diff --git "a/main/app/app.py" "b/main/app/app.py"
--- "a/main/app/app.py"
+++ "b/main/app/app.py"
@@ -1,2900 +1,2897 @@
-import os
-import re
-import ssl
-import sys
-import json
-import onnx
-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 multiprocessing import cpu_count
-
-sys.path.append(os.getcwd())
-
-from main.configs.config import Config
-from main.library.utils import pydub_convert, pydub_load
-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", "diow", "dio", "mangio-crepe-tiny", "mangio-crepe-small", "mangio-crepe-medium", "mangio-crepe-large", "mangio-crepe-full", "crepe-tiny", "crepe-small", "crepe-medium", "crepe-large", "crepe-full", "fcpe", "fcpe-legacy", "rmvpe", "rmvpe-legacy", "harvestw", "harvest", "yin", "pyin", "swipe"]
-embedders_model = ["contentvec_base", "hubert_base", "japanese_hubert_base", "korean_hubert_base", "chinese_hubert_base", "portuguese_hubert_base", "custom"]
-
-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", ".onnx")) 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")))
-f0_file = sorted([os.path.abspath(os.path.join(root, f)) for root, _, files in os.walk(os.path.join("assets", "f0")) for f in files if f.endswith(".txt")])
-
-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")
-
-logger.info(config.device)
-
-app_mode = "--app" in sys.argv
-
-if "--allow_all_disk" in sys.argv:
- import win32api
-
- allow_disk = win32api.GetLogicalDriveStrings().split('\x00')[:-1]
-else: allow_disk = []
-
-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_f0_choices():
- f0_file = sorted([os.path.abspath(os.path.join(root, f)) for root, _, files in os.walk(os.path.join("assets", "f0")) for f in files if f.endswith(".txt")])
- return {"value": f0_file[0] if len(f0_file) >= 1 else "", "choices": f0_file, "__type__": "update"}
-
-def change_audios_choices():
- audios = 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")])
- return {"value": audios[0] if len(audios) >= 1 else "", "choices": audios, "__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():
- model, index = sorted(list(model for model in os.listdir(os.path.join("assets", "weights")) if model.endswith((".pth", ".onnx")) 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")])
- return [{"value": model[0] if len(model) >= 1 else "", "choices": model, "__type__": "update"}, {"value": index[0] if len(index) >= 1 else "", "choices": 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-small", "mangio-crepe-medium", "mangio-crepe-large", "mangio-crepe-full", "fcpe", "fcpe-legacy", "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')} {sys.argv}")
-
-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", ".onnx")): return gr_warning(translations["provide_file"].format(filename=translations["model"]))
-
- zip_file_path = os.path.join("assets", "logs", pth.replace(".pth", ""), 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(".onnx", "").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)
- elif file.endswith(".onnx") and not file.startswith("D_") and not file.startswith("G_"):
- pth_path = os.path.join(dest_weights, model_name + ".onnx")
- 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(".onnx", "").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(".onnx"): huggingface.HF_download_file(url, os.path.join(weights_dir, f"{model}.onnx"))
- 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(".onnx") 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", ".onnx")):
- 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):
- pretraineds_custom_path = os.path.join("assets", "models", "pretrained_custom")
- if choices == translations["list_model"]:
- paths = fetch_pretrained_data()[model][sample_rate]
-
- 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_pth(name, pth_1, pth_2, ratio):
- 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_2.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"]
-
- vocoder = ckpt1.get("vocoder", "Default")
-
- 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)
- opt["vocoder"] = vocoder
-
- 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 extract_metadata(model):
- return {prop.key: prop.value for prop in model.metadata_props}
-
-def fushion_model_onnx(name, onnx_path1, onnx_path2, ratio=0.5):
- if not name.endswith(".onnx"): name = name + ".onnx"
-
- if not onnx_path1 or not os.path.exists(onnx_path1) or not onnx_path1.endswith(".onnx"):
- gr_warning(translations["provide_file"].format(filename=translations["model"] + " 1"))
- return [translations["provide_file"].format(filename=translations["model"] + " 1"), None]
-
- if not onnx_path2 or not os.path.exists(onnx_path2) or not onnx_path2.endswith(".onnx"):
- gr_warning(translations["provide_file"].format(filename=translations["model"] + " 2"))
- return [translations["provide_file"].format(filename=translations["model"] + " 2"), None]
-
- try:
- model1 = onnx.load(onnx_path1)
- model2 = onnx.load(onnx_path2)
-
- metadata1 = extract_metadata(model1)
- metadata2 = extract_metadata(model2)
-
- if metadata1.get("sr") != metadata2.get("sr"):
- gr_warning(translations["sr_not_same"])
- return [translations["sr_not_same"], None]
-
- gr_info(translations["start"].format(start=translations["fushion_model"]))
-
- for init1, init2 in zip(model1.graph.initializer, model2.graph.initializer):
- tensor1 = onnx.numpy_helper.to_array(init1)
- tensor2 = onnx.numpy_helper.to_array(init2)
-
- if tensor1.shape != tensor2.shape:
- gr_warning(translations["architectures_not_same"])
- return [translations["architectures_not_same"], None]
-
