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))