diff --git "a/main/app/app.py" "b/main/app/app.py" deleted file mode 100644--- "a/main/app/app.py" +++ /dev/null @@ -1,2500 +0,0 @@ -import os -import re -import ssl -import sys -import json -import torch -import codecs -import shutil -import yt_dlp -import logging -import platform -import requests -import warnings -import threading -import gradio.strings -import logging.handlers - -import gradio as gr -import pandas as pd - -from time import sleep -from subprocess import Popen -from bs4 import BeautifulSoup -from datetime import datetime -from pydub import AudioSegment -from multiprocessing import cpu_count - -sys.path.append(os.getcwd()) - -from main.configs.config import Config -from main.library.utils import pydub_convert -from main.tools import gdown, meganz, mediafire, pixeldrain, huggingface, edge_tts, google_tts - -ssl._create_default_https_context = ssl._create_unverified_context -logger = logging.getLogger(__name__) -logger.propagate = False - -if logger.hasHandlers(): logger.handlers.clear() -else: - console_handler = logging.StreamHandler() - console_formatter = logging.Formatter(fmt="\n%(asctime)s.%(msecs)03d | %(levelname)s | %(module)s | %(message)s", datefmt="%Y-%m-%d %H:%M:%S") - console_handler.setFormatter(console_formatter) - console_handler.setLevel(logging.INFO) - file_handler = logging.handlers.RotatingFileHandler(os.path.join("assets", "logs", "app.log"), maxBytes=5*1024*1024, backupCount=3, encoding='utf-8') - file_formatter = logging.Formatter(fmt="\n%(asctime)s.%(msecs)03d | %(levelname)s | %(module)s | %(message)s", datefmt="%Y-%m-%d %H:%M:%S") - file_handler.setFormatter(file_formatter) - file_handler.setLevel(logging.DEBUG) - logger.addHandler(console_handler) - logger.addHandler(file_handler) - logger.setLevel(logging.DEBUG) - -warnings.filterwarnings("ignore") -for l in ["httpx", "gradio", "uvicorn", "httpcore", "urllib3"]: - logging.getLogger(l).setLevel(logging.ERROR) - -config = Config() -python = sys.executable -translations = config.translations -configs_json = os.path.join("main", "configs", "config.json") -configs = json.load(open(configs_json, "r")) -models, model_options = {}, {} -method_f0 = ["pm", "dio", "mangio-crepe-tiny", "mangio-crepe-tiny-onnx", "mangio-crepe-small", "mangio-crepe-small-onnx", "mangio-crepe-medium", "mangio-crepe-medium-onnx", "mangio-crepe-large", "mangio-crepe-large-onnx", "mangio-crepe-full", "mangio-crepe-full-onnx", "crepe-tiny", "crepe-tiny-onnx", "crepe-small", "crepe-small-onnx", "crepe-medium", "crepe-medium-onnx", "crepe-large", "crepe-large-onnx", "crepe-full", "crepe-full-onnx", "fcpe", "fcpe-onnx", "fcpe-legacy", "fcpe-legacy-onnx", "rmvpe", "rmvpe-onnx", "rmvpe-legacy", "rmvpe-legacy-onnx", "harvest", "yin", "pyin", "hybrid"] -paths_for_files = sorted([os.path.abspath(os.path.join(root, f)) for root, _, files in os.walk("audios") for f in files if os.path.splitext(f)[1].lower() in (".wav", ".mp3", ".flac", ".ogg", ".opus", ".m4a", ".mp4", ".aac", ".alac", ".wma", ".aiff", ".webm", ".ac3")]) -model_name, index_path, delete_index = sorted(list(model for model in os.listdir(os.path.join("assets", "weights")) if model.endswith(".pth") and not model.startswith("G_") and not model.startswith("D_"))), sorted([os.path.join(root, name) for root, _, files in os.walk(os.path.join("assets", "logs"), topdown=False) for name in files if name.endswith(".index")]), sorted([os.path.join("assets", "logs", f) for f in os.listdir(os.path.join("assets", "logs")) if "mute" not in f and os.path.isdir(os.path.join("assets", "logs", f))]) -pretrainedD, pretrainedG, Allpretrained = ([model for model in os.listdir(os.path.join("assets", "models", "pretrained_custom")) if model.endswith(".pth") and "D" in model], [model for model in os.listdir(os.path.join("assets", "models", "pretrained_custom")) if model.endswith(".pth") and "G" in model], [os.path.join("assets", "models", path, model) for path in ["pretrained_v1", "pretrained_v2", "pretrained_custom"] for model in os.listdir(os.path.join("assets", "models", path)) if model.endswith(".pth") and ("D" in model or "G" in model)]) -separate_model = sorted([os.path.join("assets", "models", "uvr5", models) for models in os.listdir(os.path.join("assets", "models", "uvr5")) if models.endswith((".th", ".yaml", ".onnx"))]) -presets_file = sorted(list(f for f in os.listdir(os.path.join("assets", "presets")) if f.endswith(".json"))) -language, theme, edgetts, google_tts_voice, mdx_model, uvr_model = configs.get("language", "vi-VN"), configs.get("theme", "NoCrypt/miku"), configs.get("edge_tts", ["vi-VN-HoaiMyNeural", "vi-VN-NamMinhNeural"]), configs.get("google_tts_voice", ["vi", "en"]), configs.get("mdx_model", "MDXNET_Main"), (configs.get("demucs_model", "HD_MMI") + configs.get("mdx_model", "MDXNET_Main")) -miku_image = codecs.decode("uggcf://uhttvatsnpr.pb/NauC/Ivrganzrfr-EIP-Cebwrpg/erfbyir/znva/zvxh.cat", "rot13") -csv_path = os.path.join("assets", "spreadsheet.csv") - -if language == "vi-VN": gradio.strings.en = {"RUNNING_LOCALLY": "* Chạy trên liên kết nội bộ: {}://{}:{}", "RUNNING_LOCALLY_SSR": "* Chạy trên liên kết nội bộ: {}://{}:{}, với SSR ⚡ (thử nghiệm, để tắt hãy dùng `ssr=False` trong `launch()`)", "SHARE_LINK_DISPLAY": "* Chạy trên liên kết công khai: {}", "COULD_NOT_GET_SHARE_LINK": "\nKhông thể tạo liên kết công khai. Vui lòng kiểm tra kết nối mạng của bạn hoặc trang trạng thái của chúng tôi: https://status.gradio.app.", "COULD_NOT_GET_SHARE_LINK_MISSING_FILE": "\nKhông thể tạo liên kết công khai. Thiếu tập tin: {}. \n\nVui lòng kiểm tra kết nối internet của bạn. Điều này có thể xảy ra nếu phần mềm chống vi-rút của bạn chặn việc tải xuống tệp này. Bạn có thể cài đặt thủ công bằng cách làm theo các bước sau: \n\n1. Tải xuống tệp này: {}\n2. Đổi tên tệp đã tải xuống thành: {}\n3. Di chuyển tệp đến vị trí này: {}", "COLAB_NO_LOCAL": "Không thể hiển thị giao diện nội bộ trên google colab, liên kết công khai đã được tạo.", "PUBLIC_SHARE_TRUE": "\nĐể tạo một liên kết công khai, hãy đặt `share=True` trong `launch()`.", "MODEL_PUBLICLY_AVAILABLE_URL": "Mô hình được cung cấp công khai tại: {} (có thể mất tới một phút để sử dụng được liên kết)", "GENERATING_PUBLIC_LINK": "Đang tạo liên kết công khai (có thể mất vài giây...):", "BETA_INVITE": "\nCảm ơn bạn đã là người dùng Gradio! Nếu bạn có thắc mắc hoặc phản hồi, vui lòng tham gia máy chủ Discord của chúng tôi và trò chuyện với chúng tôi: https://discord.gg/feTf9x3ZSB", "COLAB_DEBUG_TRUE": "Đã phát hiện thấy sổ tay Colab. Ô này sẽ chạy vô thời hạn để bạn có thể xem lỗi và nhật ký. " "Để tắt, hãy đặt debug=False trong launch().", "COLAB_DEBUG_FALSE": "Đã phát hiện thấy sổ tay Colab. Để hiển thị lỗi trong sổ ghi chép colab, hãy đặt debug=True trong launch()", "COLAB_WARNING": "Lưu ý: việc mở Chrome Inspector có thể làm hỏng bản demo trong sổ tay Colab.", "SHARE_LINK_MESSAGE": "\nLiên kết công khai sẽ hết hạn sau 72 giờ. Để nâng cấp GPU và lưu trữ vĩnh viễn miễn phí, hãy chạy `gradio deploy` từ terminal trong thư mục làm việc để triển khai lên huggingface (https://huggingface.co/spaces)", "INLINE_DISPLAY_BELOW": "Đang tải giao diện bên dưới...", "COULD_NOT_GET_SHARE_LINK_CHECKSUM": "\nKhông thể tạo liên kết công khai. Tổng kiểm tra không khớp cho tập tin: {}."} -if not os.path.exists(os.path.join("assets", "miku.png")): huggingface.HF_download_file(miku_image, os.path.join("assets", "miku.png")) - -if os.path.exists(csv_path): cached_data = pd.read_csv(csv_path) -else: - cached_data = pd.read_csv(codecs.decode("uggcf://qbpf.tbbtyr.pbz/fcernqfurrgf/q/1gNHnDeRULtEfz1Yieaw14USUQjWJy0Oq9k0DrCrjApb/rkcbeg?sbezng=pfi&tvq=1977693859", "rot13")) - cached_data.to_csv(csv_path, index=False) - -for _, row in cached_data.iterrows(): - filename = row['Filename'] - url = None - for value in row.values: - if isinstance(value, str) and "huggingface" in value: - url = value - break - if url: models[filename] = url - -def gr_info(message): - gr.Info(message, duration=2) - logger.info(message) - -def gr_warning(message): - gr.Warning(message, duration=2) - logger.warning(message) - -def gr_error(message): - gr.Error(message=message, duration=6) - logger.error(message) - -def get_gpu_info(): - ngpu = torch.cuda.device_count() - gpu_infos = [f"{i}: {torch.cuda.get_device_name(i)} ({int(torch.cuda.get_device_properties(i).total_memory / 1024 / 1024 / 1024 + 0.4)} GB)" for i in range(ngpu) if torch.cuda.is_available() or ngpu != 0] - return "\n".join(gpu_infos) if len(gpu_infos) > 0 else translations["no_support_gpu"] - -def change_audios_choices(): - return {"value": "", "choices": sorted([os.path.abspath(os.path.join(root, f)) for root, _, files in os.walk("audios") for f in files if os.path.splitext(f)[1].lower() in (".wav", ".mp3", ".flac", ".ogg", ".opus", ".m4a", ".mp4", ".aac", ".alac", ".wma", ".aiff", ".webm", ".ac3")]), "__type__": "update"} - -def change_separate_choices(): - return [{"choices": sorted([os.path.join("assets", "models", "uvr5", models) for models in os.listdir(os.path.join("assets", "models", "uvr5")) if model.endswith((".th", ".yaml", ".onnx"))]), "__type__": "update"}] - -def change_models_choices(): - return [{"value": "", "choices": sorted(list(model for model in os.listdir(os.path.join("assets", "weights")) if model.endswith(".pth") and not model.startswith("G_") and not model.startswith("D_"))), "__type__": "update"}, {"value": "", "choices": sorted([os.path.join(root, name) for root, _, files in os.walk(os.path.join("assets", "logs"), topdown=False) for name in files if name.endswith(".index")]), "__type__": "update"}] - -def change_allpretrained_choices(): - return [{"choices": sorted([os.path.join("assets", "models", path, model) for path in ["pretrained_v1", "pretrained_v2", "pretrained_custom"] for model in os.listdir(os.path.join("assets", "models", path)) if model.endswith(".pth") and ("D" in model or "G" in model)]), "__type__": "update"}] - -def change_pretrained_choices(): - return [{"choices": sorted([model for model in os.listdir(os.path.join("assets", "models", "pretrained_custom")) if model.endswith(".pth") and "D" in model]), "__type__": "update"}, {"choices": sorted([model for model in os.listdir(os.path.join("assets", "models", "pretrained_custom")) if model.endswith(".pth") and "G" in model]), "__type__": "update"}] - -def change_choices_del(): - return [{"choices": sorted(list(model for model in os.listdir(os.path.join("assets", "weights")) if model.endswith(".pth") and not model.startswith("G_") and not model.startswith("D_"))), "__type__": "update"}, {"choices": sorted([os.path.join("assets", "logs", f) for f in os.listdir(os.path.join("assets", "logs")) if "mute" not in f and os.path.isdir(os.path.join("assets", "logs", f))]), "__type__": "update"}] - -def change_preset_choices(): - return {"value": "", "choices": sorted(list(f for f in os.listdir(os.path.join("assets", "presets")) if f.endswith(".json"))), "__type__": "update"} - -def change_tts_voice_choices(google): - return {"choices": google_tts_voice if google else edgetts, "value": google_tts_voice[0] if google else edgetts[0], "__type__": "update"} - -def change_backing_choices(backing, merge): - if backing or merge: return {"value": False, "interactive": False, "__type__": "update"} - elif not backing or not merge: return {"interactive": True, "__type__": "update"} - else: gr_warning(translations["option_not_valid"]) - -def change_download_choices(select): - selects = [False]*10 - - if select == translations["download_url"]: selects[0] = selects[1] = selects[2] = True - elif select == translations["download_from_csv"]: selects[3] = selects[4] = True - elif select == translations["search_models"]: selects[5] = selects[6] = True - elif select == translations["upload"]: selects[9] = True - else: gr_warning(translations["option_not_valid"]) - - return [{"visible": selects[i], "__type__": "update"} for i in range(len(selects))] - -def change_download_pretrained_choices(select): - selects = [False]*8 - - if select == translations["download_url"]: selects[0] = selects[1] = selects[2] = True - elif select == translations["list_model"]: selects[3] = selects[4] = selects[5] = True - elif select == translations["upload"]: selects[6] = selects[7] = True - else: gr_warning(translations["option_not_valid"]) - - return [{"visible": selects[i], "__type__": "update"} for i in range(len(selects))] - -def get_index(model): - model = os.path.basename(model).split("_")[0] - return {"value": next((f for f in [os.path.join(root, name) for root, _, files in os.walk(os.path.join("assets", "logs"), topdown=False) for name in files if name.endswith(".index") and "trained" not in name] if model.split(".")[0] in f), ""), "__type__": "update"} if model else None - -def index_strength_show(index): - return {"visible": index and os.path.exists(index), "value": 0.5, "__type__": "update"} - -def hoplength_show(method, hybrid_method=None): - show_hop_length_method = ["mangio-crepe-tiny", "mangio-crepe-tiny-onnx", "mangio-crepe-small", "mangio-crepe-small-onnx", "mangio-crepe-medium", "mangio-crepe-medium-onnx", "mangio-crepe-large", "mangio-crepe-large-onnx", "mangio-crepe-full", "mangio-crepe-full-onnx", "fcpe-legacy", "fcpe-legacy-onnx", "yin", "pyin"] - - if method in show_hop_length_method: visible = True - elif method == "hybrid": - methods_str = re.search("hybrid\[(.+)\]", hybrid_method) - if methods_str: methods = [method.strip() for method in methods_str.group(1).split("+")] - - for i in methods: - visible = i in show_hop_length_method - if visible: break - else: visible = False - - return {"visible": visible, "__type__": "update"} - -def visible(value): - return {"visible": value, "__type__": "update"} - -def valueFalse_interactive(inp): - return {"value": False, "interactive": inp, "__type__": "update"} - -def valueEmpty_visible1(inp1): - return {"value": "", "visible": inp1, "__type__": "update"} - -def process_input(file_path): - with open(file_path, "r", encoding="utf-8") as file: - file_contents = file.read() - - gr_info(translations["upload_success"].format(name=translations["text"])) - return file_contents - -def fetch_pretrained_data(): - response = requests.get(codecs.decode("uggcf://uhttvatsnpr.pb/NauC/Ivrganzrfr-EIP-Cebwrpg/erfbyir/znva/wfba/phfgbz_cergenvarq.wfba", "rot13")) - response.raise_for_status() - return response.json() - -def update_sample_rate_dropdown(model): - data = fetch_pretrained_data() - if model != translations["success"]: return {"choices": list(data[model].keys()), "value": list(data[model].keys())[0], "__type__": "update"} - -def if_done(done, p): - while 1: - if p.poll() is None: sleep(0.5) - else: break - - done[0] = True - -def restart_app(): - global app - - gr_info(translations["15s"]) - os.system("cls" if platform.system() == "Windows" else "clear") - - app.close() - os.system(f"{python} {os.path.join('main', 'app', 'app.py')} --share") - -def change_language(lang): - with open(configs_json, "r") as f: - configs = json.load(f) - - configs["language"] = lang - with open(configs_json, "w") as f: - json.dump(configs, f, indent=4) - - restart_app() - -def change_theme(theme): - with open(configs_json, "r") as f: - configs = json.load(f) - - configs["theme"] = theme - with open(configs_json, "w") as f: - json.dump(configs, f, indent=4) - - restart_app() - -def zip_file(name, pth, index): - pth_path = os.path.join("assets", "weights", pth) - if not pth or not os.path.exists(pth_path) or not pth.endswith(".pth"): return gr_warning(translations["provide_file"].format(filename=translations["model"])) - - zip_file_path = os.path.join("assets", name + ".zip") - gr_info(translations["start"].format(start=translations["zip"])) - - import zipfile - with zipfile.ZipFile(zip_file_path, 'w') as zipf: - zipf.write(pth_path, os.path.basename(pth_path)) - if index: zipf.write(index, os.path.basename(index)) - - gr_info(translations["success"]) - return {"visible": True, "value": zip_file_path, "__type__": "update"} - -def fetch_models_data(search): - all_table_data = [] - page = 1 - - while 1: - try: - response = requests.post(url=codecs.decode("uggcf://ibvpr-zbqryf.pbz/srgpu_qngn.cuc", "rot13"), data={"page": page, "search": search}) - - if response.status_code == 200: - table_data = response.json().get("table", "") - if not table_data.strip(): break - all_table_data.append(table_data) - page += 1 - else: - logger.debug(f"{translations['code_error']} {response.status_code}") - break - except json.JSONDecodeError: - logger.debug(translations["json_error"]) - break - except requests.RequestException as e: - logger.debug(translations["requests_error"].format(e=e)) - break - return all_table_data - -def search_models(name): - gr_info(translations["start"].format(start=translations["search"])) - tables = fetch_models_data(name) - - if len(tables) == 0: - gr_info(translations["not_found"].format(name=name)) - return [None]*2 - else: - model_options.clear() - for table in tables: - for row in BeautifulSoup(table, "html.parser").select("tr"): - name_tag, url_tag = row.find("a", {"class": "fs-5"}), row.find("a", {"class": "btn btn-sm fw-bold btn-light ms-0 p-1 ps-2 pe-2"}) - if name_tag and url_tag: model_options[name_tag.text.replace(".pth", "").replace(".index", "").replace(".zip", "").replace(" ", "_").replace("(", "").replace(")", "").replace("[", "").replace("]", "").replace(",", "").replace('"', "").replace("'", "").replace("|", "").strip()] = url_tag["href"].replace("https://easyaivoice.com/run?url=", "") - - gr_info(translations["found"].format(results=len(model_options))) - return [{"value": "", "choices": model_options, "interactive": True, "visible": True, "__type__": "update"}, {"value": translations["downloads"], "visible": True, "__type__": "update"}] - -def move_files_from_directory(src_dir, dest_weights, dest_logs, model_name): - for root, _, files in os.walk(src_dir): - for file in files: - file_path = os.path.join(root, file) - if file.endswith(".index"): - model_log_dir = os.path.join(dest_logs, model_name) - os.makedirs(model_log_dir, exist_ok=True) - - filepath = os.path.join(model_log_dir, file.replace(' ', '_').replace('(', '').replace(')', '').replace('[', '').replace(']', '').replace(",", "").replace('"', "").replace("'", "").replace("|", "").strip()) - if os.path.exists(filepath): os.remove(filepath) - shutil.move(file_path, filepath) - elif file.endswith(".pth") and not file.startswith("D_") and not file.startswith("G_"): - pth_path = os.path.join(dest_weights, model_name + ".pth") - if os.path.exists(pth_path): os.remove(pth_path) - shutil.move(file_path, pth_path) - -def download_url(url): - if not url: return gr_warning(translations["provide_url"]) - if not os.path.exists("audios"): os.makedirs("audios", exist_ok=True) - - with warnings.catch_warnings(): - warnings.filterwarnings("ignore") - ydl_opts = {"format": "bestaudio/best", "postprocessors": [{"key": "FFmpegExtractAudio", "preferredcodec": "wav", "preferredquality": "192"}], "quiet": True, "no_warnings": True, "noplaylist": True, "verbose": False} - - gr_info(translations["start"].format(start=translations["download_music"])) - - with yt_dlp.YoutubeDL(ydl_opts) as ydl: - audio_output = os.path.join("audios", re.sub(r'\s+', '-', re.sub(r'[^\w\s\u4e00-\u9fff\uac00-\ud7af\u0400-\u04FF\u1100-\u11FF]', '', ydl.extract_info(url, download=False).get('title', 'video')).strip())) - if os.path.exists(audio_output): shutil.rmtree(audio_output, ignore_errors=True) - - ydl_opts['outtmpl'] = audio_output - - with yt_dlp.YoutubeDL(ydl_opts) as ydl: - audio_output = audio_output + ".wav" - if os.path.exists(audio_output): os.remove(audio_output) - - ydl.download([url]) - - gr_info(translations["success"]) - return [audio_output, audio_output, translations["success"]] - -def download_model(url=None, model=None): - if not url: return gr_warning(translations["provide_url"]) - if not model: return gr_warning(translations["provide_name_is_save"]) - - model = model.replace(".pth", "").replace(".index", "").replace(".zip", "").replace(" ", "_").replace("(", "").replace(")", "").replace("[", "").replace("]", "").replace(",", "").replace('"', "").replace("'", "").replace("|", "").strip() - url = url.replace("/blob/", "/resolve/").replace("?download=true", "").strip() - - download_dir = os.path.join("download_model") - weights_dir = os.path.join("assets", "weights") - logs_dir = os.path.join("assets", "logs") - - if not os.path.exists(download_dir): os.makedirs(download_dir, exist_ok=True) - if not os.path.exists(weights_dir): os.makedirs(weights_dir, exist_ok=True) - if not os.path.exists(logs_dir): os.makedirs(logs_dir, exist_ok=True) - - try: - gr_info(translations["start"].format(start=translations["download"])) - - if url.endswith(".pth"): huggingface.HF_download_file(url, os.path.join(weights_dir, f"{model}.pth")) - elif url.endswith(".index"): - model_log_dir = os.path.join(logs_dir, model) - os.makedirs(model_log_dir, exist_ok=True) - - huggingface.HF_download_file(url, os.path.join(model_log_dir, f"{model}.index")) - elif url.endswith(".zip"): - output_path = huggingface.HF_download_file(url, os.path.join(download_dir, model + ".zip")) - shutil.unpack_archive(output_path, download_dir) - - move_files_from_directory(download_dir, weights_dir, logs_dir, model) - else: - if "drive.google.com" in url or "drive.usercontent.google.com" in url: - file_id = None - - if "/file/d/" in url: file_id = url.split("/d/")[1].split("/")[0] - elif "open?id=" in url: file_id = url.split("open?id=")[1].split("/")[0] - elif "/download?id=" in url: file_id = url.split("/download?id=")[1].split("&")[0] - - if file_id: - file = gdown.gdown_download(id=file_id, output=download_dir) - if file.endswith(".zip"): shutil.unpack_archive(file, download_dir) - - move_files_from_directory(download_dir, weights_dir, logs_dir, model) - elif "mega.nz" in url: - meganz.mega_download_url(url, download_dir) - - file_download = next((f for f in os.listdir(download_dir)), None) - if file_download.endswith(".zip"): shutil.unpack_archive(os.path.join(download_dir, file_download), download_dir) - - move_files_from_directory(download_dir, weights_dir, logs_dir, model) - elif "mediafire.com" in url: - file = mediafire.Mediafire_Download(url, download_dir) - if file.endswith(".zip"): shutil.unpack_archive(file, download_dir) - - move_files_from_directory(download_dir, weights_dir, logs_dir, model) - elif "pixeldrain.com" in url: - file = pixeldrain.pixeldrain(url, download_dir) - if file.endswith(".zip"): shutil.unpack_archive(file, download_dir) - - move_files_from_directory(download_dir, weights_dir, logs_dir, model) - else: - gr_warning(translations["not_support_url"]) - return translations["not_support_url"] - - gr_info(translations["success"]) - return translations["success"] - except Exception as e: - gr_error(message=translations["error_occurred"].format(e=e)) - logger.debug(e) - return translations["error_occurred"].format(e=e) - finally: - shutil.rmtree(download_dir, ignore_errors=True) - -def save_drop_model(dropbox): - weight_folder = os.path.join("assets", "weights") - logs_folder = os.path.join("assets", "logs") - save_model_temp = os.path.join("save_model_temp") - - if not os.path.exists(weight_folder): os.makedirs(weight_folder, exist_ok=True) - if not os.path.exists(logs_folder): os.makedirs(logs_folder, exist_ok=True) - if not os.path.exists(save_model_temp): os.makedirs(save_model_temp, exist_ok=True) - - shutil.move(dropbox, save_model_temp) - - try: - file_name = os.path.basename(dropbox) - - if file_name.endswith(".pth") and file_name.endswith(".index"): gr_warning(translations["not_model"]) - else: - if file_name.endswith(".zip"): - shutil.unpack_archive(os.path.join(save_model_temp, file_name), save_model_temp) - move_files_from_directory(save_model_temp, weight_folder, logs_folder, file_name.replace(".zip", "")) - elif file_name.endswith(".pth"): - output_file = os.path.join(weight_folder, file_name) - if os.path.exists(output_file): os.remove(output_file) - - shutil.move(os.path.join(save_model_temp, file_name), output_file) - elif file_name.endswith(".index"): - def extract_name_model(filename): - match = re.search(r"([A-Za-z]+)(?=_v|\.|$)", filename) - return match.group(1) if match else None - - model_logs = os.path.join(logs_folder, extract_name_model(file_name)) - if not os.path.exists(model_logs): os.makedirs(model_logs, exist_ok=True) - shutil.move(os.path.join(save_model_temp, file_name), model_logs) - else: - gr_warning(translations["unable_analyze_model"]) - return None - - gr_info(translations["upload_success"].format(name=translations["model"])) - return None - except Exception as e: - gr_error(message=translations["error_occurred"].format(e=e)) - logger.debug(e) - return None - finally: - shutil.rmtree(save_model_temp, ignore_errors=True) - -def download_pretrained_model(choices, model, sample_rate): - if choices == translations["list_model"]: - paths = fetch_pretrained_data()[model][sample_rate] - pretraineds_custom_path = os.path.join("assets", "models", "pretrained_custom") - - if not os.path.exists(pretraineds_custom_path): os.makedirs(pretraineds_custom_path, exist_ok=True) - url = codecs.decode("uggcf://uhttvatsnpr.pb/NauC/Ivrganzrfr-EIP-Cebwrpg/erfbyir/znva/cergenvarq_phfgbz/", "rot13") + paths - - gr_info(translations["download_pretrain"]) - file = huggingface.HF_download_file(url.replace("/blob/", "/resolve/").replace("?download=true", "").strip(), os.path.join(pretraineds_custom_path, paths)) - - if file.endswith(".zip"): - shutil.unpack_archive(file, pretraineds_custom_path) - os.remove(file) - - gr_info(translations["success"]) - return translations["success"] - elif choices == translations["download_url"]: - if not model: return gr_warning(translations["provide_pretrain"].format(dg="D")) - if not sample_rate: return gr_warning(translations["provide_pretrain"].format(dg="G")) - - gr_info(translations["download_pretrain"]) - - huggingface.HF_download_file(model.replace("/blob/", "/resolve/").replace("?download=true", "").strip(), pretraineds_custom_path) - huggingface.HF_download_file(sample_rate.replace("/blob/", "/resolve/").replace("?download=true", "").strip(), pretraineds_custom_path) - - gr_info(translations["success"]) - return translations["success"] - -def hubert_download(hubert): - if not hubert: - gr_warning(translations["provide_hubert"]) - return translations["provide_hubert"] - - huggingface.HF_download_file(hubert.replace("/blob/", "/resolve/").replace("?download=true", "").strip(), os.path.join("assets", "models", "embedders")) - - gr_info(translations["success"]) - return translations["success"] - -def fushion_model(name, pth_1, pth_2, ratio): - if not name: - gr_warning(translations["provide_name_is_save"]) - return [translations["provide_name_is_save"], None] - - if not name.endswith(".pth"): name = name + ".pth" - - if not pth_1 or not os.path.exists(pth_1) or not pth_1.endswith(".pth"): - gr_warning(translations["provide_file"].format(filename=translations["model"] + " 1")) - return [translations["provide_file"].format(filename=translations["model"] + " 1"), None] - - if not pth_2 or not os.path.exists(pth_2) or not pth_1.endswith(".pth"): - gr_warning(translations["provide_file"].format(filename=translations["model"] + " 2")) - return [translations["provide_file"].format(filename=translations["model"] + " 2"), None] - - from collections import OrderedDict - - def extract(ckpt): - a = ckpt["model"] - opt = OrderedDict() - opt["weight"] = {} - - for key in a.keys(): - if "enc_q" in key: continue - - opt["weight"][key] = a[key] - - return opt - - try: - ckpt1 = torch.load(pth_1, map_location="cpu") - ckpt2 = torch.load(pth_2, map_location="cpu") - - if ckpt1["sr"] != ckpt2["sr"]: - gr_warning(translations["sr_not_same"]) - return [translations["sr_not_same"], None] - - cfg = ckpt1["config"] - cfg_f0 = ckpt1["f0"] - cfg_version = ckpt1["version"] - cfg_sr = ckpt1["sr"] - - ckpt1 = extract(ckpt1) if "model" in ckpt1 else ckpt1["weight"] - ckpt2 = extract(ckpt2) if "model" in ckpt2 else ckpt2["weight"] - - if sorted(list(ckpt1.keys())) != sorted(list(ckpt2.keys())): - gr_warning(translations["architectures_not_same"]) - return [translations["architectures_not_same"], None] - - gr_info(translations["start"].format(start=translations["fushion_model"])) - - opt = OrderedDict() - opt["weight"] = {} - - for key in ckpt1.keys(): - if key == "emb_g.weight" and ckpt1[key].shape != ckpt2[key].shape: - min_shape0 = min(ckpt1[key].shape[0], ckpt2[key].shape[0]) - opt["weight"][key] = (ratio * (ckpt1[key][:min_shape0].float()) + (1 - ratio) * (ckpt2[key][:min_shape0].float())).half() - else: opt["weight"][key] = (ratio * (ckpt1[key].float()) + (1 - ratio) * (ckpt2[key].float())).half() - - opt["config"] = cfg - opt["sr"] = cfg_sr - opt["f0"] = cfg_f0 - opt["version"] = cfg_version - opt["infos"] = translations["model_fushion_info"].format(name=name, pth_1=pth_1, pth_2=pth_2, ratio=ratio) - - output_model = os.path.join("assets", "weights") - if not os.path.exists(output_model): os.makedirs(output_model, exist_ok=True) - - torch.save(opt, os.path.join(output_model, name)) - - gr_info(translations["success"]) - return [translations["success"], os.path.join(output_model, name)] - except Exception as e: - gr_error(message=translations["error_occurred"].format(e=e)) - logger.debug(e) - return [e, None] - -def model_info(path): - if not path or not os.path.exists(path) or os.path.isdir(path) or not path.endswith(".pth"): return gr_warning(translations["provide_file"].format(filename=translations["model"])) - - def prettify_date(date_str): - if date_str == translations["not_found_create_time"]: return None - - try: - return datetime.strptime(date_str, "%Y-%m-%dT%H:%M:%S.%f").strftime("%Y-%m-%d %H:%M:%S") - except ValueError as e: - logger.debug(e) - return translations["format_not_valid"] - - model_data = torch.load(path, map_location=torch.device("cpu")) - gr_info(translations["read_info"]) - - epochs = model_data.get("epoch", None) - if epochs is None: - epochs = model_data.get("info", None) - try: - epoch = epochs.replace("epoch", "").replace("e", "").isdigit() - if epoch and epochs is None: epochs = translations["not_found"].format(name=translations["epoch"]) - except: - pass - - steps = model_data.get("step", translations["not_found"].format(name=translations["step"])) - sr = model_data.get("sr", translations["not_found"].format(name=translations["sr"])) - f0 = model_data.get("f0", translations["not_found"].format(name=translations["f0"])) - version = model_data.get("version", translations["not_found"].format(name=translations["version"])) - creation_date = model_data.get("creation_date", translations["not_found_create_time"]) - model_hash = model_data.get("model_hash", translations["not_found"].format(name="model_hash")) - pitch_guidance = translations["trained_f0"] if f0 else translations["not_f0"] - creation_date_str = prettify_date(creation_date) if creation_date else translations["not_found_create_time"] - model_name = model_data.get("model_name", translations["unregistered"]) - model_author = model_data.get("author", translations["not_author"]) - - gr_info(translations["success"]) - return translations["model_info"].format(model_name=model_name, model_author=model_author, epochs=epochs, steps=steps, version=version, sr=sr, pitch_guidance=pitch_guidance, model_hash=model_hash, creation_date_str=creation_date_str) - -def audio_effects(input_path, output_path, resample, resample_sr, chorus_depth, chorus_rate, chorus_mix, chorus_delay, chorus_feedback, distortion_drive, reverb_room_size, reverb_damping, reverb_wet_level, reverb_dry_level, reverb_width, reverb_freeze_mode, pitch_shift, delay_seconds, delay_feedback, delay_mix, compressor_threshold, compressor_ratio, compressor_attack_ms, compressor_release_ms, limiter_threshold, limiter_release, gain_db, bitcrush_bit_depth, clipping_threshold, phaser_rate_hz, phaser_depth, phaser_centre_frequency_hz, phaser_feedback, phaser_mix, bass_boost_db, bass_boost_frequency, treble_boost_db, treble_boost_frequency, fade_in_duration, fade_out_duration, export_format, chorus, distortion, reverb, delay, compressor, limiter, gain, bitcrush, clipping, phaser, treble_bass_boost, fade_in_out, audio_combination, audio_combination_input): - if not input_path or not os.path.exists(input_path) or os.path.isdir(input_path): - gr_warning(translations["input_not_valid"]) - return None - - if not output_path: - gr_warning(translations["output_not_valid"]) - return None - - if os.path.isdir(output_path): output_path = os.path.join(output_path, f"audio_effects.{export_format}") - output_dir = os.path.dirname(output_path) or output_path - - if not os.path.exists(output_dir): os.makedirs(output_dir, exist_ok=True) - if os.path.exists(output_path): os.remove(output_path) - - gr_info(translations["start"].format(start=translations["apply_effect"])) - os.system(f'{python} main/inference/audio_effects.py --input_path "{input_path}" --output_path "{output_path}" --resample {resample} --resample_sr {resample_sr} --chorus_depth {chorus_depth} --chorus_rate {chorus_rate} --chorus_mix {chorus_mix} --chorus_delay {chorus_delay} --chorus_feedback {chorus_feedback} --drive_db {distortion_drive} --reverb_room_size {reverb_room_size} --reverb_damping {reverb_damping} --reverb_wet_level {reverb_wet_level} --reverb_dry_level {reverb_dry_level} --reverb_width {reverb_width} --reverb_freeze_mode {reverb_freeze_mode} --pitch_shift {pitch_shift} --delay_seconds {delay_seconds} --delay_feedback {delay_feedback} --delay_mix {delay_mix} --compressor_threshold {compressor_threshold} --compressor_ratio {compressor_ratio} --compressor_attack_ms {compressor_attack_ms} --compressor_release_ms {compressor_release_ms} --limiter_threshold {limiter_threshold} --limiter_release {limiter_release} --gain_db {gain_db} --bitcrush_bit_depth {bitcrush_bit_depth} --clipping_threshold {clipping_threshold} --phaser_rate_hz {phaser_rate_hz} --phaser_depth {phaser_depth} --phaser_centre_frequency_hz {phaser_centre_frequency_hz} --phaser_feedback {phaser_feedback} --phaser_mix {phaser_mix} --bass_boost_db {bass_boost_db} --bass_boost_frequency {bass_boost_frequency} --treble_boost_db {treble_boost_db} --treble_boost_frequency {treble_boost_frequency} --fade_in_duration {fade_in_duration} --fade_out_duration {fade_out_duration} --export_format {export_format} --chorus {chorus} --distortion {distortion} --reverb {reverb} --pitchshift {pitch_shift != 0} --delay {delay} --compressor {compressor} --limiter {limiter} --gain {gain} --bitcrush {bitcrush} --clipping {clipping} --phaser {phaser} --treble_bass_boost {treble_bass_boost} --fade_in_out {fade_in_out} --audio_combination {audio_combination} --audio_combination_input "{audio_combination_input}"') - - gr_info(translations["success"]) - return output_path - -async def TTS(prompt, voice, speed, output, pitch, google): - if not prompt: - gr_warning(translations["enter_the_text"]) - return None - - if not voice: - gr_warning(translations["choose_voice"]) - return None - - if not output: - gr_warning(translations["output_not_valid"]) - return None - - if os.path.isdir(output): output = os.path.join(output, f"tts.wav") - gr_info(translations["convert"].format(name=translations["text"])) - - output_dir = os.path.dirname(output) or output - if not os.path.exists(output_dir): os.makedirs(output_dir, exist_ok=True) - - if not google: await edge_tts.Communicate(text=prompt, voice=voice, rate=f"+{speed}%" if speed >= 0 else f"{speed}%", pitch=f"+{pitch}Hz" if pitch >= 0 else f"{pitch}Hz").save(output) - else: google_tts.google_tts(text=prompt, lang=voice, speed=speed, pitch=pitch, output_file=output) - - gr_info(translations["success"]) - return output - -def separator_music(input, output_audio, format, shifts, segments_size, overlap, clean_audio, clean_strength, denoise, separator_model, kara_model, backing, reverb, backing_reverb, hop_length, batch_size, sample_rate): - output = os.path.dirname(output_audio) or output_audio - - if not input or not os.path.exists(input) or os.path.isdir(input): - gr_warning(translations["input_not_valid"]) - return [None]*4 - - if not os.path.exists(output): - gr_warning(translations["output_not_valid"]) - return [None]*4 - - filename, _ = os.path.splitext(os.path.basename(input)) - output = os.path.join(output, filename) - - if not os.path.exists(output): os.makedirs(output) - gr_info(translations["start"].format(start=translations["separator_music"])) - - os.system(f'{python} main/inference/separator_music.py --input_path "{input}" --output_path "{output}" --format {format} --shifts {shifts} --segments_size {segments_size} --overlap {overlap} --mdx_hop_length {hop_length} --mdx_batch_size {batch_size} --clean_audio {clean_audio} --clean_strength {clean_strength} --kara_model {kara_model} --backing {backing} --mdx_denoise {denoise} --reverb {reverb} --backing_reverb {backing_reverb} --model_name "{separator_model}" --sample_rate {sample_rate}') - gr_info(translations["success"]) - - return [os.path.join(output, f"Original_Vocals_No_Reverb.