- fused_tensor = ratio * tensor1 + (1 - ratio) * tensor2
- init1.CopyFrom(onnx.numpy_helper.from_array(fused_tensor, name=init1.name))
-
- new_metadata = metadata1.copy()
- new_metadata["fusion_ratio"] = str(ratio)
- new_metadata["creation_date"] = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-
- del model1.metadata_props[:]
-
- for key, value in new_metadata.items():
- entry = model1.metadata_props.add()
- entry.key = key
- entry.value = value
-
- output_model = os.path.join("assets", "weights")
- if not os.path.exists(output_model): os.makedirs(output_model, exist_ok=True)
-
- onnx.save(model1, 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 fushion_model(name, path_1, path_2, ratio):
- if not name:
- gr_warning(translations["provide_name_is_save"])
- return [translations["provide_name_is_save"], None]
-
- if path_1.endswith(".onnx") and path_2.endswith(".onnx"): return fushion_model_onnx(name.replace(".pth", ".onnx"), path_1, path_2, ratio)
- elif path_1.endswith(".pth") and path_2.endswith(".pth"): return fushion_model_pth(name.replace(".onnx", ".pth"), path_1, path_2, ratio)
- else:
- gr_warning(translations["format_not_valid"])
- return [None, None]
-
-def onnx_export(model_path):
- from main.library.algorithm.onnx_export import onnx_exporter
-
- if not model_path.endswith(".pth"): model_path + ".pth"
- if not model_path or not os.path.exists(model_path) or not model_path.endswith(".pth"):
- gr_warning(translations["provide_file"].format(filename=translations["model"]))
- return [None, translations["provide_file"].format(filename=translations["model"])]
-
- try:
- gr_info(translations["start_onnx_export"])
- output = onnx_exporter(model_path, model_path.replace(".pth", ".onnx"))
-
- gr_info(translations["success"])
- return [output, translations["success"]]
- except Exception as e:
- return [None, e]
-
-def model_info(path):
- if not path or not os.path.exists(path) or os.path.isdir(path) or not path.endswith((".pth", ".onnx")): 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"]
-
- if path.endswith(".pth"): model_data = torch.load(path, map_location=torch.device("cpu"))
- else:
- model = onnx.load(path)
- model_data = None
-
- for prop in model.metadata_props:
- if prop.key == "model_info":
- model_data = json.loads(prop.value)
- break
-
- 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"])
- vocoder = model_data.get("vocoder", "Default")
-
- 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, vocoder=vocoder)
-
-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
-
- 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)] if os.path.isfile(input) else [None]*4
-
-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, onnx_f0_mode, embedders_onnx, formant_shifting, formant_qfrency, formant_timbre, f0_file):
- 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} --resample_sr {resample_sr} --split_audio {split_audio} --f0_autotune_strength {f0_autotune_strength} --checkpointing {checkpointing} --f0_onnx {onnx_f0_mode} --embedders_onnx {embedders_onnx} --formant_shifting {formant_shifting} --formant_qfrency {formant_qfrency} --formant_timbre {formant_timbre} --f0_file "{f0_file}"')
-
-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, onnx_f0_mode, formant_shifting, formant_qfrency, formant_timbre, f0_file, embedders_onnx):
- 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", ".onnx")):
- 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, onnx_f0_mode, embedders_onnx, formant_shifting, formant_qfrency, formant_timbre, f0_file)
-
- 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, onnx_f0_mode, embedders_onnx, formant_shifting, formant_qfrency, formant_timbre, f0_file)
-
- 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(pydub_load(output_path)).overlay(pydub_convert(pydub_load(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(pydub_load(instruments)).overlay(pydub_convert(pydub_load(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, onnx_f0_mode, embedders_onnx, formant_shifting, formant_qfrency, formant_timbre, f0_file)
-
- 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, onnx_f0_mode, embedders_onnx, formant_shifting, formant_qfrency, formant_timbre, f0_file)
-
- 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, onnx_f0_mode, formant_shifting, formant_qfrency, formant_timbre, f0_file, embedders_onnx):
- 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, onnx_f0_mode, formant_shifting, formant_qfrency, formant_timbre, f0_file, embedders_onnx)
-
- 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, onnx_f0_mode, formant_shifting, formant_qfrency, formant_timbre, f0_file, embedders_onnx)
-
- 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, onnx_f0_mode, formant_shifting, formant_qfrency, formant_timbre, f0_file, embedders_onnx):
- 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", ".onnx")):
- 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, onnx_f0_mode, embedders_onnx, formant_shifting, formant_qfrency, formant_timbre, f0_file)
-
- 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, onnx_f0_mode):
- 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} --f0_onnx {onnx_f0_mode}', 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", ".onnx")): 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", ".onnx")): 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 clean_f0_files():
- path = os.path.join("assets", "f0")
-
- if len(os.listdir(path)) < 1: gr_warning(translations["empty_folder"])
- else:
- gr_info(translations["start_clean_f0"])
-
- shutil.rmtree(path, ignore_errors=True)
