arabic": "ar",
    "Bulgarian": "bg",
    "Chinese": "zh",
    "Croatian": "hr",
    "Czech": "cs",
    "Danish": "da",
    "Dutch": "nl",
    "English-1": "en1",
    "English-2": "en2",
    "Finnish": "fi",
    "French": "fr",
    "German": "de",
    "Greek": "el",
    "Hindi": "hi",
    "Hungarian": "hu",
    "Indonesian": "id",
    "Italian": "it",
    "Japanese": "ja",
    "Korean": "ko",
    "Norwegian": "no",
    "Polish": "pl",
    "Portuguese": "pt",
    "Romanian": "ro",
    "Russian": "ru",
    "Slovak": "sk",
    "Spanish": "es",
    "Swedish": "sv",
    "Tamil": "ta",
    "Turkish": "tr",
    "Ukrainian": "uk",
    "Vietnamese": "vi"
}

# Listas de nombres para cada idioma (Solución 2: Recomendada)
show_legacy  = ['Adam', 'Alice', 'Antoni', 'Aria', 'Arnold', 'Bill', 'Brian', 'Callum', 'Charlie', 'Charlotte', 'Chris', 'Clyde', 'Daniel', 'Dave', 'David_Martin._1', 'Domi', 'Dorothy', 'Drew', 'Elli', 'Emily', 'Eric', 'Ethan', 'Fin', 'Freya', 'George', 'Gigi', 'Giovanni', 'Glinda', 'Grace', 'Harry', 'James', 'Jeremy', 'Jessica', 'Jessie', 'Joseph', 'Josh', 'Laura', 'Liam', 'Lily', 'Matilda', 'Michael', 'Mimi', 'Nicole', 'Patrick', 'Paul', 'Rachel', 'River', 'Roger', 'Sam', 'Sarah', 'Serena', 'Thomas', 'Will']

arabic_names = ['Amr', 'Anas', 'HMIDA', 'Hamid', 'Haytham', 'Haytham_-_Conversation', 'Jafar_-_Deep_Narrator', 'Mo_Wiseman', 'Mona', 'Mourad_Sami', 'Raed', 'Sana', 'Wahab_Arabic']
bulgarian_names = ['Elena', 'Julian']
chinese_names = ['Coco_Li', 'Karo_Yang', 'Liang', 'Martin_Li', 'Maya_-_Young__Calm', 'ShanShan_-_Young_Energetic_Female', 'Stacy_-_Sweet_and_Cute_Chinese', 'YT']
croatian_names = ['Ivan', 'Luka_-_Narration', 'Maja', 'Slobodan']
czech_names_names = ['Anet', 'Hana_-_CZ', 'Hanka_beta', 'Jan', 'Jan_-_kind__gentle', 'Jiri', 'Ondřej_–_vypravěč', 'Pawel_TV™️_-_High_Quality_', 'Petr_Sovadina', 'Tony']
danish_names = ['Christian_-_Danish_calm_voice', 'Constantin_Birkedal', 'Mathias_-_Storyteller', 'Peter_-___Readings__Presentations', 'Sissel', 'Thomas_Hansen']

dutch_names = ['Arno_Drost', 'Bart', 'Daniel_van_der_Meer_', 'Jaimie_from_the_Netherlands_-_Dutch_Amsterdam_Voiceover_-_Young_Male_Age_30_', 'Richard', 'Serge_de_Beer_Pro1', 'Tijs']

finnish_names = ['Christoffer_Satu']

french_names = ['Adina_-_French_teenager', 'Adrien_Piret', 'Alexandre_Boutin_-_French_Canadian', 'Audiobooks_Lady', 'Audrey', 'Camille_Martin', 'Christophe_Géradon_Belge', 'Christophe_M', 'Claire', 'Coco_-_French_-_for_E-learning_and_Tutorial', 'Corentin', 'Cyril_-_Narration__Audiobook', 'Darine_-_Narration', 'Dave_-_Pro_Narrative', 'David', 'Denis_Landrieu', 'Emilie_Lacroix', 'Eric', 'Franck_de_France', 'Frédéric_-__French_Narration', 'Gaétan_L-Pro_French_Warm_Calm_Clear_Voice_Reader_conditions', 'Guillaume_-_French_voice_-_Narration_and_Voiceover', 'Guillaume_-_Narration', 'Haseeb_-_Canadian_French', 'Hélène', 'JaySoft', 'Jean_Petit_-_jeune', 'Jeanne_-_Professional_and_captivating_voice', 'Kevin_histoire_V2', 'Laurence_-_Class__Mature', 'Liam_-_Sharp__Pro', 'Louis_Boutin', 'Lucie', 'Lucien', 'Ludovic', 'Léo_-_Quebec_French', 'Léo_Latti', 'Mademoiselle_French_-_For_Conversational', 'Mademoiselle_French_-_for_Institutional_Video', 'Manuel_Formateur_-_Français', 'Martin_Dupont_Aimable', 'Martin_Dupont_Intime', 'Martin_Dupont_Profond', 'Mat', 'Mathieu_-_French_voice_-_Narration', 'Maxime_-_French_Young_Male', 'Maxime_Lavaud_-_French_young_man', 'Maxime_Lavaud_-_French_young_man_', 'Michel', 'Miss_French_-_For_Audiobook', 'Miss_French_Papote', 'Miss_Radio', 'Nicolas_-_Narration', 'Nicolas_Petit', 'Nicolas_Petit_-_Deep_voice_narration_', 'Nicolas_animateur_', 'Olivier_Calm', 'Patrick_-_Québec_Canada', 'Peter_-_Engaging_friendly_young_adult_male_voice', 'Romain_-_Lecture', 'Sam_French', 'SkaraB', 'Sophie_-_Pro_Audiobook', 'Sébastien_-_French_Male', 'Theo_-_Smart_warm_open', 'Ulys_-_Young__Energetic', 'Vincent_FR', 'Voix_Nicolas_Petit_ton_Animateur_Radio', 'Voix_grand_père']

german_names = ['Aaron', 'Albert_-_Funny_Cartoon_Character', 'Aleks', 'Alessandro_Devigus', 'Alex_-__Professional_German_Male_Voiceover', 'Amadeus', 'Ana', 'Ana_-_Novel_Audiobook', 'Andi_Brewi_-_Moderator_advertising_spokesperson', 'Andreas_-_Clear_German', 'Andreas_-_Deep_German_Voice', 'Annika', 'Anton_Dark_Magic_-_Thriller_-_True_Crime', 'Antonia_Konstanz_-_German_Native', 'Apollo_-_Documentary__TV_Voice', 'Ava_-_youthful_and_expressive_German_female_voice', 'Bartholomeus_Bösewicht_-_Grim_and_Gruesome', 'Ben', 'Ben_Hoffmann_-_German_Ads__Trailers', 'Carlos_-_der_Spanier', 'Carola_Ferstl_Nachrichten', 'Christian', 'Christian_Ehler', 'Christian_Kinderbuch', 'Clemens_Hartmann_-_The_Berlin_Voice', 'Clemens_Hartmann_2_-_for_Ads__Trailers', 'Clemens_Hartmann_3_-_The_Narrator', 'Cornelia_', 'Daniel_DaFraVe', 'Daniel_DaFraVe_Whisper._ASMR._Meditation._Relaxing', 'David_-_Serious_voice_for_narration_and_stories', 'Der_Beamte', 'Dimawalker_', 'Dimi', 'Dirk', 'Elias_-_Radio_Host__Radio_News_Presenter_Voice', 'Elias_-_Social_Media_Podcasts_Conversations__Discussions', 'Emilia_-_German_narrator', 'Fabian', 'Felix_-_Smooth_German_Chaos', 'Felix_-_Soft_Deep_German_Narration_Voice', 'Felix_Gebhardt_-_authentisch_und_berührend_Podcast_Hörbuch_Radio', 'Finnegan_Fairytale_-_Exciting_Childrens_Stories', 'Flauschi', 'Frederick', 'Frederick_-_Calm_Meditation_Deutsch', 'Frederick_-_Calm_and_Soothing_Meditation', 'Frederick_-_Friendly__helpful_', 'Frederick_-_Old_Gnarly_Narrator', 'German_Daniel', 'German_Michael_-_Loud_Clear__Striking', 'German_Voice', 'Grandpa_Georg_-_Funny_and_Gruff', 'Günther_Goodnight_-_Relaxed_and_Slow', 'Hans_Kraft', 'Heidi_factual_Standard_German_-_with_Swiss_Accent', 'Helmut_Schwarz', 'Herr_Gruber', 'Horvath_aus_Wien', 'Isabell', 'Jan', 'Jean_Art', 'Jesper', 'Johannes_-_Documentary_film', 'Jonas', 'Juan_Schubert', 'Julia', 'Julian_-_German_Explainer_Voice', 'Julius', 'Kris_Klingenberg', 'Kurt_-_Calm', 'Lana_Weiss_-_Meditation', 'Lea', 'Lena_-_Cute_German_Voice', 'Leo_liest', 'Leo_liest_tief', 'Leon_Stern_-_Fiction__Fantasy_', 'Leonie', 'Lex_Mystery', 'Lorenz', 'Louisa_', 'Luisa', 'Lukas_Harmony', 'Manuel_-_Your_Narrator_and_Storyteller', 'Marc', 'Marc_Weber_-_Non-fiction_books_', 'Marcel__Male__Audiobook__Tutorial__Trainings_GERMAN', 'Marco_-_Gentle_German_ASMR_Narrator', 'Marcus_KvE_–_German_Voice_Over', 'Marie_-_German_Frenchwoman', 'Marko_-_German_Male_Deep_Voice', 'Markus', 'Martin_History', 'Martin_Jung', 'Martin_R._Pro', 'Max_Mustermann_-_Ernst', 'Meine_Lesestimme', 'Michael', 'Mila', 'Nader', 'Narrator_Markus', 'Niander_Wallace_', 'Otto', 'Patrick_-_German_speaker', 'Peter_Hartlapp_-_Voiceactor_Werbesprecher_und_Moderator', 'Peter_Meta_Business_Twin', 'Petra_PeFraVe_Pro_', 'Petra_PeFraVe__-_Funny', 'Phil_-_Fantasy__Thriller', 'Philipp_-_Male_with_standard_accent', 'Prinz_Pricklig_-_Whispering_Sparkling_and_Crisp_', 'Rafi_Biber', 'Reeloverlay', 'Rob', 'Robby_-_Audio_books_Speeches__Stories', 'Robert_dein_freundlicher_Assistent', 'Robert_erklaert_mit_Betonung', 'Robert_hypnotisiert_entspannte_Meditation', 'Samer', 'Sammy_Zimmermanns', 'Sascha_Pro_', 'Sebastian_Thomas', 'Stefan_Rank_der_Erzähler_Radio-Moderator', 'Susi', 'Sympathische_Stimme', 'Thomas_-_The_pragmatist', 'Timo', 'Tom_-_Deep_German_Voice', 'Tom_Magic', 'Tommy_Studio_Voice_2', 'Torsten_-_Raspy_Charmer', 'Tristan_Medersburg_-_Trustworthy_Deepness', 'Vali_-_Young_man_with_a_bass-heavy_voice', 'Vincent_-_Factual', 'Willi_-_Professional_German_Narrator']

greek_names = ['Agapi', 'Fatsis_', 'Giassiranis_Dimitrios', 'Kyriakos', 'Niki_-_native_Greek_female_', 'Niki_2_-_native_Greek_female', 'Niki_3_-_native_Greek_female', 'Stefanos_-_Calm_youthful_and_casual', 'Takis_-_native_Greek_male']

hindi_names = ['Aaditya_Kapur_-_Calm_Conversational_Hindi_Voice', 'Aakash_Aryan_-_Conversational_Voice', 'Amit_Gupta', 'Anand_-_Storytelling_and_Narration_Hindi', 'Anoop', 'Ayesha_-_Energetic_Hindi_Voice', 'Bobby_', 'Danish_Khan_-_Expressive_Old_Voice', 'Devi_-_Clear_Hindi_pronunciation', 'Faiq_-_Standard_Hindi', 'God', 'Guru_-_Rich_Bass_Hindi_Voice', 'Ishika_Singh_-__Storytelling_and_Narration_Hindi', 'Janvi_-_Expressive_Indian_Voice_', 'Jitu', 'John_-_Confident_and_Deep', 'Kaaya_-_Gentle_Hindi_', 'Kanika_-_Relatable_Hindi_Voice', 'Krishna_-_Energetic_Hindi_Voice', 'Kunal_Agarwal', 'Leo_-_Energetic_Hindi_Voice_', 'Luv_-_Hindi_Storytelling_Voice', 'Manu_-_Smooth_Modulated_Voice', 'Monika_Sogam_-_Hindi_Modulated', 'Muskaan_-_Casual_Hindi_Voice', 'Natasha_-_Energetic_Hindi_Voice', 'Neel_-_Expressive_Narrator', 'Nikita_-_Youthful_Hindi_Voice', 'Nipunn_-_Deep_Hindi_voice', 'Niraj_-_Hindi_Narrator', 'P_K_Anil_-_Clear_Hindi', 'Parmeshwar_परमेश्वर', 'Parveen_-_Hindi', 'Pratima_-_Casual_Hindi_Conversational_Voice', 'Prem_-_Connectable_Hindi_Voice', 'Priya', 'Raju_-_Relatable_Hindi_Voice', 'Ranbir_Merchant_-_Deep_Engaging_Hindi_Voice', 'Ranga_-_Authoritative_and_Deep_Hindi_Voice', 'Reva_-_Familiar_Hindi_Voice', 'Riya_K._Rao_-_Hindi_Conversational_Voice', 'Ruhaan_-_Clean_Hindi_Narration_Voice', 'Saanu_-_Soft_and_Calm', 'Sachin_-_Deep_and_thoughtful', 'Saira_-_Young_Casual_Voice', 'Samads_Realistic_Voice', 'Shakuntala_-_Expressive_Indian_Voice', 'Shrey_-_Deep_Hindi_Voice', 'Sohaib_Jasra_', 'Sonu_Indian_Male', 'Suhaan_-_Delhi_Guy', 'Sweetie', 'Vihan_Ahuja_-_Friendly_Hindi_Voice', 'Yash_A_Malhotra_-_Warm__Friendly_Hindi_Voice', 'Zadok_-_Good_for_character']

