import os import re import random from scipy.io.wavfile import write from scipy.io.wavfile import read import numpy as np import gradio as gr import yt_dlp import subprocess from pydub import AudioSegment from audio_separator.separator import Separator from lib.infer import infer_audio import edge_tts import tempfile import anyio from pathlib import Path import os import zipfile import shutil import urllib.request import gdown import subprocess main_dir = Path().resolve() print(main_dir) os.chdir(main_dir) models_dir = "models" language_dict = { 'English-Jenny (Female)': 'en-US-JennyNeural', 'English-Guy (Male)': 'en-US-GuyNeural', 'English-Ana (Female)': 'en-US-AnaNeural', 'English-Aria (Female)': 'en-US-AriaNeural', 'English-Christopher (Male)': 'en-US-ChristopherNeural', 'English-Eric (Male)': 'en-US-EricNeural', 'English-Michelle (Female)': 'en-US-MichelleNeural', 'English-Roger (Male)': 'en-US-RogerNeural', 'Spanish (Mexican)-Dalia (Female)': 'es-MX-DaliaNeural', 'Spanish (Mexican)-Jorge- (Male)': 'es-MX-JorgeNeural', 'Korean-Sun-Hi- (Female)': 'ko-KR-SunHiNeural', 'Korean-InJoon- (Male)': 'ko-KR-InJoonNeural', 'Thai-Premwadee- (Female)': 'th-TH-PremwadeeNeural', 'Thai-Niwat- (Male)': 'th-TH-NiwatNeural', 'Vietnamese-HoaiMy- (Female)': 'vi-VN-HoaiMyNeural', 'Vietnamese-NamMinh- (Male)': 'vi-VN-NamMinhNeural', 'Japanese-Nanami- (Female)': 'ja-JP-NanamiNeural', 'Japanese-Keita- (Male)': 'ja-JP-KeitaNeural', 'French-Denise- (Female)': 'fr-FR-DeniseNeural', 'French-Eloise- (Female)': 'fr-FR-EloiseNeural', 'French-Henri- (Male)': 'fr-FR-HenriNeural', 'Brazilian-Francisca- (Female)': 'pt-BR-FranciscaNeural', 'Brazilian-Antonio- (Male)': 'pt-BR-AntonioNeural', 'Indonesian-Ardi- (Male)': 'id-ID-ArdiNeural', 'Indonesian-Gadis- (Female)': 'id-ID-GadisNeural', 'Hebrew-Avri- (Male)': 'he-IL-AvriNeural', 'Hebrew-Hila- (Female)': 'he-IL-HilaNeural', 'Italian-Isabella- (Female)': 'it-IT-IsabellaNeural', 'Italian-Diego- (Male)': 'it-IT-DiegoNeural', 'Italian-Elsa- (Female)': 'it-IT-ElsaNeural', 'Dutch-Colette- (Female)': 'nl-NL-ColetteNeural', 'Dutch-Fenna- (Female)': 'nl-NL-FennaNeural', 'Dutch-Maarten- (Male)': 'nl-NL-MaartenNeural', 'Malese-Osman- (Male)': 'ms-MY-OsmanNeural', 'Malese-Yasmin- (Female)': 'ms-MY-YasminNeural', 'Norwegian-Pernille- (Female)': 'nb-NO-PernilleNeural', 'Norwegian-Finn- (Male)': 'nb-NO-FinnNeural', 'Swedish-Sofie- (Female)': 'sv-SE-SofieNeural', 'ArabicSwedish-Mattias- (Male)': 'sv-SE-MattiasNeural', 'Arabic-Hamed- (Male)': 'ar-SA-HamedNeural', 'Arabic-Zariyah- (Female)': 'ar-SA-ZariyahNeural', 'Greek-Athina- (Female)': 'el-GR-AthinaNeural', 'Greek-Nestoras- (Male)': 'el-GR-NestorasNeural', 'German-Katja- (Female)': 'de-DE-KatjaNeural', 'German-Amala- (Female)': 'de-DE-AmalaNeural', 'German-Conrad- (Male)': 'de-DE-ConradNeural', 'German-Killian- (Male)': 'de-DE-KillianNeural', 'Afrikaans-Adri- (Female)': 'af-ZA-AdriNeural', 'Afrikaans-Willem- (Male)': 'af-ZA-WillemNeural', 'Ethiopian-Ameha- (Male)': 