- os.makedirs(path, exist_ok=True)
-
- gr_info(translations["clean_f0_done"])
-
-def load_presets(presets, cleaner, autotune, pitch, clean_strength, index_strength, resample_sr, filter_radius, volume_envelope, protect, split_audio, f0_autotune_strength, formant_shifting, formant_qfrency, formant_timbre):
- 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), file.get("formant_shifting", formant_shifting), file.get("formant_qfrency", formant_qfrency), file.get("formant_timbre", formant_timbre)
-
-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, formant_shifting_chbox, formant_shifting, formant_qfrency, formant_timbre):
- 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, formant_shifting_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}), (formant_shifting_chbox, {"formant_shifting": formant_shifting, "formant_qfrency": formant_qfrency, "formant_timbre": formant_timbre})]:
- 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, "a", 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}]})
-
-def f0_extract(audio, f0_method, f0_onnx):
- if not audio or not os.path.exists(audio) or os.path.isdir(audio):
- gr_warning(translations["input_not_valid"])
- return [None]*2
-
- import librosa
-
- from matplotlib import pyplot as plt
- from main.inference.extract import FeatureInput
-
- filename, _ = os.path.splitext(os.path.basename(audio))
-
- f0_path = os.path.join("assets", "f0", filename)
- image_path = os.path.join(f0_path, "f0.png")
- txt_path = os.path.join(f0_path, "f0.txt")
-
- gr_info(translations["start_extract"])
-
- if not os.path.exists(f0_path): os.makedirs(f0_path, exist_ok=True)
-
- y, sr = librosa.load(audio, sr=None)
- f0 = FeatureInput(sample_rate=sr, device=config.device).compute_f0(y.flatten(), f0_method, 160, f0_onnx)
-
- plt.figure(figsize=(10, 4))
- plt.plot(f0)
- plt.title(f0_method)
- plt.xlabel(translations["time_frames"])
- plt.ylabel(translations["Frequency"])
- plt.savefig(image_path)
- plt.close()
-
- with open(txt_path, "w") as f:
- for i, f0_value in enumerate(f0):
- f.write(f"{i * sr / 160},{f0_value}\n")
-
- gr_info(translations["extract_done"])
-
- return [txt_path, image_path]
-
-
-
-with gr.Blocks(title="📱 Vietnamese-RVC GUI BY ANH", theme=theme) as app:
- gr.HTML(translations["display_title"])
- 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 os.path.isfile(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=model_name, value=model_name[0] if len(model_name) >= 1 else "", interactive=True, allow_custom_value=True)
- model_index = gr.Dropdown(label=translations["index_path"], choices=index_path, value=index_path[0] if len(index_path) >= 1 else "", 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):
- with gr.Group():
- onnx_f0_mode = gr.Checkbox(label=translations["f0_onnx_mode"], info=translations["f0_onnx_mode_info"], value=False, interactive=True)
- method = gr.Radio(label=translations["f0_method"], info=translations["f0_method_info"], choices=method_f0+["hybrid"], 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["f0_file"], open=False):
- upload_f0_file = gr.File(label=translations["upload_f0"], file_types=[".txt"])
- f0_file_dropdown = gr.Dropdown(label=translations["f0_file_2"], value="", choices=f0_file, allow_custom_value=True, interactive=True)
- refesh_f0_file = gr.Button(translations["refesh"])
- with gr.Accordion(translations["hubert_model"], open=False):
- onnx_embed_mode = gr.Checkbox(label=translations["embed_onnx"], info=translations["embed_onnx_info"], value=False, interactive=True)
- embedders = gr.Radio(label=translations["hubert_model"], info=translations["hubert_info"], choices=embedders_model, 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=presets_file, value=presets_file[0] if len(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)
- formant_shifting_chbox = gr.Checkbox(label=translations["formantshift"], 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.Row():
- split_audio = gr.Checkbox(label=translations["split_audio"], value=False, interactive=True)
- formant_shifting = gr.Checkbox(label=translations["formantshift"], 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():
- formant_qfrency = gr.Slider(value=1.0, label=translations["formant_qfrency"], info=translations["formant_qfrency"], minimum=0.0, maximum=16.0, step=0.1, interactive=True, visible=False)
- formant_timbre = gr.Slider(value=1.0, label=translations["formant_timbre"], info=translations["formant_timbre"], minimum=0.0, maximum=16.0, step=0.1, interactive=True, visible=False)
- 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():
- upload_f0_file.upload(fn=lambda inp: shutil.move(inp.name, os.path.join("assets", "f0")), inputs=[upload_f0_file], outputs=[f0_file_dropdown])
- refesh_f0_file.click(fn=change_f0_choices, inputs=[], outputs=[f0_file_dropdown])
- with gr.Row():
- 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,
- formant_qfrency,
- formant_timbre
- ],
- outputs=[
- cleaner0,
- autotune,
- pitch,
- clean_strength0,
- index_strength,
- resample_sr,
- filter_radius,
- volume_envelope,
- protect,
- split_audio,
- f0_autotune_strength,
- formant_shifting,
- formant_qfrency,
- formant_timbre
- ]
- )
- refesh_click.click(fn=change_preset_choices, inputs=[], outputs=[presets_name])
- 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,
- formant_shifting_chbox,
- formant_shifting,
- formant_qfrency,
- formant_timbre
- ],
- 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 os.path.isfile(audio) else None, inputs=[input_audio0], outputs=[play_audio])