hungarian_names = ['Magyar_Férfi_-_Hungarian_Male', 'Susanna_Rutkai']

indonesian_names = ['Abyasa', 'Andi', 'Andra', 'Bambang__', 'Bee_Ard_-_Clear_Dynamic_Voice', 'Blasto', 'Hendro_Atmoko', 'Jin', 'Mahaputra', 'Meraki_female_Indonesian_voice', 'Miz', 'Pramoedya_Chandra', 'Pratama', 'Putra', 'Suara_narasi', 'Tri_Nugraha_Ramadhani', 'Zephlyn']

italian_names = ['Aaron', 'Alessandro', 'Alessio_-_positive_and_professional', 'Andrea_Loco', 'Anna', 'Antonio_Farina_-_Italian_PRO_Talent_-_Audiobook_Narration', 'Carmelo_La_Rosa_-_Italian_Pro_Talent_e-learning_news_webinar_istitutional.', 'Chris_Basetta_-_Audio_Books', 'Chris_Basetta_-_Social_Media', 'Dante_-_Italian_30_years_old', 'Emanuel', 'Eray_Rio·Sae', 'Fabi', 'Francesco', 'Francesco_-_Narrative', 'Francesco_-_Premium', 'Gabriele', 'Germano_Carella', 'GianP_-_Edu_-_Clear__Upbeat', 'GianP_-_Narrative_Storytelling', 'GianP_-_News_Info_and_Documentary', 'GianP_-_Social_Media__Ads', 'Gianluigi_Toso', 'Giovanni_Rossi_-_giovane', 'Giulia_-_sweet_and_soothing', 'Gus_-_Deep_and_Pleasant', 'Kina_-_Cute_happy_girl', 'Leandro_', 'Linda_Fiore', 'Luca', 'Luca_Brasi_Gentile', 'Luca_Brasi_Intimo', 'Luca_Brasi_Profondo', 'Luna', 'Marcello_Lares_-_Soothing_Narrator', 'Marco', 'MarcoTrox_-_Italian_Pro_Voice_Actor_-_Storytelling_Audiobooks_Narration.', 'MarcoTrox_-_Italian_Professional_Voice_Talent', 'Marco_Pro', 'MrVibes', 'Nicola_Lorusso_-_Italian_Pro_-_Storytelling_Audiobooks_Narration.', 'Oceano_-_A_very_young_narrator', 'Pietro_-_Crazy_Character_Narrator', 'RenzoTech_', 'Stefano', 'Stefano_Becciolini_1']

japanese_names = ['Asahi_-_Japanese_male', 'Ena_', 'Hinata', 'Hiro_Satake', 'Ichiro', 'Ishibashi_-_Strong_Japanese_Male_Voice', 'Junichi', 'Ken', 'Ken_-_Japanese_male', 'Kozy_Male_Japanese_Narrative_Voice_-_Tokyo_Standard_Accent', 'Morioki', 'Otani', 'Sakura_Suzuki', 'Shoki']

korean_names = ['Anna_Kim', 'Bin', 'ChulSu', 'Do_Hyeon', 'Funny_Jackie_Lee', 'HYUK_', 'Hyuk', 'Hyun_Bin', 'Jaedong_Ahn', 'Jina', 'Jung_-_Narrative', 'KKC', 'KKC_-_Guided_Meditation__Narration', 'Kyungduk_Ko', 'Man_Bo', 'Min_ho']

norwegian_names = ['Johannes_-_Norwegian_-_Upbeat', 'Mia_Starset']

polish_names = ['Adam_-_Polish_narrator', 'Adygeusz', 'Aneta_-_Loud_and_confident_voice', 'Ave_Cezar', 'Bart', 'Bea', 'Damian_PL_', 'Daniel', 'Dawid_PL', 'Ignacius', 'James_-_Narrative__Story', 'Jerzy', 'Krzysztof_PL', 'Lena_Suzuki', 'Maciej', 'Maciek', 'Mark_-_Polish', 'Martin', 'MePolish', 'Mr_Lucas_', 'Oliver_Brown', 'Pawel_Pro_-_Polish', 'Piotrek_Pro', 'Pixi', 'Robert', 'Robert_Rob']

portuguese_names = ['Adriano_-_Narrador3', 'Adriano_-_Narrator', 'Adriano_-_Narrator2', 'Alcione', 'Ale_Garcia', 'Ana_-_Brazilian', 'Ana_Dias', 'Andreia_I.', 'Bia_-_Brazilian', 'Brazilian_Dudy', 'Conrado_Bueno', 'Daiane_Candido', 'Daniel_Dan', 'Davi', 'Dhyogo_Azevedo', 'Diego', 'Eddie_Barroso_-_Brazilian', 'Edna_E.', 'FMDAmbrosio', 'FRANCISCO_IA', 'Fabio_Filho', 'Flavio_Francisco_-_Narrative_-_Brazilian_Portuguese', 'Gabby', 'Gilson_Lima', 'Gustavo_Barros', 'Gustavo_Jannuzzi_', 'Gustavo_Sancho', 'Higor_Bourges', 'Hugo_Mendonça', 'João_Pedro', 'Juliana_Barbieri', 'Keren_-_Young_Brazilian_Female', 'Klaus_-__Young_Brazilian_Professional_Narrator', 'Kuhcsal', 'Lax', 'Leonardo_Hamaral', 'Locução_para_Propaganda', 'Luka', 'Marcelo_Costa_Brasileiro', 'Matheus_-_Energic_Young_Voice', 'Michele_-_Brazilian', 'Muhammad_Umm', 'Oliveir4_Music', 'Onildo_F._Rocha', 'Otto_de_La_Luna', 'Papai_Noel_', 'Rafael_Valente_-_Brazilian_Professional_Narrator', 'Rener', 'Roberto_Barbieri', 'Rodrigo_Rodrigues', 'Samuel_-_Jovem_Empreendedor', 'ScheilaSMTy', 'Slany', 'Thiago_Realista', 'Vagner_De_souza', 'Vinicius_Bergamo', 'Wesley_Bessa_', 'Weverton_', 'Will_-_Deep']

romanian_names = ['Andrei', 'Antonia', 'Apeiron', 'Ciprian_Pop', 'Corina_Ioana', 'Cristi_Romana', 'Cristina_Amza', 'Jora_Slobod', 'Liviu_Mihai', '_Bogdan_-_Advertising']

russian_names = ['Aleksandr_Petrov', 'Andrei_-_Calm_and_Friendly', 'Anna_-_Calm_and_pleasant_', 'Artem_K', 'Artemii_Levkoy', 'Dimitri', 'Dmitry', 'Felix_-_calm_friendly', 'Larisa_Actrisa', 'Marat', 'Mark_Rozenberg', 'Max_-_Clear__Professional', 'Nadia', 'Nikolay', 'Oleg_Krugliak_', 'Oleksandr_Trotsenko', 'Ranger3D.pro', 'Tyler_Soapen', 'Viktoriia_-_clear_resonant_young_female_voice']

slovak_names = ['Andrej']

spanish_names = ['AF', 'Alberto_Rodriguez', 'Alejandro_-_Mexican_male', 'Alejandro_Aragon', 'Alejandro_Ballesteros', 'Alejandro_Durán', 'Alex_-_Happy_Upbeat_Joyful_Energetic', 'Alex_Comunicando', 'Andrea', 'Andrew_V.', 'Andromeda_Thunders', 'Angie_vendedora_Colombiana', 'Ani_Egea', 'Ani_Egea_-_Expressive', 'Antonio_LV', 'Antonio_ia', 'Apex_-_Fitness_-_Nutrition_-_Coach_-_Energetic_-_Professional', 'ArthisRap_Pro', 'Ashley_Travels-_American_English_Tourist_speaking_Spanish_', 'Bardo_Limon_-_Epic_Promotional_Voice', 'Bebe_Lunita_-_Bebe_hablando', 'Beto_-_Latin_American_Spanish_Argentina', 'Bruno_-_Suspense_-_Thrill_-_Horror_-_Tense', 'Brêchet_Simon', 'CRISTINA_VOICE', 'Carles_Pujol', 'Carlos_-_Podcasting__News', 'Carmelo', 'Carmelo_Crespo', 'Carmelo_Crespo_-_Expressive', 'Carolina_-_Spanish_woman_-_es_ES', 'Christian_Avilés_-_documentales_e-learning_corporativos_y_Redes_Sociales', 'Claudia_Whispers-_Asmr_Spanish_Intimate', 'Cristi_Poot', 'Cristian_Medina', 'Damian_Valdez', 'Dan_Dan', 'Dante_-_Castilian_Spanish', 'Dany_-_Professional_narrator', 'David_Martin._1', 'David_Martin_2', 'Denilson', 'Didak_Leñero__Spanish_Spain', 'Diego_Aguado_-_Spanish_deep_voice', 'Diego_Cárdenas', 'Diego_Galán', 'Dominican_', 'Dosi_Español', 'EDGARD', 'Eduardo_-_Advertising__Commercial_voice_in_Spanish', 'Eduardo_M._-_Mexican_Spanish', 'Eduardo_Román', 'Efrayn', 'Eleguar_-_Latin_American_Spanish', 'Eleguar_-__Deep_Latin_American_Spanish', 'Emiliano_Zamora', 'Emilio_Menal', 'Enrico', 'Enrique_M._Nieto', 'Enrique_Mondragón', 'Erika_-_Raspy_and_Pleasant', 'Eva_Dorado', 'FantasyCraft_Studios', 'Fer', 'Fernanda_olea_1', 'Fernando', 'Fernando_Martinez', 'Firusho', 'Francisco', 'Frankie_San_Juan', 'Gabriela_-_Spanish_from_Mexico_', 'Gabriela_Gonzalez_', 'Gilfoy', 'Ginyin', 'Ginyin_2_-_Webpages_Narrative__Books', 'Grandma_Titina_-_70_year_old_woman', 'Guillermo_Brazález', 'Guillermo_Brazález_-_Dynamic__Cheerful', 'Haroldo_', 'Hernán_Cortés', 'Isabela_-_Spanish_Childrens_Book_Narrator', 'Jacson_Ander', 'Jaime_Fregoso_-_Professional_Annoucer', 'Jaime_Tu_Locutor_Online', 'Jarpa_Test_-_Francisco', 'Jav_-_Calm_clean_and_profound_voice', 'Javier_España', 'Javier_Madrid', 'Javisanchez', 'JeiJo_', 'Jhenny_-_Warm_Fluid_and_Smooth', 'Jhenny_Antiques_-_Calm_Soft_and_Sweet', 'Jonathan', 'Jorge', 'Jorge_Gaviria_-_Powerful_and_impactful', 'Jorge_Mario_-_Spanish_to_read_books_and_narration', 'Jose_A._del_Rio', 'José_Borda', 'José_Borda_-_Deep', 'José_Borda_-_Expressive', 'Juan', 'Juan_Carlos', 'Juan_Manuel', 'Juan_Manuel_-_Conversational', 'Juan_Pablo', 'Kiko_Hdz', 'Knight_JAVIER-Calm_Gentle', 'Lalo', 'Leo_-_Energetic_Warm_Happy_Upbeat_Inviting_Optimistic', 'Leo_Kid_Spanish-_Character', 'Leonardo', 'Ligia_Elena', 'Ligia_Mendez', 'LoidaBurgos', 'Luis', 'Luis_Guary', 'Luis_R_Casiano', 'Luis_Vega', 'Lumina_-_Clara__Natural', 'Maicolangel', 'Malena_Tango', 'Mariluz_Parras', 'Mariluz_Parras_-_Expressive', 'Martin_Osborne_1', 'Martin_Osborne_2', 'Martin_Osborne_4', 'Martin_Osborne_5', 'Martin_Osborne_6', 'Martin_Osborne_7_', 'Mary', 'María', 'Mauricio', 'Mauro_C', 'Maxi_Araya', 'Maxi_Argames', 'Memo_M_-Professional_Latin_American_Spanish', 'Mia_García-_business_narrations_and_informative', 'Mia_Instructor-_Spanish_E_learning_corporate_Conversational_training', 'Miguel', 'Mikel_-_Adulto_idioma_español', 'Miquel', 'Nina', 'Oliver_Podcasting_Refinada', 'OmarVoice', 'Omgpvoice', 'Omgpvoice_-_Expressive', 'Pablo_Vambe_AI_V2', 'Paloma_S.__-_Spanish_-_Conversational_Comforting_Compelling', 'Pilar_Corral', 'Rafael', 'Regina_Martin', 'Ricardo', 'Rodolfo_Rodriguez_', 'Rosa_-_Spanish_Calm_Old_Woman', 'Rosa_Zambrano_', 'Santiago', 'Santiago_-_calm', 'Sara_Martin_1', 'Sara_Martin_2', 'Sara_Martin_3', 'Screaming_George', 'Serena_AI', 'Sergio_Juvenal', 'Sofi', 'Soy_Luis_Cen', 'Tatiana_Martin', 'Tony_Villa', 'Valeria', 'Victor', 'Víctor_Hinojosa', 'Yinet_-Upbeat_Columbian_Woman', 'Yorman_Andres', 'Zabra_-_Commercial_Announcer', '_Medellin_-_Colombian_Voice', 'paco']

