app.py

#%cd SoniTranslate
# vc infer pipe 161 np.int
import os

os.system("pip install -r requirements_colab.txt")
os.system("pip install -r requirements_extra.txt")

os.system('apt install git-lfs')
os.system('git lfs install')
os.system('apt -y install -qq aria2')
os.system('aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt -d . -o hubert_base.pt')
os.system('wget https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/rmvpe.pt')

import numpy as np
import gradio as gr
import whisperx
from whisperx.utils import LANGUAGES as LANG_TRANSCRIPT
from whisperx.alignment import DEFAULT_ALIGN_MODELS_TORCH as DAMT, DEFAULT_ALIGN_MODELS_HF as DAMHF
from IPython.utils import capture
import torch
from gtts import gTTS
import librosa
import edge_tts
import asyncio
import gc
from pydub import AudioSegment
from tqdm import tqdm
from deep_translator import GoogleTranslator
import os
from soni_translate.audio_segments import create_translated_audio
from soni_translate.text_to_speech import make_voice_gradio
from soni_translate.translate_segments import translate_text
import time
import shutil
from urllib.parse import unquote
import zipfile
import rarfile



title = "<center><strong><font size='7'>📽️ SoniTranslate 🈷️</font></strong></center>"

news = """ ## 📖 News
        🔥 2023/07/26: New UI and add mix options.

        🔥 2023/07/27: Fix some bug processing the video and audio.

        🔥 2023/08/01: Add options for use RVC models.

        🔥 2023/08/02: Added support for Arabic, Czech, Danish, Finnish, Greek, Hebrew, Hungarian, Korean, Persian, Polish, Russian, Turkish, Urdu, Hindi, and Vietnamese languages. 🌐

        🔥 2023/08/03: Changed default options and added directory view of downloads..
        """

description = """
### 🎥 **Translate videos easily with SoniTranslate!** 📽️

Upload a video or provide a video link. Limitation: 10 seconds for CPU, but no restrictions with a GPU.

For faster results and no duration limits, try the Colab notebook with a GPU:
[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://github.com/R3gm/SoniTranslate/blob/main/SoniTranslate_Colab.ipynb)

📽️ **This a demo of SoniTranslate; GitHub repository: [SoniTranslate](https://github.com/R3gm/SoniTranslate)!**

See the tab labeled `Help` for instructions on how to use it. Let's start having fun with video translation! 🚀🎉
"""



tutorial = """

# 🔰 **Instructions for use:**

1. 📤 **Upload a video** on the first tab or 🌐 **use a video link** on the second tab.

2. 🌍 Choose the language in which you want to **translate the video**.

3. 🗣️ Specify the **number of people speaking** in the video and **assign each one a text-to-speech voice** suitable for the translation language.

4. 🚀 Press the '**Translate**' button to obtain the results.


# 🎤 How to Use RVC and RVC2 Voices 🎶

The goal is to apply a RVC (Retrieval-based Voice Conversion) to the generated TTS (Text-to-Speech) 🎙️

1. In the `Custom Voice RVC` tab, download the models you need 📥 You can use links from Hugging Face and Google Drive in formats like zip, pth, or index. You can also download complete HF space repositories, but this option is not very stable 😕

2. Now, go to `Replace voice: TTS to RVC` and check the `enable` box ✅ After this, you can choose the models you want to apply to each TTS speaker 👩‍🦰👨‍🦱👩‍🦳👨‍🦲

3. Adjust the F0 method that will be applied to all RVCs 🎛️

4. Press `APPLY CONFIGURATION` to apply the changes you made 🔄

5. Go back to the video translation tab and click on 'Translate' ▶️ Now, the translation will be done applying the RVCs 🗣️

Tip: You can use `Test RVC` to experiment and find the best TTS or configurations to apply to the RVC 🧪🔍

"""



# Check GPU
if torch.cuda.is_available():
    device = "cuda"
    list_compute_type = ['float16', 'float32']
    compute_type_default = 'float16'
    whisper_model_default = 'large-v2'
else:
    device = "cpu"
    list_compute_type = ['float32']
    compute_type_default = 'float32'
    whisper_model_default = 'medium'
print('Working in: ', device)