- formant_shifting.change(fn=lambda a: [visible(a)]*2, inputs=[formant_shifting], outputs=[formant_qfrency, formant_timbre])
- 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,
- onnx_f0_mode,
- formant_shifting,
- formant_qfrency,
- formant_timbre,
- f0_file_dropdown,
- onnx_embed_mode
- ],
- 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,
- onnx_f0_mode,
- formant_shifting,
- formant_qfrency,
- formant_timbre,
- f0_file_dropdown,
- onnx_embed_mode
- ],
- 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=model_name, value=model_name[0] if len(model_name) >= 1 else "", interactive=True, allow_custom_value=True)
- model_index0 = gr.Dropdown(label=translations["index_path"], choices=index_path, value=index_path[0] if len(index_path) >= 1 else "", 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):
- with gr.Group():
- onnx_f0_mode1 = gr.Checkbox(label=translations["f0_onnx_mode"], info=translations["f0_onnx_mode_info"], value=False, interactive=True)
- method0 = gr.Radio(label=translations["f0_method"], info=translations["f0_method_info"], choices=method_f0+["hybrid"], 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["f0_file"], open=False):
- upload_f0_file0 = gr.File(label=translations["upload_f0"], file_types=[".txt"])
- f0_file_dropdown0 = gr.Dropdown(label=translations["f0_file_2"], value="", choices=f0_file, allow_custom_value=True, interactive=True)
- refesh_f0_file0 = gr.Button(translations["refesh"])
- with gr.Accordion(translations["hubert_model"], open=False):
- onnx_embed_mode1 = gr.Checkbox(label=translations["embed_onnx"], info=translations["embed_onnx_info"], value=False, interactive=True)
- embedders0 = gr.Radio(label=translations["hubert_model"], info=translations["hubert_info"], choices=embedders_model, 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():
- formant_shifting1 = gr.Checkbox(label=translations["formantshift"], value=False, interactive=True)
- split_audio0 = gr.Checkbox(label=translations["split_audio"], value=False, interactive=True)
- cleaner1 = gr.Checkbox(label=translations["clear_audio"], value=False, interactive=True)
- 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():
- formant_qfrency1 = gr.Slider(value=1.0, label=translations["formant_qfrency"], info=translations["formant_qfrency"], minimum=0.0, maximum=16.0, step=0.1, interactive=True, visible=False)
- formant_timbre1 = gr.Slider(value=1.0, label=translations["formant_timbre"], info=translations["formant_timbre"], minimum=0.0, maximum=16.0, step=0.1, interactive=True, visible=False)
- 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():
- upload_f0_file0.upload(fn=lambda inp: shutil.move(inp.name, os.path.join("assets", "f0")), inputs=[upload_f0_file0], outputs=[f0_file_dropdown0])
- refesh_f0_file0.click(fn=change_f0_choices, inputs=[], outputs=[f0_file_dropdown0])
- 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])
- formant_shifting1.change(fn=lambda a: [visible(a)]*2, inputs=[formant_shifting1], outputs=[formant_qfrency1, formant_timbre1])
- 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,
- onnx_f0_mode1,
- formant_shifting1,
- formant_qfrency1,
- formant_timbre1,
- f0_file_dropdown0,
- onnx_embed_mode1
- ],
- 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)
- preprocess_cut = gr.Checkbox(label=translations["split_audio"], value=True, interactive=True)
- process_effects = gr.Checkbox(label=translations["preprocess_effect"], value=False, interactive=True)
- checkpointing1 = gr.Checkbox(label=translations["memory_efficient_training"], value=False, interactive=True)
- training_f0 = gr.Checkbox(label=translations["training_pitch"], value=True, interactive=True)
- upload = gr.Checkbox(label=translations["upload_dataset"], 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):
- with gr.Group():
- onnx_f0_mode2 = gr.Checkbox(label=translations["f0_onnx_mode"], info=translations["f0_onnx_mode_info"], value=False, interactive=True)
- extract_method = gr.Radio(label=translations["f0_method"], info=translations["f0_method_info"], choices=method_f0, 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):
- with gr.Group():
- onnx_embed_mode2 = gr.Checkbox(label=translations["embed_onnx"], info=translations["embed_onnx_info"], value=False, interactive=True)
- extract_embedders = gr.Radio(label=translations["hubert_model"], info=translations["hubert_info"], choices=embedders_model, 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=pretrainedD, value=pretrainedD[0] if len(pretrainedD) > 0 else '', interactive=True, allow_custom_value=True)
- pretrained_G = gr.Dropdown(label=translations["pretrain_file"].format(dg="G"), choices=pretrainedG, value=pretrainedG[0] if len(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=model_name, value=model_name[0] if len(model_name) >= 1 else "", interactive=True, allow_custom_value=True)
- index_file = gr.Dropdown(label=translations["index_path"], choices=index_path, value=index_path[0] if len(index_path) >= 1 else "", 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,
- onnx_f0_mode2,
- onnx_embed_mode2
- ],
- 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", ".onnx"])
- model_b = gr.File(label=f"{translations['model_name']} 2", file_types=[".pth", ".onnx"])
- 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"], file_types=[".pth", ".onnx"], interactive=False, 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", ".onnx"])
- 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="", placeholder="assets/weights/Model.pth", 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["convert_model"], visible=configs.get("onnx_tab", True)):