{format}") if reverb else os.path.join(output, f"Original_Vocals.{format}"), os.path.join(output, f"Instruments.{format}"), (os.path.join(output, f"Main_Vocals_No_Reverb.{format}") if reverb else os.path.join(output, f"Main_Vocals.{format}") if backing else None), (os.path.join(output, f"Backing_Vocals_No_Reverb.{format}") if backing_reverb else os.path.join(output, f"Backing_Vocals.{format}") if backing else None)] - -def convert(pitch, filter_radius, index_rate, volume_envelope, protect, hop_length, f0_method, input_path, output_path, pth_path, index_path, f0_autotune, clean_audio, clean_strength, export_format, embedder_model, resample_sr, split_audio, f0_autotune_strength, checkpointing): - os.system(f'{python} main/inference/convert.py --pitch {pitch} --filter_radius {filter_radius} --index_rate {index_rate} --volume_envelope {volume_envelope} --protect {protect} --hop_length {hop_length} --f0_method {f0_method} --input_path "{input_path}" --output_path "{output_path}" --pth_path "{pth_path}" --index_path "{index_path}" --f0_autotune {f0_autotune} --clean_audio {clean_audio} --clean_strength {clean_strength} --export_format {export_format} --embedder_model {embedder_model.replace(".pt", "")} --resample_sr {resample_sr} --split_audio {split_audio} --f0_autotune_strength {f0_autotune_strength} --checkpointing {checkpointing}') - -def convert_audio(clean, autotune, use_audio, use_original, convert_backing, not_merge_backing, merge_instrument, pitch, clean_strength, model, index, index_rate, input, output, format, method, hybrid_method, hop_length, embedders, custom_embedders, resample_sr, filter_radius, volume_envelope, protect, split_audio, f0_autotune_strength, input_audio_name, checkpointing): - model_path = os.path.join("assets", "weights", model) - return_none = [None]*6 - return_none[5] = {"visible": True, "__type__": "update"} - - if not use_audio: - if merge_instrument or not_merge_backing or convert_backing or use_original: - gr_warning(translations["turn_on_use_audio"]) - return return_none - - if use_original: - if convert_backing: - gr_warning(translations["turn_off_convert_backup"]) - return return_none - elif not_merge_backing: - gr_warning(translations["turn_off_merge_backup"]) - return return_none - - if not model or not os.path.exists(model_path) or os.path.isdir(model_path) or not model.endswith(".pth"): - gr_warning(translations["provide_file"].format(filename=translations["model"])) - return return_none - - f0method, embedder_model = (method if method != "hybrid" else hybrid_method), (embedders if embedders != "custom" else custom_embedders) - - if use_audio: - output_audio = os.path.join("audios", input_audio_name) - - def get_audio_file(label): - matching_files = [f for f in os.listdir(output_audio) if label in f] - - if not matching_files: return translations["notfound"] - return os.path.join(output_audio, matching_files[0]) - - output_path = os.path.join(output_audio, f"Convert_Vocals.{format}") - output_backing = os.path.join(output_audio, f"Convert_Backing.{format}") - output_merge_backup = os.path.join(output_audio, f"Vocals+Backing.{format}") - output_merge_instrument = os.path.join(output_audio, f"Vocals+Instruments.{format}") - - if os.path.exists(output_audio): os.makedirs(output_audio, exist_ok=True) - if os.path.exists(output_path): os.remove(output_path) - - if use_original: - original_vocal = get_audio_file('Original_Vocals_No_Reverb.') - - if original_vocal == translations["notfound"]: original_vocal = get_audio_file('Original_Vocals.') - - if original_vocal == translations["notfound"]: - gr_warning(translations["not_found_original_vocal"]) - return return_none - - input_path = original_vocal - else: - main_vocal = get_audio_file('Main_Vocals_No_Reverb.') - backing_vocal = get_audio_file('Backing_Vocals_No_Reverb.') - - if main_vocal == translations["notfound"]: main_vocal = get_audio_file('Main_Vocals.') - if not not_merge_backing and backing_vocal == translations["notfound"]: backing_vocal = get_audio_file('Backing_Vocals.') - - if main_vocal == translations["notfound"]: - gr_warning(translations["not_found_main_vocal"]) - return return_none - - if not not_merge_backing and backing_vocal == translations["notfound"]: - gr_warning(translations["not_found_backing_vocal"]) - return return_none - - input_path = main_vocal - backing_path = backing_vocal - - gr_info(translations["convert_vocal"]) - - convert(pitch, filter_radius, index_rate, volume_envelope, protect, hop_length, f0method, input_path, output_path, model_path, index, autotune, clean, clean_strength, format, embedder_model, resample_sr, split_audio, f0_autotune_strength, checkpointing) - - gr_info(translations["convert_success"]) - - if convert_backing: - if os.path.exists(output_backing): os.remove(output_backing) - - gr_info(translations["convert_backup"]) - - convert(pitch, filter_radius, index_rate, volume_envelope, protect, hop_length, f0method, backing_path, output_backing, model_path, index, autotune, clean, clean_strength, format, embedder_model, resample_sr, split_audio, f0_autotune_strength, checkpointing) - - gr_info(translations["convert_backup_success"]) - - try: - if not not_merge_backing and not use_original: - backing_source = output_backing if convert_backing else backing_vocal - - if os.path.exists(output_merge_backup): os.remove(output_merge_backup) - - gr_info(translations["merge_backup"]) - - pydub_convert(AudioSegment.from_file(output_path)).overlay(pydub_convert(AudioSegment.from_file(backing_source))).export(output_merge_backup, format=format) - - gr_info(translations["merge_success"]) - - if merge_instrument: - vocals = output_merge_backup if not not_merge_backing and not use_original else output_path - - if os.path.exists(output_merge_instrument): os.remove(output_merge_instrument) - - gr_info(translations["merge_instruments_process"]) - - instruments = get_audio_file('Instruments.') - - if instruments == translations["notfound"]: - gr_warning(translations["not_found_instruments"]) - output_merge_instrument = None - else: pydub_convert(AudioSegment.from_file(instruments)).overlay(pydub_convert(AudioSegment.from_file(vocals))).export(output_merge_instrument, format=format) - - gr_info(translations["merge_success"]) - except: - return return_none - - return [(None if use_original else output_path), output_backing, (None if not_merge_backing and use_original else output_merge_backup), (output_path if use_original else None), (output_merge_instrument if merge_instrument else None), {"visible": True, "__type__": "update"}] - else: - if not input or not os.path.exists(input): - gr_warning(translations["input_not_valid"]) - return return_none - - if not output: - gr_warning(translations["output_not_valid"]) - return return_none - - if os.path.isdir(input): - gr_info(translations["is_folder"]) - - if not [f for f in os.listdir(input) if f.lower().endswith(("wav", "mp3", "flac", "ogg", "opus", "m4a", "mp4", "aac", "alac", "wma", "aiff", "webm", "ac3"))]: - gr_warning(translations["not_found_in_folder"]) - return return_none - - gr_info(translations["batch_convert"]) - output_dir = os.path.dirname(output) or output - - convert(pitch, filter_radius, index_rate, volume_envelope, protect, hop_length, f0method, input, output_dir, model_path, index, autotune, clean, clean_strength, format, embedder_model, resample_sr, split_audio, f0_autotune_strength, checkpointing) - gr_info(translations["batch_convert_success"]) - - return return_none - else: - output_dir = os.path.dirname(output) or output - - if not os.path.exists(output_dir): os.makedirs(output_dir, exist_ok=True) - if os.path.exists(output): os.remove(output) - - gr_info(translations["convert_vocal"]) - convert(pitch, filter_radius, index_rate, volume_envelope, protect, hop_length, f0method, input, output, model_path, index, autotune, clean, clean_strength, format, embedder_model, resample_sr, split_audio, f0_autotune_strength, checkpointing) - - gr_info(translations["convert_success"]) - return_none[0] = output - return return_none - -def convert_selection(clean, autotune, use_audio, use_original, convert_backing, not_merge_backing, merge_instrument, pitch, clean_strength, model, index, index_rate, input, output, format, method, hybrid_method, hop_length, embedders, custom_embedders, resample_sr, filter_radius, volume_envelope, protect, split_audio, f0_autotune_strength, checkpointing): - if use_audio: - gr_info(translations["search_separate"]) - choice = [f for f in os.listdir("audios") if os.path.isdir(os.path.join("audios", f))] - - gr_info(translations["found_choice"].format(choice=len(choice))) - - if len(choice) == 0: - gr_warning(translations["separator==0"]) - - return [{"choices": [], "value": "", "interactive": False, "visible": False, "__type__": "update"}, None, None, None, None, None, {"visible": True, "__type__": "update"}] - elif len(choice) == 1: - convert_output = convert_audio(clean, autotune, use_audio, use_original, convert_backing, not_merge_backing, merge_instrument, pitch, clean_strength, model, index, index_rate, None, None, format, method, hybrid_method, hop_length, embedders, custom_embedders, resample_sr, filter_radius, volume_envelope, protect, split_audio, f0_autotune_strength, choice[0], checkpointing) - return [{"choices": [], "value": "", "interactive": False, "visible": False, "__type__": "update"}, convert_output[0], convert_output[1], convert_output[2], convert_output[3], convert_output[4], {"visible": True, "__type__": "update"}] - else: return [{"choices": choice, "value": "", "interactive": True, "visible": True, "__type__": "update"}, None, None, None, None, None, {"visible": False, "__type__": "update"}] - else: - main_convert = convert_audio(clean, autotune, use_audio, use_original, convert_backing, not_merge_backing, merge_instrument, pitch, clean_strength, model, index, index_rate, input, output, format, method, hybrid_method, hop_length, embedders, custom_embedders, resample_sr, filter_radius, volume_envelope, protect, split_audio, f0_autotune_strength, None, checkpointing) - return [{"choices": [], "value": "", "interactive": False, "visible": False, "__type__": "update"}, main_convert[0], None, None, None, None, {"visible": True, "__type__": "update"}] - -def convert_tts(clean, autotune, pitch, clean_strength, model, index, index_rate, input, output, format, method, hybrid_method, hop_length, embedders, custom_embedders, resample_sr, filter_radius, volume_envelope, protect, split_audio, f0_autotune_strength, checkpointing): - model_path = os.path.join("assets", "weights", model) - - if not model_path or not os.path.exists(model_path) or os.path.isdir(model_path) or not model.endswith(".pth"): - gr_warning(translations["provide_file"].format(filename=translations["model"])) - return None - - if not input or not os.path.exists(input): - gr_warning(translations["input_not_valid"]) - return None - - if os.path.isdir(input): - input_audio = [f for f in os.listdir(input) if "tts" in f and f.lower().endswith(("wav", "mp3", "flac", "ogg", "opus", "m4a", "mp4", "aac", "alac", "wma", "aiff", "webm", "ac3"))] - - if not input_audio: - gr_warning(translations["not_found_in_folder"]) - return None - - input = os.path.join(input, input_audio[0]) - - if not output: - gr_warning(translations["output_not_valid"]) - return None - - if os.path.isdir(output): output = os.path.join(output, f"tts.{format}") - - output_dir = os.path.dirname(output) - if not os.path.exists(output_dir): os.makedirs(output_dir, exist_ok=True) - - if os.path.exists(output): os.remove(output) - - f0method = method if method != "hybrid" else hybrid_method - embedder_model = embedders if embedders != "custom" else custom_embedders - - gr_info(translations["convert_vocal"]) - convert(pitch, filter_radius, index_rate, volume_envelope, protect, hop_length, f0method, input, output, model_path, index, autotune, clean, clean_strength, format, embedder_model, resample_sr, split_audio, f0_autotune_strength, checkpointing) - - gr_info(translations["convert_success"]) - return output - -def log_read(log_file, done): - f = open(log_file, "w", encoding="utf-8") - f.close() - - while 1: - with open(log_file, "r", encoding="utf-8") as f: - yield "".join(line for line in f.readlines() if "DEBUG" not in line and line.strip() != "") - - sleep(1) - if done[0]: break - - with open(log_file, "r", encoding="utf-8") as f: - log = "".join(line for line in f.readlines() if "DEBUG" not in line and line.strip() != "") - - yield log - -def create_dataset(input_audio, output_dataset, clean_dataset, clean_strength, separator_reverb, kim_vocals_version, overlap, segments_size, denoise_mdx, skip, skip_start, skip_end, hop_length, batch_size, sample_rate): - version = 1 if kim_vocals_version == "Version-1" else 2 - gr_info(translations["start"].format(start=translations["create"])) - - p = Popen(f'{python} main/inference/create_dataset.py --input_audio "{input_audio}" --output_dataset "{output_dataset}" --clean_dataset {clean_dataset} --clean_strength {clean_strength} --separator_reverb {separator_reverb} --kim_vocal_version {version} --overlap {overlap} --segments_size {segments_size} --mdx_hop_length {hop_length} --mdx_batch_size {batch_size} --denoise_mdx {denoise_mdx} --skip {skip} --skip_start_audios "{skip_start}" --skip_end_audios "{skip_end}" --sample_rate {sample_rate}', shell=True) - done = [False] - - threading.Thread(target=if_done, args=(done, p)).start() - - for log in log_read(os.path.join("assets", "logs", "create_dataset.log"), done): - yield log - -def preprocess(model_name, sample_rate, cpu_core, cut_preprocess, process_effects, path, clean_dataset, clean_strength): - dataset = os.path.join(path) - sr = int(float(sample_rate.rstrip("k")) * 1000) - - if not model_name: return gr_warning(translations["provide_name"]) - if not any(f.lower().endswith(("wav", "mp3", "flac", "ogg", "opus", "m4a", "mp4", "aac", "alac", "wma", "aiff", "webm", "ac3")) for f in os.listdir(dataset) if os.path.isfile(os.path.join(dataset, f))): return gr_warning(translations["not_found_data"]) - - model_dir = os.path.join("assets", "logs", model_name) - if os.path.exists(model_dir): shutil.rmtree(model_dir, ignore_errors=True) - - p = Popen(f'{python} main/inference/preprocess.py --model_name "{model_name}" --dataset_path "{dataset}" --sample_rate {sr} --cpu_cores {cpu_core} --cut_preprocess {cut_preprocess} --process_effects {process_effects} --clean_dataset {clean_dataset} --clean_strength {clean_strength}', shell=True) - done = [False] - - threading.Thread(target=if_done, args=(done, p)).start() - os.makedirs(model_dir, exist_ok=True) - - for log in log_read(os.path.join(model_dir, "preprocess.log"), done): - yield log - -def extract(model_name, version, method, pitch_guidance, hop_length, cpu_cores, gpu, sample_rate, embedders, custom_embedders): - embedder_model = embedders if embedders != "custom" else custom_embedders - sr = int(float(sample_rate.rstrip("k")) * 1000) - - if not model_name: return gr_warning(translations["provide_name"]) - - model_dir = os.path.join("assets", "logs", model_name) - if not any(os.path.isfile(os.path.join(model_dir, "sliced_audios", f)) for f in os.listdir(os.path.join(model_dir, "sliced_audios"))) or not any(os.path.isfile(os.path.join(model_dir, "sliced_audios_16k", f)) for f in os.listdir(os.path.join(model_dir, "sliced_audios_16k"))): return gr_warning(translations["not_found_data_preprocess"]) - - p = Popen(f'{python} main/inference/extract.py --model_name "{model_name}" --rvc_version {version} --f0_method {method} --pitch_guidance {pitch_guidance} --hop_length {hop_length} --cpu_cores {cpu_cores} --gpu {gpu} --sample_rate {sr} --embedder_model {embedder_model}', shell=True) - done = [False] - - threading.Thread(target=if_done, args=(done, p)).start() - os.makedirs(model_dir, exist_ok=True) - - for log in log_read(os.path.join(model_dir, "extract.log"), done): - yield log - -def create_index(model_name, rvc_version, index_algorithm): - if not model_name: return gr_warning(translations["provide_name"]) - model_dir = os.path.join("assets", "logs", model_name) - - if not any(os.path.isfile(os.path.join(model_dir, f"{rvc_version}_extracted", f)) for f in os.listdir(os.path.join(model_dir, f"{rvc_version}_extracted"))): return gr_warning(translations["not_found_data_extract"]) - - p = Popen(f'{python} main/inference/create_index.py --model_name "{model_name}" --rvc_version {rvc_version} --index_algorithm {index_algorithm}', shell=True) - done = [False] - - threading.Thread(target=if_done, args=(done, p)).start() - os.makedirs(model_dir, exist_ok=True) - - for log in log_read(os.path.join(model_dir, "create_index.log"), done): - yield log - -def training(model_name, rvc_version, save_every_epoch, save_only_latest, save_every_weights, total_epoch, sample_rate, batch_size, gpu, pitch_guidance, not_pretrain, custom_pretrained, pretrain_g, pretrain_d, detector, threshold, clean_up, cache, model_author, vocoder, checkpointing): - sr = int(float(sample_rate.rstrip("k")) * 1000) - if not model_name: return gr_warning(translations["provide_name"]) - - model_dir = os.path.join("assets", "logs", model_name) - if not any(os.path.isfile(os.path.join(model_dir, f"{rvc_version}_extracted", f)) for f in os.listdir(os.path.join(model_dir, f"{rvc_version}_extracted"))): return gr_warning(translations["not_found_data_extract"]) - - if not not_pretrain: - if not custom_pretrained: - pretrained_selector = {True: {32000: ("f0G32k.pth", "f0D32k.pth"), 40000: ("f0G40k.pth", "f0D40k.pth"), 44100: ("f0G44k.pth", "f0D44k.pth"), 48000: ("f0G48k.pth", "f0D48k.pth")}, False: {32000: ("G32k.pth", "D32k.pth"), 40000: ("G40k.pth", "D40k.pth"), 44100: ("G44k.pth", "D44k.pth"), 48000: ("G48k.pth", "D48k.pth")}} - pg, pd = pretrained_selector[pitch_guidance][sr] - else: - if not pretrain_g: return gr_warning(translations["provide_pretrained"].format(dg="G")) - if not pretrain_d: return gr_warning(translations["provide_pretrained"].format(dg="D")) - - pg, pd = pretrain_g, pretrain_d - - pretrained_G, pretrained_D = (os.path.join("assets", "models", f"pretrained_{rvc_version}", f"{vocoder if vocoder != 'Default' else ''}{pg}"), os.path.join("assets", "models", f"pretrained_{rvc_version}", f"{vocoder