swedish_names = ['Adam_Composer_Stockholm', 'Jonas_calm__informative_Swedish_voice', 'Sanna_Hartfield_-_Sassy_Swedish_', 'Sanna_Hartfield_-_Swedish_Conversational', 'Sanna_Hartfield_-_Swedish_Narration']

tamil_names = ['Ashwin_-_Relatable_Tamil_Voice', 'Madsri_-_Friendly_Tamil_Voice', 'Madsri_-_Tamil_Narrator', 'Meera_-_Conversational_Tamil_Voice', 'Nila_-_Warm__Expressive_Tamil_Voice', 'Ramaa_–_Energetic_Conversational_Tamil', 'Ramaa_–_Energetic_Tamil_Narrator']

turkish_names = ['Adilcan_Demirel', 'Ahmed', 'Ahmet_Evlice', 'Ahmet_Çiçek', 'Arman_Yılmazkurt', 'Belma_-_Dynamic_Playful_Clear_Narrator', 'Burak_Yoglu', 'Burcu_Basyigit', 'Cagatay_A.', 'Calm_Turkish_AudioGuide', 'Cavit_Pancar_-_Epic_Powerful_Historical', 'Cem', 'Cicek_-_Joyful_Dynamic_Storyteller', 'Derin_Roman_-_Epic_Dark_Powerful', 'Doacast_', 'Doga', 'Eda_Atlas', 'Emre', 'Emre_Gökçe', 'Farshid', 'Fatih', 'Fatih_Çetinkaya', 'Furkan_Keser', 'Gokce', 'Gokce_lx', 'Gozde_Arikan', 'Gönül_Filiz', 'Hakan_Turk', 'Halil_', 'Hulya', 'Hurrem_-_Confident_Turkish_Actress', 'Ipek_-_Professional_Confident_Narrator', 'Irem', 'Kamil', 'MUHAMMER_ARABACI', 'Mad_Scientist_-_For_All_Languages', 'Mahidevran_-_Playful_Clear_Powerful_Narrator', 'Mert', 'Mertkan_Erkan', 'Mustafa_Can', 'Onur_Can', 'Onur_Naci_Ozturkler_-_spunkram', 'Ramazan', 'Recep_Arkiş_', 'Rıdvan_Elitez', 'Se_-_Young_Male_Reading', 'Sedat', 'Sencer', 'Seyda_-_Eğlenceli_Anlaşılabilir_Fun_Fluent_Clear', 'Sohbet_Adami_-_Natural_Chat_Friend', 'Sultan_-_Charming_Seductive_Narrator', 'Tarik', 'Tuba_Velidede', 'Tuncay_Saran', 'Valperga', 'Walter_BJ', 'Whispering_Irem', 'Yigit', 'Zafer_', 'bilgehan', 'İbrahim_Halil_Acioglu', 'İbrahim_Khan_İpek']

ukrainian_names = ['Anton', 'Danylo_Fedirko', 'Dmytro_UA', 'Oleksii_Safin', 'Olena', 'Volodymyr_Pro']

vietnamese_names = ['Actor_Pham_Hung', 'Announcer_Van_Phuc', 'Ca_Dao', 'Kim_Tuyến', 'Ly_Hai', 'MC_Duy_Minh', 'Mai', 'Nhung', 'Sơn', 'Trang', 'Trung_Caha', 'Tuan_TLU']