'am-ET-AmehaNeural', 'Ethiopian-Mekdes- (Female)': 'am-ET-MekdesNeural', 'Arabic (UAD)-Fatima- (Female)': 'ar-AE-FatimaNeural', 'Arabic (UAD)-Hamdan- (Male)': 'ar-AE-HamdanNeural', 'Arabic (Bahrain)-Ali- (Male)': 'ar-BH-AliNeural', 'Arabic (Bahrain)-Laila- (Female)': 'ar-BH-LailaNeural', 'Arabic (Algeria)-Ismael- (Male)': 'ar-DZ-IsmaelNeural', 'Arabic (Egypt)-Salma- (Female)': 'ar-EG-SalmaNeural', 'Arabic (Egypt)-Shakir- (Male)': 'ar-EG-ShakirNeural', 'Arabic (Iraq)-Bassel- (Male)': 'ar-IQ-BasselNeural', 'Arabic (Iraq)-Rana- (Female)': 'ar-IQ-RanaNeural', 'Arabic (Jordan)-Sana- (Female)': 'ar-JO-SanaNeural', 'Arabic (Jordan)-Taim- (Male)': 'ar-JO-TaimNeural', 'Arabic (Kuwait)-Fahed- (Male)': 'ar-KW-FahedNeural', 'Arabic (Kuwait)-Noura- (Female)': 'ar-KW-NouraNeural', 'Arabic (Lebanon)-Layla- (Female)': 'ar-LB-LaylaNeural', 'Arabic (Lebanon)-Rami- (Male)': 'ar-LB-RamiNeural', 'Arabic (Libya)-Iman- (Female)': 'ar-LY-ImanNeural', 'Arabic (Libya)-Omar- (Male)': 'ar-LY-OmarNeural', 'Arabic (Morocco)-Jamal- (Male)': 'ar-MA-JamalNeural', 'Arabic (Morocco)-Mouna- (Female)': 'ar-MA-MounaNeural', 'Arabic (Oman)-Abdullah- (Male)': 'ar-OM-AbdullahNeural', 'Arabic (Oman)-Aysha- (Female)': 'ar-OM-AyshaNeural', 'Arabic (Qatar)-Amal- (Female)': 'ar-QA-AmalNeural', 'Arabic (Qatar)-Moaz- (Male)': 'ar-QA-MoazNeural', 'Arabic (Syrian Arab Republic)-Amany- (Female)': 'ar-SY-AmanyNeural', 'Arabic (Syrian Arab Republic)-Laith- (Male)': 'ar-SY-LaithNeural', 'Arabic (Tunisia)-Hedi- (Male)': 'ar-TN-HediNeural', 'Arabic (Tunisia)-Reem- (Female)': 'ar-TN-ReemNeural', 'Arabic (Yemen )-Maryam- (Female)': 'ar-YE-MaryamNeural', 'Arabic (Yemen )-Saleh- (Male)': 'ar-YE-SalehNeural', 'Azerbaijani-Babek- (Male)': 'az-AZ-BabekNeural', 'Azerbaijani-Banu- (Female)': 'az-AZ-BanuNeural', 'Bulgarian-Borislav- (Male)': 'bg-BG-BorislavNeural', 'Bulgarian-Kalina- (Female)': 'bg-BG-KalinaNeural', 'Bengali (Bangladesh)-Nabanita- (Female)': 'bn-BD-NabanitaNeural', 'Bengali (Bangladesh)-Pradeep- (Male)': 'bn-BD-PradeepNeural', 'Bengali (India)-Bashkar- (Male)': 'bn-IN-BashkarNeural', 'Bengali (India)-Tanishaa- (Female)': 'bn-IN-TanishaaNeural', 'Bosniak (Bosnia and Herzegovina)-Goran- (Male)': 'bs-BA-GoranNeural', 'Bosniak (Bosnia and Herzegovina)-Vesna- (Female)': 'bs-BA-VesnaNeural', 'Catalan (Spain)-Joana- (Female)': 'ca-ES-JoanaNeural', 'Catalan (Spain)-Enric- (Male)': 'ca-ES-EnricNeural', 'Czech (Czech Republic)-Antonin- (Male)': 'cs-CZ-AntoninNeural', 'Czech (Czech Republic)-Vlasta- (Female)': 'cs-CZ-VlastaNeural', 'Welsh (UK)-Aled- (Male)': 'cy-GB-AledNeural', 'Welsh (UK)-Nia- (Female)': 'cy-GB-NiaNeural', 'Danish (Denmark)-Christel- (Female)': 'da-DK-ChristelNeural', 'Danish (Denmark)-Jeppe- (Male)': 'da-DK-JeppeNeural', 'German (Austria)-Ingrid- (Female)': 'de-AT-IngridNeural', 'German (Austria)-Jonas- (Male)': 'de-AT-JonasNeural', 'German (Switzerland)-Jan- (Male)': 'de-CH-JanNeural', 'German (Switzerland)-Leni- (Female)': 'de-CH-LeniNeural', 'English (Australia)-Natasha- (Female)': 'en-AU-NatashaNeural', 'English (Australia)-William- (Male)': 'en-AU-WilliamNeural', 'English (Canada)-Clara- (Female)': 'en-CA-ClaraNeural', 'English (Canada)-Liam- (Male)': 'en-CA-LiamNeural', 'English (UK)-Libby- (Female)': 'en-GB-LibbyNeural', 'English (UK)-Maisie- (Female)': 'en-GB-MaisieNeural', 'English (UK)-Ryan- (Male)': 'en-GB-RyanNeural', 'English (UK)-Sonia- (Female)': 'en-GB-SoniaNeural', 'English (UK)-Thomas- (Male)': 'en-GB-ThomasNeural', 'English (Hong Kong)-Sam- (Male)': 'en-HK-SamNeural', 'English (Hong Kong)-Yan- (Female)': 'en-HK-YanNeural', 'English (Ireland)-Connor- (Male)': 'en-IE-ConnorNeural', 'English (Ireland)-Emily- (Female)': 'en-IE-EmilyNeural', 'English (India)-Neerja- (Female)': 'en-IN-NeerjaNeural', 'English (India)-Prabhat- (Male)': 'en-IN-PrabhatNeural', 'English (Kenya)-Asilia- (Female)': 'en-KE-AsiliaNeural', 'English (Kenya)-Chilemba- (Male)': 'en-KE-ChilembaNeural', 'English (Nigeria)-Abeo- (Male)': 'en-NG-AbeoNeural', 'English (Nigeria)-Ezinne- (Female)': 'en-NG-EzinneNeural', 'English (New Zealand)-Mitchell- (Male)': 'en-NZ-MitchellNeural', 'English (Philippines)-James- (Male)': 'en-PH-JamesNeural', 'English (Philippines)-Rosa- (Female)': 'en-PH-RosaNeural', 'English (Singapore)-Luna- (Female)': 'en-SG-LunaNeural', 'English (Singapore)-Wayne- (Male)': 'en-SG-WayneNeural', 'English (Tanzania)-Elimu- (Male)': 'en-TZ-ElimuNeural', 'English (Tanzania)-Imani- (Female)': 'en-TZ-ImaniNeural', 'English (South Africa)-Leah- (Female)': 'en-ZA-LeahNeural', 'English (South Africa)-Luke- (Male)': 'en-ZA-LukeNeural', 'Spanish (Argentina)-Elena- (Female)': 'es-AR-ElenaNeural', 'Spanish (Argentina)-Tomas- (Male)': 'es-AR-TomasNeural', 'Spanish (Bolivia)-Marcelo- (Male)': 'es-BO-MarceloNeural', 'Spanish (Bolivia)-Sofia- (Female)': 'es-BO-SofiaNeural', 'Spanish (Colombia)-Gonzalo- (Male)': 'es-CO-GonzaloNeural', 'Spanish (Colombia)-Salome- (Female)': 'es-CO-SalomeNeural', 'Spanish (Costa Rica)-Juan- (Male)': 'es-CR-JuanNeural', 'Spanish (Costa Rica)-Maria- (Female)': 'es-CR-MariaNeural', 'Spanish (Cuba)-Belkys- (Female)': 'es-CU-BelkysNeural', 'Spanish (Dominican Republic)-Emilio- (Male)': 'es-DO-EmilioNeural', 'Spanish (Dominican Republic)-Ramona- (Female)': 'es-DO-RamonaNeural', 'Spanish (Ecuador)-Andrea- (Female)': 'es-EC-AndreaNeural', 'Spanish (Ecuador)-Luis- (Male)': 'es-EC-LuisNeural', 'Spanish (Spain)-Alvaro- (Male)': 'es-ES-AlvaroNeural', 'Spanish (Spain)-Elvira- (Female)': 'es-ES-ElviraNeural', 'Spanish (Equatorial Guinea)-Teresa- (Female)': 'es-GQ-TeresaNeural', 'Spanish (Guatemala)-Andres- (Male)': 'es-GT-AndresNeural', 'Spanish (Guatemala)-Marta- (Female)': 'es-GT-MartaNeural', 'Spanish (Honduras)-Carlos- (Male)': 'es-HN-CarlosNeural', 'Spanish (Honduras)-Karla- (Female)': 'es-HN-KarlaNeural', 'Spanish (Nicaragua)-Federico- (Male)': 'es-NI-FedericoNeural', 'Spanish (Nicaragua)-Yolanda- (Female)': 'es-NI-YolandaNeural', 'Spanish (Panama)-Margarita- (Female)': 