list_tts = ['af-ZA-AdriNeural-Female', 'af-ZA-WillemNeural-Male', 'am-ET-AmehaNeural-Male', 'am-ET-MekdesNeural-Female', 'ar-AE-FatimaNeural-Female', 'ar-AE-HamdanNeural-Male', 'ar-BH-AliNeural-Male', 'ar-BH-LailaNeural-Female', 'ar-DZ-AminaNeural-Female', 'ar-DZ-IsmaelNeural-Male', 'ar-EG-SalmaNeural-Female', 'ar-EG-ShakirNeural-Male', 'ar-IQ-BasselNeural-Male', 'ar-IQ-RanaNeural-Female', 'ar-JO-SanaNeural-Female', 'ar-JO-TaimNeural-Male', 'ar-KW-FahedNeural-Male', 'ar-KW-NouraNeural-Female', 'ar-LB-LaylaNeural-Female', 'ar-LB-RamiNeural-Male', 'ar-LY-ImanNeural-Female', 'ar-LY-OmarNeural-Male', 'ar-MA-JamalNeural-Male', 'ar-MA-MounaNeural-Female', 'ar-OM-AbdullahNeural-Male', 'ar-OM-AyshaNeural-Female', 'ar-QA-AmalNeural-Female', 'ar-QA-MoazNeural-Male', 'ar-SA-HamedNeural-Male', 'ar-SA-ZariyahNeural-Female', 'ar-SY-AmanyNeural-Female', 'ar-SY-LaithNeural-Male', 'ar-TN-HediNeural-Male', 'ar-TN-ReemNeural-Female', 'ar-YE-MaryamNeural-Female', 'ar-YE-SalehNeural-Male', 'az-AZ-BabekNeural-Male', 'az-AZ-BanuNeural-Female', 'bg-BG-BorislavNeural-Male', 'bg-BG-KalinaNeural-Female', 'bn-BD-NabanitaNeural-Female', 'bn-BD-PradeepNeural-Male', 'bn-IN-BashkarNeural-Male', 'bn-IN-TanishaaNeural-Female', 'bs-BA-GoranNeural-Male', 'bs-BA-VesnaNeural-Female', 'ca-ES-EnricNeural-Male', 'ca-ES-JoanaNeural-Female', 'cs-CZ-AntoninNeural-Male', 'cs-CZ-VlastaNeural-Female', 'cy-GB-AledNeural-Male', 'cy-GB-NiaNeural-Female', 'da-DK-ChristelNeural-Female', 'da-DK-JeppeNeural-Male', 'de-AT-IngridNeural-Female', 'de-AT-JonasNeural-Male', 'de-CH-JanNeural-Male', 'de-CH-LeniNeural-Female', 'de-DE-AmalaNeural-Female', 'de-DE-ConradNeural-Male', 'de-DE-KatjaNeural-Female', 'de-DE-KillianNeural-Male', 'el-GR-AthinaNeural-Female', 'el-GR-NestorasNeural-Male', 'en-AU-NatashaNeural-Female', 'en-AU-WilliamNeural-Male', 'en-CA-ClaraNeural-Female', 'en-CA-LiamNeural-Male', 'en-GB-LibbyNeural-Female', 'en-GB-MaisieNeural-Female', 'en-GB-RyanNeural-Male', 'en-GB-SoniaNeural-Female', 'en-GB-ThomasNeural-Male', 'en-HK-SamNeural-Male', 'en-HK-YanNeural-Female', 'en-IE-ConnorNeural-Male', 'en-IE-EmilyNeural-Female', 'en-IN-NeerjaExpressiveNeural-Female', 'en-IN-NeerjaNeural-Female', 'en-IN-PrabhatNeural-Male', 'en-KE-AsiliaNeural-Female', 'en-KE-ChilembaNeural-Male', 'en-NG-AbeoNeural-Male', 'en-NG-EzinneNeural-Female', 'en-NZ-MitchellNeural-Male', 'en-NZ-MollyNeural-Female', 'en-PH-JamesNeural-Male', 'en-PH-RosaNeural-Female', 'en-SG-LunaNeural-Female', 'en-SG-WayneNeural-Male', 'en-TZ-ElimuNeural-Male', 'en-TZ-ImaniNeural-Female', 'en-US-AnaNeural-Female', 'en-US-AriaNeural-Female', 'en-US-ChristopherNeural-Male', 'en-US-EricNeural-Male', 'en-US-GuyNeural-Male', 'en-US-JennyNeural-Female', 'en-US-MichelleNeural-Female', 'en-US-RogerNeural-Male', 'en-US-SteffanNeural-Male', 'en-ZA-LeahNeural-Female', 'en-ZA-LukeNeural-Male', 'es-AR-ElenaNeural-Female', 'es-AR-TomasNeural-Male', 'es-BO-MarceloNeural-Male', 'es-BO-SofiaNeural-Female', 'es-CL-CatalinaNeural-Female', 'es-CL-LorenzoNeural-Male', 'es-CO-GonzaloNeural-Male', 'es-CO-SalomeNeural-Female', 'es-CR-JuanNeural-Male', 'es-CR-MariaNeural-Female', 'es-CU-BelkysNeural-Female', 'es-CU-ManuelNeural-Male', 'es-DO-EmilioNeural-Male', 'es-DO-RamonaNeural-Female', 'es-EC-AndreaNeural-Female', 'es-EC-LuisNeural-Male', 'es-ES-AlvaroNeural-Male', 'es-ES-ElviraNeural-Female', 'es-GQ-JavierNeural-Male', 'es-GQ-TeresaNeural-Female', 'es-GT-AndresNeural-Male', 'es-GT-MartaNeural-Female', 'es-HN-CarlosNeural-Male', 'es-HN-KarlaNeural-Female', 'es-MX-DaliaNeural-Female', 'es-MX-JorgeNeural-Male', 'es-NI-FedericoNeural-Male', 'es-NI-YolandaNeural-Female', 'es-PA-MargaritaNeural-Female', 'es-PA-RobertoNeural-Male', 'es-PE-AlexNeural-Male', 'es-PE-CamilaNeural-Female', 'es-PR-KarinaNeural-Female', 'es-PR-VictorNeural-Male', 'es-PY-MarioNeural-Male', 'es-PY-TaniaNeural-Female', 'es-SV-LorenaNeural-Female', 'es-SV-RodrigoNeural-Male', 'es-US-AlonsoNeural-Male', 'es-US-PalomaNeural-Female', 'es-UY-MateoNeural-Male', 'es-UY-ValentinaNeural-Female', 'es-VE-PaolaNeural-Female', 'es-VE-SebastianNeural-Male', 'et-EE-AnuNeural-Female', 'et-EE-KertNeural-Male', 'fa-IR-DilaraNeural-Female', 'fa-IR-FaridNeural-Male', 'fi-FI-HarriNeural-Male', 'fi-FI-NooraNeural-Female', 'fil-PH-AngeloNeural-Male', 'fil-PH-BlessicaNeural-Female', 'fr-BE-CharlineNeural-Female', 'fr-BE-GerardNeural-Male', 'fr-CA-AntoineNeural-Male', 'fr-CA-JeanNeural-Male', 'fr-CA-SylvieNeural-Female', 'fr-CH-ArianeNeural-Female', 'fr-CH-FabriceNeural-Male', 'fr-FR-DeniseNeural-Female', 'fr-FR-EloiseNeural-Female', 'fr-FR-HenriNeural-Male', 'ga-IE-ColmNeural-Male', 'ga-IE-OrlaNeural-Female', 'gl-ES-RoiNeural-Male', 'gl-ES-SabelaNeural-Female', 'gu-IN-DhwaniNeural-Female', 'gu-IN-NiranjanNeural-Male', 'he-IL-AvriNeural-Male', 'he-IL-HilaNeural-Female', 'hi-IN-MadhurNeural-Male', 'hi-IN-SwaraNeural-Female', 'hr-HR-GabrijelaNeural-Female', 'hr-HR-SreckoNeural-Male', 'hu-HU-NoemiNeural-Female', 'hu-HU-TamasNeural-Male', 'id-ID-ArdiNeural-Male', 'id-ID-GadisNeural-Female', 'is-IS-GudrunNeural-Female', 'is-IS-GunnarNeural-Male', 'it-IT-DiegoNeural-Male', 'it-IT-ElsaNeural-Female', 'it-IT-IsabellaNeural-Female', 'ja-JP-KeitaNeural-Male', 'ja-JP-NanamiNeural-Female', 'jv-ID-DimasNeural-Male', 'jv-ID-SitiNeural-Female', 'ka-GE-EkaNeural-Female', 'ka-GE-GiorgiNeural-Male', 'kk-KZ-AigulNeural-Female', 'kk-KZ-DauletNeural-Male', 'km-KH-PisethNeural-Male', 'km-KH-SreymomNeural-Female', 'kn-IN-GaganNeural-Male', 'kn-IN-SapnaNeural-Female', 'ko-KR-InJoonNeural-Male', 'ko-KR-SunHiNeural-Female', 'lo-LA-ChanthavongNeural-Male', 'lo-LA-KeomanyNeural-Female', 'lt-LT-LeonasNeural-Male', 'lt-LT-OnaNeural-Female', 'lv-LV-EveritaNeural-Female', 'lv-LV-NilsNeural-Male', 'mk-MK-AleksandarNeural-Male', 'mk-MK-MarijaNeural-Female', 'ml-IN-MidhunNeural-Male', 'ml-IN-SobhanaNeural-Female', 'mn-MN-BataaNeural-Male', 'mn-MN-YesuiNeural-Female', 'mr-IN-AarohiNeural-Female', 'mr-IN-ManoharNeural-Male', 'ms-MY-OsmanNeural-Male', 'ms-MY-YasminNeural-Female', 'mt-MT-GraceNeural-Female', 'mt-MT-JosephNeural-Male', 'my-MM-NilarNeural-Female', 'my-MM-ThihaNeural-Male', 'nb-NO-FinnNeural-Male', 'nb-NO-PernilleNeural-Female', 'ne-NP-HemkalaNeural-Female', 'ne-NP-SagarNeural-Male', 'nl-BE-ArnaudNeural-Male', 'nl-BE-DenaNeural-Female', 'nl-NL-ColetteNeural-Female', 'nl-NL-FennaNeural-Female', 'nl-NL-MaartenNeural-Male', 'pl-PL-MarekNeural-Male', 'pl-PL-ZofiaNeural-Female', 'ps-AF-GulNawazNeural-Male', 'ps-AF-LatifaNeural-Female', 'pt-BR-AntonioNeural-Male', 'pt-BR-FranciscaNeural-Female', 'pt-PT-DuarteNeural-Male', 'pt-PT-RaquelNeural-Female', 'ro-RO-AlinaNeural-Female', 'ro-RO-EmilNeural-Male', 'ru-RU-DmitryNeural-Male', 'ru-RU-SvetlanaNeural-Female', 'si-LK-SameeraNeural-Male', 'si-LK-ThiliniNeural-Female', 'sk-SK-LukasNeural-Male', 'sk-SK-ViktoriaNeural-Female', 'sl-SI-PetraNeural-Female', 'sl-SI-RokNeural-Male', 'so-SO-MuuseNeural-Male', 'so-SO-UbaxNeural-Female', 'sq-AL-AnilaNeural-Female', 'sq-AL-IlirNeural-Male', 'sr-RS-NicholasNeural-Male', 'sr-RS-SophieNeural-Female', 'su-ID-JajangNeural-Male', 'su-ID-TutiNeural-Female', 'sv-SE-MattiasNeural-Male', 'sv-SE-SofieNeural-Female', 'sw-KE-RafikiNeural-Male', 'sw-KE-ZuriNeural-Female', 'sw-TZ-DaudiNeural-Male', 'sw-TZ-RehemaNeural-Female', 'ta-IN-PallaviNeural-Female', 'ta-IN-ValluvarNeural-Male', 'ta-LK-KumarNeural-Male', 'ta-LK-SaranyaNeural-Female', 'ta-MY-KaniNeural-Female', 'ta-MY-SuryaNeural-Male', 'ta-SG-AnbuNeural-Male', 'ta-SG-VenbaNeural-Female', 'te-IN-MohanNeural-Male', 'te-IN-ShrutiNeural-Female', 'th-TH-NiwatNeural-Male', 'th-TH-PremwadeeNeural-Female', 'tr-TR-AhmetNeural-Male', 'tr-TR-EmelNeural-Female', 'uk-UA-OstapNeural-Male', 'uk-UA-PolinaNeural-Female', 'ur-IN-GulNeural-Female', 'ur-IN-SalmanNeural-Male', 'ur-PK-AsadNeural-Male', 'ur-PK-UzmaNeural-Female', 'uz-UZ-MadinaNeural-Female', 'uz-UZ-SardorNeural-Male', 'vi-VN-HoaiMyNeural-Female', 'vi-VN-NamMinhNeural-Male', 'zh-CN-XiaoxiaoNeural-Female', 'zh-CN-XiaoyiNeural-Female', 'zh-CN-YunjianNeural-Male', 'zh-CN-YunxiNeural-Male', 'zh-CN-YunxiaNeural-Male', 'zh-CN-YunyangNeural-Male', 'zh-CN-liaoning-XiaobeiNeural-Female', 'zh-CN-shaanxi-XiaoniNeural-Female']