english1_names = ['2B_Impression', 'ANDREA_CUTE_female_voice', 'Aakash_Aryan_-_Conversational_English_Voice', 'Aaron_-_Monotone_tech_narrator', 'Aaron_-_trusted_and_engaged', 'Abandoned_school', 'Abigail_-_arrogant_and_snobbish', 'Abrogail_', 'Ada', 'Adam__-_Newscaster', 'Adina_-_Teen_Girl', 'Aditi', 'Adriano_-_44', 'Aerylla', 'Aiden_-_Happy_Video_Host', 'Ailema_-_calm__Soft', 'Akwasi_-_Young_Ghanaian_man', 'Al', 'Alan', 'Alec_-_Energetic_Confident_and_Charismatic', 'Alex', 'Alex_-_Vibrant_Engaging_and_Lively', 'Alex_the_Performer_-_Commercial_Warm_Inviting_Expressive', 'Alexander_-_Mature_and_confident', 'Alexi', 'Alexite', 'Ali_', 'Alice_-_calm_and_soft_narrator', 'Alisha_-_Soft_and_Engaging', 'Alton', 'Alyx_-_Vibrant_British_Male', 'Amada', 'Amanda_-_a_natural_narrator', 'Amar', 'Amelia', 'Amelia_-_young_and_soft', 'Amilia', 'Amina_-_regal', 'Amritanshu_Professional_voice', 'Amy_-_Clear_and_Concise', 'Amy_-_Smart_Teacher_Narration', 'Amy_-_Witty_College_Girl', 'Andre_LeDoux_-_Romantic_Fancy_Talking_male_', 'Andrew', 'Andrew_-_Old_slow_voice', 'Andrew_-_Smooth_audio_books', 'Andrew_Radio', 'Andy_Berg', 'Angie_-_Upbeat_Book_Narrator_Professional_Videos_Engaging_Conversations_Radio_News_Meditation', 'Anjali', 'Anjina', 'Anna_-_Modern', 'Annie', 'Anthony_-_emotive__expressive', 'Arabella', 'Arayah_-_Mature_and_Professional', 'Archer', 'Aria_-_Sexy_Female_Villain_Voice', 'Armando', 'Armando_realistic', 'Asarte', 'Ash_', 'Asher_Avery_Alex_-_Engaging_and_Real__Storyteller_and_Performer', 'Asmodia_-_earnest', 'Aspexia_-_Grand__Clear', 'Athena_-_Stern_serious_and_powerful', 'Attention_Grabbing_Male_Narrator', 'Aunt_Annie_-_calm_and_professional', 'Aurelia_-_High_Quality_Realistic_Princess_', 'Aurion_-_Wise_Narrator', 'Austin_-_Dramatic_Narration', 'Austin_Boy', 'Austin_the_Cowboy', 'Ava', 'Ayden', 'Ayesha_-_Energetic_Indian_Voice', 'Ayinde_-_young_British_Nigerian', 'Bailey_-_twenty-something_earnest_confident', 'Bails', 'Barry_Bob_Alone', 'Bateman_-_Deep_Masculine_and_Authoritative', 'Befutig_-_Steady_Robust__Engaging', 'Befutig_Safiza_Uj-alet_-_Resonant_Commanding__Authentic', 'Belinda_-_Curious_and_Soft', 'Bella-_sensual_allurin_beautiful', 'Bella_-_Direct_and_Understanding', 'Belle_-_Clear_Well-Modulated_Expressive', 'Ben', 'Ben_-_British_male_young', 'Benjamin_-_The_Frenchy_Guy', 'Benjamin_S_Powell', 'Benny', 'Bert', 'Beth_-_gentle_and_nurturing', 'Betsy_-_Wise_and_Thoughtful', 'Betty', 'Beyond_Average_Joe', 'Bhavna_-_Insightful_Storyteller', 'Bianca_-_City_girl_', 'Bill_-__A_deep_voice_narrator', 'Bill_Oxley_-_Clear_informative_mature_forthright_and_understandable', 'Blaire_Frost', 'Blkking407_', 'Bob_-_old_man', 'Bogdan_-_Soft_Male_Narration', 'Boi', 'Booney_-_calm_and_cute', 'Brandon_-_Young_Male_American_Voice_Over', 'Brandon_Cole', 'Brandon_VO_Artist_Clone', 'Brayden_-_Conversational_Older_Teen', 'Brenda', 'Brenda_-_Raspy_female_', 'Bria_-_Young_and_Soft', 'Brittney_-_Male_Child_-_Youthful_Raspy_Cute__Excitable_', 'Brittney_-_Social_Media_Voice_-_Fun_Youthful__Informative', 'Brody_-_Serious', 'Broom', 'Bruce_-_vibrant_and_baritone', 'Bryan', 'Bryn_-_Calm_and_Expressive', 'Bud', 'Cal_-_confident_professor', 'Calliope_-_ancient_muse', 'Camelia', 'Cara_-_Expressive_and_Direct', 'Carl_-_Big_Voice', 'Caroline_-_clear_and_confident', 'Carter_-_Caring_and_Rational_British_Male', 'Cassandra', 'Cassandra_-_Confident_and_Vibrant', 'Cassia', 'Catherine_-_Professional_and_Direct', 'Cecile_-_Confident_and_Strict', 'Charlie_-_Posh_and_Royal', 'Charlotte_-_precise', 'Chazza_Hypno', 'Chechi_for_first_video', 'Chelsea_Boddie', 'Chinmay_-_Calm_Energetic__Relatable_', 'Chloe_-_sharp', 'Chris_-_irritable_boss', 'Chris_C_-_Mid_30s_-_Podcast_Reviewer_good_for_shorts', 'Chris___Young_and_Inspired', 'Chrissy_-_Millenial_Female', 'Christian_Rivera', 'Christina_-_Trained_on_over_900_characters_with_emotional_dialogue', 'Christopher_', 'Christopher_-_friendly_guy_next_door', 'Chrisva', 'Ciro_-_real_intense_twentyish', 'Clara', 'Claw_Benn', 'Cody_-_Energetic_Upbeat_Educator', 'Cody_McAvoy', 'Cole_-_Gritty-Rough-Strong', 'Cooper', 'Cornelis', 'Creator', 'Cristiano', 'Crystal', 'Cyrus', 'Dan', 'Dana', 'Danbee', 'Daniel', 'Daniel_-_American_Game_Show_Host', 'Daniel_-_expressive_and_wise', 'Danny_-_highschool_jockish', 'Daphne_-_alluring_goth', 'Dara_-_loud_and_Intense_', 'Darwin_-_Rich_Mature_Voice', 'Daryl', 'Dath_Ilan', 'David', 'David_-_British_Storyteller', 'David_-_Deep_British', 'David_Bent', 'David_Castlemore_-_Newsreader_and_Educator', 'David_DeWitt', 'David_Eclipse', 'David_Esposito', 'David_Hertel', 'Dean_-_Goody_Two_Shoes', 'Deb_-_emotive_and_expressive', 'Deja', 'DellaRayne', 'DellaRayne_-_Smooth_and_Assertive', 'Demon_Monster', 'Derrick_-_melancholy', 'Desdemona_-_sassy', 'Desmond_-_clear_sincere_angst', 'Dezzy_-_Young_and_Soft', 'Dhyogo_azevedo', 'Diana_-_Meditative_Calm', 'Donny_-_Real_New_Yorker', 'Donny_-_very_deep', 'DrRenetta_Weaver', 'Dr_Lovejoy_-_Pro_Whisper_ASMR_', 'Drake__Warm_Canadian_English', 'Drew', 'Drew_-_Deep_Soothing_Guided_Meditation', 'Duke', 'Durgesh', 'Eamon_-_old_lecturer', 'Ed_Holderness', 'Edward', 'Egbert_-_upbeat_meditations', 'Elisa', 'Elisabeth_-_meditative', 'Elizabeth', 'Elizabeth_-_Wise_and_wistful', 'Elizabeth_-_calm_commanding_classic', 'Ella_-_Old_And_Deep_', 'Ellie', 'Emily_-_Australian_Female', 'Emily_-_relaxed_and_conversational', 'Emily_-_sweet', 'Emma', 'Emma_', 'Emma_-_A_brilliant_young_magician', 'Emma_-_sharp', 'Emma_Taylor', 'Emmeline_-_a_young_clear_and_confident', 'Epiktet_Philosoph_', 'Erdem_-_Educational_and_Instructional', 'Erika', 'Erin_-_Meditation_Guide', 'Ethan', 'Ethan_-_expressive_wise', 'Eustis_', 'Evan_-_showbiz_excited_happy', 'Eve_-_young_Australian_girl', 'Ezreal', 'Ezreal_-_energetic', 'Faith', 'Feeven', 'Female_Romance_Novel_', 'Foxy_-_Futuristic_Robotic_Personal_AGI', 'Francesca', 'Frank', 'Frank-_scary_stories', 'Frank_Johnson', 'Fucia_-_Youthful_and_Confident', 'Gabriella_-_deep', 'Garrett_Wasny', 'Gault_-_Youngish_excitable_high-strung.', 'General_Joe_-_WWII_Narrator', 'George', 'George_-_Serious_and_Experienced', 'Gerhard_Bakker', 'Gertrude_-_Childrens_Narrator', 'Gijs', 'Gladys', 'Goddess_Freyja_-_A_Mysterious__Magical_Muse', 'Graham_-_Old_and_Wise', 'Greg_Murphy', 'Gregoria', 'Gruhastha_-_Energetic_Enthusiastic__Articulate', 'Guy', 'Hakim_-_Audiobook_English__Arabic_Gulf_Accent', 'Halbert', 'Halley_McClure', 'Hallie_-_soft-spoken_and_subtle', 'Hallie_-_youthful_girl_voice', 'Hamlin_-_Deep_and_Booming', 'Hannah___Confident_Teacher', 'Hardcore_Henry_-_Intense_Storyteller', 'Harold', 'Harry_-_Proper_and_Academic', 'Harry___Sad_Emotional_Reck_', 'Harvey_-_Knowledgeful_Upfront', 'Haven_Sands', 'Helena_-_British_female_gentle_and_smart', 'Hemaka', 'Herbie_-_Lisp_and_whistle_S_sounds', 'Hermes_-_frank_abrupt_messenger', 'Hobbs_-_Casual_Narration', 'Horace_-_intense_deep_elder', 'Huckleberry_-_Southern_Charm', 'Hyde', 'Ian', 'Indian', 'Ingmar_-_Intimately_Mysterious', 'Investigator_Jane', 'Iomedae', 'Iris', 'Isabel_-_emotional__lisp', 'Isabella_', 'Isla_-_Strong_British_accent', 'Isla_Reid', 'Ivan_the_Mighty', 'Ivy_-_Free_Spirit', 'J._Thorn', 'J._Tyson', 'Jack', 'Jack-_Raspy__deep', 'Jacme', 'Jade', 'Jakobi_-_Emotive_and_Intriguing', 'James_-_Deep_', 'James_-_Deep_and_Booming', 'James_-_cool_and_expressive', 'James_-_deep_and_to_the_point', 'James_Fitzgerald', 'Jami_-_Mature_and_Clear', 'Jannice', 'Jason', 'Jason_Jordan', 'Jason_Pike', 'Jasper_-_androgynous_and_rebellious', 'Jasper_-_erudite_and_inquiring', 'Jeff', 'Jeff_-_Australian_Male', 'Jeff_-_Smooth_and_Confidant', 'Jennifer_-_expressive_and_cheerful_narrator', 'Jeremy_-_meditative', 'Jeremy_Clarkson', 'Jeremy_Smith', 'Jerry', 'Jerry_-_Energetic_and_Upbeat', 'Jessica_Anne_Bogart_-_Conversations', 'Jeż', 'Jim', 'JimBob_', 'Joan', 'Jodie_-_Assertive_and_Intelligent', 'Joe_-_American_Male_Narrator', 'Joe_-_professional_British_male_voiceover', 'Joe_02', 'Joey_Reeve', 'John', 'John_-_Deep', 'John_-_Guided_Meditation__Narration', 'John_-_Ultra_Brutal_Man', 'John_Beamer', 'John_Doe_Gentle', 'John_Domus_Cruo_-_Serious', 'John_Fernandes_-_Energetic__Friendly', 'Johnny_-_Upbeat_Professional_American_Male', 'Johnny_Boy_-_Action_Movie_Narrator', 'Johnny_Kid__-_Serious', 'Johnson_-_American_Male_voice_', 'Jones_-_Articulate_Gruff_Raspy', 'Jordan', 'Jordan_-_Warm_Narrator', 'Josh', 'Josh_-_Quiet_Person', 'Josh_T.', 'Joshua_-_Authoritative_Warm_and_Articulate', 'Joshua_-_Young_Soft_Warm_Male_Voice', 'Judy_-_Aged_and_Confident_Elder', 'Julie_-_expressive_and_energetic_romance_narrator', 'Justin_Time_-_eLearning_Narration', 'Justine_-_Expressive_Teen_Boy', 'Kade_Murdock_2.0', 'Kala', 'Kallen', 'Karen', 'Karma_-_Professional_and_Thoughtful', 'Kasi', 'Kathleen_Julie_-_alto_serious_articulate_focused_and_direct', 'Katy_-_sassy_teen', 'Kayla_-_Nurturing_and_Caring', 'Kelli-_Young_Mature_Southern', 'Kelly', 'Kelly_-_clear_teen_voice', 'Kenneth_-_strange_eccentric_old_gentleman', 'Kenny_-_Volume_2', 'Kevin', 'Khaled_', 'Khushi_-_New_Indian_Voice', 'Kieran_-_newsreader_male', 'Kik', 'Kim_-_Swedish_accent', 'Kim_Selch_-_Pro_Studio_Recording', 'King_Chuku', 'Kingsley_-_dapper_and_deep_narrator', 'Kirsten_-_Elegant_Knowledgeable_and_Reassuring', 'Kirt', 'Kitten_Kaley_Rose', 'Kiwi_-_Holistic_Educator', 'Kristopher_-_Gentle_ASMR_', 'Kuk', 'Kurrayah_-_young_and_friendly', 'Kuthon', 'Kwame', 'Kyana_Cook', 'LIAM_DALE', 'Lalitha_J_-_Tamil_Old_Woman', 'Lamar_Lincoln-_Black_Male', 'Latisha', 'Laurance', 'Lawrence_Mayles', 'Lee__Middle-Aged_Australian_Male', 'Leif_-_husky_male', 'Lena_-_crispt_and_confident', 'Leonardo_', 'Lerato', 'Liam', 'Liam_', 'Lila_-_Intelligent_and_emotive', 'Lily', 'Linus_-_A_young_American_tech_video_narrator', 'Lisa___Stern_and_Assertive_', 'LiveCat', 'LiveChi_', 'Liz', 'Lloyd', 'Lucas_-_motivational_speaker', 'Ludo_-_Storyteller_-_Your_epic_story_narrator', 'Luis_Gabriel', 'Lukas', 'Luna_-_Well_rounded_insightful_charismatic', 'Lyle', 'MANSHI', 'Magnolia_-_Mature_and_Wellspoken', 'Magpie', 'Marc_--_Smart_Soothing_Man', 'Marco-_hot_male_voice', 'Margot_', 'Mariam', 'Marianne_-_Narrative_Friendly_British_', 'Maribeth_-_A_Southern_Sweetheart_', 'Marie_KC', 'Marilyn_-_confident', 'Marissa_-_Friendly_and_Sociable', 'Marissa_from_ElevenLabs', 'Marjorie_', 'Mark', 'Mark_-_Very_Deep_Confident_Professional', 'Mark_-_Young_and_Calm_', 'Mark_-_clear_and_professional_newscaster', 'Mark_-_confident', 'Mark_-_raspy', 'Markus_-_Mature_and_Chill', 'Marshal_-_Dandy_Brit', 'Marshal_-_Toon_Character', 'Marta_-_Officious', 'Matt_Landon_', 'Matt_Rogo', 'Matt_Washer', 'Matthew_-_American_Male_Narrator', 'Matthew_-_Friendly_Clear_and_Perfect_for_Educational_Content', 'Matthew_MacGyver', 'Max_-_YouTube_Professional', 'Melina_CTC', 'Melissa_-_Female_Soothing_Narrator', 'Melville__Euro-accented_narrator.', 'Melvin_-_soothing_and_gentle', 'Mia', 'Mia_-_confident_and_annoyed', 'Mia_Chou', 'Michael', 'Michael_Anthony', 'Michelle_-_Old_and_Daring', 'Mike', 'Milan_Diekstra', 'Milean_-_bassy_with_plosives', 'Miller', 'Milo_-_Casual_Chill_Relatable_Young_Male', 'Mina', 'Miriam_-_Casual_and_Wry', 'Miss_Brittany_Andrews', 'Mkves_-_Calm_', 'Molly', 'Monika_Sogam', 'Mono', 'Morgan', 'Motivational_Coach_-_Leader', 'Mr._P_-_the_fun_guy', 'Mr_Novella_Main_Voice_-_Kobe_Black_British_Male_Young', 'Mun_W', 'My_Fortress', 'Nakiso', 'Nana-chan', 'Narender_Sharma', 'Narrador-34', 'Nata_Professional', 'Natasha_', 'Natasha_-_Sensual_Hypnosis', 'Nathaniel_C._-_Deep_Rich_Mature_British_voice', 'Naty_Heals_voice', 'Neal_-_Perfect_for_documentaries', 'Neil_-_cheerful_upbeat_youth', 'Nellie_-_soft', 'Newton', 'Niamh', 'Nichalia_Schwartz', 'Nick_Colter', 'Nicki', 'Nigel_-_Mysterious_Intriguing', 'Niladri_Mahapatra', 'Nina_-_nerdy', 'Nipunn_-_deep_captivating', 'Noah_-_scary_story_voice', 'Nora_', 'Nova_-_Wise_and_Tranquil_', 'Old_Joshua', 'Old_man_with_a_soft_voice', 'Olivia', 'Omeo', 'Oscar_-_Older_Narrative_Epic', 'Osiris_-_Deep_and_commanding_rumble_', 'Oswald_-_intelligent_professor', 'Pace_-_Deep_Menacing_and_Raspy', 'Page', 'Paladin', 'Parki_-_expressive_and_loud_elder', 'Paul_Henry_Smith_-_gentle_patient_clear', 'Paul_J._-_Calm_and_soothing_', 'Paula_Moon_-__Sleepy-time_true_crime_vocal', 'Paxti_-_Young_and_Earnest', 'Penelope_-_relaxed_and_breathy', 'Penfist_-_Military_Broadcaster', 'Penny_-_sweet_story_teller', 'Peter_-_Eastern_European_English', 'Peter_-_Hungarian_accent', 'Phil_-_Author_Non-fiction', 'Phillip', 'Phoebe', 'Pixy', 'Planty_-_raspy_voice', 'Prakash', 'Priya_-_Beautiful_and_melodic_Indian_accent', 'Priyam_-_Deep_Indian_Voice', 'Quasi-Jude-Lw', 'Rabih_Rizk', 'Rainbow', 'Raj', 'Raja_Babu', 'Ran', 'Rasper', 'Raven_Nightshade', 'Raven_Reed', 'Ray_-_Male_Soothing_Narrator', 'Raymond_Baxter', 'Raymond_Elliott', 'ReadingSam', 'Recvoice', 'RedGlassesVoiceovers', 'Remus_-_Fantasy_Professor', 'Rex_-_Throaty_and_World_Weary', 'Richard_-_enthusiastic_young_male', 'Richard_Yu', 'Ricky_The_K', 'Riley_-_loud_and_intense', 'Rinoa_-_Middle_Aged_Lady', 'Robert', 'Robert_-_American_standard_broadcaster', 'Robert_-_Business_Book_Narrator', 'Ronald_Wang', 'Rosalind_-_Classy_British_Actress', 'Rose', 'Rowan_-_gruff_and_raspy', 'Rufus', 'Rupert___Strong_British', 'Russel-_clear_realistic_pleasant', 'Russell', 'Russo_-_Dramatic_Australian_TV', 'Ryan', 'Ryan_-_Calm_Masculine_Teenager', 'Ryan_-_Dynamic_', 'Ryan_-_rough', 'Ryder_-_cool_and_balanced', 'SAVVAS', 'Sahand_RZ', 'Sally_Ford', 'Sam', 'Sam_-_English_Storyteller', 'Samantha', 'Sandy', 'Sanjana_', 'Sara_Jay', 'Sarah', 'Sarah_Lawson', 'Sash', 'Sassy_Aerisita', 'Satyam_1', 'Scar', 'Scott_-_Mature_and_Deep', 'Scott_-_Young_male_Canadian_voice', 'Scott_-_drill_instructor', 'Sean', 'Sean_-_deliberate_low_voice_authoritative_narration', 'Sean_Michael', 'Security', 'Serenity', 'Sexy_American_Female_voice', 'Seán', 'Shannon', 'Shannon_-_High_Quality_American_Male_Voice', 'Shanny_-_Soothing_Calm_American_Woman', 'Shelley_-_Clear_and_confident_British_female', 'Sheriff_Ben_-_Deep_Gruff_Authoritative', 'Shianne_-_Young_and_Confident', 'Shiv_-_Mature_Deep_Voice', 'Shoobu_-_Old_British_Man', 'Shot_List_voice_Girl', 'Sieu_Muoi', 'Sigrid_-_solemn_raspy_wise', 'Silas_-_stern_british_male', 'Silvia_-_upbeat_british_lady', 'Simeon', 'Simon_J_Kidson', 'Sina_-_Your_Narrator', 'SirEden_', 'Smart_Sara', 'Smarty_Pants_Amy', 'Smokey_McSmoker_-_Deep_and_Motivational', 'SocraGPTs', 'Sofy', 'Sophia', 'Sophia_Florence', 'Southern_Ann', 'Stan', 'Starry', 'Stella_-_Calm', 'Stephanie_P_-_Casual_feminine_great_for_storytelling', 'Stephen_-_Calm_British_Narrator_', 'Steve_Maughan', 'Steven_-_Calm_British_Deep_Soothing', 'Steven_-_Vibrant_Resonant_and_Inspiring', 'Stuart', 'Subirachs', 'Subu', 'Sully', 'Susan', 'Swara_-_Young__Calm_Voice', 'Tamara', 'Tanya-_Upbeat_and_Expressive', 'Tara', 'Tarini_-_Expressive__Cheerful_Narrator', 'Tarnish', 'Taro-_Young_Japanese_Accented_Guy', 'Tatsuya_Suzuki', 'Technical_Narrator_-_Precise_Knowledgeable_Engaging', 'Technical_Southerner', 'Temos_Sevandivasen_-_Resolute_Philosophical_Empathetic', 'Test_Aaron_2', 'Test_Plumb_2', 'Thaddeus_-_ancient_historian', 'Theodor_-_deep_american', 'Theodore-Old_Man__Deep_Husky_Voice', 'Theresa', 'Thomas', 'Tiffany_Kim_-_versatile_and_engaged_narrator', 'TikTok_Male_Voice', 'Todd_-_Universal_Crossover', 'Tom', 'Tom_-_trailer_narrator', 'Tommy_-_Reedy_Annoyed', 'Tony_-_Middle-aged_with_American_accent_', 'Trent_-_quirky', 'Tulipe', 'Twilight_Zone_Guy', 'Tyler_Kurk', 'Tyrone_-_Deep_Strong_Masculine_Narrator', 'Tyson', 'Upbeat_Teacher', 'Vee_-_Soft_Spoken_British_Male', 'Vicki_', 'Victoria_Queen_of_England', 'Vieux', 'Vivian', 'Vivie_2_Upbeat', 'W._Sillyman_Oxley', 'Wade_-_powerful', 'Walker', 'Wally_-_Warm_Deep_Masculine', 'Walter_-_Intelligent_and_Resolute', 'Wanda_-_calm_female', 'Wesley_-_nervous_cowardly_fellow', 'Will_', 'William', 'Winston_-_Distinguished_Erudite_and_Genteel', 'Winston__Authoritative_British_Man', 'Yee', 'Yoel', 'Young_brit', 'Yousef_-_Passionate_Sympathetic', 'Zara_-_understanding_friend', 'Zashikix', 'Zee_-_Childish', 'Zeus_-_arrogant', 'Zeus_Epic', 'Zoe_-_emphatic_and_pleasant', 'Zon-Kuthon', 'Zuri_-_New_Yorker', '_Luca_-_ calm_soothing_steady', '_Martha_-_Narration', '_Vicky_-_Posh_Voice_With_A_Lipse_', 'adriano_-_41', 'emily_', 'harry_deep_and_warm', 'madeline', 'neuris', 'sebastian_']