'es-PA-MargaritaNeural', 'Spanish (Panama)-Roberto- (Male)': 'es-PA-RobertoNeural', 'Spanish (Peru)-Alex- (Male)': 'es-PE-AlexNeural', 'Spanish (Peru)-Camila- (Female)': 'es-PE-CamilaNeural', 'Spanish (Puerto Rico)-Karina- (Female)': 'es-PR-KarinaNeural', 'Spanish (Puerto Rico)-Victor- (Male)': 'es-PR-VictorNeural', 'Spanish (Paraguay)-Mario- (Male)': 'es-PY-MarioNeural', 'Spanish (Paraguay)-Tania- (Female)': 'es-PY-TaniaNeural', 'Spanish (El Salvador)-Lorena- (Female)': 'es-SV-LorenaNeural', 'Spanish (El Salvador)-Rodrigo- (Male)': 'es-SV-RodrigoNeural', 'Spanish (United States)-Alonso- (Male)': 'es-US-AlonsoNeural', 'Spanish (United States)-Paloma- (Female)': 'es-US-PalomaNeural', 'Spanish (Uruguay)-Mateo- (Male)': 'es-UY-MateoNeural', 'Spanish (Uruguay)-Valentina- (Female)': 'es-UY-ValentinaNeural', 'Spanish (Venezuela)-Paola- (Female)': 'es-VE-PaolaNeural', 'Spanish (Venezuela)-Sebastian- (Male)': 'es-VE-SebastianNeural', 'Estonian (Estonia)-Anu- (Female)': 'et-EE-AnuNeural', 'Estonian (Estonia)-Kert- (Male)': 'et-EE-KertNeural', 'Persian (Iran)-Dilara- (Female)': 'fa-IR-DilaraNeural', 'Persian (Iran)-Farid- (Male)': 'fa-IR-FaridNeural', 'Finnish (Finland)-Harri- (Male)': 'fi-FI-HarriNeural', 'Finnish (Finland)-Noora- (Female)': 'fi-FI-NooraNeural', 'French (Belgium)-Charline- (Female)': 'fr-BE-CharlineNeural', 'French (Belgium)-Gerard- (Male)': 'fr-BE-GerardNeural', 'French (Canada)-Sylvie- (Female)': 'fr-CA-SylvieNeural', 'French (Canada)-Antoine- (Male)': 'fr-CA-AntoineNeural', 'French (Canada)-Jean- (Male)': 'fr-CA-JeanNeural', 'French (Switzerland)-Ariane- (Female)': 'fr-CH-ArianeNeural', 'French (Switzerland)-Fabrice- (Male)': 'fr-CH-FabriceNeural', 'Irish (Ireland)-Colm- (Male)': 'ga-IE-ColmNeural', 'Irish (Ireland)-Orla- (Female)': 'ga-IE-OrlaNeural', 'Galician (Spain)-Roi- (Male)': 'gl-ES-RoiNeural', 'Galician (Spain)-Sabela- (Female)': 'gl-ES-SabelaNeural', 'Gujarati (India)-Dhwani- (Female)': 'gu-IN-DhwaniNeural', 'Gujarati (India)-Niranjan- (Male)': 'gu-IN-NiranjanNeural', 'Hindi (India)-Madhur- (Male)': 'hi-IN-MadhurNeural', 'Hindi (India)-Swara- (Female)': 'hi-IN-SwaraNeural', 'Croatian (Croatia)-Gabrijela- (Female)': 'hr-HR-GabrijelaNeural', 'Croatian (Croatia)-Srecko- (Male)': 'hr-HR-SreckoNeural', 'Hungarian (Hungary)-Noemi- (Female)': 'hu-HU-NoemiNeural', 'Hungarian (Hungary)-Tamas- (Male)': 'hu-HU-TamasNeural', 'Icelandic (Iceland)-Gudrun- (Female)': 'is-IS-GudrunNeural', 'Icelandic (Iceland)-Gunnar- (Male)': 'is-IS-GunnarNeural', 'Javanese (Indonesia)-Dimas- (Male)': 'jv-ID-DimasNeural', 'Javanese (Indonesia)-Siti- (Female)': 'jv-ID-SitiNeural', 'Georgian (Georgia)-Eka- (Female)': 'ka-GE-EkaNeural', 'Georgian (Georgia)-Giorgi- (Male)': 'ka-GE-GiorgiNeural', 'Kazakh (Kazakhstan)-Aigul- (Female)': 'kk-KZ-AigulNeural', 'Kazakh (Kazakhstan)-Daulet- (Male)': 'kk-KZ-DauletNeural', 'Khmer (Cambodia)-Piseth- (Male)': 'km-KH-PisethNeural', 'Khmer (Cambodia)-Sreymom- (Female)': 'km-KH-SreymomNeural', 'Kannada (India)-Gagan- (Male)': 'kn-IN-GaganNeural', 'Kannada (India)-Sapna- (Female)': 'kn-IN-SapnaNeural', 'Lao (Laos)-Chanthavong- (Male)': 'lo-LA-ChanthavongNeural', 'Lao (Laos)-Keomany- (Female)': 'lo-LA-KeomanyNeural', 'Lithuanian (Lithuania)-Leonas- (Male)': 'lt-LT-LeonasNeural', 'Lithuanian (Lithuania)-Ona- (Female)': 'lt-LT-OnaNeural', 'Latvian (Latvia)-Everita- (Female)': 'lv-LV-EveritaNeural', 'Latvian (Latvia)-Nils- (Male)': 'lv-LV-NilsNeural', 'Macedonian (North Macedonia)-Aleksandar- (Male)': 'mk-MK-AleksandarNeural', 'Macedonian (North Macedonia)-Marija- (Female)': 'mk-MK-MarijaNeural', 'Malayalam (India)-Midhun- (Male)': 'ml-IN-MidhunNeural', 'Malayalam (India)-Sobhana- (Female)': 'ml-IN-SobhanaNeural', 'Mongolian (Mongolia)-Bataa- (Male)': 'mn-MN-BataaNeural', 'Mongolian (Mongolia)-Yesui- (Female)': 'mn-MN-YesuiNeural', 'Marathi (India)-Aarohi- (Female)': 'mr-IN-AarohiNeural', 'Marathi (India)-Manohar- (Male)': 'mr-IN-ManoharNeural', 'Maltese (Malta)-Grace- (Female)': 'mt-MT-GraceNeural', 'Maltese (Malta)-Joseph- (Male)': 'mt-MT-JosephNeural', 'Burmese (Myanmar)-Nilar- (Female)': 'my-MM-NilarNeural', 'Burmese (Myanmar)-Thiha- (Male)': 'my-MM-ThihaNeural', 'Nepali (Nepal)-Hemkala- (Female)': 'ne-NP-HemkalaNeural', 'Nepali (Nepal)-Sagar- (Male)': 'ne-NP-SagarNeural', 'Dutch (Belgium)-Arnaud- (Male)': 'nl-BE-ArnaudNeural', 'Dutch (Belgium)-Dena- (Female)': 'nl-BE-DenaNeural', 'Polish (Poland)-Marek- (Male)': 'pl-PL-MarekNeural', 'Polish (Poland)-Zofia- (Female)': 'pl-PL-ZofiaNeural', 'Pashto (Afghanistan)-Gul Nawaz- (Male)': 'ps-AF-Gul', } def download_audio(url): ydl_opts = { 'format': 'bestaudio/best', 'outtmpl': 'ytdl/%(title)s.%(ext)s', 'postprocessors': [{ 'key': 'FFmpegExtractAudio', 'preferredcodec': 'wav', 'preferredquality': '192', }], } with yt_dlp.YoutubeDL(ydl_opts) as ydl: info_dict = ydl.extract_info(url, download=True) file_path = ydl.prepare_filename(info_dict).rsplit('.', 1)[0] + '.wav' sample_rate, audio_data = read(file_path) audio_array = np.asarray(audio_data, dtype=np.int16) return sample_rate, audio_array # Define a function to handle the entire separation process def separate_audio(input_audio, output_dir, model_voc_inst, model_deecho, model_back_voc): # Create output directory if it doesn't exist if not os.path.exists(output_dir): os.makedirs(output_dir) separator = Separator(output_dir=output_dir) # Define output files vocals = os.path.join(output_dir, 'Vocals.wav') instrumental = os.path.join(output_dir, 'Instrumental.wav') vocals_reverb = os.path.join(output_dir, 'Vocals (Reverb).wav') vocals_no_reverb = os.path.join(output_dir, 'Vocals (No Reverb).wav') lead_vocals = os.path.join(output_dir, 'Lead Vocals.wav') backing_vocals = os.path.join(output_dir, 'Backing Vocals.wav') # Splitting a track into Vocal and Instrumental separator.load_model(model_filename=model_voc_inst) voc_inst = separator.separate(input_audio) os.rename(os.path.join(output_dir, voc_inst[0]), instrumental) # Rename