### voices
with capture.capture_output() as cap:
    os.system('mkdir downloads')
    os.system('mkdir logs')
    os.system('mkdir weights')
    os.system('mkdir downloads')
    del cap


def print_tree_directory(root_dir, indent=''):
    if not os.path.exists(root_dir):
        print(f"{indent}Invalid directory or file: {root_dir}")
        return

    items = os.listdir(root_dir)

    for index, item in enumerate(sorted(items)):
        item_path = os.path.join(root_dir, item)
        is_last_item = index == len(items) - 1

        if os.path.isfile(item_path) and item_path.endswith('.zip'):
            with zipfile.ZipFile(item_path, 'r') as zip_file:
                print(f"{indent}{'└──' if is_last_item else '├──'} {item} (zip file)")
                zip_contents = zip_file.namelist()
                for zip_item in sorted(zip_contents):
                    print(f"{indent}{'    ' if is_last_item else '│   '}{zip_item}")
        else:
            print(f"{indent}{'└──' if is_last_item else '├──'} {item}")

            if os.path.isdir(item_path):
                new_indent = indent + ('    ' if is_last_item else '│   ')
                print_tree_directory(item_path, new_indent)


def upload_model_list():
    weight_root = "weights"
    models = []
    for name in os.listdir(weight_root):
        if name.endswith(".pth"):
            models.append(name)

    index_root = "logs"
    index_paths = []
    for name in os.listdir(index_root):
        if name.endswith(".index"):
            index_paths.append("logs/"+name)

    print(models, index_paths)
    return models, index_paths

def manual_download(url, dst):
    token = os.getenv("YOUR_HF_TOKEN")
    user_header = f"\"Authorization: Bearer {token}\""

    if 'drive.google' in url:
        print("Drive link")
        if 'folders' in url:
            print("folder")
            os.system(f'gdown --folder "{url}" -O {dst} --fuzzy -c')
        else:
            print("single")
            os.system(f'gdown "{url}" -O {dst} --fuzzy -c')
    elif 'huggingface' in url:
        print("HuggingFace link")
        if '/blob/' in url or '/resolve/' in url:
          if '/blob/' in url:
              url = url.replace('/blob/', '/resolve/')
          #parsed_link = '\n{}\n\tout={}'.format(url, unquote(url.split('/')[-1]))
          #os.system(f'echo -e "{parsed_link}" | aria2c --header={user_header} --console-log-level=error --summary-interval=10 -i- -j5 -x16 -s16 -k1M -c -d "{dst}"')
          os.system(f"wget -P {dst} {url}")
        else:
          os.system(f"git clone {url} {dst+'repo/'}")
    elif 'http' in url or 'magnet' in url:
        parsed_link = '"{}"'.format(url)
        os.system(f'aria2c --optimize-concurrent-downloads --console-log-level=error --summary-interval=10 -j5 -x16 -s16 -k1M -c -d {dst} -Z {parsed_link}')


def download_list(text_downloads):
    try:
      urls = [elem.strip() for elem in text_downloads.split(',')]
    except:
      return 'No valid link'

    os.system('mkdir downloads')
    os.system('mkdir logs')
    os.system('mkdir weights')
    path_download = "downloads/"
    for url in urls:
      manual_download(url, path_download)
    
    # Tree
    print('####################################')
    print_tree_directory("downloads", indent='')
    print('####################################')

    # Place files
    select_zip_and_rar_files("downloads/")

    models, _ = upload_model_list()
    os.system("rm -rf downloads/repo")

    return f"Downloaded = {models}"


def select_zip_and_rar_files(directory_path="downloads/"):
    #filter
    zip_files = []
    rar_files = []

    for file_name in os.listdir(directory_path):
        if file_name.endswith(".zip"):
            zip_files.append(file_name)
        elif file_name.endswith(".rar"):
            rar_files.append(file_name)

    # extract
    for file_name in zip_files:
        file_path = os.path.join(directory_path, file_name)
        with zipfile.ZipFile(file_path, 'r') as zip_ref:
            zip_ref.extractall(directory_path)

    for file_name in rar_files:
        file_path = os.path.join(directory_path, file_name)
        with rarfile.RarFile(file_path, 'r') as rar_ref:
            rar_ref.extractall(directory_path)

    # set in path
    def move_files_with_extension(src_dir, extension, destination_dir):
        for root, _, files in os.walk(src_dir):
            for file_name in files:
                if file_name.endswith(extension):
                    source_file = os.path.join(root, file_name)
                    destination = os.path.join(destination_dir, file_name)
                    shutil.move(source_file, destination)

    move_files_with_extension(directory_path, ".index", "logs/")
    move_files_with_extension(directory_path, ".pth", "weights/")

    return 'Download complete'

def custom_model_voice_enable(enable_custom_voice):
    if enable_custom_voice:
      os.environ["VOICES_MODELS"] = 'ENABLE'
    else:
      os.environ["VOICES_MODELS"] = 'DISABLE'


models, index_paths = upload_model_list()

f0_methods_voice = ["pm", "harvest", "crepe", "rmvpe"]


from voice_main import ClassVoices
voices = ClassVoices()

'''
def translate_from_video(video, WHISPER_MODEL_SIZE, batch_size, compute_type,
                         TRANSLATE_AUDIO_TO, min_speakers, max_speakers,
                         tts_voice00, tts_voice01,tts_voice02,tts_voice03,tts_voice04,tts_voice05):