english2_names = ['19keys', 'ADAM', 'ADAM_v2', 'ALESSANDRO_DEEP', 'ATAKAN_ARISOY', 'A_Top_Narrator_VO_PRO', 'Aaditya_Kapur_-_Calm_Conversational_Voice', 'Aarav_-_Deep_and_wise_Indian', 'Aaron_-_Narration_Voice__A_Voice_thats_One_in_a_Million_NOT_like_a_Million_Others', 'Aaron_Davis_Emerson', 'Aaron_Sage_-_Friendly__Conversational_', 'Abigail', 'Adam_-_Calm_Smart', 'Adam_-_deep_voice_Australian', 'Adam_-_low_rough_and_full', 'Adam_-_old_and_knowing', 'Addie_-_Podcast_Princess', 'Adeline', 'Adi', 'Adriano_-_narrador_37', 'Aelar', 'Agatha', 'Agent_L', 'Ajay', 'Akua', 'Albert_-_Pleasant_deep_voice', 'Albert_-_Strong_German_Accent_', 'Albert_-_deep_slurred_meditations', 'Albert_Banoy', 'Alden_-_Resolute_Gravitas', 'Alex_-_Australian_Male_-_Casual_-_Melbourne_City', 'Alex_-_Business_Book_Narrator', 'Alex_-_Young_American_Male', 'Alex_-_expressive_narrator', 'Alex_Ozwyn', 'Alex_Wright', 'Alexander_-_Deep_Calm_and_Authoritative', 'Alexis_-_chic_and_cosmopolitan', 'Alfie', 'Ali', 'Alice_-_calm__composed', 'Alice_-_young_and_confident', 'Alita', 'Allison_-_millennial', 'Aly_-_Serious_and_Strict', 'Amaniri_-_British_Stalwart_Lass', 'Amelia_-_haughty', 'Amrut_Deshmukh_-_Booklet_Guy', 'Amy_-_Spunky_Cartoon_Girl_Voice', 'Amy_-_mean', 'Ana', 'Ana-Rita', 'Andrea_Wolff_-_clear_youthful_evenly_paced_', 'Andrew_-_tech_wizard', 'Andrews', 'Android_X.Y._Z._-_AI_Robot_of_the_Future', 'Angel', 'Ann_the_neighbor_', 'Anna_-_Cute_Calming_Narrator', 'Anne_Marie', 'Anthony10', 'Antoine', 'Antonio', 'Antonio_-_English_with_Subtle_Italian_Accent', 'Anup_Chugh_', 'Anushri_-_Natural_Young_Indian_Voice', 'Archie_-_English_teen_youth', 'Ardian', 'Ariah', 'Aristocrat', 'Arjun', 'Arnold', 'Arthur', 'Arthur_-_Energetic_American_Male_Narrator', 'Arthur_-_Geeky_Masculine_Deep', 'Arthur_-_Royal_Narrator', 'Arthur_the_anchorman', 'Arun', 'Ash', 'Asher_-_Confident_Aristocratic_Male', 'Ashley_American_Mom', 'Astrid', 'Astro_-_Audiobook_Excellence', 'Athena_-_corporate_supervisor', 'Aurelius_-_Calming_Deep_Serious_', 'Ava_Said_2', 'B._Hardscrabble_Oxley', 'Baron_Theatricus_-_Dramatic_Elocution', 'Beatrice_-_energetic_older_female_voice', 'Bedlam', 'Belinda', 'Ben_-_Masculine_Authorative', 'Ben_-_Scary_Stories', 'Benjamin', 'Benjamin_-_Deep_Warm_Calming', 'Benjamin_-_strong_and_confident', 'Bert_-_Mystical__Whimsical', 'Bill_Oxley_', 'Biquette_-_sad_and_resigned', 'Blake_-_bassy_and_gruff', 'Bob__-_Young_Deep-voice', 'Brian', 'Brian_-_Broadcast_News_Anchor', 'Brian_-_deep_narrator', 'Brian_Overturf', 'Brittany', 'Brittney', 'Brittney_-_Young_Peppy_Female_-_Social_Media_How_Tos_Explainers', 'Brody_-_Cool_Deep_Chilled', 'Bruce_-_Deep_Warm_Strong', 'Bruce_Actor', 'Brucifer', 'Brutus_-_Profound_Slow-paced_Inspiring', 'Bryan-Deep_Narration', 'Bryan_-_Narration', 'Bryan_-_Professional_Narrator', 'Bubba_Marshal', 'Burak_-_accented_storyteller', 'CAMILO_-_AMERICAN_VOICE_NARRATOR', 'CJ_Murph', 'CS_New_', 'Cal_-_Deep_and_Calming', 'Caleb', 'Cali_-_American_Female_voice_for_Promos', 'Cally_-_Young_and_Sweet', 'Calvin_', 'Cameron_-_deep_and_emotive', 'Can', 'Capt_Lynch_-_Sophisticated_Wise_Calm', 'Carlo_', 'Carlos_', 'CarterSutra', 'Carters_Edge', 'Casey_-_Clean_crisp_female_voice', 'Cecil_-_Profound_and_Precise', 'Cecilia', 'Charles_-_Deep_Hoarseness_Voice', 'Charlie_-_gentle_knowledgeable_old_voice___', 'Charlotte_-_sweet', 'Charmion_-_Soft_and_husky', 'Chloe_-_Girl_Next_Door', 'Chris_-_British_Friendly_Advertising_', 'Christian', 'Christina-_friendly_and_energetic', 'Christine_-_calm_teacher', 'Christopher', 'Christopher_-_Immersive', 'Christopher_-_scientific_mind', 'Chuck', 'Chuck_-_True_Crime', 'Claire', 'Clarice_-_Kind_and_Trustworthy', 'Clover_-_Calm_and_Collected', 'Cody_-_Authoritative__Deep_Motivational_Narration', 'Connor', 'Conny_-_Old_and_Stubborn', 'Consuelo', 'Conversational_Joe_-_A_chatty_casual_voice_British_RP_male', 'Courtney', 'Courtney_-_Soothing_and_Calm', 'Crime_Channel', 'Crystal_-_Pleasant_sultry_Voice_for_Audio_Experience', 'DJ_Marathon', 'DR_Dean_British', 'Dakota_H', 'Dalia_', 'Damon_-_Deep_and_Strong', 'Dan_-_Young_British_friendly_voice', 'Daniel_Lappisto', 'Daniel_R', 'Danielle_-_Canadian_Narrator', 'Dave', 'Dave_-_Dry_Quirky_Wit', 'David_-_American_Narrator', 'David_-_Epic_Movie_Trailer_', 'David_-_Gentle_Engaging_Soothing', 'David_-_Mature_Engaging_Male_Voice_American_accent', 'David_-_knowledgeable_old_soul_', 'Davy_-_Deep_Pirate_Voice', 'Dean_-_British_RP_Warm_and_Friendly', 'Dean_Jones', 'Delegate_-_Bright_and_Airy', 'Demeter_-_expressive_and_sincere_mother', 'Denis_-_Authoritative_and_Deep_Narrator', 'Denzel', 'Denzel_-_Casual_Narration_', 'Desdemona_-_balanced', 'Dez', 'Dispater_-_Refined_Strong__Authoritative_', 'Divija_-_A_female_voice_young_and_vibrant', 'Djali_Vesela', 'Don_-_Deep_Warm_Realistic', 'Don_Kim', 'Donald_-_American_70_years_old', 'Dorian_-_fast_paced_mediations', 'Dragonia_-_Dragon_Rider', 'Drake', 'Duncan_--_the_Melancholy_Intellectual_', 'Dying_story_teller', 'Ebony', 'Ebsa-_Realistic_Deep_male_voice', 'Ed_-_sweet_and_soft', 'Eddy', 'Edgar_-_nerdy', 'Edmund', 'Edris_-_deep_and_powerful', 'Edward-_muffled_and_distorted', 'Elaine_-_Sweet_and_Lively', 'Elaine_-_emotionally_versatile_narrator', 'Ele_-_Elegant_Youthful', 'Eli_-_American_voice_for_promos_and_explainers', 'Elijah_-_Narrative_Reader', 'Ella_', 'Ella_-_soft_and_sweet', 'Ellie_-_Tender_young_woman', 'Emily', 'Emily__-_pleasant_teen_voice', 'Emma_watson', 'Emms', 'Emre', 'Erecura_-_Walm_and_Nurturing', 'Erin', 'Erin_', 'Eris_-_strong', 'Eugene_-_nerd_and_geek', 'Evan_-_deep_narrator_voice', 'Evan_Byers', 'Evy_-_endearing_textured', 'Extraordinary_Joe_', 'Faheem_Ahmed_', 'Felicity_-_young_and_well-spoken', 'Finn_-_Serious_and_Sincere', 'Florence_-_Mature_Educated', 'Fowler_-_scary_and_authoratative', 'Franklin', 'Frederick_Surrey', 'Gandalf_', 'Garretts_Groove', 'Gemma_-_Refined_Witty_and_Warm', 'Gemma_-_Young_Australian_Female', 'Gene_-_informative_and_trustworthy', 'German_Petra_-_English_with_hard_accent', 'Giovanni', 'Godfrey_a_National_Treasure', 'Godot_-_Wise_and_Serene', 'Gordon_', 'Grace', 'GrandMaester_Game_of_Thrones', 'Grandma_Margaret_-_Storybook_Narrator', 'Grandpa_Slow_Reading', 'Grandpa_Spuds_Oxley', 'Gravitas_-_The_deep_narrator_voice', 'Gregory_-_British_Nature_Narrator', 'Gwen_-_Calm_and_Pleasant', 'Hades_-_grim_gravitas', 'Hamza', 'Hannah_-_assertive__refined', 'Hannah_-_soft-spoken', 'Harrison_-_Deep_and_Cinematic', 'Harrison_Gale_–_The_Velvet_Voice__deep_resonant_powerful_smooth_rich_storytelling_narrator', 'Haseeb_-_Canadian_Narration', 'Haven_Glass', 'Hector', 'Hector_-_Deep_Narrative', 'Hephaestus_-_steady_and_patient_teacher', 'Hiro', 'Hope_-_natural_conversations', 'Hope_-_upbeat_and_clear', 'Huss', 'Hyznberg_-_Crime_Time_Cool', 'Ian_Cartwell_-_Suspense_Mystery_and_Thriller', 'Igor_Radio', 'Illya_-_Soft_and_neutral', 'Isabel_-_Soft_Spoken_Teen_Youth', 'Isac', 'Isadore_', 'Ivy_-_Female_Childish_-_Young_Innocent__Bubbly', 'Ivys_Allure', 'Jace_Nox_-_Mellow_Gentle_and_Diverse', 'Jack_-_Calm_Monotone_Measured_Speech', 'Jack_the_Pirate', 'Jackson_-_Confident_Charismatic_and_Approachable', 'Jacob_-_Teen_and_Popular', 'Jacob_Dayi', 'Jacqui_Griffin', 'Jada_-_confident_and_direct', 'Jalia_-_soothing_female_voice', 'Jamal_', 'James', 'James_-_British_TV_presenter', 'James_-_classic_narrator', 'James_-_professional_and_authoritative', 'James_Lindsay_Pro', 'Jameson_-_Guided_Meditation__Narration', 'Jamie_-_young_child_voice', 'Jan', 'Janet', 'Jaquon', 'Jarvis_-_Polite_and_Upfront', 'Jason_-_Authoritative_Smooth_and_Approachable', 'Jay_-_Proper_Mancunian', 'Jeevan_-_Expressive_Indian_Voice', 'Jenny', 'Jerry_-_Presenter_Announcer_Event', 'Jerry_Beharry_-_Conversational', 'Jessica', 'Jessica_-_Cali_girl', 'Jessica_-_meditative', 'Jessica_-_smart_coach', 'Jessica_Anne_Bogart_-_A_VO_Professional_now_cloned', 'Jhon_-_casual_and_friendly', 'Jhonny_-_Agradable_reading', 'Joe', 'Joe_-_British_male_in_high_quality', 'Joey', 'Joey_-_Upbeat_Popular_News_Host', 'Joey_-_Youthful_and_Energetic', 'John2', 'John_-_American_War_Speech', 'John_-_Old_and_kind_', 'John_-_The_Heart_Of_America', 'John_Adams', 'John_Doe_-_Deep', 'John_Fernandes_-_Vibrant_British_Voice', 'John_Martin_-_Funny', 'Johnny_-_deep_and_gruff', 'Johnny_Lefors', 'Jona_-_man_of_the_Desert', 'Jonah_-_sassy_young_male', 'Jonathon', 'Jose_Feliciano_Voice_Clone', 'Joseph_-_Comforting_', 'Joseph_-_Cool_calm_and_great_for_narration', 