to “Instrumental.wav” os.rename(os.path.join(output_dir, voc_inst[1]), vocals) # Rename to “Vocals.wav” # Applying DeEcho-DeReverb to Vocals separator.load_model(model_filename=model_deecho) voc_no_reverb = separator.separate(vocals) os.rename(os.path.join(output_dir, voc_no_reverb[0]), vocals_no_reverb) # Rename to “Vocals (No Reverb).wav” os.rename(os.path.join(output_dir, voc_no_reverb[1]), vocals_reverb) # Rename to “Vocals (Reverb).wav” # Separating Back Vocals from Main Vocals separator.load_model(model_filename=model_back_voc) backing_voc = separator.separate(vocals_no_reverb) os.rename(os.path.join(output_dir, backing_voc[0]), backing_vocals) # Rename to “Backing Vocals.wav” os.rename(os.path.join(output_dir, backing_voc[1]), lead_vocals) # Rename to “Lead Vocals.wav” return instrumental, vocals, vocals_reverb, vocals_no_reverb, lead_vocals, backing_vocals # Main function to process audio (Inference) def process_audio(MODEL_NAME, SOUND_PATH, F0_CHANGE, F0_METHOD, MIN_PITCH, MAX_PITCH, CREPE_HOP_LENGTH, INDEX_RATE, FILTER_RADIUS, RMS_MIX_RATE, PROTECT, SPLIT_INFER, MIN_SILENCE, SILENCE_THRESHOLD, SEEK_STEP, KEEP_SILENCE, FORMANT_SHIFT, QUEFRENCY, TIMBRE, F0_AUTOTUNE, OUTPUT_FORMAT, upload_audio=None): # If no sound path is given, use the uploaded file if not SOUND_PATH and upload_audio is not None: SOUND_PATH = os.path.join("uploaded_audio", upload_audio.name) with open(SOUND_PATH, "wb") as f: f.write(upload_audio.read()) # Check if a model name is provided if not MODEL_NAME: return "Please provide a model name." # Run the inference os.system("chmod +x stftpitchshift") inferred_audio = infer_audio( MODEL_NAME, SOUND_PATH, F0_CHANGE, F0_METHOD, MIN_PITCH, MAX_PITCH, CREPE_HOP_LENGTH, INDEX_RATE, FILTER_RADIUS, RMS_MIX_RATE, PROTECT, SPLIT_INFER, MIN_SILENCE, SILENCE_THRESHOLD, SEEK_STEP, KEEP_SILENCE, FORMANT_SHIFT, QUEFRENCY, TIMBRE, F0_AUTOTUNE, OUTPUT_FORMAT ) return inferred_audio async def text_to_speech_edge(text, language_code): voice = language_dict.get(language_code, "default_voice") communicate = edge_tts.Communicate(text, voice) with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file: tmp_path = tmp_file.name await communicate.save(tmp_path) return tmp_path def extract_zip(extraction_folder, zip_name): os.makedirs(extraction_folder) with zipfile.ZipFile(zip_name, 'r') as zip_ref: zip_ref.extractall(extraction_folder) os.remove(zip_name) index_filepath, model_filepath = None, None for root, dirs, files in os.walk(extraction_folder): for name in files: if name.endswith('.index') and os.stat(os.path.join(root, name)).st_size > 1024 * 100: index_filepath = os.path.join(root, name) if name.endswith('.pth') and os.stat(os.path.join(root, name)).st_size > 1024 * 1024 * 40: model_filepath = os.path.join(root, name) if not model_filepath: raise Exception(f'No .pth model file was found in the extracted zip. Please check {extraction_folder}.') # move model and index file to extraction folder