- gr.Markdown(translations["pytorch2onnx"])
- with gr.Row():
- gr.Markdown(translations["pytorch2onnx_markdown"])
- with gr.Row():
- model_pth_upload = gr.File(label=translations["drop_model"], file_types=[".pth"])
- with gr.Row():
- convert_onnx = gr.Button(translations["convert_model"], variant="primary", scale=2)
- with gr.Row():
- model_pth_path = gr.Textbox(label=translations["model_path"], value="", placeholder="assets/weights/Model.pth", info=translations["model_path_info"], interactive=True)
- with gr.Row():
- output_model2 = gr.File(label=translations["output_model_path"], file_types=[".pth", ".onnx"], interactive=False, visible=False)
- with gr.Row():
- model_pth_upload.upload(fn=lambda model_pth_upload: shutil.move(model_pth_upload.name, os.path.join("assets", "weights")), inputs=[model_pth_upload], outputs=[model_pth_path])
- convert_onnx.click(
- fn=onnx_export,
- inputs=[model_pth_path],
- outputs=[output_model2, output_info],
- api_name="model_onnx_export"
- )
- convert_onnx.click(fn=lambda: visible(True), inputs=[], outputs=[output_model2])
-
- 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", ".onnx", ".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["f0_extractor_tab"], visible=configs.get("f0_extractor_tab", True)):
- gr.Markdown(translations["f0_extractor_markdown"])
- with gr.Row():
- gr.Markdown(translations["f0_extractor_markdown_2"])
- with gr.Row():
- extractor_button = gr.Button(translations["extract_button"].replace("2. ", ""), variant="primary")
- with gr.Row():
- with gr.Column():
- upload_audio_file = gr.File(label=translations["drop_audio"], file_types=[".wav", ".mp3", ".flac", ".ogg", ".opus", ".m4a", ".mp4", ".aac", ".alac", ".wma", ".aiff", ".webm", ".ac3"])
- audioplay = gr.Audio(show_download_button=True, interactive=False, label=translations["input_audio"])
- with gr.Column():
- with gr.Accordion(translations["f0_method"], open=False):
- with gr.Group():
- onnx_f0_mode3 = gr.Checkbox(label=translations["f0_onnx_mode"], info=translations["f0_onnx_mode_info"], value=False, interactive=True)
- f0_method_extract = gr.Radio(label=translations["f0_method"], info=translations["f0_method_info"], choices=method_f0, value="rmvpe", interactive=True)
- with gr.Accordion(translations["input_output"], open=True):
- input_audio_path = gr.Dropdown(label=translations["audio_path"], value="", choices=paths_for_files, allow_custom_value=True, interactive=True)
- refesh_audio_button = gr.Button(translations["refesh"])
- with gr.Row():
- gr.Markdown("___")
- with gr.Row():
- file_output = gr.File(label="", file_types=[".txt"], interactive=False)
- image_output = gr.Image(label="", interactive=False, show_download_button=True)
- with gr.Row():
- upload_audio_file.upload(fn=lambda audio_in: shutil.move(audio_in.name, os.path.join("audios")), inputs=[upload_audio_file], outputs=[input_audio_path])
- input_audio_path.change(fn=lambda audio: audio if os.path.isfile(audio) else None, inputs=[input_audio_path], outputs=[audioplay])
- refesh_audio_button.click(fn=change_audios_choices, inputs=[], outputs=[input_audio_path])
- with gr.Row():
- extractor_button.click(
- fn=f0_extract,
- inputs=[
- input_audio_path,
- f0_method_extract,
- onnx_f0_mode3
- ],
- outputs=[file_output, image_output],
- api_name="f0_extract"
- )
-
- 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=model_name, value="", interactive=True, allow_custom_value=True)
- index_select = gr.Dropdown(label=translations["index_path"], choices=delete_index, value=delete_index[0] if len(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=Allpretrained, value=Allpretrained[0] if len(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=separate_model, value=separate_model[0] if len(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=presets_file, value=presets_file[0] if len(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")
- clean_f0_file = gr.Button(translations["clean_f0_file"], 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=[])
- clean_f0_file.click(fn=clean_f0_files, 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=[])
-
- 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"])
-
- 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 and not app_mode,
- share="--share" in sys.argv and not app_mode,
- allowed_paths=allow_disk,
- prevent_thread_lock=app_mode
- )
- break
- except OSError:
- logger.debug(translations["port"].format(port=port))
- port -= 1
- except Exception as e:
- logger.error(translations["error_occurred"].format(e=e))
- sys.exit(1)
-
-if app_mode:
- import webview
-
- def on_closed():
- logger.info(translations["close"])
- sys.exit(0)
-
- window = webview.create_window("Vietnamese RVC", f"localhost:{port}", width=1600, height=900, min_size=(800, 600))
- window.events.closed += on_closed
-
- webview.start(icon=os.path.join("assets", "miku.png"), debug=False)
\ No newline at end of file
+import os
+import re
+import ssl
+import sys
+import json
+import onnx
+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 multiprocessing import cpu_count
+
+sys.path.append(os.getcwd())
+
+from main.configs.config import Config
+from main.library.utils import pydub_convert, pydub_load
+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", "diow", "dio", "mangio-crepe-tiny", "mangio-crepe-small", "mangio-crepe-medium", "mangio-crepe-large", "mangio-crepe-full", "crepe-tiny", "crepe-small", "crepe-medium", "crepe-large", "crepe-full", "fcpe", "fcpe-legacy", "rmvpe", "rmvpe-legacy", "harvestw", "harvest", "yin", "pyin", "swipe"]