'Joseph_-_motivational_speaker', 'Joy_Love', 'Judith_-_calm_and_confident', 'Julia_-_soft_and_shy', 'Julian_-_deep_rich_mature_British_voice', 'JuniorDT', 'Justin_-_hyped', 'Kai_Selekwa', 'Kamwe', 'Karan', 'Karan_-_EnglishStandup_Comedian_', 'Karl_C._Shroff_-_Professional_Calm_Voice', 'Karl_Stuke', 'Kass_-_Energetic_Casual_Engaging', 'Kat_Dollar', 'Kavya_-_Energetic_Kids_Voice_', 'Keel_-_confident_dramatic_narrator', 'Kelcey_-_Teen_and_Adventurous', 'Kellan_-_soft_and_gentle', 'Ken_-_African_man_with_heavy_accent', 'Ken_-_Influential_British_Male', 'Kenneth_-_calm_newcaster', 'Kevin_W._Krause_', 'Khemet_-_Deep_and_Powerful', 'Kostiantyn', 'Kyle_-_narrator', 'Lachita', 'Lana-_Robin_Rekia', 'Landon_Bailey', 'Laura_-_emphatic', 'Lauren_-Confident_Quick_Talking_No_Nonsense_Gal', 'Layo_Queen', 'Lee_-_Calm_and_Relaxed', 'Lena_-_emotive_and_expressive', 'Leo', 'Leo_-_Energetic_Indian_Voice', 'Leoni_Vergara_', 'Lily_Wolff_-_Expressive_Clear_Youthful_Calming', 'Lisa_-_pleasant', 'Lisa__-_Pleasant_calm_and_dynamic', 'Long_Storyteller', 'Lowy_-_soothing_gentle_and_warm', 'Lucan_Rook__-_Energetic_Male', 'Lucia_Reid_', 'Lucy_-_British_Storyteller', 'Lucy_-_sweet_and_sensual', 'Lucy_-_yound_anime_girl', 'Luis_-_Relaxed_and_Calm_Narration_-_Pro_Recording', 'Luke_old_and_deep', 'Luminessence_-_Light_Mirror', 'Luna_Spencer', 'Lydia_-_squeaky_', 'MW', 'Maccabaeus_-_Audiobook_Narration', 'Mael_-_deep_raspy_male', 'Mahmood', 'Manohar_-_Gruff_Seasoned_and_Wise', 'Marcus', 'Maria', 'Marie-Alice', 'Mark_-_Natural_Conversations', 'Mark_-_Robust_Dependable_and_Engaging', 'Mark_-_calm_and_wise_teacher', 'Marques_-_Young_and_Wary', 'Marshal_-_Grumpy_Sourpuss', 'Marshal_-_New_Jersey_Male', 'Martas', 'Mary_-_soft_and_warm', 'Matilda', 'Mats', 'Matt', 'Matt_Snowden', 'Matthew_-_calm_and_peaceful', 'Matthew_Wayne_-_Natural_calm_steady', 'Max_-_fast_friendly_and_direct', 'Maxwell_-_deep_and_dramatic', 'Maya', 'Meera', 'Melissa', 'Mellow_Matt', 'Merlin', 'Merlin_the_Wizard_Protector_of_King_Arthur', 'Mia_-_Clear_Smooth_Professional', 'Mia_-_Old_And_Confident_', 'Micah', 'Michael_', 'Michael_-_A_narrator_with_a_buttery-smooth_deep_voice', 'Michael_-_Confident', 'Michael_-_Excited_and_Ready_to_Speak', 'Michael_Filce', 'Michela', 'Middle_age_Southern_Male', 'Mike_Adams_-_All_things_space', 'Mike_G', 'Milo', 'Mine', 'Minerva_-_Fantasy_Professor', 'Mira_Gold_-_Dystopian', 'Miranda', 'Mirilene', 'Misti_-_English_Technology_Virtual_Training_Teacher', 'Mistress_Regina', 'Modavian_-_Dignified_Experienced_Authoritative', 'Moe', 'Mohammed', 'Mohanapriya', 'Monotone_Mike', 'Mora_of_Maragall_-_Resilient_Compassionate_Inspiring', 'Mouse', 'Mr_Clem', 'Mr_President_-_Strong_Fast_and_Impactful', 'Mrs_Novella_Main_Voice_-_Althea_Female_Young_European', 'Mwika_Kayange', 'Myriam_-_sweet_Teen_Girl', 'NEW_AMREEN', 'Nadya', 'Naina_-_Sophisticated_Indian_Girl', 'Nala_-_African_Female', 'Narrador_-_documentarios', 'Natalie_-_Posh_British', 'Natasha_-_Gentle_Meditation_', 'Natasha_-_Valley_girl', 'Nate_the_Great_-_American_Male', 'Neha', 'Neil', 'Neil_-_calm_and_deep', 'Neville', 'Nia_-_Black_Female', 'Niall_-_dramatic_male', 'Nichalia_Schwartz_-_Gentle_Kind_Sweet_GenAm', 'Nicholas_-_Raspy_Mature', 'Nickrad_', 'Nicola', 'Nicoletta', 'Nicolette_-_Strong_and_Stern', 'Nicolette_-_Young_Woman_Clear_Accented', 'Nigel_-_classic', 'Nigel_J.', 'Noah_-_The_stoic_narrator', 'Nolan_-_Emotive_and_Smooth', 'Northern_Irish_Peter', 'Northern_Terry', 'ONeal', 'Ocean_•_Monotonous_Voice', 'Oi', 'Okole', 'Old_Osirion_Woman_-_Timeless_Mystical_Nurturing', 'Older_British_gangster_-_Gravelly_and_Rough', 'Oliver_-_Documentary_Narration', 'Olivia-_sweet_and_soft', 'Omari_African_Voice_VERY_foreign_sounding.', 'Ophelia_Rose', 'Opsy', 'Oscar', 'Outstanding_for_Side_Character', 'Oxley_-_Evil_Character', 'Pablo_Marshal', 'Panda_Montana', 'Patino_-_Columbian_Spanish', 'Patrick_International', 'Patsy_Dahling', 'Paul_Martin', 'Pedro_Costa', 'Penelope', 'Persephone_-_lively', 'Peter', 'Peter_-_annoyingly_pitchy_and_enforcing', 'Peter_Owen_-_non-fiction_audiobooks_and_factual_VO', 'Philemon_-_serious_old_scientist', 'Pilar_-_Young_and_Cheerful', 'Piper', 'Pirate_Marshal', 'Pop', 'Pratheep_Tharan', 'Pro_Narrator_-_Convincing_story_teller', 'Prometheus', 'Queen_Rosamund_-_British_Older_Woman', 'Rachel', 'Rachel_M_-_Pro_British_Radio_Presenter_', 'Rachel__McGrath', 'Raju_-_Relatable_Indian_Voice', 'Rakhat_Eje_', 'Ralf_Eisend', 'Rama_-_wise_and_philosophical_sage', 'Randell-_Glone_Rekia', 'Randolph_-_Trustworthy_and_wise', 'Red_-_Dynamic_Expressive_and_Invigorating', 'Reginald_-_intense_villain', 'Researcher_-_Nerdy_and_Hesitant', 'Rhett_Sutton', 'Rhys_--_Sexy_British_Twink', 'Rich_Baritone_American_Radio_Announcer', 'Richard-2', 'Rike_Fischer', 'Road_Dawg__', 'Rob', 'Rob_-_confident_and_formal', 'Robert-__British_Narrator', 'Roberto_Riva', 'Ron_', 'Ron_-_Older_American_Story_Teller', 'Ruhaan_-_Clean_narration_voice', 'Rupert_-_British_60_years_old', 'Russell_-_Dramatic_British_TV', 'Ruth_-_grandmother_storyteller', 'Ryan_-_subtle_accent_and_deep_timbre', 'Ryan_Kurk', 'Ryan_Quin', 'Sagar_-_Voice_of_India', 'Sahara_-_Soothing_Meditation-Hypnosis-Romance', 'Sally', 'Sam_-_Chill_Southern_California_Male', 'Sam_-_Slight_Welsh_Accent', 'Sam_Bragg', 'Samantha_', 'Samantha_Narrations', 'Sanjay_-_profound_and_deep_', 'Sanna_Hartfield_Beta_1.0', 'Saphira_-_Teen_and_Nerdy', 'Sara', 'Sarcini_-_Snarky_Quick-witted_Unapologetic', 'Sasha_-_Soothing_and_Chill', 'Sayn_Awal', 'Scary_Story_', 'Scoobie_-_American_Male_enthusiastic_sharp_smart', 'Scot_Combs_Narration', 'Scott', 'Scott_Woodworth', 'Sean_John_-_Top_Quality', 'Sebastion-Young_uncertain._', 'Selena_-_Introspective_Intuitive', 'Seth_-_Vibrant_Engaging_and_Genuine', 'Sevan_Bomar_-_Black_Motivational_Speaker', 'Sexy_Female_Villain_Voice', 'Sgt_Hayes_-_Authority_Deep_Masculine', 'Shannon_B_-_Sad_Emo_Teenage_Girl', 'Shannon_B_-_Warm_Southern_Woman', 'Shayne_-_Narrator_RJ_Voice', 'Sheba', 'Shelby', 'Shelby_-_Erratic_and_Confident', 'Shells', 'Sheps_Rocky', 'Shrey_-_Deep__Engaging_', 'Simba', 'Sir_Linus_Warmheart', 'Sita_2', 'Soft_Daria_-_Meditation', 'Soft_Demure_Garden_Voice', 'Soft_young_male_voice', 'Sohaib_Jasra', 'Soothing_Narrator', 'Sophia_-_Female_UK_Accent_-_Audiobooks_E-learning_Courses_Adverts', 'Southern_Stewart', 'Sparrow_Lee_', 'Stanley', 'Stanley_', 'Starina_Jr_Pro', 'Stephen', 'Stephen_-_Narrator', 'Steve_-_Australian_Male_', 'Steve_-_British_-_Clean_Smooth_Professional', 'Steve_V', 'Steven', 'Steven_-_Business_Book_Narrator', 'Stuart_-_Energetic_and_enthusiastic', 'Summer', 'Sylvia_-_confident_sensible_wise', 'THE_PROTOTYPE_LIVE_aka_Ana_Daugherty', 'Tara_-_Conversational_Expressive_Voice', 'Tarun_C._Dhanraj_-_Rich_Warm_and_friendly', 'Tass', 'Tatiana', 'Taylor_Andrew_Commercial-Driven', 'Tere', 'Terry_Blackburn_', 'Tessa', 'Thalia_-_Mysteriously_Captivating', 'Thalias_Engine_-_Mysteriously_Captivating', 'The_Great_Conversationalist_', 'Theodore_-_Oldschool_Cool', 'Thomas_-_Measured_Clear_Informative_', 'Thomas_Candia', 'Thomas_Fischer_-_Authentic_German_Accent', 'Tiffany', 'Tim_Rooney', 'Tira_Shabbar_-_Spirited_Irreverent_Young-at-Heart', 'Tommy_-_Teen_Cool__Nonchalant_', 'Tony_-_King_of_New_York', 'Tony_-_middle_aged_male_Northern_English_native_accent', 'Tyler_', 'Tyrell', 'Tyrone', 'UK_Teen_-_Black_man_Marquess_Germain', 'Val_3.0', 'Valentino', 'Valentyna_-_Soft_and_calm', 'Veda', 'Very_Vlad_-_Soviet_Comrade', 'Victor_-_the_motivational_speaker', 'Victoria_-_classy_and_mature', 'Victorian_-_a_lady_of_quality', 'Victorino_-_Deep', 'Vidhi_-_Young__Bold', 'Vidura', 'Vikrant_-_Indian_', 'Vincent_Sparks_-_Deep_American_Voice', 'Vivian_-_knowledgeable_voice__', 'Von_-_Perfect_Storytelling_Clean_Realistic', 'W._Storytime_Oxley', 'Whimsy_-_Kids_Cartoon_Character', 'Whispering_Joe_-_a_storytelling_whisper_ASMR_British_RP_male', 'Wildebeest', 'Will_-_Young_Australian_Male', 'William_Shanks', 'Xanthippe_Abelló_-_Exuberant_Inquisitive_Unconventional', 'Yagiz', 'Yaisa', 'Yash_A_Malhotra_-_Warm__Friendly', 'Yomiee', 'Young_Jamal', 'Yuan_-_emotional_artist_poem_romantic_sensible', 'Zach_-_Storyteller_Narrator_Audiobooks_Podcasts', 'Zakirah_-_Chill_and_Calm', 'Zara_-_Soft_and_Serene_Indian_Voice', 'Zoe', 'Zoe_-_crisp_and_strong', '_Ethan_-_Calm_Intense_and_Compelling', '_Haseeb_-_Canadian_Presentation', '_Louis_Bloom', 'glenda-_soft_and_friendly', 'wise-woman']