if vocoder != 'Default' else ''}{pd}")) if not custom_pretrained else (os.path.join("assets", "models", f"pretrained_custom", pg), os.path.join("assets", "models", f"pretrained_custom", pd)) - download_version = codecs.decode(f"uggcf://uhttvatsnpr.pb/NauC/Ivrganzrfr-EIP-Cebwrpg/erfbyir/znva/cergenvarq_i{'2' if rvc_version == 'v2' else '1'}/", "rot13") - - if not custom_pretrained: - try: - if not os.path.exists(pretrained_G): - gr_info(translations["download_pretrained"].format(dg="G", rvc_version=rvc_version)) - huggingface.HF_download_file(f"{download_version}{pg}", os.path.join("assets", "models", f"pretrained_{rvc_version}", f"{vocoder if vocoder != 'Default' else ''}{pg}")) - - if not os.path.exists(pretrained_D): - gr_info(translations["download_pretrained"].format(dg="D", rvc_version=rvc_version)) - huggingface.HF_download_file(f"{download_version}{pd}", os.path.join("assets", "models", f"pretrained_{rvc_version}", f"{vocoder if vocoder != 'Default' else ''}{pd}")) - except: - gr_warning(translations["not_use_pretrain_error_download"]) - pretrained_G, pretrained_D = None, None - else: - if not os.path.exists(pretrained_G): return gr_warning(translations["not_found_pretrain"].format(dg="G")) - if not os.path.exists(pretrained_D): return gr_warning(translations["not_found_pretrain"].format(dg="D")) - else: gr_warning(translations["not_use_pretrain"]) - - gr_info(translations["start"].format(start=translations["training"])) - - p = Popen(f'{python} main/inference/train.py --model_name "{model_name}" --rvc_version {rvc_version} --save_every_epoch {save_every_epoch} --save_only_latest {save_only_latest} --save_every_weights {save_every_weights} --total_epoch {total_epoch} --sample_rate {sr} --batch_size {batch_size} --gpu {gpu} --pitch_guidance {pitch_guidance} --overtraining_detector {detector} --overtraining_threshold {threshold} --cleanup {clean_up} --cache_data_in_gpu {cache} --g_pretrained_path "{pretrained_G}" --d_pretrained_path "{pretrained_D}" --model_author "{model_author}" --vocoder {vocoder} --checkpointing {checkpointing}', shell=True) - done = [False] - - threading.Thread(target=if_done, args=(done, p)).start() - if not os.path.exists(model_dir): os.makedirs(model_dir, exist_ok=True) - - for log in log_read(os.path.join(model_dir, "train.log"), done): - if len(log.split("\n")) > 100: log = log[-100:] - yield log - -def stop_pid(pid_file, model_name=None): - try: - pid_file_path = os.path.join("assets", f"{pid_file}.txt") if model_name is None else os.path.join("assets", "logs", model_name, f"{pid_file}.txt") - - if not os.path.exists(pid_file_path): return gr_warning(translations["not_found_pid"]) - else: - with open(pid_file_path, "r") as pid_file: - pids = [int(pid) for pid in pid_file.readlines()] - - for pid in pids: - os.kill(pid, 9) - - gr_info(translations["end_pid"]) - if os.path.exists(pid_file_path): os.remove(pid_file_path) - except: - pass - -def stop_train(model_name): - try: - pid_file_path = os.path.join("assets", "logs", model_name, "config.json") - - if not os.path.exists(pid_file_path): return gr_warning(translations["not_found_pid"]) - else: - with open(pid_file_path, "r") as pid_file: - pid_data = json.load(pid_file) - pids = pid_data.get("process_pids", []) - - with open(pid_file_path, "w") as pid_file: - pid_data.pop("process_pids", None) - - json.dump(pid_data, pid_file, indent=4) - - for pid in pids: - os.kill(pid, 9) - - gr_info(translations["end_pid"]) - except: - pass - -def delete_audios(files): - if not os.path.exists(files) or os.path.isdir(files): return gr_warning(translations["input_not_valid"]) - else: - gr_info(translations["clean_audios"]) - os.remove(files) - - for item in os.listdir("audios"): - item_path = os.path.join("audios", item) - - if os.path.isdir(item_path) and len([f for f in os.listdir(item_path)]) < 1: shutil.rmtree(item_path, ignore_errors=True) - - gr_info(translations["clean_audios_success"]) - return change_audios_choices() - -def delete_separated(files): - if not os.path.exists(files) or os.path.isdir(files): return gr_warning(translations["input_not_valid"]) - else: - gr_info(translations["clean_separate"]) - os.remove(files) - - gr_info(translations["clean_separate_success"]) - return change_separate_choices() - -def delete_model(model, index): - files = os.path.join("assets", "weights", model) - - if model: - if not os.path.exists(files) or not model.endswith(".pth"): return gr_warning(translations["provide_file"].format(filename=translations["model"])) - else: - gr_info(translations["clean_model"]) - os.remove(files) - gr_info(translations["clean_model_success"]) - - if index: - if not os.path.exists(index): return gr_warning(translations["provide_file"].format(filename=translations["index"])) - else: - gr_info(translations["clean_index"]) - shutil.rmtree(index, ignore_errors=True) - gr_info(translations["clean_index_success"]) - - return change_choices_del() - -def delete_pretrained(pretrain): - if not os.path.exists(pretrain) or os.path.isdir(pretrain): return gr_warning(translations["input_not_valid"]) - else: - gr_info(translations["clean_pretrain"]) - os.remove(pretrain) - gr_info(translations["clean_pretrain_success"]) - - return change_allpretrained_choices() - -def delete_presets(json_file): - files = os.path.join("assets", "presets", json_file) - - if not os.path.exists(files) or not json_file.endswith(".json"): return gr_warning(translations["provide_file_settings"]) - else: - gr_info(translations["clean_presets_2"]) - os.remove(files) - gr_info(translations["clean_presets_success"]) - - return change_preset_choices() - -def delete_all_audios(): - dir = "audios" - - if len(os.listdir(dir)) < 1: return gr_warning(translations["not_found_in_folder"]) - else: - gr_info(translations["clean_all_audios"]) - - shutil.rmtree(dir, ignore_errors=True) - os.makedirs(dir, exist_ok=True) - - gr_info(translations["clean_all_audios_success"]) - return {"choices": [], "value": "", "__type__": "update"} - -def delete_all_separated(): - dir = os.path.join("assets", "models", "uvr5") - - if len(os.listdir(dir)) < 1: return gr_warning(translations["not_found_separate_model"]) - else: - gr_info(translations["clean_all_separate_model"]) - - shutil.rmtree(dir, ignore_errors=True) - os.makedirs(dir, exist_ok=True) - - gr_info(translations["clean_all_separate_model_success"]) - return {"choices": [], "value": "", "__type__": "update"} - -def delete_all_model(): - model = os.listdir(os.path.join("assets", "weights")) - index = list(f for f in os.listdir(os.path.join("assets", "logs")) if os.path.isdir(os.path.join("assets", "logs", f)) and f != "mute") - - if len(model) < 1: return gr_warning(translations["not_found"].format(name=translations["model"])) - if len(index) < 1: return gr_warning(translations["not_found"].format(name=translations["index"])) - - gr_info(translations["start_clean_model"]) - - for f in model: - file = os.path.join("assets", "weights", f) - if os.path.exists(file) and f.endswith(".pth"): os.remove(file) - - for f in index: - file = os.path.join("assets", "logs", f) - if os.path.exists(file): shutil.rmtree(file, ignore_errors=True) - - gr_info(translations["clean_all_models_success"]) - return [{"choices": [], "value": "", "__type__": "update"}]*2 - -def delete_all_pretrained(): - Allpretrained = [os.path.join("assets", "models", path, model) for path in ["pretrained_v1", "pretrained_v2", "pretrained_custom"] for model in os.listdir(os.path.join("assets", "models", path)) if model.endswith(".pth") and ("D" in model or "G" in model)] - - if len(Allpretrained) < 1: return gr_warning(translations["not_found_pretrained"]) - else: - gr_info(translations["clean_all_pretrained"]) - for f in Allpretrained: - if os.path.exists(f): os.remove(f) - - gr_info(translations["clean_all_pretrained_success"]) - return {"choices": [], "value": "", "__type__": "update"} - -def delete_all_presets(): - dir = os.path.join("assets", "presets") - - if len(os.listdir(dir)) < 1: return gr_warning(translations["not_found_presets"]) - else: - gr_info(translations["clean_all_presets"]) - - shutil.rmtree(dir, ignore_errors=True) - os.makedirs(dir, exist_ok=True) - - gr_info(translations["clean_all_presets_success"]) - return {"choices": [], "value": "", "__type__": "update"} - -def delete_all_log(): - log_path = [os.path.join(root, f) for root, _, files in os.walk(os.path.join("assets", "logs"), topdown=False) for f in files if f.endswith(".log")] - - if len(log_path) < 1: return gr_warning(translations["not_found_log"]) - else: - gr_info(translations["clean_all_log"]) - - for f in log_path: - if os.path.exists(f): os.remove(f) - - open(os.path.join("assets", "logs", "app.log"), "w", encoding="utf-8") - gr_info(translations["clean_all_log_success"]) - -def delete_all_predictors(): - dir = os.path.join("assets", "models", "predictors") - - if len(os.listdir(dir)) < 1: return gr_warning(translations["not_found_predictors"]) - else: - gr_info(translations["clean_all_predictors"]) - - shutil.rmtree(dir, ignore_errors=True) - os.makedirs(dir, exist_ok=True) - - gr_info(translations["clean_all_predictors_success"]) - return {"choices": [], "value": "", "__type__": "update"} - -def delete_all_embedders(): - dir = os.path.join("assets", "models", "embedders") - - if len(os.listdir(dir)) < 1: return gr_warning(translations["not_found_embedders"]) - else: - gr_info(translations["clean_all_embedders"]) - - shutil.rmtree(dir, ignore_errors=True) - os.makedirs(dir, exist_ok=True) - - gr_info(translations["clean_all_embedders_success"]) - return {"choices": [], "value": "", "__type__": "update"} - -def delete_dataset(name): - if not name or not os.path.exists(name) or not os.path.isdir(name): return gr_warning(translations["provide_folder"]) - else: - if len(os.listdir(name)) < 1: gr_warning(translations["empty_folder"]) - else: - gr_info(translations["clean_dataset"]) - - shutil.rmtree(name, ignore_errors=True) - os.makedirs(name, exist_ok=True) - - gr_info(translations["clean_dataset_success"]) - -def load_presets(presets, cleaner, autotune, pitch, clean_strength, index_strength, resample_sr, filter_radius, volume_envelope, protect, split_audio, f0_autotune_strength): - if not presets: return gr_warning(translations["provide_file_settings"]) - with open(os.path.join("assets", "presets", presets)) as f: - file = json.load(f) - - gr_info(translations["load_presets"].format(presets=presets)) - return file.get("cleaner", cleaner), file.get("autotune", autotune), file.get("pitch", pitch), file.get("clean_strength", clean_strength), file.get("index_strength", index_strength), file.get("resample_sr", resample_sr), file.get("filter_radius", filter_radius), file.get("volume_envelope", volume_envelope), file.get("protect", protect), file.get("split_audio", split_audio), file.get("f0_autotune_strength", f0_autotune_strength) - -def save_presets(name, cleaner, autotune, pitch, clean_strength, index_strength, resample_sr, filter_radius, volume_envelope, protect, split_audio, f0_autotune_strength, cleaner_chbox, autotune_chbox, pitch_chbox, index_strength_chbox, resample_sr_chbox, filter_radius_chbox, volume_envelope_chbox, protect_chbox, split_audio_chbox): - if not name: return gr_warning(translations["provide_filename_settings"]) - if not any([cleaner_chbox, autotune_chbox, pitch_chbox, index_strength_chbox, resample_sr_chbox, filter_radius_chbox, volume_envelope_chbox, protect_chbox, split_audio_chbox]): return gr_warning(translations["choose1"]) - - settings = {} - for checkbox, data in [(cleaner_chbox, {"cleaner": cleaner, "clean_strength": clean_strength}), (autotune_chbox, {"autotune": autotune, "f0_autotune_strength": f0_autotune_strength}), (pitch_chbox, {"pitch": pitch}), (index_strength_chbox, {"index_strength": index_strength}), (resample_sr_chbox, {"resample_sr": resample_sr}), (filter_radius_chbox, {"filter_radius": filter_radius}), (volume_envelope_chbox, {"volume_envelope": volume_envelope}), (protect_chbox, {"protect": protect}), (split_audio_chbox, {"split_audio": split_audio})]: - if checkbox: settings.update(data) - - with open(os.path.join("assets", "presets", name + ".json"), "w") as f: - json.dump(settings, f, indent=4) - - gr_info(translations["export_settings"]) - return change_preset_choices() - -def report_bug(error_info, provide): - report_path = os.path.join("assets", "logs", "report_bugs.log") - if os.path.exists(report_path): os.remove(report_path) - - report_url = codecs.decode(requests.get(codecs.decode("uggcf://uhttvatsnpr.pb/NauC/Ivrganzrfr-EIP-Cebwrpg/erfbyir/znva/jroubbx.gkg", "rot13")).text, "rot13") - if not error_info: error_info = "Không Có" - - gr_info(translations["thank"]) - - if provide: - try: - for log in [os.path.join(root, name) for root, _, files in os.walk(os.path.join("assets", "logs"), topdown=False) for name in files if name.endswith(".log")]: - with open(log, "r", encoding="utf-8") as r: - with open(report_path, "w", encoding="utf-8") as w: - w.write(str(r.read())) - w.write("\n") - except Exception as e: - gr_error(translations["error_read_log"]) - logger.debug(e) - - try: - with open(report_path, "r", encoding="utf-8") as f: - content = f.read() - - requests.post(report_url, json={"embeds": [{"title": "Báo Cáo Lỗi", "description": f"Mô tả lỗi: {error_info}", "color": 15158332, "author": {"name": "Vietnamese_RVC", "icon_url": miku_image, "url": codecs.decode("uggcf://tvguho.pbz/CunzUhlauNau16/Ivrganzrfr-EIP/gerr/znva","rot13")}, "thumbnail": {"url": codecs.decode("uggcf://p.grabe.pbz/7dADJbv-36fNNNNq/grabe.tvs", "rot13")}, "fields": [{"name": "Số Lượng Gỡ Lỗi", "value": content.count("DEBUG")}, {"name": "Số Lượng Thông Tin", "value": content.count("INFO")}, {"name": "Số Lượng Cảnh Báo", "value": content.count("WARNING")}, {"name": "Số Lượng Lỗi", "value": content.count("ERROR")}], "footer": {"text": f"Tên Máy: {platform.uname().node} - Hệ Điều Hành: {platform.system()}-{platform.version()}\nThời Gian Báo Cáo Lỗi: {datetime.now()}."}}]}) - - with open(report_path, "rb") as f: - requests.post(report_url, files={"file": f}) - except Exception as e: - gr_error(translations["error_send"]) - logger.debug(e) - finally: - if os.path.exists(report_path): os.remove(report_path) - else: requests.post(report_url, json={"embeds": [{"title": "Báo Cáo Lỗi", "description": error_info}]}) - -with gr.Blocks(title="📱 Vietnamese-RVC GUI BY ANH", theme=theme) as app: - gr.HTML(translations["display_title"]) - with gr.Row(): - gr.Markdown(translations["rick_roll"].format(rickroll=codecs.decode('uggcf://jjj.lbhghor.pbz/jngpu?i=qDj4j9JtKpD', 'rot13'))) - with gr.Row(): - gr.Markdown(translations["terms_of_use"]) - with gr.Row(): - gr.Markdown(translations["exemption"]) - with gr.Tabs(): - with gr.TabItem(translations["separator_tab"], visible=configs.get("separator_tab", True)): - gr.Markdown(f"## {translations['separator_tab']}") - with gr.Row(): - gr.Markdown(translations["4_part"]) - with gr.Row(): - with gr.Column(): - with gr.Group(): - with gr.Row(): - cleaner = gr.Checkbox(label=translations["clear_audio"], value=False, interactive=True, min_width=140) - backing = gr.Checkbox(label=translations["separator_backing"], value=False, interactive=True, min_width=140) - reverb = gr.Checkbox(label=translations["dereveb_audio"], value=False, interactive=True, min_width=140) - backing_reverb = gr.Checkbox(label=translations["dereveb_backing"], value=False, interactive=False, min_width=140) - denoise = gr.Checkbox(label=translations["denoise_mdx"], value=False, interactive=False, min_width=140) - with gr.Row(): - separator_model = gr.Dropdown(label=translations["separator_model"], value=uvr_model[0], choices=uvr_model, interactive=True) - separator_backing_model = gr.Dropdown(label=translations["separator_backing_model"], value="Version-1", choices=["Version-1", "Version-2"], interactive=True, visible=backing.value) - with gr.Row(): - with gr.Column(): - separator_button = gr.Button(translations["separator_tab"], variant="primary") - with gr.Row(): - with gr.Column(): - with gr.Group(): - with gr.Row(): - shifts = gr.Slider(label=translations["shift"], info=translations["shift_info"], minimum=1, maximum=20, value=2, step=1, interactive=True) - segment_size = gr.Slider(label=translations["segments_size"], info=translations["segments_size_info"], minimum=32, maximum=3072, value=256, step=32, interactive=True) - with gr.Row(): - mdx_batch_size = gr.Slider(label=translations["batch_size"], info=translations["mdx_batch_size_info"], minimum=1, maximum=64, value=1, step=1, interactive=True, visible=backing.value or reverb.value or separator_model.value in mdx_model) - with gr.Column(): - with gr.Group(): - with gr.Row(): - overlap = gr.Radio(label=translations["overlap"], info=translations["overlap_info"], choices=["0.25", "0.5", "0.75", "0.99"], value="0.25", interactive=True) - with gr.Row(): - mdx_hop_length = gr.Slider(label="Hop length", info=translations["hop_length_info"], minimum=1, maximum=8192, value=1024, step=1, interactive=True, visible=backing.value or reverb.value or separator_model.value in mdx_model) - with gr.Row(): - with gr.Column(): - input = gr.File(label=translations["drop_audio"], file_types=[".wav", ".mp3", ".flac", ".ogg", ".opus", ".m4a", ".mp4", ".aac", ".alac", ".wma", ".aiff", ".webm", ".ac3"]) - with gr.Accordion(translations["use_url"], open=False): - url = gr.Textbox(label=translations["url_audio"], value="", placeholder="https://www.youtube.com/...", scale=6) - download_button = gr.Button(translations["downloads"]) - with gr.Column(): - with gr.Row(): - clean_strength = gr.Slider(label=translations["clean_strength"], info=translations["clean_strength_info"], minimum=0, maximum=1, value=0.5, step=0.1, interactive=True, visible=cleaner.value) - sample_rate1 = gr.Slider(minimum=0, maximum=96000, step=1, value=44100, label=translations["sr"], info=translations["sr_info"], interactive=True) - with gr.Accordion(translations["input_output"], open=False): - format = gr.Radio(label=translations["export_format"], info=translations["export_info"], choices=["wav", "mp3", "flac", "ogg", "opus", "m4a", "mp4", "aac", "alac", "wma", "aiff", "webm", "ac3"], value="wav", interactive=True) - input_audio = gr.Dropdown(label=translations["audio_path"], value="", choices=paths_for_files, allow_custom_value=True, interactive=True) - refesh_separator = gr.Button(translations["refesh"]) - output_separator = gr.Textbox(label=translations["output_folder"], value="audios", placeholder="audios", info=translations["output_folder_info"], interactive=True) - audio_input = gr.Audio(show_download_button=True, interactive=False, label=translations["input_audio"]) - with gr.Row(): - gr.Markdown(translations["output_separator"]) - with gr.Row(): - instruments_audio = gr.Audio(show_download_button=True, interactive=False, label=translations["instruments"]) - original_vocals = gr.Audio(show_download_button=True, interactive=False, label=translations["original_vocal"]) - main_vocals = gr.Audio(show_download_button=True, interactive=False, label=translations["main_vocal"], visible=backing.value) - backing_vocals = gr.Audio(show_download_button=True, interactive=False, label=translations["backing_vocal"], visible=backing.value) - with gr.Row(): - separator_model.change(fn=lambda a, b, c: [visible(a or b or c in mdx_model), visible(a or b or c in mdx_model), valueFalse_interactive(a or b or c in mdx_model), visible(c not in mdx_model)], inputs=[backing, reverb, separator_model], outputs=[mdx_batch_size, mdx_hop_length, denoise, shifts]) - backing.change(fn=lambda a, b, c: [visible(a or b or c in mdx_model), visible(a or b or c in mdx_model), valueFalse_interactive(a or b or c in mdx_model), visible(a), visible(a), visible(a), valueFalse_interactive(a and b)], inputs=[backing, reverb, separator_model], outputs=[mdx_batch_size, mdx_hop_length, denoise, separator_backing_model, main_vocals, backing_vocals, backing_reverb]) - reverb.change(fn=lambda a, b, c: [visible(a or b or c in mdx_model), visible(a or b or c in mdx_model), valueFalse_interactive(a or b or c in mdx_model), valueFalse_interactive(a and b)], inputs=[backing, reverb, separator_model], outputs=[mdx_batch_size, mdx_hop_length, denoise, backing_reverb]) - with gr.Row(): - input_audio.change(fn=lambda audio: audio if audio else None, inputs=[input_audio], outputs=[audio_input]) - cleaner.change(fn=visible, inputs=[cleaner], outputs=[clean_strength]) - with gr.Row(): - input.upload(fn=lambda audio_in: shutil.move(audio_in.name, os.path.join("audios")), inputs=[input], outputs=[input_audio]) - refesh_separator.click(fn=change_audios_choices, inputs=[], outputs=[input_audio]) - with gr.Row(): - download_button.click( - fn=download_url, - inputs=[url], - outputs=[input_audio, audio_input, url], - api_name='download_url' - ) - separator_button.click( - fn=separator_music, - inputs=[ - input_audio, - output_separator, - format, - shifts, - segment_size, - overlap, - cleaner, - clean_strength, - denoise, - separator_model, - separator_backing_model, - backing, - reverb, - backing_reverb, - mdx_hop_length, - mdx_batch_size, - sample_rate1 - ], - outputs=[original_vocals, instruments_audio, main_vocals, backing_vocals], - api_name='separator_music' - ) - - with gr.TabItem(translations["convert_audio"], visible=configs.get("convert_tab", True)): - gr.Markdown(f"## {translations['convert_audio']}") - with gr.Row(): - gr.Markdown(translations["convert_info"]) - with gr.Row(): - with gr.Column(): - with gr.Group(): - with gr.Row(): - cleaner0 = gr.Checkbox(label=translations["clear_audio"], value=False, interactive=True) - autotune = gr.Checkbox(label=translations["autotune"], value=False, interactive=True) - use_audio = gr.Checkbox(label=translations["use_audio"], value=False, interactive=True) - checkpointing = gr.Checkbox(label=translations["memory_efficient_training"], value=False, interactive=True) - with gr.Row(): - use_original = gr.Checkbox(label=translations["convert_original"], value=False, interactive=True, visible=use_audio.value) - convert_backing = gr.Checkbox(label=translations["convert_backing"], value=False, interactive=True, visible=use_audio.value) - not_merge_backing = gr.Checkbox(label=translations["not_merge_backing"], value=False, interactive=True, visible=use_audio.value) - merge_instrument = gr.Checkbox(label=translations["merge_instruments"], value=False, interactive=True, visible=use_audio.value) - with gr.Row(): - pitch = gr.Slider(minimum=-20, maximum=20, step=1, info=translations["pitch_info"], label=translations["pitch"], value=0, interactive=True) - clean_strength0 = gr.Slider(label=translations["clean_strength"], info=translations["clean_strength_info"], minimum=0, maximum=1, value=0.5, step=0.1, interactive=True, visible=cleaner0.value) - with gr.Row(): - with gr.Column(): - audio_select = gr.Dropdown(label=translations["select_separate"], choices=[], value="", interactive=True, allow_custom_value=True, visible=False) - convert_button_2 = gr.Button(translations["convert_audio"], visible=False) - with gr.Row(): - with gr.Column(): - convert_button = gr.Button(translations["convert_audio"], variant="primary") - with gr.Row(): - with gr.Column(): - input0 = gr.File(label=translations["drop_audio"], file_types=[".wav", ".mp3", ".flac", ".ogg", ".opus", ".m4a", ".mp4", ".aac", ".alac", ".wma", ".aiff", ".webm", ".ac3"]) - play_audio = gr.Audio(show_download_button=True, interactive=False, label=translations["input_audio"]) - with gr.Column(): - with gr.Accordion(translations["model_accordion"], open=True): - with gr.Row(): - model_pth = gr.Dropdown(label=translations["model_name"], choices=sorted(model_name), value="", interactive=True, allow_custom_value=True) - model_index = gr.Dropdown(label=translations["index_path"], choices=sorted(index_path), value="", interactive=True, allow_custom_value=True) - with gr.Row(): - refesh = gr.Button(translations["refesh"]) - with gr.Row(): - index_strength = gr.Slider(label=translations["index_strength"], info=translations["index_strength_info"], minimum=0, maximum=1, value=0.5, step=0.01, interactive=True, visible=model_index.value != "") - with gr.Accordion(translations["input_output"], open=False): - with gr.Column(): - export_format = gr.Radio(label=translations["export_format"], info=translations["export_info"], choices=["wav", "mp3", "flac", "ogg", "opus", "m4a", "mp4", "aac", "alac", "wma", "aiff", "webm", "ac3"], value="wav", interactive=True) - input_audio0 = gr.Dropdown(label=translations["audio_path"], value="", choices=paths_for_files, info=translations["provide_audio"], allow_custom_value=True, interactive=True) - output_audio = gr.Textbox(label=translations["output_path"], value="audios/output.wav", placeholder="audios/output.wav", info=translations["output_path_info"], interactive=True) - with gr.Column(): - refesh0 = gr.Button(translations["refesh"]) - with gr.Accordion(translations["setting"], open=False): - with gr.Accordion(translations["f0_method"], open=False): - method = gr.Radio(label=translations["f0_method"], info=translations["f0_method_info"], choices=method_f0, value="rmvpe", interactive=True) - hybrid_method = gr.Dropdown(label=translations["f0_method_hybrid"], info=translations["f0_method_hybrid_info"], choices=["hybrid[pm+dio]", "hybrid[pm+crepe-tiny]", "hybrid[pm+crepe]", "hybrid[pm+fcpe]", "hybrid[pm+rmvpe]", "hybrid[pm+harvest]", "hybrid[pm+yin]", "hybrid[dio+crepe-tiny]", "hybrid[dio+crepe]", "hybrid[dio+fcpe]", "hybrid[dio+rmvpe]", "hybrid[dio+harvest]", "hybrid[dio+yin]", "hybrid[crepe-tiny+crepe]", "hybrid[crepe-tiny+fcpe]", "hybrid[crepe-tiny+rmvpe]", "hybrid[crepe-tiny+harvest]", "hybrid[crepe+fcpe]", "hybrid[crepe+rmvpe]", "hybrid[crepe+harvest]", "hybrid[crepe+yin]", "hybrid[fcpe+rmvpe]", "hybrid[fcpe+harvest]", "hybrid[fcpe+yin]", "hybrid[rmvpe+harvest]", "hybrid[rmvpe+yin]", "hybrid[harvest+yin]"], value="hybrid[pm+dio]", interactive=True, allow_custom_value=True, visible=method.value == "hybrid") - hop_length = gr.Slider(label="Hop length", info=translations["hop_length_info"], minimum=1, maximum=512, value=128, step=1, interactive=True, visible=False) - with gr.Accordion(translations["hubert_model"], open=False): - embedders = gr.Radio(label=translations["hubert_model"], info=translations["hubert_info"], choices=["contentvec_base", "hubert_base", "japanese_hubert_base", "korean_hubert_base", "chinese_hubert_base", "Hidden_Rabbit_last", "portuguese_hubert_base", "custom"], value="contentvec_base", interactive=True) - custom_embedders = gr.Textbox(label=translations["modelname"], info=translations["modelname_info"], value="", placeholder="hubert_base", interactive=True, visible=embedders.value == "custom") - with gr.Accordion(translations["use_presets"], open=False): - with gr.Row(): - presets_name = gr.Dropdown(label=translations["file_preset"], choices=sorted(presets_file), value=sorted(presets_file)[0] if len(sorted(presets_file)) > 0 else '', interactive=True, allow_custom_value=True) - with gr.Row(): - load_click = gr.Button(translations["load_file"], variant="primary") - refesh_click = gr.Button(translations["refesh"]) - with gr.Accordion(translations["export_file"], open=False): - with gr.Row(): - with gr.Column(): - with gr.Group(): - with gr.Row(): - cleaner_chbox = gr.Checkbox(label=translations["save_clean"], value=True, interactive=True) - autotune_chbox = gr.Checkbox(label=translations["save_autotune"], value=True, interactive=True) - pitch_chbox = gr.Checkbox(label=translations["save_pitch"], value=True, interactive=True) - index_strength_chbox = gr.Checkbox(label=translations["save_index_2"], value=True, interactive=True) - resample_sr_chbox = gr.Checkbox(label=translations["save_resample"], value=True, interactive=True) - filter_radius_chbox = gr.Checkbox(label=translations["save_filter"], value=True, interactive=True) - volume_envelope_chbox = gr.Checkbox(label=translations["save_envelope"], value=True, interactive=True) - protect_chbox = gr.Checkbox(label=translations["save_protect"], value=True, interactive=True) - split_audio_chbox = gr.Checkbox(label=translations["save_split"], value=True, interactive=True) - with gr.Row(): - with gr.Column(): - name_to_save_file = gr.Textbox(label=translations["filename_to_save"]) - save_file_button = gr.Button(translations["export_file"]) - with gr.Row(): - upload_presets = gr.File(label=translations["upload_presets"], file_types=[".json"]) - with gr.Column(): - with gr.Group(): - split_audio = gr.Checkbox(label=translations["split_audio"], value=False, interactive=True) - f0_autotune_strength = gr.Slider(minimum=0, maximum=1, label=translations["autotune_rate"], info=translations["autotune_rate_info"], value=1, step=0.1, interactive=True, visible=autotune.value) - resample_sr = gr.Slider(minimum=0, maximum=96000, label=translations["resample"], info=translations["resample_info"], value=0, step=1, interactive=True) - filter_radius = gr.Slider(minimum=0, maximum=7, label=translations["filter_radius"], info=translations["filter_radius_info"], value=3, step=1, interactive=True) - volume_envelope = gr.Slider(minimum=0, maximum=1, label=translations["volume_envelope"], info=translations["volume_envelope_info"], value=1, step=0.1, interactive=True) - protect = gr.Slider(minimum=0, maximum=1, label=translations["protect"], info=translations["protect_info"], value=0.33, step=0.01, interactive=True) - with gr.Row(): - gr.Markdown(translations["output_convert"]) - with gr.Row(): - main_convert = gr.Audio(show_download_button=True, interactive=False, label=translations["main_convert"]) - backing_convert = gr.Audio(show_download_button=True, interactive=False, label=translations["convert_backing"], visible=convert_backing.value) - main_backing = gr.Audio(show_download_button=True, interactive=False, label=translations["main_or_backing"], visible=convert_backing.value) - with gr.Row(): - original_convert = gr.Audio(show_download_button=True, interactive=False, label=translations["convert_original"], visible=use_original.value) - vocal_instrument = gr.Audio(show_download_button=True, interactive=False, label=translations["voice_or_instruments"], visible=merge_instrument.value) - with gr.Row(): - refesh_click.click(fn=change_preset_choices, inputs=[], outputs=[presets_name]) - load_click.click(fn=load_presets, inputs=[presets_name, cleaner0, autotune, pitch, clean_strength0, index_strength, resample_sr, filter_radius, volume_envelope, protect, split_audio, f0_autotune_strength], outputs=[cleaner0, autotune, pitch, clean_strength0, index_strength, resample_sr, filter_radius, volume_envelope, protect, split_audio, f0_autotune_strength]) - save_file_button.click(fn=save_presets, inputs=[name_to_save_file, cleaner0, autotune, pitch, clean_strength0, index_strength, resample_sr, filter_radius, volume_envelope, protect, split_audio, f0_autotune_strength, cleaner_chbox, autotune_chbox, pitch_chbox, index_strength_chbox, resample_sr_chbox, filter_radius_chbox, volume_envelope_chbox, protect_chbox, split_audio_chbox], outputs=[presets_name]) - with gr.Row(): - upload_presets.upload(fn=lambda audio_in: shutil.move(audio_in.name, os.path.join("assets", "presets")), inputs=[upload_presets], outputs=[presets_name]) - autotune.change(fn=visible, inputs=[autotune], outputs=[f0_autotune_strength]) - use_audio.change(fn=lambda a: [visible(a), visible(a), visible(a), visible(a), visible(a), valueFalse_interactive(a), valueFalse_interactive(a), valueFalse_interactive(a), valueFalse_interactive(a), visible(not a), visible(not a), visible(not a), visible(not a)], inputs=[use_audio], outputs=[main_backing, use_original, convert_backing, not_merge_backing, merge_instrument, use_original, convert_backing, not_merge_backing, merge_instrument, input_audio0, output_audio, input0, play_audio]) - with gr.Row(): - convert_backing.change(fn=lambda a,b: [change_backing_choices(a, b), visible(a)], inputs=[convert_backing, not_merge_backing], outputs=[use_original, backing_convert]) - use_original.change(fn=lambda audio, original: [visible(original), visible(not original), visible(audio and not original), valueFalse_interactive(not original), valueFalse_interactive(not original)], inputs=[use_audio, use_original], outputs=[original_convert, main_convert, main_backing, convert_backing, not_merge_backing]) - cleaner0.change(fn=visible, inputs=[cleaner0], outputs=[clean_strength0]) - with gr.Row(): - merge_instrument.change(fn=visible, inputs=[merge_instrument], outputs=[vocal_instrument]) - not_merge_backing.change(fn=lambda audio, merge, cvb: [visible(audio and not merge), change_backing_choices(cvb, merge)], inputs=[use_audio, not_merge_backing, convert_backing], outputs=[main_backing, use_original]) - method.change(fn=lambda method, hybrid: [visible(method == "hybrid"), hoplength_show(method, hybrid)], inputs=[method, hybrid_method], outputs=[hybrid_method, hop_length]) - with gr.Row(): - hybrid_method.change(fn=hoplength_show, inputs=[method, hybrid_method], outputs=[hop_length]) - refesh.click(fn=change_models_choices, inputs=[], outputs=[model_pth, model_index]) - model_pth.change(fn=get_index, inputs=[model_pth], outputs=[model_index]) - with gr.Row(): - input0.upload(fn=lambda audio_in: shutil.move(audio_in.name, os.path.join("audios")), inputs=[input0], outputs=[input_audio0]) - input_audio0.change(fn=lambda audio: audio if audio else None, inputs=[input_audio0], outputs=[play_audio]) - with gr.Row(): - embedders.change(fn=lambda embedders: visible(embedders == "custom"), inputs=[embedders], outputs=[custom_embedders]) - refesh0.click(fn=change_audios_choices, inputs=[], outputs=[input_audio0]) - model_index.change(fn=index_strength_show, inputs=[model_index], outputs=[index_strength]) - with gr.Row(): - audio_select.change(fn=lambda: visible(True), inputs=[], outputs=[convert_button_2]) - convert_button.click(fn=lambda: visible(False), inputs=[], outputs=[convert_button]) - convert_button_2.click(fn=lambda: [visible(False), visible(False)], inputs=[], outputs=[audio_select, convert_button_2]) - with gr.Row(): - convert_button.click( - fn=convert_selection, - inputs=[ - cleaner0, - autotune, - use_audio, - use_original, - convert_backing, - not_merge_backing, - merge_instrument, - pitch, - clean_strength0, - model_pth, - model_index, - index_strength, - input_audio0, - output_audio, - export_format, - method, - hybrid_method, - hop_length, - embedders, - custom_embedders, - resample_sr, - filter_radius, - volume_envelope, - protect, - split_audio, - f0_autotune_strength, - checkpointing - ], - outputs=[audio_select, main_convert, backing_convert, main_backing, original_convert, vocal_instrument, convert_button], - api_name="convert_selection" - ) - convert_button_2.click( - fn=convert_audio, - inputs=[ - cleaner0, - autotune, - use_audio, - use_original, - convert_backing, - not_merge_backing, - merge_instrument, - pitch, - clean_strength0, - model_pth, - model_index, - index_strength, - input_audio0, - output_audio, - export_format, - method, - hybrid_method, - hop_length, - embedders, - custom_embedders, - resample_sr, - filter_radius, - volume_envelope, - protect, - split_audio, - f0_autotune_strength, - audio_select, - checkpointing - ], - outputs=[main_convert, backing_convert, main_backing, original_convert, vocal_instrument, convert_button], - api_name="convert_audio" - ) - - with gr.TabItem(translations["convert_text"], visible=configs.get("tts_tab", True)): - gr.Markdown(translations["convert_text_markdown"]) - with gr.Row(): - gr.Markdown(translations["convert_text_markdown_2"]) - with gr.Row(): - with gr.Column(): - with gr.Group(): - with gr.Row(): - use_txt = gr.Checkbox(label=translations["input_txt"], value=False, interactive=True) - google_tts_check_box = gr.Checkbox(label=translations["googletts"], value=False, interactive=True) - prompt = gr.Textbox(label=translations["text_to_speech"], value="", placeholder="Hello Words", lines=3) - with gr.Column(): - speed = gr.Slider(label=translations["voice_speed"], info=translations["voice_speed_info"], minimum=-100, maximum=100, value=0, step=1) - pitch0 = gr.Slider(minimum=-20, maximum=20, step=1, info=translations["pitch_info"], label=translations["pitch"], value=0, interactive=True) - with gr.Row(): - tts_button = gr.Button(translations["tts_1"], variant="primary", scale=2) - convert_button0 = gr.Button(translations["tts_2"], variant="secondary", scale=2) - with gr.Row(): - with gr.Column(): - txt_input = gr.File(label=translations["drop_text"], file_types=[".txt"], visible=use_txt.value) - tts_voice = gr.Dropdown(label=translations["voice"], choices=edgetts, interactive=True, value="vi-VN-NamMinhNeural") - tts_pitch = gr.Slider(minimum=-20, maximum=20, step=1, info=translations["pitch_info_2"], label=translations["pitch"], value=0, interactive=True) - with gr.Column(): - with gr.Accordion(translations["model_accordion"], open=True): - with