    YOUR_HF_TOKEN = os.getenv("My_hf_token")

    create_translated_audio(result_diarize, audio_files, Output_name_file)

    os.system("rm audio_dub_stereo.wav")
    os.system("ffmpeg -i audio_dub_solo.wav -ac 1 audio_dub_stereo.wav")

    os.system(f"rm {mix_audio}")
    os.system(f'ffmpeg -y -i audio.wav -i audio_dub_stereo.wav -filter_complex "[0:0]volume=0.15[a];[1:0]volume=1.90[b];[a][b]amix=inputs=2:duration=longest" -c:a libmp3lame {mix_audio}')

    os.system(f"rm {video_output}")
    os.system(f"ffmpeg -i {OutputFile} -i {mix_audio} -c:v copy -c:a copy -map 0:v -map 1:a -shortest {video_output}")

    return video_output
'''

def translate_from_video(
    video,
    YOUR_HF_TOKEN,
    preview=False,
    WHISPER_MODEL_SIZE="large-v1",
    batch_size=16,
    compute_type="float16",
    SOURCE_LANGUAGE= "Automatic detection",
    TRANSLATE_AUDIO_TO="English (en)",
    min_speakers=1,
    max_speakers=2,
    tts_voice00="en-AU-WilliamNeural-Male",
    tts_voice01="en-CA-ClaraNeural-Female",
    tts_voice02="en-GB-ThomasNeural-Male",
    tts_voice03="en-GB-SoniaNeural-Female",
    tts_voice04="en-NZ-MitchellNeural-Male",
    tts_voice05="en-GB-MaisieNeural-Female",
    video_output="video_dub.mp4",
    AUDIO_MIX_METHOD='Adjusting volumes and mixing audio',
    progress=gr.Progress(),
    ):

    if YOUR_HF_TOKEN == "" or YOUR_HF_TOKEN == None:
      YOUR_HF_TOKEN = os.getenv("YOUR_HF_TOKEN")
      if YOUR_HF_TOKEN == None:
        print('No valid token')
        return "No valid token"
      else:
        os.environ["YOUR_HF_TOKEN"] = YOUR_HF_TOKEN

    video = video if isinstance(video, str) else video.name
    print(video)

    if "SET_LIMIT" == os.getenv("DEMO"):
      preview=True
      print("DEMO; set preview=True; The generation is **limited to 10 seconds** to prevent errors with the CPU. If you use a GPU, you won't have any of these limitations.")
      AUDIO_MIX_METHOD='Adjusting volumes and mixing audio'
      print("DEMO; set Adjusting volumes and mixing audio")
      WHISPER_MODEL_SIZE="medium"
      print("DEMO; set whisper model to medium")

    LANGUAGES = {
        'Automatic detection': 'Automatic detection',
        'Arabic (ar)': 'ar',
        'Chinese (zh)': 'zh',
        'Czech (cs)': 'cs',
        'Danish (da)': 'da',
        'Dutch (nl)': 'nl',
        'English (en)': 'en',
        'Finnish (fi)': 'fi',
        'French (fr)': 'fr',
        'German (de)': 'de',
        'Greek (el)': 'el',
        'Hebrew (he)': 'he',
        'Hungarian (hu)': 'hu',
        'Italian (it)': 'it',
        'Japanese (ja)': 'ja',
        'Korean (ko)': 'ko',
        'Persian (fa)': 'fa',
        'Polish (pl)': 'pl',
        'Portuguese (pt)': 'pt',
        'Russian (ru)': 'ru',
        'Spanish (es)': 'es',
        'Turkish (tr)': 'tr',
        'Ukrainian (uk)': 'uk',
        'Urdu (ur)': 'ur',
        'Vietnamese (vi)': 'vi',
        'Hindi (hi)': 'hi',
    }

    TRANSLATE_AUDIO_TO = LANGUAGES[TRANSLATE_AUDIO_TO]
    SOURCE_LANGUAGE = LANGUAGES[SOURCE_LANGUAGE]


    if not os.path.exists('audio'):
        os.makedirs('audio')

    if not os.path.exists('audio2/audio'):
        os.makedirs('audio2/audio')

    # Check GPU
    device = "cuda" if torch.cuda.is_available() else "cpu"
    compute_type = "float32" if device == "cpu" else compute_type

    OutputFile = 'Video.mp4'
    audio_wav = "audio.wav"
    Output_name_file = "audio_dub_solo.ogg"
    mix_audio = "audio_mix.mp3"

    os.system("rm Video.mp4")
    os.system("rm audio.webm")
    os.system("rm audio.wav")

    progress(0.15, desc="Processing video...")
    if os.path.exists(video):
        if preview:
            print('Creating a preview video of 10 seconds, to disable this option, go to advanced settings and turn off preview.')
            os.system(f'ffmpeg -y -i "{video}" -ss 00:00:20 -t 00:00:10 -c:v libx264 -c:a aac -strict experimental Video.mp4')
        else:
            # Check if the file ends with ".mp4" extension
            if video.endswith(".mp4"):
                destination_path = os.path.join(os.getcwd(), "Video.mp4")
                shutil.copy(video, destination_path)
            else:
                print("File does not have the '.mp4' extension. Converting video.")
                os.system(f'ffmpeg -y -i "{video}" -c:v libx264 -c:a aac -strict experimental Video.mp4')

        for i in range (120):
            time.sleep(1)
            print('process video...')
            if os.path.exists(OutputFile):
                time.sleep(1)
                os.system("ffmpeg -y -i Video.mp4 -vn -acodec pcm_s16le -ar 44100 -ac 2 audio.wav")
                time.sleep(1)
                break
            if i == 119:
              print('Error processing video')
              return

        for i in range (120):
            time.sleep(1)
            print('process audio...')
            if os.path.exists(audio_wav):
                break
            if i == 119:
              print("Error can't create the audio")
              return

    else:
        if preview:
            print('Creating a preview from the link, 10 seconds to disable this option, go to advanced settings and turn off preview.')
            #https://github.com/yt-dlp/yt-dlp/issues/2220
            mp4_ = f'yt-dlp -f "mp4" --downloader ffmpeg --downloader-args "ffmpeg_i: -ss 00:00:20 -t 00:00:10" --force-overwrites --max-downloads 1 --no-warnings --no-abort-on-error --ignore-no-formats-error --restrict-filenames -o {OutputFile} {video}'
            wav_ = "ffmpeg -y -i Video.mp4 -vn -acodec pcm_s16le -ar 44100 -ac 2 audio.wav"
            os.system(mp4_)
            os.system(wav_)
        else:
            mp4_ = f'yt-dlp -f "mp4" --force-overwrites --max-downloads 1 --no-warnings --no-abort-on-error --ignore-no-formats-error --restrict-filenames -o {OutputFile} {video}'
            wav_ = f'python -m yt_dlp --output {audio_wav} --force-overwrites --max-downloads 1 --no-warnings --no-abort-on-error --ignore-no-formats-error --extract-audio --audio-format wav {video}'

            os.system(wav_)

            for i in range (120):
                time.sleep(1)
                print('process audio...')
                if os.path.exists(audio_wav) and not os.path.exists('audio.webm'):
                    time.sleep(1)
                    os.system(mp4_)
                    break
                if i == 119:
                  print('Error donwloading the audio')
                  return

    print("Set file complete.")
    progress(0.30, desc="Transcribing...")