+embedders_model = ["contentvec_base", "hubert_base", "japanese_hubert_base", "korean_hubert_base", "chinese_hubert_base", "portuguese_hubert_base", "custom"]
+
+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", ".onnx")) 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")))
+f0_file = sorted([os.path.abspath(os.path.join(root, f)) for root, _, files in os.walk(os.path.join("assets", "f0")) for f in files if f.endswith(".txt")])
+
+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"))
+
+csv_path = os.path.join("assets", "spreadsheet.csv")
+
+logger.info(config.device)
+
+app_mode = "--app" in sys.argv
+
+if "--allow_all_disk" in sys.argv:
+ import win32api
+
+ allow_disk = win32api.GetLogicalDriveStrings().split('\x00')[:-1]
+else: allow_disk = []
+
+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 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_f0_choices():
+ f0_file = sorted([os.path.abspath(os.path.join(root, f)) for root, _, files in os.walk(os.path.join("assets", "f0")) for f in files if f.endswith(".txt")])
+ return {"value": f0_file[0] if len(f0_file) >= 1 else "", "choices": f0_file, "__type__": "update"}
+
+def change_audios_choices():
+ audios = 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")])
+ return {"value": audios[0] if len(audios) >= 1 else "", "choices": audios, "__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():
+ model, index = sorted(list(model for model in os.listdir(os.path.join("assets", "weights")) if model.endswith((".pth", ".onnx")) 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")])
+ return [{"value": model[0] if len(model) >= 1 else "", "choices": model, "__type__": "update"}, {"value": index[0] if len(index) >= 1 else "", "choices": 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-small", "mangio-crepe-medium", "mangio-crepe-large", "mangio-crepe-full", "fcpe", "fcpe-legacy", "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')} {sys.argv}")
+
+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", ".onnx")): return gr_warning(translations["provide_file"].format(filename=translations["model"]))
+
+ zip_file_path = os.path.join("assets", "logs", pth.replace(".pth", ""), 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(".onnx", "").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)
+ elif file.endswith(".onnx") and not file.startswith("D_") and not file.startswith("G_"):
+ pth_path = os.path.join(dest_weights, model_name + ".onnx")
+ 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(".onnx", "").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(".onnx"): huggingface.HF_download_file(url, os.path.join(weights_dir, f"{model}.onnx"))
+ 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(".onnx") 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", ".onnx")):
+ 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):
+ pretraineds_custom_path = os.path.join("assets", "models", "pretrained_custom")
+ if choices == translations["list_model"]:
+ paths = fetch_pretrained_data()[model][sample_rate]
+
+ 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_pth(name, pth_1, pth_2, ratio):
+ 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_2.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"]
+
+ vocoder = ckpt1.get("vocoder", "Default")
+
+ 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)
+ opt["vocoder"] = vocoder
+
+ 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 extract_metadata(model):
+ return {prop.key: prop.value for prop in model.metadata_props}
+
+def fushion_model_onnx(name, onnx_path1, onnx_path2, ratio=0.5):
+ if not name.endswith(".onnx"): name = name + ".onnx"
+
+ if not onnx_path1 or not os.path.exists(onnx_path1) or not onnx_path1.endswith(".onnx"):
+ gr_warning(translations["provide_file"].format(filename=translations["model"] + " 1"))
+ return [translations["provide_file"].format(filename=translations["model"] + " 1"), None]
+
+ if not onnx_path2 or not os.path.exists(onnx_path2) or not onnx_path2.endswith(".onnx"):
+ gr_warning(translations["provide_file"].format(filename=translations["model"] + " 2"))
+ return [translations["provide_file"].format(filename=translations["model"] + " 2"), None]
+
+ try:
+ model1 = onnx.load(onnx_path1)
+ model2 = onnx.load(onnx_path2)
+
+ metadata1 = extract_metadata(model1)
+ metadata2 = extract_metadata(model2)
+
+ if metadata1.get("sr") != metadata2.get("sr"):
+ gr_warning(translations["sr_not_same"])
+ return [translations["sr_not_same"], None]
+
+ gr_info(translations["start"].format(start=translations["fushion_model"]))
+
+ for init1, init2 in zip(model1.graph.initializer, model2.graph.initializer):
+ tensor1 = onnx.numpy_helper.to_array(init1)
+ tensor2 = onnx.numpy_helper.to_array(init2)
+
+ if tensor1.shape != tensor2.shape:
+ gr_warning(translations["architectures_not_same"])
+ return [translations["architectures_not_same"], None]
+
+ fused_tensor = ratio * tensor1 + (1 - ratio) * tensor2
+ init1.CopyFrom(onnx.numpy_helper.from_array(fused_tensor, name=init1.name))
+
+ new_metadata = metadata1.copy()
+ new_metadata["fusion_ratio"] = str(ratio)
+ new_metadata["creation_date"] = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+
+ del model1.metadata_props[:]
+
+ for key, value in new_metadata.items():
+ entry = model1.metadata_props.add()
+ entry.key = key
+ entry.value = value
+
+ output_model = os.path.join("assets", "weights")
+ if not os.path.exists(output_model): os.makedirs(output_model, exist_ok=True)
+
+ onnx.save(model1, 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 fushion_model(name, path_1, path_2, ratio):