gr.Row(): - model_pth0 = gr.Dropdown(label=translations["model_name"], choices=sorted(model_name), value="", interactive=True, allow_custom_value=True) - model_index0 = gr.Dropdown(label=translations["index_path"], choices=sorted(index_path), value="", interactive=True, allow_custom_value=True) - with gr.Row(): - refesh1 = gr.Button(translations["refesh"]) - with gr.Row(): - index_strength0 = gr.Slider(label=translations["index_strength"], info=translations["index_strength_info"], minimum=0, maximum=1, value=0.5, step=0.01, interactive=True, visible=model_index0.value != "") - with gr.Accordion(translations["output_path"], open=False): - export_format0 = gr.Radio(label=translations["export_format"], info=translations["export_info"], choices=["wav", "mp3", "flac", "ogg", "opus", "m4a", "mp4", "aac", "alac", "wma", "aiff", "webm", "ac3"], value="wav", interactive=True) - output_audio0 = gr.Textbox(label=translations["output_tts"], value="audios/tts.wav", placeholder="audios/tts.wav", info=translations["tts_output"], interactive=True) - output_audio1 = gr.Textbox(label=translations["output_tts_convert"], value="audios/tts-convert.wav", placeholder="audios/tts-convert.wav", info=translations["tts_output"], interactive=True) - with gr.Accordion(translations["setting"], open=False): - with gr.Accordion(translations["f0_method"], open=False): - method0 = gr.Radio(label=translations["f0_method"], info=translations["f0_method_info"], choices=method_f0, value="rmvpe", interactive=True) - hybrid_method0 = gr.Dropdown(label=translations["f0_method_hybrid"], info=translations["f0_method_hybrid_info"], choices=["hybrid[pm+dio]", "hybrid[pm+crepe-tiny]", "hybrid[pm+crepe]", "hybrid[pm+fcpe]", "hybrid[pm+rmvpe]", "hybrid[pm+harvest]", "hybrid[pm+yin]", "hybrid[dio+crepe-tiny]", "hybrid[dio+crepe]", "hybrid[dio+fcpe]", "hybrid[dio+rmvpe]", "hybrid[dio+harvest]", "hybrid[dio+yin]", "hybrid[crepe-tiny+crepe]", "hybrid[crepe-tiny+fcpe]", "hybrid[crepe-tiny+rmvpe]", "hybrid[crepe-tiny+harvest]", "hybrid[crepe+fcpe]", "hybrid[crepe+rmvpe]", "hybrid[crepe+harvest]", "hybrid[crepe+yin]", "hybrid[fcpe+rmvpe]", "hybrid[fcpe+harvest]", "hybrid[fcpe+yin]", "hybrid[rmvpe+harvest]", "hybrid[rmvpe+yin]", "hybrid[harvest+yin]"], value="hybrid[pm+dio]", interactive=True, allow_custom_value=True, visible=method0.value == "hybrid") - hop_length0 = gr.Slider(label="Hop length", info=translations["hop_length_info"], minimum=1, maximum=512, value=128, step=1, interactive=True, visible=False) - with gr.Accordion(translations["hubert_model"], open=False): - embedders0 = gr.Radio(label=translations["hubert_model"], info=translations["hubert_info"], choices=["contentvec_base", "hubert_base", "japanese_hubert_base", "korean_hubert_base", "chinese_hubert_base", "Hidden_Rabbit_last", "portuguese_hubert_base", "custom"], value="contentvec_base", interactive=True) - custom_embedders0 = gr.Textbox(label=translations["modelname"], info=translations["modelname_info"], value="", placeholder="hubert_base", interactive=True, visible=embedders0.value == "custom") - with gr.Group(): - with gr.Row(): - split_audio0 = gr.Checkbox(label=translations["split_audio"], value=False, interactive=True) - cleaner1 = gr.Checkbox(label=translations["clear_audio"], value=False, interactive=True) - with gr.Row(): - autotune3 = gr.Checkbox(label=translations["autotune"], value=False, interactive=True) - checkpointing0 = gr.Checkbox(label=translations["memory_efficient_training"], value=False, interactive=True) - with gr.Column(): - f0_autotune_strength0 = gr.Slider(minimum=0, maximum=1, label=translations["autotune_rate"], info=translations["autotune_rate_info"], value=1, step=0.1, interactive=True, visible=autotune3.value) - clean_strength1 = gr.Slider(label=translations["clean_strength"], info=translations["clean_strength_info"], minimum=0, maximum=1, value=0.5, step=0.1, interactive=True, visible=cleaner1.value) - resample_sr0 = gr.Slider(minimum=0, maximum=96000, label=translations["resample"], info=translations["resample_info"], value=0, step=1, interactive=True) - filter_radius0 = gr.Slider(minimum=0, maximum=7, label=translations["filter_radius"], info=translations["filter_radius_info"], value=3, step=1, interactive=True) - volume_envelope0 = gr.Slider(minimum=0, maximum=1, label=translations["volume_envelope"], info=translations["volume_envelope_info"], value=1, step=0.1, interactive=True) - protect0 = gr.Slider(minimum=0, maximum=1, label=translations["protect"], info=translations["protect_info"], value=0.33, step=0.01, interactive=True) - with gr.Row(): - gr.Markdown(translations["output_tts_markdown"]) - with gr.Row(): - tts_voice_audio = gr.Audio(show_download_button=True, interactive=False, label=translations["output_text_to_speech"]) - tts_voice_convert = gr.Audio(show_download_button=True, interactive=False, label=translations["output_file_tts_convert"]) - with gr.Row(): - autotune3.change(fn=visible, inputs=[autotune3], outputs=[f0_autotune_strength0]) - model_pth0.change(fn=get_index, inputs=[model_pth0], outputs=[model_index0]) - with gr.Row(): - cleaner1.change(fn=visible, inputs=[cleaner1], outputs=[clean_strength1]) - method0.change(fn=lambda method, hybrid: [visible(method == "hybrid"), hoplength_show(method, hybrid)], inputs=[method0, hybrid_method0], outputs=[hybrid_method0, hop_length0]) - hybrid_method0.change(fn=hoplength_show, inputs=[method0, hybrid_method0], outputs=[hop_length0]) - with gr.Row(): - refesh1.click(fn=change_models_choices, inputs=[], outputs=[model_pth0, model_index0]) - embedders0.change(fn=lambda embedders: visible(embedders == "custom"), inputs=[embedders0], outputs=[custom_embedders0]) - with gr.Row(): - model_index0.change(fn=index_strength_show, inputs=[model_index0], outputs=[index_strength0]) - txt_input.upload(fn=process_input, inputs=[txt_input], outputs=[prompt]) - use_txt.change(fn=visible, inputs=[use_txt], outputs=[txt_input]) - with gr.Row(): - google_tts_check_box.change(fn=change_tts_voice_choices, inputs=[google_tts_check_box], outputs=[tts_voice]) - tts_button.click( - fn=TTS, - inputs=[ - prompt, - tts_voice, - speed, - output_audio0, - tts_pitch, - google_tts_check_box - ], - outputs=[tts_voice_audio], - api_name="text-to-speech" - ) - convert_button0.click( - fn=convert_tts, - inputs=[ - cleaner1, - autotune3, - pitch0, - clean_strength1, - model_pth0, - model_index0, - index_strength0, - output_audio0, - output_audio1, - export_format0, - method0, - hybrid_method0, - hop_length0, - embedders0, - custom_embedders0, - resample_sr0, - filter_radius0, - volume_envelope0, - protect0, - split_audio0, - f0_autotune_strength0, - checkpointing0 - ], - outputs=[tts_voice_convert], - api_name="convert_tts" - ) - - with gr.TabItem(translations["audio_effects"], visible=configs.get("effects_tab", True)): - gr.Markdown(translations["apply_audio_effects"]) - with gr.Row(): - gr.Markdown(translations["audio_effects_edit"]) - with gr.Row(): - with gr.Column(): - with gr.Row(): - reverb_check_box = gr.Checkbox(label=translations["reverb"], value=False, interactive=True) - chorus_check_box = gr.Checkbox(label=translations["chorus"], value=False, interactive=True) - delay_check_box = gr.Checkbox(label=translations["delay"], value=False, interactive=True) - phaser_check_box = gr.Checkbox(label=translations["phaser"], value=False, interactive=True) - compressor_check_box = gr.Checkbox(label=translations["compressor"], value=False, interactive=True) - more_options = gr.Checkbox(label=translations["more_option"], value=False, interactive=True) - with gr.Row(): - with gr.Accordion(translations["input_output"], open=False): - with gr.Row(): - upload_audio = gr.File(label=translations["drop_audio"], file_types=[".wav", ".mp3", ".flac", ".ogg", ".opus", ".m4a", ".mp4", ".aac", ".alac", ".wma", ".aiff", ".webm", ".ac3"]) - with gr.Row(): - audio_in_path = gr.Dropdown(label=translations["input_audio"], value="", choices=paths_for_files, info=translations["provide_audio"], interactive=True, allow_custom_value=True) - audio_out_path = gr.Textbox(label=translations["output_audio"], value="audios/audio_effects.wav", placeholder="audios/audio_effects.wav", info=translations["provide_output"], interactive=True) - with gr.Row(): - with gr.Column(): - audio_combination = gr.Checkbox(label=translations["merge_instruments"], value=False, interactive=True) - audio_combination_input = gr.Dropdown(label=translations["input_audio"], value="", choices=paths_for_files, info=translations["provide_audio"], interactive=True, allow_custom_value=True, visible=audio_combination.value) - with gr.Row(): - audio_effects_refesh = gr.Button(translations["refesh"]) - with gr.Row(): - audio_output_format = gr.Radio(label=translations["export_format"], info=translations["export_info"], choices=["wav", "mp3", "flac", "ogg", "opus", "m4a", "mp4", "aac", "alac", "wma", "aiff", "webm", "ac3"], value="wav", interactive=True) - with gr.Row(): - apply_effects_button = gr.Button(translations["apply"], variant="primary", scale=2) - with gr.Row(): - with gr.Column(): - with gr.Row(): - with gr.Accordion(translations["reverb"], open=False, visible=reverb_check_box.value) as reverb_accordion: - reverb_freeze_mode = gr.Checkbox(label=translations["reverb_freeze"], info=translations["reverb_freeze_info"], value=False, interactive=True) - reverb_room_size = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.5, label=translations["room_size"], info=translations["room_size_info"], interactive=True) - reverb_damping = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.5, label=translations["damping"], info=translations["damping_info"], interactive=True) - reverb_wet_level = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.3, label=translations["wet_level"], info=translations["wet_level_info"], interactive=True) - reverb_dry_level = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.7, label=translations["dry_level"], info=translations["dry_level_info"], interactive=True) - reverb_width = gr.Slider(minimum=0, maximum=1, step=0.01, value=1, label=translations["width"], info=translations["width_info"], interactive=True) - with gr.Row(): - with gr.Accordion(translations["chorus"], open=False, visible=chorus_check_box.value) as chorus_accordion: - chorus_depth = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.5, label=translations["chorus_depth"], info=translations["chorus_depth_info"], interactive=True) - chorus_rate_hz = gr.Slider(minimum=0.1, maximum=10, step=0.1, value=1.5, label=translations["chorus_rate_hz"], info=translations["chorus_rate_hz_info"], interactive=True) - chorus_mix = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.5, label=translations["chorus_mix"], info=translations["chorus_mix_info"], interactive=True) - chorus_centre_delay_ms = gr.Slider(minimum=0, maximum=50, step=1, value=10, label=translations["chorus_centre_delay_ms"], info=translations["chorus_centre_delay_ms_info"], interactive=True) - chorus_feedback = gr.Slider(minimum=-1, maximum=1, step=0.01, value=0, label=translations["chorus_feedback"], info=translations["chorus_feedback_info"], interactive=True) - with gr.Row(): - with gr.Accordion(translations["delay"], open=False, visible=delay_check_box.value) as delay_accordion: - delay_second = gr.Slider(minimum=0, maximum=5, step=0.01, value=0.5, label=translations["delay_seconds"], info=translations["delay_seconds_info"], interactive=True) - delay_feedback = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.5, label=translations["delay_feedback"], info=translations["delay_feedback_info"], interactive=True) - delay_mix = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.5, label=translations["delay_mix"], info=translations["delay_mix_info"], interactive=True) - with gr.Column(): - with gr.Row(): - with gr.Accordion(translations["more_option"], open=False, visible=more_options.value) as more_accordion: - with gr.Row(): - fade = gr.Checkbox(label=translations["fade"], value=False, interactive=True) - bass_or_treble = gr.Checkbox(label=translations["bass_or_treble"], value=False, interactive=True) - limiter = gr.Checkbox(label=translations["limiter"], value=False, interactive=True) - resample_checkbox = gr.Checkbox(label=translations["resample"], value=False, interactive=True) - with gr.Row(): - distortion_checkbox = gr.Checkbox(label=translations["distortion"], value=False, interactive=True) - gain_checkbox = gr.Checkbox(label=translations["gain"], value=False, interactive=True) - bitcrush_checkbox = gr.Checkbox(label=translations["bitcrush"], value=False, interactive=True) - clipping_checkbox = gr.Checkbox(label=translations["clipping"], value=False, interactive=True) - with gr.Accordion(translations["fade"], open=True, visible=fade.value) as fade_accordion: - with gr.Row(): - fade_in = gr.Slider(minimum=0, maximum=10000, step=100, value=0, label=translations["fade_in"], info=translations["fade_in_info"], interactive=True) - fade_out = gr.Slider(minimum=0, maximum=10000, step=100, value=0, label=translations["fade_out"], info=translations["fade_out_info"], interactive=True) - with gr.Accordion(translations["bass_or_treble"], open=True, visible=bass_or_treble.value) as bass_treble_accordion: - with gr.Row(): - bass_boost = gr.Slider(minimum=0, maximum=20, step=1, value=0, label=translations["bass_boost"], info=translations["bass_boost_info"], interactive=True) - bass_frequency = gr.Slider(minimum=20, maximum=200, step=10, value=100, label=translations["bass_frequency"], info=translations["bass_frequency_info"], interactive=True) - with gr.Row(): - treble_boost = gr.Slider(minimum=0, maximum=20, step=1, value=0, label=translations["treble_boost"], info=translations["treble_boost_info"], interactive=True) - treble_frequency = gr.Slider(minimum=1000, maximum=10000, step=500, value=3000, label=translations["treble_frequency"], info=translations["treble_frequency_info"], interactive=True) - with gr.Accordion(translations["limiter"], open=True, visible=limiter.value) as limiter_accordion: - with gr.Row(): - limiter_threashold_db = gr.Slider(minimum=-60, maximum=0, step=1, value=-1, label=translations["limiter_threashold_db"], info=translations["limiter_threashold_db_info"], interactive=True) - limiter_release_ms = gr.Slider(minimum=10, maximum=1000, step=1, value=100, label=translations["limiter_release_ms"], info=translations["limiter_release_ms_info"], interactive=True) - with gr.Column(): - pitch_shift_semitones = gr.Slider(minimum=-20, maximum=20, step=1, value=0, label=translations["pitch"], info=translations["pitch_info"], interactive=True) - audio_effect_resample_sr = gr.Slider(minimum=0, maximum=96000, step=1, value=0, label=translations["resample"], info=translations["resample_info"], interactive=True, visible=resample_checkbox.value) - distortion_drive_db = gr.Slider(minimum=0, maximum=50, step=1, value=20, label=translations["distortion"], info=translations["distortion_info"], interactive=True, visible=distortion_checkbox.value) - gain_db = gr.Slider(minimum=-60, maximum=60, step=1, value=0, label=translations["gain"], info=translations["gain_info"], interactive=True, visible=gain_checkbox.value) - clipping_threashold_db = gr.Slider(minimum=-60, maximum=0, step=1, value=-1, label=translations["clipping_threashold_db"], info=translations["clipping_threashold_db_info"], interactive=True, visible=clipping_checkbox.value) - bitcrush_bit_depth = gr.Slider(minimum=1, maximum=24, step=1, value=16, label=translations["bitcrush_bit_depth"], info=translations["bitcrush_bit_depth_info"], interactive=True, visible=bitcrush_checkbox.value) - with gr.Row(): - with gr.Accordion(translations["phaser"], open=False, visible=phaser_check_box.value) as phaser_accordion: - phaser_depth = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.5, label=translations["phaser_depth"], info=translations["phaser_depth_info"], interactive=True) - phaser_rate_hz = gr.Slider(minimum=0.1, maximum=10, step=0.1, value=1, label=translations["phaser_rate_hz"], info=translations["phaser_rate_hz_info"], interactive=True) - phaser_mix = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.5, label=translations["phaser_mix"], info=translations["phaser_mix_info"], interactive=True) - phaser_centre_frequency_hz = gr.Slider(minimum=50, maximum=5000, step=10, value=1000, label=translations["phaser_centre_frequency_hz"], info=translations["phaser_centre_frequency_hz_info"], interactive=True) - phaser_feedback = gr.Slider(minimum=-1, maximum=1, step=0.01, value=0, label=translations["phaser_feedback"], info=translations["phaser_feedback_info"], interactive=True) - with gr.Row(): - with gr.Accordion(translations["compressor"], open=False, visible=compressor_check_box.value) as compressor_accordion: - compressor_threashold_db = gr.Slider(minimum=-60, maximum=0, step=1, value=-20, label=translations["compressor_threashold_db"], info=translations["compressor_threashold_db_info"], interactive=True) - compressor_ratio = gr.Slider(minimum=1, maximum=20, step=0.1, value=1, label=translations["compressor_ratio"], info=translations["compressor_ratio_info"], interactive=True) - compressor_attack_ms = gr.Slider(minimum=0.1, maximum=100, step=0.1, value=10, label=translations["compressor_attack_ms"], info=translations["compressor_attack_ms_info"], interactive=True) - compressor_release_ms = gr.Slider(minimum=10, maximum=1000, step=1, value=100, label=translations["compressor_release_ms"], info=translations["compressor_release_ms_info"], interactive=True) - with gr.Row(): - gr.Markdown(translations["output_audio"]) - with gr.Row(): - audio_play_input = gr.Audio(show_download_button=True, interactive=False, label=translations["input_audio"]) - audio_play_output = gr.Audio(show_download_button=True, interactive=False, label=translations["output_audio"]) - with gr.Row(): - reverb_check_box.change(fn=visible, inputs=[reverb_check_box], outputs=[reverb_accordion]) - chorus_check_box.change(fn=visible, inputs=[chorus_check_box], outputs=[chorus_accordion]) - delay_check_box.change(fn=visible, inputs=[delay_check_box], outputs=[delay_accordion]) - with gr.Row(): - compressor_check_box.change(fn=visible, inputs=[compressor_check_box], outputs=[compressor_accordion]) - phaser_check_box.change(fn=visible, inputs=[phaser_check_box], outputs=[phaser_accordion]) - more_options.change(fn=visible, inputs=[more_options], outputs=[more_accordion]) - with