    SOURCE_LANGUAGE = None if SOURCE_LANGUAGE == 'Automatic detection' else SOURCE_LANGUAGE

    # 1. Transcribe with original whisper (batched)
    with capture.capture_output() as cap:
      model = whisperx.load_model(
          WHISPER_MODEL_SIZE,
          device,
          compute_type=compute_type,
          language= SOURCE_LANGUAGE,
          )
      del cap
    audio = whisperx.load_audio(audio_wav)
    result = model.transcribe(audio, batch_size=batch_size)
    gc.collect(); torch.cuda.empty_cache(); del model
    print("Transcript complete")

    

    # 2. Align whisper output
    progress(0.45, desc="Aligning...")
    DAMHF.update(DAMT) #lang align
    EXTRA_ALIGN = {
        "hi": "theainerd/Wav2Vec2-large-xlsr-hindi"
    } # add new align models here
    #print(result['language'], DAM.keys(), EXTRA_ALIGN.keys())
    if not result['language'] in DAMHF.keys() and not result['language'] in EXTRA_ALIGN.keys():
        audio = result = None
        print("Automatic detection: Source language not incompatible")
        print(f"Detected language {LANG_TRANSCRIPT[result['language']]}  incompatible, you can select the source language to avoid this error.")
        return

    model_a, metadata = whisperx.load_align_model(
        language_code=result["language"],
        device=device,
        model_name = None if result["language"] in DAMHF.keys() else EXTRA_ALIGN[result["language"]]
        )
    result = whisperx.align(
        result["segments"],
        model_a,
        metadata,
        audio,
        device,
        return_char_alignments=True,
        )
    gc.collect(); torch.cuda.empty_cache(); del model_a
    print("Align complete")

    if result['segments'] == []:
        print('No active speech found in audio')
        return

    # 3. Assign speaker labels
    progress(0.60, desc="Diarizing...")
    with capture.capture_output() as cap:
      diarize_model = whisperx.DiarizationPipeline(use_auth_token=YOUR_HF_TOKEN, device=device)
      del cap
    diarize_segments = diarize_model(
        audio_wav,
        min_speakers=min_speakers,
        max_speakers=max_speakers)
    result_diarize = whisperx.assign_word_speakers(diarize_segments, result)
    gc.collect(); torch.cuda.empty_cache(); del diarize_model
    print("Diarize complete")

    progress(0.75, desc="Translating...")
    if TRANSLATE_AUDIO_TO == "zh":
        TRANSLATE_AUDIO_TO = "zh-CN"
    if TRANSLATE_AUDIO_TO == "he":
        TRANSLATE_AUDIO_TO = "iw"
    result_diarize['segments'] = translate_text(result_diarize['segments'], TRANSLATE_AUDIO_TO)
    print("Translation complete")

    progress(0.85, desc="Text_to_speech...")
    audio_files = []
    speakers_list = []

    # Mapping speakers to voice variables
    speaker_to_voice = {
        'SPEAKER_00': tts_voice00,
        'SPEAKER_01': tts_voice01,
        'SPEAKER_02': tts_voice02,
        'SPEAKER_03': tts_voice03,
        'SPEAKER_04': tts_voice04,
        'SPEAKER_05': tts_voice05
    }

    for segment in tqdm(result_diarize['segments']):

        text = segment['text']
        start = segment['start']
        end = segment['end']

        try:
            speaker = segment['speaker']
        except KeyError:
            segment['speaker'] = "SPEAKER_99"
            speaker = segment['speaker']
            print(f"NO SPEAKER DETECT IN SEGMENT: TTS auxiliary will be used in the segment time {segment['start'], segment['text']}")

        # make the tts audio
        filename = f"audio/{start}.ogg"

        if speaker in speaker_to_voice and speaker_to_voice[speaker] != 'None':
            make_voice_gradio(text, speaker_to_voice[speaker], filename, TRANSLATE_AUDIO_TO)
        elif speaker == "SPEAKER_99":
            try:
                tts = gTTS(text, lang=TRANSLATE_AUDIO_TO)
                tts.save(filename)
                print('Using GTTS')
            except:
                tts = gTTS('a', lang=TRANSLATE_AUDIO_TO)
                tts.save(filename)
                print('Error: Audio will be replaced.')

        # duration
        duration_true = end - start
        duration_tts = librosa.get_duration(filename=filename)

        # porcentaje
        porcentaje = duration_tts / duration_true

        if porcentaje > 2.1:
            porcentaje = 2.1
        elif porcentaje <= 1.2 and porcentaje >= 0.8:
            porcentaje = 1.0
        elif porcentaje <= 0.79:
            porcentaje = 0.8

        # Smoth and round
        porcentaje = round(porcentaje+0.0, 1)

        # apply aceleration or opposite to the audio file in audio2 folder
        os.system(f"ffmpeg -y -loglevel panic -i {filename} -filter:a atempo={porcentaje} audio2/{filename}")

        duration_create = librosa.get_duration(filename=f"audio2/{filename}")
        audio_files.append(filename)
        speakers_list.append(speaker)

    # custom voice
    if os.getenv('VOICES_MODELS') == 'ENABLE':
        progress(0.90, desc="Applying customized voices...")
        voices(speakers_list, audio_files)

    # replace files with the accelerates
    os.system("mv -f audio2/audio/*.ogg audio/")

    os.system(f"rm {Output_name_file}")

    progress(0.95, desc="Creating final translated video...")

    create_translated_audio(result_diarize, audio_files, Output_name_file)

    os.system(f"rm {mix_audio}")

    # TYPE MIX AUDIO
    if AUDIO_MIX_METHOD == 'Adjusting volumes and mixing audio':
        # volume mix
        os.system(f'ffmpeg -y -i {audio_wav} -i {Output_name_file} -filter_complex "[0:0]volume=0.15[a];[1:0]volume=1.90[b];[a][b]amix=inputs=2:duration=longest" -c:a libmp3lame {mix_audio}')
    else:
        try:
            # background mix
            os.system(f'ffmpeg -i {audio_wav} -i {Output_name_file} -filter_complex "[1:a]asplit=2[sc][mix];[0:a][sc]sidechaincompress=threshold=0.003:ratio=20[bg]; [bg][mix]amerge[final]" -map [final] {mix_audio}')
        except:
            # volume mix except
            os.system(f'ffmpeg -y -i {audio_wav} -i {Output_name_file} -filter_complex "[0:0]volume=0.25[a];[1:0]volume=1.80[b];[a][b]amix=inputs=2:duration=longest" -c:a libmp3lame {mix_audio}')

    os.system(f"rm {video_output}")
    os.system(f"ffmpeg -i {OutputFile} -i {mix_audio} -c:v copy -c:a copy -map 0:v -map 1:a -shortest {video_output}")

    return video_output

import sys

class Logger:
    def __init__(self, filename):
        self.terminal = sys.stdout
        self.log = open(filename, "w")

    def write(self, message):
        self.terminal.write(message)
        self.log.write(message)

    def flush(self):
        self.terminal.flush()
        self.log.flush()

    def isatty(self):
        return False

sys.stdout = Logger("output.log")

def read_logs():
    sys.stdout.flush()
    with open("output.log", "r") as f:
        return f.read()

def submit_file_func(file):
    print(file.name)
    return file.name, file.name

# max tts
MAX_TTS = 6

theme='Taithrah/Minimal'

with gr.Blocks(theme=theme) as demo:
    gr.Markdown(title)
    gr.Markdown(description)

#### video
    with gr.Tab("Audio Translation for a Video"):
        with gr.Row():
            with gr.Column():
                #video_input = gr.UploadButton("Click to Upload a video", file_types=["video"], file_count="single") #gr.Video() # height=300,width=300
                video_input = gr.Video(label="Submit a video") #gr.File(label="VIDEO")
                #link = gr.HTML()
                #video_input.change(submit_file_func, video_input, [video_input, link], show_progress='full')