os.rename(model_filepath, os.path.join(extraction_folder, os.path.basename(model_filepath))) if index_filepath: os.rename(index_filepath, os.path.join(extraction_folder, os.path.basename(index_filepath))) # remove any unnecessary nested folders for filepath in os.listdir(extraction_folder): if os.path.isdir(os.path.join(extraction_folder, filepath)): shutil.rmtree(os.path.join(extraction_folder, filepath)) def download_online_model(url, dir_name): try: print(f'[~] Downloading voice model with name {dir_name}...') zip_name = url.split('/')[-1] extraction_folder = os.path.join(models_dir, dir_name) if os.path.exists(extraction_folder): raise Exception(f'Voice model directory {dir_name} already exists! Choose a different name for your voice model.') if 'pixeldrain.com' in url: url = f'https://pixeldrain.com/api/file/{zip_name}' if 'drive.google.com' in url: zip_name = dir_name + ".zip" gdown.download(url, output=zip_name, use_cookies=True, quiet=True, fuzzy=True) else: urllib.request.urlretrieve(url, zip_name) print(f'[~] Extracting zip file...') extract_zip(extraction_folder, zip_name) print(f'[+] {dir_name} Model successfully downloaded!') except Exception as e: raise Exception(str(e)) # Gradio Blocks Interface with Tabs with gr.Blocks(title="Hex RVC") as app: gr.Markdown("# Hex RVC") with gr.Tab("Inference"): with gr.Row(): MODEL_NAME = gr.Textbox(label="Model Name", placeholder="Enter model name") SOUND_PATH = gr.Textbox(label="Audio Path (Optional)", placeholder="Leave blank to upload audio") upload_audio = gr.File(label="Upload Audio", type='filepath', file_types=["audio"]) with gr.Row(): F0_CHANGE = gr.Number(label="Pitch Change (semitones)", value=0) F0_METHOD = gr.Dropdown(choices=["crepe", "harvest", "mangio-crepe", "rmvpe", "rmvpe+", "fcpe", "hybrid[mangio-crepe+rmvpe]", "hybrid[mangio-crepe+fcpe]", "hybrid[rmvpe+fcpe]", "hybrid[mangio-crepe+rmvpe+fcpe]"], label="F0 Method", value="fcpe") with gr.Row(): MIN_PITCH = gr.Textbox(label="Min Pitch", value="50") MAX_PITCH = gr.Textbox(label="Max Pitch", value="1100") CREPE_HOP_LENGTH = gr.Number(label="Crepe Hop Length", value=120) INDEX_RATE = gr.Slider(label="Index Rate", minimum=0, maximum=1, value=0.75) FILTER_RADIUS = gr.Number(label="Filter Radius", value=3) RMS_MIX_RATE = gr.Slider(label="RMS Mix Rate", minimum=0, maximum=1, value=0.25) PROTECT = gr.Slider(label="Protect", minimum=0, maximum=1, value=0.33) with gr.Accordion("Hex TTS"): input_text = gr.Textbox(lines=5, label="Input Text") #output_text = gr.Textbox(label="Output Text") #output_audio = gr.Audio(type="filepath", label="Exported Audio") language = gr.Dropdown(choices=list(language_dict.keys()), label="Choose the Voice Model") tts_convert = gr.Button("Convert") tts_convert.click(fn=text_to_speech_edge inputs=[input_text, language], output=upload_audio) with gr.Accordion("Advanced Settings", open=False): SPLIT_INFER = gr.Checkbox(label="Enable Split Inference", value=False) MIN_SILENCE = gr.Number(label="Min Silence (ms)", value=500) SILENCE_THRESHOLD = gr.Number(label="Silence Threshold (dBFS)", value=-50) SEEK_STEP = gr.Slider(label="Seek Step (ms)", minimum=1, maximum=10, value=1) KEEP_SILENCE = gr.Number(label="Keep Silence (ms)", value=200) FORMANT_SHIFT = gr.Checkbox(label="Enable Formant Shift", value=False) QUEFRENCY = gr.Number(label="Quefrency", value=0) TIMBRE = gr.Number(label="Timbre", value=1) F0_AUTOTUNE = gr.Checkbox(label="Enable F0 Autotune", value=False) OUTPUT_FORMAT = gr.Dropdown(choices=["wav", "flac", "mp3"], label="Output Format", value="wav") run_button = gr.Button("Run Inference") output_audio = gr.Audio(label="Generated Audio", type='filepath') run_button.click( process_audio, inputs=[MODEL_NAME, SOUND_PATH, F0_CHANGE, F0_METHOD, MIN_PITCH, MAX_PITCH, CREPE_HOP_LENGTH, INDEX_RATE, FILTER_RADIUS, RMS_MIX_RATE, PROTECT, SPLIT_INFER, MIN_SILENCE, SILENCE_THRESHOLD, SEEK_STEP, KEEP_SILENCE, FORMANT_SHIFT, QUEFRENCY, TIMBRE, F0_AUTOTUNE, OUTPUT_FORMAT, upload_audio], outputs=output_audio ) with gr.Tqb("Download RVC Model"): url = gr.Textbox(label="Your model URL") dirname = gr.Textbox(label="Your Model name") button_model = gr.Button("Download model") button_model.click(fn=download_online_model, inputs=url, dirname, output=dirname) with gr.Tab("Audio Separation"): with gr.Row(): input_audio = gr.Audio(source="upload", type="filepath", label="Upload Audio File") output_dir = gr.Textbox(value="/content/output", label="Output Directory") with gr.Accordion("Separation by Link", open = False): with gr.Row(): roformer_link = gr.Textbox( label = "Link", placeholder = "Paste the link here", interactive = True ) with gr.Row(): gr.Markdown("You can paste the link to the video/audio from many sites, check the complete list [here](https://github.com/yt-dlp/yt-dlp/blob/master/supportedsites.md)") with gr.Row(): roformer_download_button = gr.Button( "Download!", variant = "primary" ) roformer_download_button.click(download_audio, [roformer_link], [input_audio]) with gr.Row(): model_voc_inst = gr.Textbox(value='model_bs_roformer_ep_317_sdr_12.9755.ckpt', label="Vocal & Instrumental Model") model_deecho = gr.Textbox(value='UVR-DeEcho-DeReverb.pth', label="DeEcho-DeReverb Model") model_back_voc = gr.Textbox(value='mel_band_roformer_karaoke_aufr33_viperx_sdr_10.1956.ckpt', label="Backing Vocals Model") separate_button = gr.Button("Separate Audio") with gr.Row(): instrumental_out = gr.Audio(label="Instrumental") vocals_out = gr.Audio(label="Vocals") vocals_reverb_out = gr.Audio(label="Vocals (Reverb)") vocals_no_reverb_out = gr.Audio(label="Vocals (No Reverb)") lead_vocals_out = gr.Audio(label="Lead Vocals") backing_vocals_out = gr.Audio(label="Backing Vocals") separate_button.click( separate_audio, inputs=[input_audio, output_dir, model_voc_inst, model_deecho, model_back_voc], outputs=[instrumental_out, vocals_out, vocals_reverb_out, vocals_no_reverb_out, lead_vocals_out, backing_vocals_out] ) # Launch the Gradio app app.launch()