gr.Row(): - fade.change(fn=visible, inputs=[fade], outputs=[fade_accordion]) - bass_or_treble.change(fn=visible, inputs=[bass_or_treble], outputs=[bass_treble_accordion]) - limiter.change(fn=visible, inputs=[limiter], outputs=[limiter_accordion]) - resample_checkbox.change(fn=visible, inputs=[resample_checkbox], outputs=[audio_effect_resample_sr]) - with gr.Row(): - distortion_checkbox.change(fn=visible, inputs=[distortion_checkbox], outputs=[distortion_drive_db]) - gain_checkbox.change(fn=visible, inputs=[gain_checkbox], outputs=[gain_db]) - clipping_checkbox.change(fn=visible, inputs=[clipping_checkbox], outputs=[clipping_threashold_db]) - bitcrush_checkbox.change(fn=visible, inputs=[bitcrush_checkbox], outputs=[bitcrush_bit_depth]) - with gr.Row(): - upload_audio.upload(fn=lambda audio_in: shutil.move(audio_in.name, os.path.join("audios")), inputs=[upload_audio], outputs=[audio_in_path]) - audio_in_path.change(fn=lambda audio: audio if audio else None, inputs=[audio_in_path], outputs=[audio_play_input]) - audio_effects_refesh.click(fn=lambda: [change_audios_choices()]*2, inputs=[], outputs=[audio_in_path, audio_combination_input]) - with gr.Row(): - more_options.change(fn=lambda: [False]*8, inputs=[], outputs=[fade, bass_or_treble, limiter, resample_checkbox, distortion_checkbox, gain_checkbox, clipping_checkbox, bitcrush_checkbox]) - audio_combination.change(fn=visible, inputs=[audio_combination], outputs=[audio_combination_input]) - with gr.Row(): - apply_effects_button.click( - fn=audio_effects, - inputs=[ - audio_in_path, - audio_out_path, - resample_checkbox, - audio_effect_resample_sr, - chorus_depth, - chorus_rate_hz, - chorus_mix, - chorus_centre_delay_ms, - chorus_feedback, - distortion_drive_db, - reverb_room_size, - reverb_damping, - reverb_wet_level, - reverb_dry_level, - reverb_width, - reverb_freeze_mode, - pitch_shift_semitones, - delay_second, - delay_feedback, - delay_mix, - compressor_threashold_db, - compressor_ratio, - compressor_attack_ms, - compressor_release_ms, - limiter_threashold_db, - limiter_release_ms, - gain_db, - bitcrush_bit_depth, - clipping_threashold_db, - phaser_rate_hz, - phaser_depth, - phaser_centre_frequency_hz, - phaser_feedback, - phaser_mix, - bass_boost, - bass_frequency, - treble_boost, - treble_frequency, - fade_in, - fade_out, - audio_output_format, - chorus_check_box, - distortion_checkbox, - reverb_check_box, - delay_check_box, - compressor_check_box, - limiter, - gain_checkbox, - bitcrush_checkbox, - clipping_checkbox, - phaser_check_box, - bass_or_treble, - fade, - audio_combination, - audio_combination_input - ], - outputs=[audio_play_output], - api_name="audio_effects" - ) - - with gr.TabItem(translations["createdataset"], visible=configs.get("create_dataset_tab", True)): - gr.Markdown(translations["create_dataset_markdown"]) - with gr.Row(): - gr.Markdown(translations["create_dataset_markdown_2"]) - with gr.Row(): - dataset_url = gr.Textbox(label=translations["url_audio"], info=translations["create_dataset_url"], value="", placeholder="https://www.youtube.com/...", interactive=True) - output_dataset = gr.Textbox(label=translations["output_data"], info=translations["output_data_info"], value="dataset", placeholder="dataset", interactive=True) - with gr.Row(): - with gr.Column(): - with gr.Group(): - with gr.Row(): - separator_reverb = gr.Checkbox(label=translations["dereveb_audio"], value=False, interactive=True) - denoise_mdx = gr.Checkbox(label=translations["denoise"], value=False, interactive=True) - with gr.Row(): - kim_vocal_version = gr.Radio(label=translations["model_ver"], info=translations["model_ver_info"], choices=["Version-1", "Version-2"], value="Version-2", interactive=True) - kim_vocal_overlap = gr.Radio(label=translations["overlap"], info=translations["overlap_info"], choices=["0.25", "0.5", "0.75", "0.99"], value="0.25", interactive=True) - with gr.Row(): - kim_vocal_hop_length = gr.Slider(label="Hop length", info=translations["hop_length_info"], minimum=1, maximum=8192, value=1024, step=1, interactive=True) - kim_vocal_batch_size = gr.Slider(label=translations["batch_size"], info=translations["mdx_batch_size_info"], minimum=1, maximum=64, value=1, step=1, interactive=True) - with gr.Row(): - kim_vocal_segments_size = gr.Slider(label=translations["segments_size"], info=translations["segments_size_info"], minimum=32, maximum=3072, value=256, step=32, interactive=True) - with gr.Row(): - sample_rate0 = gr.Slider(minimum=0, maximum=96000, step=1, value=44100, label=translations["sr"], info=translations["sr_info"], interactive=True) - with gr.Column(): - create_button = gr.Button(translations["createdataset"], variant="primary", scale=2, min_width=4000) - with gr.Group(): - with gr.Row(): - clean_audio = gr.Checkbox(label=translations["clear_audio"], value=False, interactive=True) - skip = gr.Checkbox(label=translations["skip"], value=False, interactive=True) - with gr.Row(): - dataset_clean_strength = gr.Slider(minimum=0, maximum=1, step=0.1, value=0.5, label=translations["clean_strength"], info=translations["clean_strength_info"], interactive=True, visible=clean_audio.value) - with gr.Row(): - skip_start = gr.Textbox(label=translations["skip_start"], info=translations["skip_start_info"], value="", placeholder="0,...", interactive=True, visible=skip.value) - skip_end = gr.Textbox(label=translations["skip_end"], info=translations["skip_end_info"], value="", placeholder="0,...", interactive=True, visible=skip.value) - create_dataset_info = gr.Textbox(label=translations["create_dataset_info"], value="", interactive=False) - with gr.Row(): - clean_audio.change(fn=visible, inputs=[clean_audio], outputs=[dataset_clean_strength]) - skip.change(fn=lambda a: [valueEmpty_visible1(a)]*2, inputs=[skip], outputs=[skip_start, skip_end]) - with gr.Row(): - create_button.click( - fn=create_dataset, - inputs=[ - dataset_url, - output_dataset, - clean_audio, - dataset_clean_strength, - separator_reverb, - kim_vocal_version, - kim_vocal_overlap, - kim_vocal_segments_size, - denoise_mdx, - skip, - skip_start, - skip_end, - kim_vocal_hop_length, - kim_vocal_batch_size, - sample_rate0 - ], - outputs=[create_dataset_info], - api_name="create_dataset" - ) - - with gr.TabItem(translations["training_model"], visible=configs.get("training_tab", True)): - gr.Markdown(f"## {translations['training_model']}") - with gr.Row(): - gr.Markdown(translations["training_markdown"]) - with gr.Row(): - with gr.Column(): - with gr.Row(): - with gr.Column(): - training_name = gr.Textbox(label=translations["modelname"], info=translations["training_model_name"], value="", placeholder=translations["modelname"], interactive=True) - training_sr = gr.Radio(label=translations["sample_rate"], info=translations["sample_rate_info"], choices=["32k", "40k", "44.1k", "48k"], value="48k", interactive=True) - training_ver = gr.Radio(label=translations["training_version"], info=translations["training_version_info"], choices=["v1", "v2"], value="v2", interactive=True) - with gr.Row(): - clean_dataset = gr.Checkbox(label=translations["clear_dataset"], value=False, interactive=True) - checkpointing1 = gr.Checkbox(label=translations["memory_efficient_training"], value=False, interactive=True) - training_f0 = gr.Checkbox(label=translations["training_pitch"], info=translations["training_pitch_info"], value=True, interactive=True) - upload = gr.Checkbox(label=translations["upload"], info=translations["upload_dataset"], value=False, interactive=True) - preprocess_cut = gr.Checkbox(label=translations["split_audio"], info=translations["preprocess_split"], value=True, interactive=True) - process_effects = gr.Checkbox(label=translations["preprocess_effect"], info=translations["preprocess_effect_info"], value=False, interactive=True) - with gr.Row(): - clean_dataset_strength = gr.Slider(label=translations["clean_strength"], info=translations["clean_strength_info"], minimum=0, maximum=1, value=0.7, step=0.1, interactive=True, visible=clean_dataset.value) - with gr.Column(): - preprocess_button = gr.Button(translations["preprocess_button"], scale=2) - upload_dataset = gr.Files(label=translations["drop_audio"], file_types=[".wav", ".mp3", ".flac", ".ogg", ".opus", ".m4a", ".mp4", ".aac", ".alac", ".wma", ".aiff", ".webm", ".ac3"], visible=upload.value) - preprocess_info = gr.Textbox(label=translations["preprocess_info"], value="", interactive=False) - with gr.Column(): - with gr.Row(): - with gr.Column(): - with gr.Accordion(label=translations["f0_method"], open=False): - extract_method = gr.Radio(label=translations["f0_method"], info=translations["f0_method_info"], choices=["pm", "dio", "mangio-crepe-tiny", "mangio-crepe-tiny-onnx", "mangio-crepe-small", "mangio-crepe-small-onnx", "mangio-crepe-medium", "mangio-crepe-medium-onnx", "mangio-crepe-large", "mangio-crepe-large-onnx", "mangio-crepe-full", "mangio-crepe-full-onnx", "crepe-tiny", "crepe-tiny-onnx", "crepe-small", "crepe-small-onnx", "crepe-medium", "crepe-medium-onnx", "crepe-large", "crepe-large-onnx", "crepe-full", "crepe-full-onnx", "fcpe", "fcpe-onnx", "fcpe-legacy", "fcpe-legacy-onnx", "rmvpe", "rmvpe-onnx", "rmvpe-legacy", "rmvpe-legacy-onnx", "harvest", "yin", "pyin"], value="rmvpe", interactive=True) - extract_hop_length = gr.Slider(label="Hop length", info=translations["hop_length_info"], minimum=1, maximum=512, value=128, step=1, interactive=True, visible=False) - with gr.Accordion(label=translations["hubert_model"], open=False): - extract_embedders = gr.Radio(label=translations["hubert_model"], info=translations["hubert_info"], choices=["contentvec_base", "hubert_base", "japanese_hubert_base", "korean_hubert_base", "chinese_hubert_base", "Hidden_Rabbit_last", "portuguese_hubert_base", "custom"], value="contentvec_base", interactive=True) - with gr.Row(): - extract_embedders_custom = gr.Textbox(label=translations["modelname"], info=translations["modelname_info"], value="", placeholder="hubert_base", interactive=True, visible=extract_embedders.value == "custom") - with gr.Column(): - extract_button = gr.Button(translations["extract_button"], scale=2) - extract_info = gr.Textbox(label=translations["extract_info"], value="", interactive=False) - with gr.Column(): - with gr.Row(): - with gr.Column(): - total_epochs = gr.Slider(label=translations["total_epoch"], info=translations["total_epoch_info"], minimum=1, maximum=10000, value=300, step=1, interactive=True) - save_epochs = gr.Slider(label=translations["save_epoch"], info=translations["save_epoch_info"], minimum=1, maximum=10000, value=50, step=1, interactive=True) - with gr.Column(): - index_button = gr.Button(f"3. {translations['create_index']}", variant="primary", scale=2) - training_button = gr.Button(f"4. {translations['training_model']}", variant="primary", scale=2) - with gr.Row(): - with gr.Accordion(label=translations["setting"], open=False): - with gr.Row(): - index_algorithm = gr.Radio(label=translations["index_algorithm"], info=translations["index_algorithm_info"], choices=["Auto", "Faiss", "KMeans"], value="Auto", interactive=True) - with gr.Row(): - custom_dataset = gr.Checkbox(label=translations["custom_dataset"], info=translations["custom_dataset_info"], value=False, interactive=True) - overtraining_detector = gr.Checkbox(label=translations["overtraining_detector"], info=translations["overtraining_detector_info"], value=False, interactive=True) - clean_up = gr.Checkbox(label=translations["cleanup_training"], info=translations["cleanup_training_info"], value=False, interactive=True) - cache_in_gpu = gr.Checkbox(label=translations["cache_in_gpu"], info=translations["cache_in_gpu_info"], value=False, interactive=True) - with gr.Column(): - dataset_path = gr.Textbox(label=translations["dataset_folder"], value="dataset", interactive=True, visible=custom_dataset.value) - with gr.Column(): - threshold = gr.Slider(minimum=1, maximum=100, value=50, step=1, label=translations["threshold"], interactive=True, visible=overtraining_detector.value) - with gr.Accordion(translations["setting_cpu_gpu"], open=False): - with gr.Column(): - gpu_number = gr.Textbox(label=translations["gpu_number"], value=str("-".join(map(str, range(torch.cuda.device_count()))) if torch.cuda.is_available() else "-"), info=translations["gpu_number_info"], interactive=True) - gpu_info = gr.Textbox(label=translations["gpu_info"], value=get_gpu_info(), info=translations["gpu_info_2"], interactive=False) - cpu_core = gr.Slider(label=translations["cpu_core"], info=translations["cpu_core_info"], minimum=0, maximum=cpu_count(), value=cpu_count(), step=1, interactive=True) - train_batch_size = gr.Slider(label=translations["batch_size"], info=translations["batch_size_info"], minimum=1, maximum=64, value=8, step=1, interactive=True) - with gr.Row(): - save_only_latest = gr.Checkbox(label=translations["save_only_latest"], info=translations["save_only_latest_info"], value=True, interactive=True) - save_every_weights = gr.Checkbox(label=translations["save_every_weights"], info=translations["save_every_weights_info"], value=True, interactive=True) - not_use_pretrain = gr.Checkbox(label=translations["not_use_pretrain_2"], info=translations["not_use_pretrain_info"], value=False, interactive=True) - custom_pretrain = gr.Checkbox(label=translations["custom_pretrain"], info=translations["custom_pretrain_info"], value=False, interactive=True) - with gr.Row(): - vocoders = gr.Radio(label=translations["vocoder"], info=translations["vocoder_info"], choices=["Default", "MRF HiFi-GAN", "RefineGAN"], value="Default", interactive=True) - with gr.Row(): - model_author = gr.Textbox(label=translations["training_author"], info=translations["training_author_info"], value="", placeholder=translations["training_author"], interactive=True) - with gr.Row(): - with gr.Column(): - with gr.Accordion(translations["custom_pretrain_info"], open=False, visible=custom_pretrain.value and not not_use_pretrain.value) as pretrain_setting: - pretrained_D = gr.Dropdown(label=translations["pretrain_file"].format(dg="D"), choices=sorted(pretrainedD), value=sorted(pretrainedD)[0] if len(sorted(pretrainedD)) > 0 else '', interactive=True, allow_custom_value=True) - pretrained_G = gr.Dropdown(label=translations["pretrain_file"].format(dg="G"), choices=sorted(pretrainedG), value=sorted(pretrainedG)[0] if len(sorted(pretrainedG)) > 0 else '', interactive=True, allow_custom_value=True) - refesh_pretrain = gr.Button(translations["refesh"], scale=2) - with gr.Row(): - training_info = gr.Textbox(label=translations["train_info"], value="", interactive=False) - with gr.Row(): - with gr.Column(): - with gr.Accordion(translations["export_model"], open=False): - with gr.Row(): - model_file= gr.Dropdown(label=translations["model_name"], choices=sorted(model_name), value="", interactive=True, allow_custom_value=True) - index_file = gr.Dropdown(label=translations["index_path"], choices=sorted(index_path), value="", interactive=True, allow_custom_value=True) - with gr.Row(): - refesh_file = gr.Button(f"1. {translations['refesh']}", scale=2) - zip_model = gr.Button(translations["zip_model"], variant="primary", scale=2) - with gr.Row(): - zip_output = gr.File(label=translations["output_zip"], file_types=[".zip"], interactive=False, visible=False) - with gr.Row(): - refesh_file.click(fn=change_models_choices, inputs=[], outputs=[model_file, index_file]) - zip_model.click(fn=zip_file, inputs=[training_name, model_file, index_file], outputs=[zip_output]) - dataset_path.change(fn=lambda folder: os.makedirs(folder, exist_ok=True), inputs=[dataset_path], outputs=[]) - with gr.Row(): - upload.change(fn=visible, inputs=[upload], outputs=[upload_dataset]) - overtraining_detector.change(fn=visible, inputs=[overtraining_detector], outputs=[threshold]) - clean_dataset.change(fn=visible, inputs=[clean_dataset], outputs=[clean_dataset_strength]) - with gr.Row(): - custom_dataset.change(fn=lambda custom_dataset: [visible(custom_dataset), "dataset"],inputs=[custom_dataset], outputs=[dataset_path, dataset_path]) - upload_dataset.upload( - fn=lambda files, folder: [shutil.move(f.name, os.path.join(folder, os.path.split(f.name)[1])) for f in files] if folder != "" else gr_warning(translations["dataset_folder1"]), - inputs=[upload_dataset, dataset_path], - outputs=[], - api_name="upload_dataset" - ) - with gr.Row(): - not_use_pretrain.change(fn=lambda a, b: visible(a and not b), inputs=[custom_pretrain, not_use_pretrain], outputs=[pretrain_setting]) - custom_pretrain.change(fn=lambda a, b: visible(a and not b), inputs=[custom_pretrain, not_use_pretrain], outputs=[pretrain_setting]) - refesh_pretrain.click(fn=change_pretrained_choices, inputs=[], outputs=[pretrained_D, pretrained_G]) - with gr.Row(): - preprocess_button.click( - fn=preprocess, - inputs=[ - training_name, - training_sr, - cpu_core, - preprocess_cut, - process_effects, - dataset_path, - clean_dataset, - clean_dataset_strength - ], - outputs=[preprocess_info], - api_name="preprocess" - ) - with gr.Row(): - extract_method.change(fn=hoplength_show, inputs=[extract_method], outputs=[extract_hop_length]) - extract_embedders.change(fn=lambda extract_embedders: visible(extract_embedders == "custom"), inputs=[extract_embedders], outputs=[extract_embedders_custom]) - with gr.Row(): - extract_button.click( - fn=extract, - inputs=[ - training_name, - training_ver, - extract_method, - training_f0, - extract_hop_length, - cpu_core, - gpu_number, - training_sr, - extract_embedders, - extract_embedders_custom - ], - outputs=[extract_info], - api_name="extract" - ) - with gr.Row(): - index_button.click( - fn=create_index, - inputs=[ - training_name, - training_ver, - index_algorithm - ], - outputs=[training_info], - api_name="create_index" - ) - with gr.Row(): - training_button.click( - fn=training, - inputs=[ - training_name, - training_ver, - save_epochs, - save_only_latest, - save_every_weights, - total_epochs, - training_sr, - train_batch_size, - gpu_number, - training_f0, - not_use_pretrain, - custom_pretrain, - pretrained_G, - pretrained_D, - overtraining_detector, - threshold, - clean_up, - cache_in_gpu, - model_author, - vocoders, - checkpointing1 - ], - outputs=[training_info], - api_name="training_model" - ) - - with gr.TabItem(translations["fushion"], visible=configs.get("fushion_tab", True)): - gr.Markdown(translations["fushion_markdown"]) - with gr.Row(): - gr.Markdown(translations["fushion_markdown_2"]) - with gr.Row(): - name_to_save = gr.Textbox(label=translations["modelname"], placeholder="Model.pth", value="", max_lines=1, interactive=True) - with gr.Row(): - fushion_button = gr.Button(translations["fushion"], variant="primary", scale=4) - with gr.Column(): - with gr.Row(): - model_a = gr.File(label=f"{translations['model_name']} 1", file_types=[".pth"]) - model_b = gr.File(label=f"{translations['model_name']} 2", file_types=[".pth"]) - with gr.Row(): - model_path_a = gr.Textbox(label=f"{translations['model_path']} 1", value="", placeholder="assets/weights/Model_1.pth") - model_path_b = gr.Textbox(label=f"{translations['model_path']} 2", value="", placeholder="assets/weights/Model_2.pth") - with gr.Row(): - ratio = gr.Slider(minimum=0, maximum=1, label=translations["model_ratio"], info=translations["model_ratio_info"], value=0.5, interactive=True) - with gr.Row(): - output_model = gr.File(label=translations["output_model_path"], visible=False) - with gr.Row(): - model_a.upload(fn=lambda model: shutil.move(model.name, os.path.join("assets", "weights")), inputs=[model_a], outputs=[model_path_a]) - model_b.upload(fn=lambda model: shutil.move(model.name, os.path.join("assets", "weights")), inputs=[model_b], outputs=[model_path_b]) - with gr.Row(): - fushion_button.click( - fn=fushion_model, - inputs=[ - name_to_save, - model_path_a, - model_path_b, - ratio - ], - outputs=[name_to_save, output_model], - api_name="fushion_model" - ) - fushion_button.click(fn=lambda: visible(True), inputs=[], outputs=[output_model]) - - with gr.TabItem(translations["read_model"], visible=configs.get("read_tab", True)): - gr.Markdown(translations["read_model_markdown"]) - with gr.Row(): - gr.Markdown(translations["read_model_markdown_2"]) - with gr.Row(): - model = gr.File(label=translations["drop_model"], file_types=[".pth"]) - with gr.Row(): - read_button = gr.Button(translations["readmodel"], variant="primary", scale=2) - with gr.Column(): - model_path = gr.Textbox(label=translations["model_path"], value="", info=translations["model_path_info"], interactive=True) - output_info = gr.Textbox(label=translations["modelinfo"], value="", interactive=False, scale=6) - with gr.Row(): - model.upload(fn=lambda model: shutil.move(model.name, os.path.join("assets", "weights")), inputs=[model], outputs=[model_path]) - read_button.click( - fn=model_info, - inputs=[model_path], - outputs=[output_info], - api_name="read_model" - ) - - with gr.TabItem(translations["downloads"], visible=configs.get("downloads_tab", True)): - gr.Markdown(translations["download_markdown"]) - with gr.Row(): - gr.Markdown(translations["download_markdown_2"]) - with gr.Row(): - with gr.Accordion(translations["model_download"], open=True): - with gr.Row(): - downloadmodel = gr.Radio(label=translations["model_download_select"], choices=[translations["download_url"], translations["download_from_csv"], translations["search_models"], translations["upload"]], interactive=True, value=translations["download_url"]) - with gr.Row(): - gr.Markdown("___") - with gr.Column(): - with gr.Row(): - url_input = gr.Textbox(label=translations["model_url"], value="", placeholder="https://...", scale=6) - download_model_name = gr.Textbox(label=translations["modelname"], value="", placeholder=translations["modelname"], scale=2) - url_download = gr.Button(value=translations["downloads"], scale=2) - with gr.Column(): - model_browser = gr.Dropdown(choices=models.keys(), label=translations["model_warehouse"], scale=8, allow_custom_value=True, visible=False) - download_from_browser = gr.Button(value=translations["get_model"], scale=2, variant="primary", visible=False) - with gr.Column(): - search_name = gr.Textbox(label=translations["name_to_search"], placeholder=translations["modelname"], interactive=True, scale=8, visible=False) - search = gr.Button(translations["search_2"], scale=2, visible=False) - search_dropdown = gr.Dropdown(label=translations["select_download_model"], value="", choices=[], allow_custom_value=True, interactive=False, visible=False) - download = gr.Button(translations["downloads"], variant="primary", visible=False) - with gr.Column(): - model_upload = gr.File(label=translations["drop_model"], file_types=[".pth", ".index", ".zip"], visible=False) - with gr.Row(): - with gr.Accordion(translations["download_pretrained_2"], open=False): - with gr.Row(): - pretrain_download_choices = gr.Radio(label=translations["model_download_select"], choices=[translations["download_url"], translations["list_model"], translations["upload"]], value=translations["download_url"], interactive=True) - with gr.Row(): - gr.Markdown("___") - with gr.Column(): - with gr.Row(): - pretrainD = gr.Textbox(label=translations["pretrained_url"].format(dg="D"), value="", info=translations["only_huggingface"], placeholder="https://...", interactive=True, scale=4) - pretrainG = gr.Textbox(label=translations["pretrained_url"].format(dg="G"), value="", info=translations["only_huggingface"], placeholder="https://...", interactive=True, scale=4) - download_pretrain_button = gr.Button(translations["downloads"], scale=2) - with gr.Column(): - with gr.Row(): - pretrain_choices = gr.Dropdown(label=translations["select_pretrain"], info=translations["select_pretrain_info"], choices=list(fetch_pretrained_data().keys()), value="Titan_Medium", allow_custom_value=True, interactive=True, scale=6, visible=False) - sample_rate_pretrain = gr.Dropdown(label=translations["pretrain_sr"], info=translations["pretrain_sr"], choices=["48k", "40k", "44.1k", "32k"], value="48k", interactive=True, visible=False) - download_pretrain_choices_button = gr.Button(translations["downloads"], scale=2, variant="primary", visible=False) - with gr.Row(): - pretrain_upload_g = gr.File(label=translations["drop_pretrain"].format(dg="G"), file_types=[".pth"], visible=False) - pretrain_upload_d = gr.File(label=translations["drop_pretrain"].format(dg="D"), file_types=[".pth"], visible=False) - with gr.Row(): - with gr.Accordion(translations["hubert_download"], open=False): - with gr.Column(): - hubert_url = gr.Textbox(label=translations["hubert_url"], value="", info=translations["only_huggingface"], placeholder="https://...", interactive=True, scale=8) - hubert_button = gr.Button(translations["downloads"], scale=2, variant="primary") - with gr.Row(): - hubert_input = gr.File(label=translations["drop_hubert"], file_types=[".pt"]) - with gr.Row(): - url_download.click( - fn=download_model, - inputs=[ - url_input, - download_model_name - ], - outputs=[url_input], - api_name="download_model" - ) - download_from_browser.click( - fn=lambda model: download_model(models[model], model), - inputs=[model_browser], - outputs=[model_browser], - api_name="download_browser" - ) - with gr.Row(): - downloadmodel.change(fn=change_download_choices, inputs=[downloadmodel], outputs=[url_input, download_model_name, url_download, model_browser, download_from_browser, search_name, search, search_dropdown, download, model_upload]) - search.click(fn=search_models, inputs=[search_name], outputs=[search_dropdown, download]) - model_upload.upload(fn=save_drop_model, inputs=[model_upload], outputs=[model_upload]) - download.click( - fn=lambda model: download_model(model_options[model], model), - inputs=[search_dropdown], - outputs=[search_dropdown], - api_name="search_models" - ) - with gr.Row(): - pretrain_download_choices.change(fn=change_download_pretrained_choices, inputs=[pretrain_download_choices], outputs=[pretrainD, pretrainG, download_pretrain_button, pretrain_choices, sample_rate_pretrain, download_pretrain_choices_button, pretrain_upload_d, pretrain_upload_g]) - pretrain_choices.change(fn=update_sample_rate_dropdown, inputs=[pretrain_choices], outputs=[sample_rate_pretrain]) - with gr.Row(): - download_pretrain_button.click( - fn=download_pretrained_model, - inputs=[ - pretrain_download_choices, - pretrainD, - pretrainG - ], - outputs=[pretrainD], - api_name="download_pretrain_link" - ) - download_pretrain_choices_button.click( - fn=download_pretrained_model, - inputs=[ - pretrain_download_choices, - pretrain_choices, - sample_rate_pretrain - ], - outputs=[pretrain_choices], - api_name="download_pretrain_choices" - ) - pretrain_upload_g.upload( - fn=lambda pretrain_upload_g: shutil.move(pretrain_upload_g.name, os.path.join("assets", "models", "pretrained_custom")), - inputs=[pretrain_upload_g], - outputs=[], - api_name="upload_pretrain_g" - ) - pretrain_upload_d.upload( - fn=lambda pretrain_upload_d: shutil.move(pretrain_upload_d.name, os.path.join("assets", "models", "pretrained_custom")), - inputs=[pretrain_upload_d], - outputs=[], - api_name="upload_pretrain_d" - ) - with gr.Row(): - hubert_button.click( - fn=hubert_download, - inputs=[hubert_url], - outputs=[hubert_url], - api_name="hubert_download" - ) - hubert_input.upload( - fn=lambda hubert: shutil.move(hubert.name, os.path.join("assets", "models", "embedders")), - inputs=[hubert_input], - outputs=[], - api_name="upload_embedder" - ) - - with gr.TabItem(translations["settings"], visible=configs.get("settings_tab", True)): - gr.Markdown(translations["settings_markdown"]) - with gr.Row(): - gr.Markdown(translations["settings_markdown_2"]) - with gr.Row(): - toggle_button = gr.Button(translations["change_light_dark"], variant=["secondary"], scale=2) - with gr.Row(): - with gr.Column(): - language_dropdown = gr.Dropdown(label=translations["lang"], interactive=True, info=translations["lang_restart"], choices=configs.get("support_language", "vi-VN"), value=language) - change_lang = gr.Button(translations["change_lang"], variant="primary", scale=2) - with gr.Column(): - theme_dropdown = gr.Dropdown(label=translations["theme"], interactive=True, info=translations["theme_restart"], choices=configs.get("themes", theme), value=theme, allow_custom_value=True) - changetheme = gr.Button(translations["theme_button"], variant="primary", scale=2) - with gr.Row(): - with gr.Column(): - with gr.Accordion(translations["stop"], open=False): - separate_stop = gr.Button(translations["stop_separate"]) - convert_stop = gr.Button(translations["stop_convert"]) - create_dataset_stop = gr.Button(translations["stop_create_dataset"]) - with gr.Accordion(translations["stop_training"], open=False): - model_name_stop = gr.Textbox(label=translations["modelname"], info=translations["training_model_name"], value="", placeholder=translations["modelname"], interactive=True) - preprocess_stop = gr.Button(translations["stop_preprocess"]) - extract_stop = gr.Button(translations["stop_extract"]) - train_stop = gr.Button(translations["stop_training"]) - with gr.Column(): - with gr.Accordion(translations["cleaner"], open=False): - with gr.Accordion(translations["clean_audio"], open=False): - with gr.Row(): - audio_file_select = gr.Dropdown(label=translations["audio_path"], value="", choices=paths_for_files, info=translations["provide_audio"], allow_custom_value=True, interactive=True) - with gr.Column(): - refesh_audio_select = gr.Button(translations["refesh"]) - with gr.Row(): - delete_all_audio = gr.Button(translations["clean_all"]) - delete_audio = gr.Button(translations["clean_file"], variant="primary") - with gr.Accordion(translations["clean_models"], open=False): - with gr.Row(): - model_select = gr.Dropdown(label=translations["model_name"], choices=sorted(model_name), value="", interactive=True, allow_custom_value=True) - index_select = gr.Dropdown(label=translations["index_path"], choices=sorted(delete_index), value=sorted(delete_index)[0] if len(sorted(delete_index)) > 0 else '', interactive=True, allow_custom_value=True) - with gr.Row(): - refesh_model_select = gr.Button(translations["refesh"]) - with gr.Row(): - delete_all_model_button = gr.Button(translations["clean_all"]) - delete_model_button = gr.Button(translations["clean_file"], variant="primary") - with gr.Accordion(translations["clean_pretrained"], open=False): - with gr.Row(): - pretrain_select = gr.Dropdown(label=translations["pretrain_file"].format(dg=" "), choices=sorted(Allpretrained), value=sorted(Allpretrained)[0] if len(sorted(Allpretrained)) > 0 else '', interactive=True, allow_custom_value=True) - with gr.Column(): - refesh_pretrain_select = gr.Button(translations["refesh"]) - with gr.Row(): - delete_all_pretrain = gr.Button(translations["clean_all"]) - delete_pretrain = gr.Button(translations["clean_file"], variant="primary") - with gr.Accordion(translations["clean_separated"], open=False): - with gr.Row(): - separate_select = gr.Dropdown(label=translations["separator_model"], choices=sorted(separate_model), value=sorted(separate_model)[0] if len(sorted(separate_model)) > 0 else '', interactive=True, allow_custom_value=True) - with gr.Column(): - refesh_separate_select = gr.Button(translations["refesh"]) - with gr.Row(): - delete_all_separate = gr.Button(translations["clean_all"]) - delete_separate = gr.Button(translations["clean_file"], variant="primary") - with gr.Accordion(translations["clean_presets"], open=False): - with gr.Row(): - presets_select = gr.Dropdown(label=translations["file_preset"], choices=sorted(presets_file), value=sorted(presets_file)[0] if len(sorted(presets_file)) > 0 else '', interactive=True, allow_custom_value=True) - with gr.Column(): - refesh_presets_select = gr.Button(translations["refesh"]) - with gr.Row(): - delete_all_presets_button = gr.Button(translations["clean_all"]) - delete_presets_button = gr.Button(translations["clean_file"], variant="primary") - with gr.Accordion(translations["clean_datasets"], open=False): - dataset_folder_name = gr.Textbox(label=translations["dataset_folder"], value="dataset", interactive=True) - delete_dataset_button = gr.Button(translations["clean_dataset_folder"], variant="primary") - with gr.Row(): - clean_log = gr.Button(translations["clean_log"], variant="primary") - clean_predictor = gr.Button(translations["clean_predictors"], variant="primary") - clean_embedders = gr.Button(translations["clean_embed"], variant="primary") - with gr.Row(): - toggle_button.click(fn=None, js="() => {document.body.classList.toggle('dark')}") - with gr.Row(): - change_lang.click(fn=change_language, inputs=[language_dropdown], outputs=[]) - changetheme.click(fn=change_theme, inputs=[theme_dropdown], outputs=[]) - with gr.Row(): - change_lang.click(fn=None, js="setTimeout(function() {location.reload()}, 15000)", inputs=[], outputs=[]) - changetheme.click(fn=None, js="setTimeout(function() {location.reload()}, 15000)", inputs=[], outputs=[]) - with gr.Row(): - separate_stop.click(fn=lambda: stop_pid("separate_pid", None), inputs=[], outputs=[]) - convert_stop.click(fn=lambda: stop_pid("convert_pid", None), inputs=[], outputs=[]) - create_dataset_stop.click(fn=lambda: stop_pid("create_dataset_pid", None), inputs=[], outputs=[]) - with gr.Row(): - preprocess_stop.click(fn=lambda model_name_stop: stop_pid("preprocess_pid", model_name_stop), inputs=[model_name_stop], outputs=[]) - extract_stop.click(fn=lambda model_name_stop: stop_pid("extract_pid", model_name_stop), inputs=[model_name_stop], outputs=[]) - train_stop.click(fn=lambda model_name_stop: stop_train(model_name_stop), inputs=[model_name_stop], outputs=[]) - with gr.Row(): - refesh_audio_select.click(fn=change_audios_choices, inputs=[], outputs=[audio_file_select]) - delete_all_audio.click(fn=delete_all_audios, inputs=[], outputs=[audio_file_select]) - delete_audio.click(fn=delete_audios, inputs=[audio_file_select], outputs=[audio_file_select]) - with gr.Row(): - refesh_model_select.click(fn=change_choices_del, inputs=[], outputs=[model_select, index_select]) - delete_all_model_button.click(fn=delete_all_model, inputs=[], outputs=[model_select, index_select]) - delete_model_button.click(fn=delete_model, inputs=[model_select, index_select], outputs=[model_select, index_select]) - with gr.Row(): - refesh_pretrain_select.click(fn=change_allpretrained_choices, inputs=[], outputs=[pretrain_select]) - delete_all_pretrain.click(fn=delete_all_pretrained, inputs=[], outputs=[pretrain_select]) - delete_pretrain.click(fn=delete_pretrained, inputs=[pretrain_select], outputs=[pretrain_select]) - with gr.Row(): - refesh_separate_select.click(fn=change_separate_choices, inputs=[], outputs=[separate_select]) - delete_all_separate.click(fn=delete_all_separated, inputs=[], outputs=[separate_select]) - delete_separate.click(fn=delete_separated, inputs=[separate_select], outputs=[separate_select]) - with gr.Row(): - refesh_presets_select.click(fn=change_preset_choices, inputs=[], outputs=[presets_select]) - delete_all_presets_button.click(fn=delete_all_presets, inputs=[], outputs=[presets_select]) - delete_presets_button.click(fn=delete_presets, inputs=[presets_select], outputs=[presets_select]) - with gr.Row(): - delete_dataset_button.click(fn=delete_dataset, inputs=[dataset_folder_name], outputs=[]) - with gr.Row(): - clean_log.click(fn=delete_all_log, inputs=[], outputs=[]) - clean_predictor.click(fn=delete_all_predictors, inputs=[], outputs=[]) - clean_embedders.click(fn=delete_all_embedders, inputs=[], outputs=[]) - - with gr.TabItem(translations["report_bugs"], visible=configs.get("report_bug_tab", True)): - gr.Markdown(translations["report_bugs"]) - with gr.Row(): - gr.Markdown(translations["report_bug_info"]) - with gr.Row(): - with gr.Column(): - with gr.Group(): - agree_log = gr.Checkbox(label=translations["agree_log"], value=True, interactive=True) - report_text = gr.Textbox(label=translations["error_info"], info=translations["error_info_2"], interactive=True) - report_button = gr.Button(translations["report_bugs"], variant="primary", scale=2) - with gr.Row(): - gr.Markdown(translations["report_info"].format(github=codecs.decode("uggcf://tvguho.pbz/CunzUhlauNau16/Ivrganzrfr-EIP/vffhrf", "rot13"))) - with gr.Row(): - report_button.click(fn=report_bug, inputs=[report_text, agree_log], outputs=[]) - - logger.info(translations["start_app"]) - logger.info(translations["set_lang"].format(lang=language)) - - port = configs.get("app_port", 7860) - - for i in range(configs.get("num_of_restart", 5)): - try: - app.queue().launch(favicon_path=os.path.join("assets", "miku.png"), server_name=configs.get("server_name", "0.0.0.0"), server_port=port, show_error=configs.get("app_show_error", False), inbrowser="--open" in sys.argv, share="--share" in sys.argv) - break - except OSError: - logger.debug(translations["port"].format(port=port)) - port -= 1 - except Exception as e: - logger.error(translations["error_occurred"].format(e=e)) \ No newline at end of file