                SOURCE_LANGUAGE = gr.Dropdown(['Automatic detection', 'Arabic (ar)', 'Chinese (zh)', 'Czech (cs)', 'Danish (da)', 'Dutch (nl)', 'English (en)', 'Finnish (fi)', 'French (fr)', 'German (de)', 'Greek (el)', 'Hebrew (he)', 'Hindi (hi)', 'Hungarian (hu)', 'Italian (it)', 'Japanese (ja)', 'Korean (ko)', 'Persian (fa)', 'Polish (pl)', 'Portuguese (pt)', 'Russian (ru)', 'Spanish (es)', 'Turkish (tr)', 'Ukrainian (uk)', 'Urdu (ur)', 'Vietnamese (vi)'], value='Automatic detection',label = 'Source language', info="This is the original language of the video")
                TRANSLATE_AUDIO_TO = gr.Dropdown(['Arabic (ar)', 'Chinese (zh)', 'Czech (cs)', 'Danish (da)', 'Dutch (nl)', 'English (en)', 'Finnish (fi)', 'French (fr)', 'German (de)', 'Greek (el)', 'Hebrew (he)', 'Hindi (hi)', 'Hungarian (hu)', 'Italian (it)', 'Japanese (ja)', 'Korean (ko)', 'Persian (fa)', 'Polish (pl)', 'Portuguese (pt)', 'Russian (ru)', 'Spanish (es)', 'Turkish (tr)', 'Ukrainian (uk)', 'Urdu (ur)', 'Vietnamese (vi)'], value='English (en)',label = 'Translate audio to', info="Select the target language, and make sure to select the language corresponding to the speakers of the target language to avoid errors in the process.")

                line_ = gr.HTML("<hr></h2>")
                gr.Markdown("Select how many people are speaking in the video.")
                min_speakers = gr.Slider(1, MAX_TTS, default=1, label="min_speakers", step=1, visible=False)
                max_speakers = gr.Slider(1, MAX_TTS, value=2, step=1, label="Max speakers", interative=True)
                gr.Markdown("Select the voice you want for each speaker.")
                def submit(value):
                    visibility_dict = {
                        f'tts_voice{i:02d}': gr.update(visible=i < value) for i in range(6)
                    }
                    return [value for value in visibility_dict.values()]
                tts_voice00 = gr.Dropdown(list_tts, value='en-AU-WilliamNeural-Male', label = 'TTS Speaker 1', visible=True, interactive= True)
                tts_voice01 = gr.Dropdown(list_tts, value='en-CA-ClaraNeural-Female', label = 'TTS Speaker 2', visible=True, interactive= True)
                tts_voice02 = gr.Dropdown(list_tts, value='en-GB-ThomasNeural-Male', label = 'TTS Speaker 3', visible=False, interactive= True)
                tts_voice03 = gr.Dropdown(list_tts, value='en-GB-SoniaNeural-Female', label = 'TTS Speaker 4', visible=False, interactive= True)
                tts_voice04 = gr.Dropdown(list_tts, value='en-NZ-MitchellNeural-Male', label = 'TTS Speaker 5', visible=False, interactive= True)
                tts_voice05 = gr.Dropdown(list_tts, value='en-GB-MaisieNeural-Female', label = 'TTS Speaker 6', visible=False, interactive= True)
                max_speakers.change(submit, max_speakers, [tts_voice00, tts_voice01, tts_voice02, tts_voice03, tts_voice04, tts_voice05])

                with gr.Column():
                      with gr.Accordion("Advanced Settings", open=False):

                          AUDIO_MIX = gr.Dropdown(['Mixing audio with sidechain compression', 'Adjusting volumes and mixing audio'], value='Adjusting volumes and mixing audio', label = 'Audio Mixing Method', info="Mix original and translated audio files to create a customized, balanced output with two available mixing modes.")

                          gr.HTML("<hr></h2>")
                          gr.Markdown("Default configuration of Whisper.")
                          WHISPER_MODEL_SIZE = gr.inputs.Dropdown(['tiny', 'base', 'small', 'medium', 'large-v1', 'large-v2'], default=whisper_model_default, label="Whisper model")
                          batch_size = gr.inputs.Slider(1, 32, default=16, label="Batch size", step=1)
                          compute_type = gr.inputs.Dropdown(list_compute_type, default=compute_type_default, label="Compute type")

                          gr.HTML("<hr></h2>")
                          VIDEO_OUTPUT_NAME = gr.Textbox(label="Translated file name" ,value="video_output.mp4", info="The name of the output file")
                          PREVIEW = gr.Checkbox(label="Preview", info="Preview cuts the video to only 10 seconds for testing purposes. Please deactivate it to retrieve the full video duration.")

            with gr.Column(variant='compact'):
                with gr.Row():
                    video_button = gr.Button("TRANSLATE", )
                with gr.Row():
                    video_output = gr.Video() #gr.outputs.File(label="DOWNLOAD TRANSLATED VIDEO") 

                line_ = gr.HTML("<hr></h2>")
                if os.getenv("YOUR_HF_TOKEN") == None or os.getenv("YOUR_HF_TOKEN") == "":
                  HFKEY = gr.Textbox(visible= True, label="HF Token", info="One important step is to accept the license agreement for using Pyannote. You need to have an account on Hugging Face and accept the license to use the models: https://huggingface.co/pyannote/speaker-diarization and https://huggingface.co/pyannote/segmentation. Get your KEY TOKEN here: https://hf.co/settings/tokens", placeholder="Token goes here...")
                else:
                  HFKEY = gr.Textbox(visible= False, label="HF Token", info="One important step is to accept the license agreement for using Pyannote. You need to have an account on Hugging Face and accept the license to use the models: https://huggingface.co/pyannote/speaker-diarization and https://huggingface.co/pyannote/segmentation. Get your KEY TOKEN here: https://hf.co/settings/tokens", placeholder="Token goes here...")

                gr.Examples(
                    examples=[
                        [
                            "./assets/Video_main.mp4",
                            "",
                            False,
                            "large-v2",
                            16,
                            "float16",
                            "Spanish (es)",
                            "English (en)",
                            1,
                            2,
                            'en-AU-WilliamNeural-Male',
                            'en-CA-ClaraNeural-Female',
                            'en-GB-ThomasNeural-Male',
                            'en-GB-SoniaNeural-Female',
                            'en-NZ-MitchellNeural-Male',
                            'en-GB-MaisieNeural-Female',
                            "video_output.mp4",
                            'Adjusting volumes and mixing audio',
                        ],
                    ],
                    fn=translate_from_video,
                    inputs=[
                    video_input,
                    HFKEY,
                    PREVIEW,
                    WHISPER_MODEL_SIZE,
                    batch_size,
                    compute_type,
                    SOURCE_LANGUAGE,
                    TRANSLATE_AUDIO_TO,
                    min_speakers,
                    max_speakers,
                    tts_voice00,
                    tts_voice01,
                    tts_voice02,
                    tts_voice03,
                    tts_voice04,
                    tts_voice05,
                    VIDEO_OUTPUT_NAME,
                    AUDIO_MIX,
                    ],
                    outputs=[video_output],
                    cache_examples=True,
                )

### link

    with gr.Tab("Audio Translation via Video Link"):
        with gr.Row():
            with gr.Column():

                blink_input = gr.Textbox(label="Media link.", info="Example: www.youtube.com/watch?v=g_9rPvbENUw", placeholder="URL goes here...")