# Función para cargar nombres en el dropdown según el idioma seleccionado
"""def load_names(selected_language):
    print(f"Idioma seleccionado: {selected_language}")
    if selected_language == "Arabic":
        return gr.update(choices=arabic_names, value=arabic_names[0] if arabic_names else None)
    elif selected_language == "Bulgarian":
        return gr.update(choices=bulgarian_names, value=bulgarian_names[0] if bulgarian_names else None)
    elif selected_language == "Chinese":
        return gr.update(choices=chinese_names, value=chinese_names[0] if chinese_names else None)
    else:
        return gr.update(choices=[], value=None)"""

def load_names(selected_language):
    print(f"Idioma seleccionado: {selected_language}")

    # Diccionario para mapear idiomas a listas de nombres
    nombres_por_idioma = {
        "Voices Legacy": show_legacy,
        "Arabic": arabic_names,
        "Bulgarian": bulgarian_names,
        "Chinese": chinese_names,
        "Croatian": croatian_names,
        "Czech": czech_names_names,  # Corregido el nombre de la variable
        "Danish": danish_names,
        "Dutch": dutch_names,
        "Finnish": finnish_names,
        "French": french_names,
        "German": german_names,
        "Greek": greek_names,
        "Hindi": hindi_names,
        "Hungarian": hungarian_names,
        "Indonesian": indonesian_names,
        "Italian": italian_names,
        "Japanese": japanese_names,
        "Korean": korean_names,
        "Norwegian": norwegian_names,
        "Polish": polish_names,
        "Portuguese": portuguese_names,
        "Romanian": romanian_names,
        "Russian": russian_names,
        "Slovak": slovak_names,
        "Spanish": spanish_names,
        "Swedish": swedish_names,
        "Tamil": tamil_names,
        "Turkish": turkish_names,
        "Ukrainian": ukrainian_names,
        "Vietnamese": vietnamese_names,
        "English-1": english1_names,
        "English-2": english2_names
    }

    nombres = nombres_por_idioma.get(selected_language, []) # Obtener la lista de nombres o una lista vacía si no se encuentra
    
    return gr.update(choices=nombres, value=nombres[0] if nombres else None)

def load_text(selected_name, selected_language):
    # Mapeo de idiomas a directorios
    directorios_por_idioma = {
        "Voices Legacy": "show_legacy",
        "Arabic": "ar",
        "Bulgarian": "bg",
        "Chinese": "zh",
        "Croatian": "hr",
        "Czech": "cs",
        "Danish": "da",
        "Dutch": "nl",
        "English-1": "en1",  # Asegúrate de que estos nombres coincidan
        "English-2": "en2",  # con las claves de leng_and_ids
        "Finnish": "fi",
        "French": "fr",
        "German": "de",
        "Greek": "el",
        "Hindi": "hi",
        "Hungarian": "hu",
        "Indonesian": "id",
        "Italian": "it",
        "Japanese": "ja",
        "Korean": "ko",
        "Norwegian": "no",
        "Polish": "pl",
        "Portuguese": "pt",
        "Romanian": "ro",
        "Russian": "ru",
        "Slovak": "sk",
        "Spanish": "es",
        "Swedish": "sv",
        "Tamil": "ta",
        "Turkish": "tr",
        "Ukrainian": "uk",
        "Vietnamese": "vi"
    }
    dir_idioma = directorios_por_idioma.get(selected_language)
    if not dir_idioma:
        return "" # Manejar el caso en que el idioma no tenga directorio

    ruta_archivo = f"/tmp/Voice/{dir_idioma}/{selected_name}.txt"

    try:
        with open(ruta_archivo, "r", encoding="utf-8") as f:
            texto = f.read()
        return texto
    except FileNotFoundError:
        return f"Archivo no encontrado: {ruta_archivo}"  # Mostrar un mensaje de error si el archivo no existe


def load_text_langs(selected_language):
    # Mapeo de idiomas a directorios
    directorios_por_idioma = {
        "Voices Legacy": "show_legacy",
        "Arabic": "ar",
        "Bulgarian": "bg",
        "Chinese": "zh",
        "Croatian": "hr",
        "Czech": "cs",
        "Danish": "da",
        "Dutch": "nl",
        "English-1": "en1",  # Asegúrate de que estos nombres coincidan
        "English-2": "en2",  # con las claves de leng_and_ids
        "Finnish": "fi",
        "French": "fr",
        "German": "de",
        "Greek": "el",
        "Hindi": "hi",
        "Hungarian": "hu",
        "Indonesian": "id",
        "Italian": "it",
        "Japanese": "ja",
        "Korean": "ko",
        "Norwegian": "no",
        "Polish": "pl",
        "Portuguese": "pt",
        "Romanian": "ro",
        "Russian": "ru",
        "Slovak": "sk",
        "Spanish": "es",
        "Swedish": "sv",
        "Tamil": "ta",
        "Turkish": "tr",
        "Ukrainian": "uk",
        "Vietnamese": "vi"
    }
    dir_idioma = directorios_por_idioma.get(selected_language)

    return dir_idioma

# Función para cargar el texto y el audio de referencia
def update_reference_info(speaker_reference_audio, selected_language):
    # Actualizar el texto de referencia
    #print(speaker_reference_audio, load_text_langs(selected_language))
    text_info = load_text(speaker_reference_audio, selected_language)
    
    # Generar la ruta del archivo de audio
    audio_path = f"/tmp/Voice/{load_text_langs(selected_language)}/{speaker_reference_audio}.mp3"
    
    # Retornar ambos valores
    return text_info, audio_path

def load_params_tts(out_path,version):
    
    out_path = Path(out_path)

    # base_model_path = Path.cwd() / "models" / version 

    # if not base_model_path.exists():
    #     return "Base model not found !","","",""

    ready_model_path = out_path / "ready" 

    vocab_path =  ready_model_path / "vocab.json"
    config_path = ready_model_path / "config.json"
    speaker_path =  ready_model_path / "speakers_xtts.pth"
    reference_path  = ready_model_path / "reference.wav"

    model_path = ready_model_path / "model.pth"

    if not model_path.exists():
        model_path = ready_model_path / "unoptimize_model.pth"
        if not model_path.exists():
          return "Params for TTS not found", "", "", ""         

    return "Params for TTS loaded", model_path, config_path, vocab_path,speaker_path, reference_path
     
def upload_audio(audio, current_path):
    if audio is None:
        return current_path

    upload_dir = "speaker_reference_audio"
    os.makedirs(upload_dir, exist_ok=True)

    if isinstance(audio, str):  # If it's a string (filepath)
        audio_path = audio  # Use the provided path directly
        if not os.path.exists(audio_path):  # Check if the file exists
            print(f"Error: File not found at {audio_path}")
            return current_path  # Or return an error message
        return audio_path     # Return the valid path


    elif hasattr(audio, "name"):  # If it's an UploadedFile object
        audio_path = os.path.join(upload_dir, audio.name)
        try:
            with open(audio_path, "wb") as f:
                f.write(audio.read())
            return audio_path
        except Exception as e:
            print(f"Error saving uploaded audio: {e}")
            return current_path
    else:
         print("The reference audio input format is not recognized")  
         return current_path


if __name__ == "__main__":

    parser = argparse.ArgumentParser(
        description="""XTTS fine-tuning demo\n\n"""
        """
        Example runs:
        python3 TTS/demos/xtts_ft_demo/xtts_demo.py --port 
        """,
        formatter_class=argparse.RawTextHelpFormatter,
    )
    parser.add_argument(
        "--whisper_model",
        type=str,
        help="Name of the whisper model selected by default (Optional) Choices are: ['large-v3','large-v2', 'large', 'medium', 'small']   Default Value: 'large-v3'",
        default="large-v3",
    )
    parser.add_argument(
        "--audio_folder_path",
        type=str,
        help="Path to the folder with audio files (optional)",
        default="",
    )
    parser.add_argument(
        "--share",
        action="store_true",
        default=False,
        help="Enable sharing of the Gradio interface via public link.",
    )
    parser.add_argument(
        "--port",
        type=int,
        help="Port to run the gradio demo. Default: 5003",
        default=5003,
    )
    parser.add_argument(
        "--out_path",
        type=str,
        help="Output path (where data and checkpoints will be saved) Default: output/",
        default=str(Path.cwd() / "train_models"),
    )

    parser.add_argument(
        "--num_epochs",
        type=int,
        help="Number of epochs to train. Default: 6",
        default=6,
    )
    parser.add_argument(
        "--batch_size",
        type=int,
        help="Batch size. Default: 2",
        default=2,
    )
    parser.add_argument(
        "--grad_acumm",
        type=int,
        help="Grad accumulation steps. Default: 1",
        default=1,
    )
    parser.add_argument(
        "--max_audio_length",
        type=int,
        help="Max permitted audio size in seconds. Default: 11",
        default=11,
    )

    args = parser.parse_args()

    language_names = {
    "en": "English",
    "es": "Spanish",
    "fr": "French",
    "de": "German",
    "it": "Italian",
    "pt": "Portuguese",
    "pl": "Polish",
    "tr": "Turkish",
    "ru": "Russian",
    "nl": "Dutch",
    "cs": "Czech",
    "ar": "Arabic",
    "zh": "Chinese",
    "hu": "Hungarian",
    "ko": "Korean",
    "ja": "Japanese",
    }

    with gr.Blocks(title=os.environ.get("APP_NAME", "Gradio")) as demo:
        with gr.Tab("1 - Data processing"):
            out_path = gr.Textbox(
                label="Output path (where data and checkpoints will be saved):",
                value=args.out_path,
            )
            # upload_file = gr.Audio(
            #     sources="upload",
            #     label="Select here the audio files that you want to use for XTTS trainining !",
            #     type="filepath",
            # )
            upload_file = gr.File(
                file_count="multiple",
                label="Select here the audio files that you want to use for XTTS trainining (Supported formats: wav, mp3, and flac)",
            )
            
            audio_folder_path = gr.Textbox(
                label="Path to the folder with audio files (optional):",
                value=args.audio_folder_path,
            )

            whisper_model = gr.Dropdown(
                label="Whisper Model",
                value=args.whisper_model,
                choices=[
                    "large-v3",
                    "large-v2",
                    "large",
                    "medium",
                    "small"
                ],
            )

            lang = gr.Dropdown(
                label="Dataset Language",
                value="en",
                choices=list(zip(language_names.values(), language_names.keys()))
            )
            progress_data = gr.Label(
                label="Progress:"
            )
            # demo.load(read_logs, None, logs, every=1)

            prompt_compute_btn = gr.Button(value="Step 1 - Create dataset")
        
            def preprocess_dataset(audio_path, audio_folder_path, language, whisper_model, out_path, train_csv, eval_csv, progress=gr.Progress(track_tqdm=True)):
                clear_gpu_cache()
            
                train_csv = ""
                eval_csv = ""
            
                out_path = os.path.join(out_path, "dataset")
                os.makedirs(out_path, exist_ok=True)
            
                if audio_folder_path:
                    audio_files = list(list_audios(audio_folder_path))
                else:
                    audio_files = audio_path
            
                if not audio_files:
                    return "No audio files found! Please provide files via Gradio or specify a folder path.", "", ""
                else:
                    try:
                        # Loading Whisper
                        device = "cuda" if torch.cuda.is_available() else "cpu" 
                        
                        # Detect compute type 
                        if torch.cuda.is_available():
                            compute_type = "float16"
                        else:
                            compute_type = "float32"
                        
                        asr_model = WhisperModel(whisper_model, device=device, compute_type=compute_type)
                        train_meta, eval_meta, audio_total_size = format_audio_list(audio_files, asr_model=asr_model, target_language=language, out_path=out_path, gradio_progress=progress)
                    except:
                        traceback.print_exc()
                        error = traceback.format_exc()
                        return f"The data processing was interrupted due an error !! Please check the console to verify the full error message! \n Error summary: {error}", "", ""
            
                # clear_gpu_cache()
            
                # if audio total len is less than 2 minutes raise an error
                if audio_total_size < 120:
                    message = "The sum of the duration of the audios that you provided should be at least 2 minutes!"
                    print(message)
                    return message, "", ""
            
                print("Dataset Processed!")
                return "Dataset Processed!", train_meta, eval_meta


        with gr.Tab("2 - XTTS Encoder"):
            load_params_btn = gr.Button(value="Load Params from output folder")
            version = gr.Dropdown(
                label="XTTS base version",
                value="v2.0.2",
                choices=[
                    "v2.0.3",
                    "v2.0.2",
                    "v2.0.1",
                    "v2.0.0",
                    "main"
                ],
            )
            train_csv = gr.Textbox(
                label="Train CSV:",
            )
            eval_csv = gr.Textbox(
                label="Eval CSV:",
            )
            custom_model = gr.Textbox(
                label="(Optional) Custom model.pth file , leave blank if you want to use the base file.",
                value="",
            )
            num_epochs =  gr.Slider(
                label="Number of epochs:",
                minimum=1,
                maximum=100,
                step=1,
                value=args.num_epochs,
            )
            batch_size = gr.Slider(
                label="Batch size:",
                minimum=2,
                maximum=512,
                step=1,
                value=args.batch_size,
            )
            grad_acumm = gr.Slider(
                label="Grad accumulation steps:",
                minimum=2,
                maximum=128,
                step=1,
                value=args.grad_acumm,
            )
            max_audio_length = gr.Slider(
                label="Max permitted audio size in seconds:",
                minimum=2,
                maximum=20,
                step=1,
                value=args.max_audio_length,
            )
            clear_train_data = gr.Dropdown(
                label="Clear train data, you will delete selected folder, after optimizing",
                value="none",
                choices=[
                    "none",
                    "run",
                    "dataset",
                    "all"
                ])
            
            progress_train = gr.Label(
                label="Progress:"
            )

            # demo.load(read_logs, None, logs_tts_train, every=1)
            train_btn = gr.Button(value="Step 2 - Run the training")
            optimize_model_btn = gr.Button(value="Step 2.5 - Optimize the model")
            
            import os
            import shutil
            from pathlib import Path
            import traceback
            
            def train_model(custom_model, version, language, train_csv, eval_csv, num_epochs, batch_size, grad_acumm, output_path, max_audio_length):
                clear_gpu_cache()
          
                # Check if `custom_model` is a URL and download it if true.
                if custom_model.startswith("http"):
                    print("Downloading custom model from URL...")
                    custom_model = download_file(custom_model, "custom_model.pth")
                    if not custom_model:
                        return "Failed to download the custom model.", "", "", "", ""
            
                run_dir = Path(output_path) / "run"
            
                # Remove train dir
                if run_dir.exists():
                    shutil.rmtree(run_dir)
                
                # Check if the dataset language matches the language you specified 
                lang_file_path = Path(output_path) / "dataset" / "lang.txt"
            