+ if not name:
+ gr_warning(translations["provide_name_is_save"])
+ return [translations["provide_name_is_save"], None]
+
+ if path_1.endswith(".onnx") and path_2.endswith(".onnx"): return fushion_model_onnx(name.replace(".pth", ".onnx"), path_1, path_2, ratio)
+ elif path_1.endswith(".pth") and path_2.endswith(".pth"): return fushion_model_pth(name.replace(".onnx", ".pth"), path_1, path_2, ratio)
+ else:
+ gr_warning(translations["format_not_valid"])
+ return [None, None]
+
+def onnx_export(model_path):
+ from main.library.algorithm.onnx_export import onnx_exporter
+
+ if not model_path.endswith(".pth"): model_path + ".pth"
+ if not model_path or not os.path.exists(model_path) or not model_path.endswith(".pth"):
+ gr_warning(translations["provide_file"].format(filename=translations["model"]))
+ return [None, translations["provide_file"].format(filename=translations["model"])]
+
+ try:
+ gr_info(translations["start_onnx_export"])
+ output = onnx_exporter(model_path, model_path.replace(".pth", ".onnx"))
+
+ gr_info(translations["success"])
+ return [output, translations["success"]]
+ except Exception as e:
+ return [None, e]
+
+def model_info(path):
+ if not path or not os.path.exists(path) or os.path.isdir(path) or not path.endswith((".pth", ".onnx")): 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"]
+
+ if path.endswith(".pth"): model_data = torch.load(path, map_location=torch.device("cpu"))
+ else:
+ model = onnx.load(path)
+ model_data = None
+
+ for prop in model.metadata_props:
+ if prop.key == "model_info":
+ model_data = json.loads(prop.value)
+ break
+
+ 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"])
+ vocoder = model_data.get("vocoder", "Default")
+
+ 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, vocoder=vocoder)
+
+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
+
+ 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)] if os.path.isfile(input) else [None]*4
+
+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, onnx_f0_mode, embedders_onnx, formant_shifting, formant_qfrency, formant_timbre, f0_file):
+ 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} --resample_sr {resample_sr} --split_audio {split_audio} --f0_autotune_strength {f0_autotune_strength} --checkpointing {checkpointing} --f0_onnx {onnx_f0_mode} --embedders_onnx {embedders_onnx} --formant_shifting {formant_shifting} --formant_qfrency {formant_qfrency} --formant_timbre {formant_timbre} --f0_file "{f0_file}"')
+
+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, onnx_f0_mode, formant_shifting, formant_qfrency, formant_timbre, f0_file, embedders_onnx):
+ 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", ".onnx")):
+ 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, onnx_f0_mode, embedders_onnx, formant_shifting, formant_qfrency, formant_timbre, f0_file)
+
+ 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, onnx_f0_mode, embedders_onnx, formant_shifting, formant_qfrency, formant_timbre, f0_file)
+
+ 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(pydub_load(output_path)).overlay(pydub_convert(pydub_load(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(pydub_load(instruments)).overlay(pydub_convert(pydub_load(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, onnx_f0_mode, embedders_onnx, formant_shifting, formant_qfrency, formant_timbre, f0_file)
+
+ 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, onnx_f0_mode, embedders_onnx, formant_shifting, formant_qfrency, formant_timbre, f0_file)
+
+ 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, onnx_f0_mode, formant_shifting, formant_qfrency, formant_timbre, f0_file, embedders_onnx):
+ 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, onnx_f0_mode, formant_shifting, formant_qfrency, formant_timbre, f0_file, embedders_onnx)
+
+ 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, onnx_f0_mode, formant_shifting, formant_qfrency, formant_timbre, f0_file, embedders_onnx)
+
+ 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, onnx_f0_mode, formant_shifting, formant_qfrency, formant_timbre, f0_file, embedders_onnx):
+ 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", ".onnx")):
+ 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, onnx_f0_mode, embedders_onnx, formant_shifting, formant_qfrency, formant_timbre, f0_file)
+
+ 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, onnx_f0_mode):
+ 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} --f0_onnx {onnx_f0_mode}', 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", ".onnx")): 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", ".onnx")): 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 clean_f0_files():
+ path = os.path.join("assets", "f0")
+
+ if len(os.listdir(path)) < 1: gr_warning(translations["empty_folder"])
+ else:
+ gr_info(translations["start_clean_f0"])
+
+ shutil.rmtree(path, ignore_errors=True)
+ os.makedirs(path, exist_ok=True)
+
+ gr_info(translations["clean_f0_done"])
+
+def load_presets(presets, cleaner, autotune, pitch, clean_strength, index_strength, resample_sr, filter_radius, volume_envelope, protect, split_audio, f0_autotune_strength, formant_shifting, formant_qfrency, formant_timbre):
+ 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), file.get("formant_shifting", formant_shifting), file.get("formant_qfrency", formant_qfrency), file.get("formant_timbre", formant_timbre)
+
+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, formant_shifting_chbox, formant_shifting, formant_qfrency, formant_timbre):