                bSOURCE_LANGUAGE = gr.Dropdown(['Automatic detection', 'Arabic (ar)', 'Chinese (zh)', 'Czech (cs)', 'Danish (da)', 'Dutch (nl)', 'English (en)', 'Finnish (fi)', 'French (fr)', 'German (de)', 'Greek (el)', 'Hebrew (he)', 'Hindi (hi)', 'Hungarian (hu)', 'Italian (it)', 'Japanese (ja)', 'Korean (ko)', 'Persian (fa)', 'Polish (pl)', 'Portuguese (pt)', 'Russian (ru)', 'Spanish (es)', 'Turkish (tr)', 'Ukrainian (uk)', 'Urdu (ur)', 'Vietnamese (vi)'], value='Automatic detection',label = 'Source language', info="This is the original language of the video")
                bTRANSLATE_AUDIO_TO = gr.Dropdown(['Arabic (ar)', 'Chinese (zh)', 'Czech (cs)', 'Danish (da)', 'Dutch (nl)', 'English (en)', 'Finnish (fi)', 'French (fr)', 'German (de)', 'Greek (el)', 'Hebrew (he)', 'Hindi (hi)', 'Hungarian (hu)', 'Italian (it)', 'Japanese (ja)', 'Korean (ko)', 'Persian (fa)', 'Polish (pl)', 'Portuguese (pt)', 'Russian (ru)', 'Spanish (es)', 'Turkish (tr)', 'Ukrainian (uk)', 'Urdu (ur)', 'Vietnamese (vi)'], value='English (en)',label = 'Translate audio to', info="Select the target language, and make sure to select the language corresponding to the speakers of the target language to avoid errors in the process.")

                bline_ = gr.HTML("<hr></h2>")
                gr.Markdown("Select how many people are speaking in the video.")
                bmin_speakers = gr.Slider(1, MAX_TTS, default=1, label="min_speakers", step=1, visible=False)
                bmax_speakers = gr.Slider(1, MAX_TTS, value=2, step=1, label="Max speakers", interative=True)
                gr.Markdown("Select the voice you want for each speaker.")
                def bsubmit(value):
                    visibility_dict = {
                        f'btts_voice{i:02d}': gr.update(visible=i < value) for i in range(6)
                    }
                    return [value for value in visibility_dict.values()]
                btts_voice00 = gr.Dropdown(list_tts, value='en-AU-WilliamNeural-Male', label = 'TTS Speaker 1', visible=True, interactive= True)
                btts_voice01 = gr.Dropdown(list_tts, value='en-CA-ClaraNeural-Female', label = 'TTS Speaker 2', visible=True, interactive= True)
                btts_voice02 = gr.Dropdown(list_tts, value='en-GB-ThomasNeural-Male', label = 'TTS Speaker 3', visible=False, interactive= True)
                btts_voice03 = gr.Dropdown(list_tts, value='en-GB-SoniaNeural-Female', label = 'TTS Speaker 4', visible=False, interactive= True)
                btts_voice04 = gr.Dropdown(list_tts, value='en-NZ-MitchellNeural-Male', label = 'TTS Speaker 5', visible=False, interactive= True)
                btts_voice05 = gr.Dropdown(list_tts, value='en-GB-MaisieNeural-Female', label = 'TTS Speaker 6', visible=False, interactive= True)
                bmax_speakers.change(bsubmit, bmax_speakers, [btts_voice00, btts_voice01, btts_voice02, btts_voice03, btts_voice04, btts_voice05])


                with gr.Column():
                      with gr.Accordion("Advanced Settings", open=False):

                          bAUDIO_MIX = gr.Dropdown(['Mixing audio with sidechain compression', 'Adjusting volumes and mixing audio'], value='Adjusting volumes and mixing audio', label = 'Audio Mixing Method', info="Mix original and translated audio files to create a customized, balanced output with two available mixing modes.")

                          gr.HTML("<hr></h2>")
                          gr.Markdown("Default configuration of Whisper.")
                          bWHISPER_MODEL_SIZE = gr.inputs.Dropdown(['tiny', 'base', 'small', 'medium', 'large-v1', 'large-v2'], default=whisper_model_default, label="Whisper model")
                          bbatch_size = gr.inputs.Slider(1, 32, default=16, label="Batch size", step=1)
                          bcompute_type = gr.inputs.Dropdown(list_compute_type, default=compute_type_default, label="Compute type")

                          gr.HTML("<hr></h2>")
                          bVIDEO_OUTPUT_NAME = gr.Textbox(label="Translated file name" ,value="video_output.mp4", info="The name of the output file")
                          bPREVIEW = gr.Checkbox(label="Preview", info="Preview cuts the video to only 10 seconds for testing purposes. Please deactivate it to retrieve the full video duration.")

            with gr.Column(variant='compact'):
                with gr.Row():
                    text_button = gr.Button("TRANSLATE")
                with gr.Row():
                    blink_output = gr.Video() #gr.outputs.File(label="DOWNLOAD TRANSLATED VIDEO") # gr.Video()


                bline_ = gr.HTML("<hr></h2>")
                if os.getenv("YOUR_HF_TOKEN") == None or os.getenv("YOUR_HF_TOKEN") == "":
                  bHFKEY = gr.Textbox(visible= True, label="HF Token", info="One important step is to accept the license agreement for using Pyannote. You need to have an account on Hugging Face and accept the license to use the models: https://huggingface.co/pyannote/speaker-diarization and https://huggingface.co/pyannote/segmentation. Get your KEY TOKEN here: https://hf.co/settings/tokens", placeholder="Token goes here...")
                else:
                  bHFKEY = gr.Textbox(visible= False, label="HF Token", info="One important step is to accept the license agreement for using Pyannote. You need to have an account on Hugging Face and accept the license to use the models: https://huggingface.co/pyannote/speaker-diarization and https://huggingface.co/pyannote/segmentation. Get your KEY TOKEN here: https://hf.co/settings/tokens", placeholder="Token goes here...")

                gr.Examples(
                    examples=[
                        [
                            "https://www.youtube.com/watch?v=5ZeHtRKHl7Y",
                            "",
                            False,
                            "large-v2",
                            16,
                            "float16",
                            "Japanese (ja)",
                            "English (en)",
                            1,
                            2,
                            'en-CA-ClaraNeural-Female',
                            'en-AU-WilliamNeural-Male',
                            'en-GB-ThomasNeural-Male',
                            'en-GB-SoniaNeural-Female',
                            'en-NZ-MitchellNeural-Male',
                            'en-GB-MaisieNeural-Female',
                            "video_output.mp4",
                            'Adjusting volumes and mixing audio',
                        ],
                    ],
                    fn=translate_from_video,
                    inputs=[
                    blink_input,
                    bHFKEY,
                    bPREVIEW,
                    bWHISPER_MODEL_SIZE,
                    bbatch_size,
                    bcompute_type,
                    bSOURCE_LANGUAGE,
                    bTRANSLATE_AUDIO_TO,
                    bmin_speakers,
                    bmax_speakers,
                    btts_voice00,
                    btts_voice01,
                    btts_voice02,
                    btts_voice03,
                    btts_voice04,
                    btts_voice05,
                    bVIDEO_OUTPUT_NAME,
                    bAUDIO_MIX
                    ],
                    outputs=[blink_output],
                    cache_examples=True,
                )


    with gr.Tab("Custom voice RVC"):
        with gr.Column():
          with gr.Accordion("Download RVC Models", open=True):
            url_links = gr.Textbox(label="URLs", value="",info="Automatically download the RVC models from the URL. You can use links from HuggingFace or Drive, and you can include several links, each one separated by a comma.", placeholder="urls here...", lines=1)
            download_finish = gr.HTML()
            download_button = gr.Button("DOWNLOAD MODELS")

            def update_models():
              models, index_paths = upload_model_list()
              for i in range(8):                      
                dict_models = {
                    f'model_voice_path{i:02d}': gr.update(choices=models) for i in range(8)
                }
                dict_index = {
                    f'file_index2_{i:02d}': gr.update(choices=index_paths) for i in range(8)
                }
                dict_changes = {**dict_models, **dict_index}
                return [value for value in dict_changes.values()]

        with gr.Column():
          with gr.Accordion("Replace voice: TTS to RVC", open=False):
            with gr.Column(variant='compact'):
              with gr.Column():
                gr.Markdown("### 1. To enable its use, mark it as enable.")
                enable_custom_voice = gr.Checkbox(label="ENABLE", info="Check this to enable the use of the models.")
                enable_custom_voice.change(custom_model_voice_enable, [enable_custom_voice], [])

                gr.Markdown("### 2. Select a voice that will be applied to each TTS of each corresponding speaker and apply the configurations.")
                