                # Check if lang.txt already exists and contains a different language
                current_language = None
                if lang_file_path.exists():
                    with open(lang_file_path, 'r', encoding='utf-8') as existing_lang_file:
                        current_language = existing_lang_file.read().strip()
                        if current_language != language:
                            print("The language that was prepared for the dataset does not match the specified language. Change the language to the one specified in the dataset")
                            language = current_language
                        
                if not train_csv or not eval_csv:
                    return "You need to run the data processing step or manually set `Train CSV` and `Eval CSV` fields !", "", "", "", ""
                try:
                    # convert seconds to waveform frames
                    max_audio_length = int(max_audio_length * 22050)
                    speaker_xtts_path, config_path, original_xtts_checkpoint, vocab_file, exp_path, speaker_wav = train_gpt(custom_model, version, language, num_epochs, batch_size, grad_acumm, train_csv, eval_csv, output_path=output_path, max_audio_length=max_audio_length)
                except:
                    traceback.print_exc()
                    error = traceback.format_exc()
                    return f"The training was interrupted due to an error !! Please check the console to check the full error message! \n Error summary: {error}", "", "", "", ""
            
                ready_dir = Path(output_path) / "ready"
            
                ft_xtts_checkpoint = os.path.join(exp_path, "best_model.pth")
            
                shutil.copy(ft_xtts_checkpoint, ready_dir / "unoptimize_model.pth")
            
                ft_xtts_checkpoint = os.path.join(ready_dir, "unoptimize_model.pth")
            
                # Move reference audio to output folder and rename it
                speaker_reference_path = Path(speaker_wav)
                speaker_reference_new_path = ready_dir / "reference.wav"
                shutil.copy(speaker_reference_path, speaker_reference_new_path)
            
                print("Model training done!")
                return "Model training done!", config_path, vocab_file, ft_xtts_checkpoint, speaker_xtts_path, speaker_reference_new_path

            def optimize_model(out_path, clear_train_data):
                # print(out_path)
                out_path = Path(out_path)  # Ensure that out_path is a Path object.
            
                ready_dir = out_path / "ready"
                run_dir = out_path / "run"
                dataset_dir = out_path / "dataset"
            
                # Clear specified training data directories.
                if clear_train_data in {"run", "all"} and run_dir.exists():
                    try:
                        shutil.rmtree(run_dir)
                    except PermissionError as e:
                        print(f"An error occurred while deleting {run_dir}: {e}")
            
                if clear_train_data in {"dataset", "all"} and dataset_dir.exists():
                    try:
                        shutil.rmtree(dataset_dir)
                    except PermissionError as e:
                        print(f"An error occurred while deleting {dataset_dir}: {e}")
            
                # Get full path to model
                model_path = ready_dir / "unoptimize_model.pth"

                if not model_path.is_file():
                    return "Unoptimized model not found in ready folder", ""
            
                # Load the checkpoint and remove unnecessary parts.
                checkpoint = torch.load(model_path, map_location=torch.device("cpu"))
                del checkpoint["optimizer"]

                for key in list(checkpoint["model"].keys()):
                    if "dvae" in key:
                        del checkpoint["model"][key]

                # Make sure out_path is a Path object or convert it to Path
                os.remove(model_path)

                  # Save the optimized model.
                optimized_model_file_name="model.pth"
                optimized_model=ready_dir/optimized_model_file_name
            
                torch.save(checkpoint, optimized_model)
                ft_xtts_checkpoint=str(optimized_model)

                clear_gpu_cache()
        
                return f"Model optimized and saved at {ft_xtts_checkpoint}!", ft_xtts_checkpoint

            def load_params(out_path):
                path_output = Path(out_path)
                
                dataset_path = path_output / "dataset"

                if not dataset_path.exists():
                    return "The output folder does not exist!", "", ""

                eval_train = dataset_path / "metadata_train.csv"
                eval_csv = dataset_path / "metadata_eval.csv"

                # Write the target language to lang.txt in the output directory
                lang_file_path =  dataset_path / "lang.txt"

                # Check if lang.txt already exists and contains a different language
                current_language = None
                if os.path.exists(lang_file_path):
                    with open(lang_file_path, 'r', encoding='utf-8') as existing_lang_file:
                        current_language = existing_lang_file.read().strip()

                clear_gpu_cache()

                print(current_language)
                return "The data has been updated", eval_train, eval_csv, current_language

        
        with gr.Tab("3 - Inference"):
            with gr.Row():
                with gr.Column() as col1:
                    load_params_tts_btn = gr.Button(value="Load params for TTS from output folder")
                    xtts_checkpoint = gr.Textbox(
                        label="XTTS checkpoint path:",
                        value="/content/xtts-webui/model/model.pth",
                    )
                    xtts_config = gr.Textbox(
                        label="XTTS config path:",
                        value="/content/xtts-webui/model/config.json",
                    )

                    xtts_vocab = gr.Textbox(
                        label="XTTS vocab path:",
                        value="/content/xtts-webui/model/vocab.json",
                    )
                    xtts_speaker = gr.Textbox(
                        label="XTTS speaker path:",
                        value="/content/xtts-webui/model/speakers_xtts.pth",
                    )
                    progress_load = gr.Label(
                        label="Progress:"
                    )
                    load_btn = gr.Button(value="Step 3 - Load XTTS model")

                with gr.Column() as col2:
                    speaker_reference_audio = gr.Textbox(
                        label="Speaker Reference Audio:",  # More descriptive label
                        value="/content/xtts-webui/model/reference.wav",
                        interactive=True,  # Allow users to edit path manually
                    )
                    speaker_audio_upload = gr.Audio(
                        label="Upload Speaker Audio (wav, mp3, flac)",
                        type="filepath",  # Just keep type="filepath"
                    )
                    tts_language = gr.Dropdown(
                        label="Language",
                        value="pt",
                        choices=list(zip(language_names.values(), language_names.keys()))
                    )
                    tts_text = gr.Textbox(
                        label="Input Text.",
                        value="This model sounds really good and above all, it's reasonably fast.",
                    )
                    with gr.Accordion("Advanced settings", open=False) as acr:
                        temperature = gr.Slider(
                            label="temperature",
                            minimum=0,
                            maximum=1,
                            step=0.05,
                            value=0.75,
                        )
                        length_penalty  = gr.Slider(
                            label="length_penalty",
                            minimum=-10.0,
                            maximum=10.0,
                            step=0.5,
                            value=1,
                        )
                        repetition_penalty = gr.Slider(
                            label="repetition penalty",
                            minimum=1,
                            maximum=10,
                            step=0.5,
                            value=5,
                        )
                        top_k = gr.Slider(
                            label="top_k",
                            minimum=1,
                            maximum=100,
                            step=1,
                            value=50,
                        )
                        top_p = gr.Slider(
                            label="top_p",
                            minimum=0,
                            maximum=1,
                            step=0.05,
                            value=0.85,
                        )
                        sentence_split = gr.Checkbox(
                            label="Enable text splitting",
                            value=True,
                        )
                        use_config = gr.Checkbox(
                            label="Use Inference settings from config, if disabled use the settings above",
                            value=False,
                        )
                    tts_btn = gr.Button(value="Step 4 - Inference")
                    

                with gr.Column() as col3:
                    progress_gen = gr.Label(
                        label="Progress:"
                    )
                    tts_output_audio = gr.Audio(label="Generated Audio.")
                    reference_audio = gr.Audio(label="Reference audio used.")


        with gr.Tab("2161 Voices"):
            with gr.Row():
                with gr.Column() as col1:
                    #load_params_tts_btn = gr.Button(value="Load params for TTS from output folder")
                    xtts_checkpoint0 = gr.Textbox(
                        label="XTTS checkpoint path:",
                        value="/content/xtts-webui/model/model.pth",
                    )
                    xtts_config0 = gr.Textbox(
                        label="XTTS config path:",
                        value="/content/xtts-webui/model/config.json",
                    )

                    xtts_vocab0 = gr.Textbox(
                        label="XTTS vocab path:",
                        value="/content/xtts-webui/model/vocab.json",
                    )
                    xtts_speaker0 = gr.Textbox(
                        label="XTTS speaker path:",
                        value="/content/xtts-webui/model/speakers_xtts.pth",
                    )
                    progress_load0 = gr.Label(
                        label="Progress:"
                    )
                    load_btn0 = gr.Button(value="Load model")

                with gr.Column() as col2:

                    # Dropdown de selección de idioma
                    selected_language0 = gr.Dropdown(list(leng_and_ids.keys()), value="Select language", label="Language reference audio")
                    speaker_reference_audio0 = gr.Dropdown(interactive=True, allow_custom_value=True, label="Speaker reference audio:")
                    text_output0 = gr.Textbox(label="Audio reference information")

                    tts_language0 = gr.Dropdown(
                        label="Language",
                        value="en",
                        choices=list(zip(language_names.values(), language_names.keys()))
                    )
                    tts_text0 = gr.Textbox(
                        label="Input Text.",
                        value="This model sounds really good and above all, it's reasonably fast.",
                    )
                    with gr.Accordion("Advanced settings", open=False) as acr:
                        temperature0 = gr.Slider(
                            label="temperature",
                            minimum=0,
                            maximum=1,
                            step=0.05,
                            value=0.75,
                        )
                        length_penalty0  = gr.Slider(
                            label="length_penalty",
                            minimum=-10.0,
                            maximum=10.0,
                            step=0.5,
                            value=1,
                        )
                        repetition_penalty0 = gr.Slider(
                            label="repetition penalty",
                            minimum=1,
                            maximum=10,
                            step=0.5,
                            value=5,
                        )
                        top_k0 = gr.Slider(
                            label="top_k",
                            minimum=1,
                            maximum=100,
                            step=1,
                            value=50,
                        )
                        top_p0 = gr.Slider(
                            label="top_p",
                            minimum=0,
                            maximum=1,
                            step=0.05,
                            value=0.85,
                        )
                        sentence_split0 = gr.Checkbox(
                            label="Enable text splitting",
                            value=True,
                        )
                        use_config0 = gr.Checkbox(
                            label="Use Inference settings from config, if disabled use the settings above",
                            value=False,
                        )
                    tts_btn0 = gr.Button(value="Generate")
                  

                    selected_language0.change(load_names, inputs=selected_language0, outputs=speaker_reference_audio0)
                    #speaker_reference_audio0.change(load_text, inputs=[speaker_reference_audio0, selected_language0], outputs=text_output0)
                    

                with gr.Column() as col3:
                    progress_gen0 = gr.Label(
                        label="Progress:"
                    )
                    tts_output_audio0 = gr.Audio(label="Generated Audio.")
                    reference_audio0 = gr.Audio(label="Reference audio used.")
                    speaker_reference_audio0.change(update_reference_info, inputs=[speaker_reference_audio0, selected_language0], outputs=[text_output0, reference_audio0])





            prompt_compute_btn.click(
                fn=preprocess_dataset,
                inputs=[
                    upload_file,
                    audio_folder_path,
                    lang,
                    whisper_model,
                    out_path,
                    train_csv,
                    eval_csv
                ],
                outputs=[
                    progress_data,
                    train_csv,
                    eval_csv,
                ],
            )


            load_params_btn.click(
                fn=load_params,
                inputs=[out_path],
                outputs=[
                    progress_train,
                    train_csv,
                    eval_csv,
                    lang
                ]
            )


            train_btn.click(
                fn=train_model,
                inputs=[
                    custom_model,
                    version,
                    lang,
                    train_csv,
                    eval_csv,
                    num_epochs,
                    batch_size,
                    grad_acumm,
                    out_path,
                    max_audio_length,
                ],
                outputs=[progress_train, xtts_config, xtts_vocab, xtts_checkpoint,xtts_speaker, speaker_reference_audio],
            )

            optimize_model_btn.click(
                fn=optimize_model,
                inputs=[
                    out_path,
                    clear_train_data
                ],
                outputs=[progress_train,xtts_checkpoint0],
            )
            

            load_btn0.click(
                fn=load_model,
                inputs=[
                    xtts_checkpoint0,
                    xtts_config0,
                    xtts_vocab0,
                    xtts_speaker0
                ],
                outputs=[progress_load0],
            )

            tts_btn0.click(
                fn=run_tts0,
                inputs=[
                    selected_language0,
                    tts_language0,
                    tts_text0,
                    speaker_reference_audio0,
                    temperature0,
                    length_penalty0,
                    repetition_penalty0,
                    top_k0,
                    top_p0,
                    sentence_split0,
                    use_config0
                ],
                outputs=[progress_gen0, tts_output_audio0,reference_audio0],
            )

            load_btn.click(
                fn=load_model,
                inputs=[
                    xtts_checkpoint,
                    xtts_config,
                    xtts_vocab,
                    xtts_speaker
                ],
                outputs=[progress_load],
            )



            tts_btn.click(
                fn=run_tts,
                inputs=[
                    tts_language,
                    tts_text,
                    speaker_reference_audio,
                    temperature,
                    length_penalty,
                    repetition_penalty,
                    top_k,
                    top_p,
                    sentence_split,
                    use_config
                ],
                outputs=[progress_gen, tts_output_audio,reference_audio],
            )

            load_params_tts_btn.click(
                fn=load_params_tts,
                inputs=[
                    out_path,
                    version
                    ],
                outputs=[progress_load,xtts_checkpoint,xtts_config,xtts_vocab,xtts_speaker,speaker_reference_audio],
            )

            speaker_audio_upload.upload(
                upload_audio,
                inputs=[speaker_audio_upload, speaker_reference_audio],
                outputs=speaker_reference_audio,
            )


             

    demo.launch(
        share=args.share,
        debug=False,
        server_port=args.port,
        # inweb=True,
        # server_name="localhost"
    )