                gr.Markdown("Voice to apply to the first speaker.")
                with gr.Row():
                  model_voice_path00 = gr.Dropdown(models, label = 'Model-1', visible=True, interactive= True)
                  file_index2_00 = gr.Dropdown(index_paths, label = 'Index-1', visible=True, interactive= True)
                  name_transpose00 = gr.Number(label = 'Transpose-1', value=0, visible=True, interactive= True)
                gr.HTML("<hr></h2>")
                gr.Markdown("Voice to apply to the second speaker.")
                with gr.Row():
                  model_voice_path01 = gr.Dropdown(models, label='Model-2', visible=True, interactive=True)
                  file_index2_01 = gr.Dropdown(index_paths, label='Index-2', visible=True, interactive=True)
                  name_transpose01 = gr.Number(label='Transpose-2', value=0, visible=True, interactive=True)
                gr.HTML("<hr></h2>")
                gr.Markdown("Voice to apply to the third speaker.")
                with gr.Row():
                  model_voice_path02 = gr.Dropdown(models, label='Model-3', visible=True, interactive=True)
                  file_index2_02 = gr.Dropdown(index_paths, label='Index-3', visible=True, interactive=True)
                  name_transpose02 = gr.Number(label='Transpose-3', value=0, visible=True, interactive=True)
                gr.HTML("<hr></h2>")
                gr.Markdown("Voice to apply to the fourth speaker.")
                with gr.Row():
                  model_voice_path03 = gr.Dropdown(models, label='Model-4', visible=True, interactive=True)
                  file_index2_03 = gr.Dropdown(index_paths, label='Index-4', visible=True, interactive=True)
                  name_transpose03 = gr.Number(label='Transpose-4', value=0, visible=True, interactive=True)
                gr.HTML("<hr></h2>")
                gr.Markdown("Voice to apply to the fifth speaker.")
                with gr.Row():
                  model_voice_path04 = gr.Dropdown(models, label='Model-5', visible=True, interactive=True)
                  file_index2_04 = gr.Dropdown(index_paths, label='Index-5', visible=True, interactive=True)
                  name_transpose04 = gr.Number(label='Transpose-5', value=0, visible=True, interactive=True)
                gr.HTML("<hr></h2>")
                gr.Markdown("Voice to apply to the sixth speaker.")
                with gr.Row():
                  model_voice_path05 = gr.Dropdown(models, label='Model-6', visible=True, interactive=True)
                  file_index2_05 = gr.Dropdown(index_paths, label='Index-6', visible=True, interactive=True)
                  name_transpose05 = gr.Number(label='Transpose-6', value=0, visible=True, interactive=True)
                gr.HTML("<hr></h2>")
                gr.Markdown("- Voice to apply in case a speaker is not detected successfully.")
                with gr.Row():
                  model_voice_path06 = gr.Dropdown(models, label='Model-Aux', visible=True, interactive=True)
                  file_index2_06 = gr.Dropdown(index_paths, label='Index-Aux', visible=True, interactive=True)
                  name_transpose06 = gr.Number(label='Transpose-Aux', value=0, visible=True, interactive=True)
                gr.HTML("<hr></h2>")
                with gr.Row():
                  f0_method_global = gr.Dropdown(f0_methods_voice, value='pm', label = 'Global F0 method', visible=True, interactive= True)

            with gr.Row(variant='compact'):
              button_config = gr.Button("APPLY CONFIGURATION")

              confirm_conf = gr.HTML()

            button_config.click(voices.apply_conf, inputs=[
                f0_method_global,
                model_voice_path00, name_transpose00, file_index2_00,
                model_voice_path01, name_transpose01, file_index2_01,
                model_voice_path02, name_transpose02, file_index2_02,
                model_voice_path03, name_transpose03, file_index2_03,
                model_voice_path04, name_transpose04, file_index2_04,
                model_voice_path05, name_transpose05, file_index2_05,
                model_voice_path06, name_transpose06, file_index2_06,
                ], outputs=[confirm_conf])


          with gr.Column():
                with gr.Accordion("Test RVC", open=False):

                  with gr.Row(variant='compact'):
                    text_test = gr.Textbox(label="Text", value="This is an example",info="write a text", placeholder="...", lines=5)
                    with gr.Column(): 
                      tts_test = gr.Dropdown(list_tts, value='en-GB-ThomasNeural-Male', label = 'TTS', visible=True, interactive= True)
                      model_voice_path07 = gr.Dropdown(models, label = 'Model', visible=True, interactive= True) #value=''
                      file_index2_07 = gr.Dropdown(index_paths, label = 'Index', visible=True, interactive= True) #value=''
                      transpose_test = gr.Number(label = 'Transpose', value=0, visible=True, interactive= True, info="integer, number of semitones, raise by an octave: 12, lower by an octave: -12")
                      f0method_test = gr.Dropdown(f0_methods_voice, value='pm', label = 'F0 method', visible=True, interactive= True) 
                  with gr.Row(variant='compact'):
                    button_test = gr.Button("Test audio")

                  with gr.Column():
                    with gr.Row():
                      original_ttsvoice = gr.Audio()
                      ttsvoice = gr.Audio()

                    button_test.click(voices.make_test, inputs=[
                        text_test,
                        tts_test,
                        model_voice_path07,
                        file_index2_07,
                        transpose_test,
                        f0method_test,
                        ], outputs=[ttsvoice, original_ttsvoice])

                download_button.click(download_list, [url_links], [download_finish]).then(update_models, [], 
                                  [
                                    model_voice_path00, model_voice_path01, model_voice_path02, model_voice_path03, model_voice_path04, model_voice_path05, model_voice_path06, model_voice_path07,
                                    file_index2_00, file_index2_01, file_index2_02, file_index2_03, file_index2_04, file_index2_05, file_index2_06, file_index2_07
                                  ])


    with gr.Tab("Help"):
        gr.Markdown(tutorial)
        gr.Markdown(news)

    with gr.Accordion("Logs", open = False):
        logs = gr.Textbox()
        demo.load(read_logs, None, logs, every=1)

    # run
    video_button.click(translate_from_video, inputs=[
        video_input,
        HFKEY,
        PREVIEW,
        WHISPER_MODEL_SIZE,
        batch_size,
        compute_type,
        SOURCE_LANGUAGE,
        TRANSLATE_AUDIO_TO,
        min_speakers,
        max_speakers,
        tts_voice00,
        tts_voice01,
        tts_voice02,
        tts_voice03,
        tts_voice04,
        tts_voice05,
        VIDEO_OUTPUT_NAME,
        AUDIO_MIX,
        ], outputs=video_output)
    text_button.click(translate_from_video, inputs=[
        blink_input,
        bHFKEY,
        bPREVIEW,
        bWHISPER_MODEL_SIZE,
        bbatch_size,
        bcompute_type,
        bSOURCE_LANGUAGE,
        bTRANSLATE_AUDIO_TO,
        bmin_speakers,
        bmax_speakers,
        btts_voice00,
        btts_voice01,
        btts_voice02,
        btts_voice03,
        btts_voice04,
        btts_voice05,
        bVIDEO_OUTPUT_NAME,
        bAUDIO_MIX,
        ], outputs=blink_output)

demo.launch(debug=False, enable_queue=True)
#demo.launch(share=True, enable_queue=True, quiet=True, debug=False)