+ 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, formant_shifting_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}), (formant_shifting_chbox, {"formant_shifting": formant_shifting, "formant_qfrency": formant_qfrency, "formant_timbre": formant_timbre})]:
+ 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, "a", 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}]})
+
+def f0_extract(audio, f0_method, f0_onnx):
+ if not audio or not os.path.exists(audio) or os.path.isdir(audio):
+ gr_warning(translations["input_not_valid"])
+ return [None]*2
+
+ import librosa
+
+ from matplotlib import pyplot as plt
+ from main.inference.extract import FeatureInput
+
+ filename, _ = os.path.splitext(os.path.basename(audio))
+
+ f0_path = os.path.join("assets", "f0", filename)
+ image_path = os.path.join(f0_path, "f0.png")
+ txt_path = os.path.join(f0_path, "f0.txt")
+
+ gr_info(translations["start_extract"])
+
+ if not os.path.exists(f0_path): os.makedirs(f0_path, exist_ok=True)
+
+ y, sr = librosa.load(audio, sr=None)
+ f0 = FeatureInput(sample_rate=sr, device=config.device).compute_f0(y.flatten(), f0_method, 160, f0_onnx)
+
+ plt.figure(figsize=(10, 4))
+ plt.plot(f0)
+ plt.title(f0_method)
+ plt.xlabel(translations["time_frames"])
+ plt.ylabel(translations["Frequency"])
+ plt.savefig(image_path)
+ plt.close()
+
+ with open(txt_path, "w") as f:
+ for i, f0_value in enumerate(f0):
+ f.write(f"{i * sr / 160},{f0_value}\n")
+
+ gr_info(translations["extract_done"])
+
+ return [txt_path, image_path]
+
+
+
+with gr.Blocks(title="📱 Vietnamese-RVC GUI BY ANH", theme=theme) as app:
+ gr.HTML(translations["display_title"])
+ 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 os.path.isfile(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=model_name, value=model_name[0] if len(model_name) >= 1 else "", interactive=True, allow_custom_value=True)
+ model_index = gr.Dropdown(label=translations["index_path"], choices=index_path, value=index_path[0] if len(index_path) >= 1 else "", 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):
+ with gr.Group():
+ onnx_f0_mode = gr.Checkbox(label=translations["f0_onnx_mode"], info=translations["f0_onnx_mode_info"], value=False, interactive=True)
+ method = gr.Radio(label=translations["f0_method"], info=translations["f0_method_info"], choices=method_f0+["hybrid"], 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["f0_file"], open=False):
+ upload_f0_file = gr.File(label=translations["upload_f0"], file_types=[".txt"])
+ f0_file_dropdown = gr.Dropdown(label=translations["f0_file_2"], value="", choices=f0_file, allow_custom_value=True, interactive=True)
+ refesh_f0_file = gr.Button(translations["refesh"])
+ with gr.Accordion(translations["hubert_model"], open=False):
+ onnx_embed_mode = gr.Checkbox(label=translations["embed_onnx"], info=translations["embed_onnx_info"], value=False, interactive=True)
+ embedders = gr.Radio(label=translations["hubert_model"], info=translations["hubert_info"], choices=embedders_model, 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=presets_file, value=presets_file[0] if len(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)
+ formant_shifting_chbox = gr.Checkbox(label=translations["formantshift"], 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.Row():
+ split_audio = gr.Checkbox(label=translations["split_audio"], value=False, interactive=True)
+ formant_shifting = gr.Checkbox(label=translations["formantshift"], 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():
+ formant_qfrency = gr.Slider(value=1.0, label=translations["formant_qfrency"], info=translations["formant_qfrency"], minimum=0.0, maximum=16.0, step=0.1, interactive=True, visible=False)
+ formant_timbre = gr.Slider(value=1.0, label=translations["formant_timbre"], info=translations["formant_timbre"], minimum=0.0, maximum=16.0, step=0.1, interactive=True, visible=False)
+ 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():
+ upload_f0_file.upload(fn=lambda inp: shutil.move(inp.name, os.path.join("assets", "f0")), inputs=[upload_f0_file], outputs=[f0_file_dropdown])
+ refesh_f0_file.click(fn=change_f0_choices, inputs=[], outputs=[f0_file_dropdown])
+ with gr.Row():
+ 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,
+ formant_qfrency,
+ formant_timbre
+ ],
+ outputs=[
+ cleaner0,
+ autotune,
+ pitch,
+ clean_strength0,
+ index_strength,
+ resample_sr,
+ filter_radius,
+ volume_envelope,
+ protect,
+ split_audio,
+ f0_autotune_strength,
+ formant_shifting,
+ formant_qfrency,
+ formant_timbre
+ ]
+ )
+ refesh_click.click(fn=change_preset_choices, inputs=[], outputs=[presets_name])
+ 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,
+ formant_shifting_chbox,
+ formant_shifting,
+ formant_qfrency,
+ formant_timbre
+ ],
+ 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 os.path.isfile(audio) else None, inputs=[input_audio0], outputs=[play_audio])
+ formant_shifting.change(fn=lambda a: [visible(a)]*2, inputs=[formant_shifting], outputs=[formant_qfrency, formant_timbre])
+ 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,
+ onnx_f0_mode,
+ formant_shifting,
+ formant_qfrency,
+ formant_timbre,
+ f0_file_dropdown,
+ onnx_embed_mode
+ ],
+ 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,
+ onnx_f0_mode,
+ formant_shifting,
+ formant_qfrency,
+ formant_timbre,
+ f0_file_dropdown,
+ onnx_embed_mode
+ ],
+ 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=model_name, value=model_name[0] if len(model_name) >= 1 else "", interactive=True, allow_custom_value=True)
+ model_index0 = gr.Dropdown(label=translations["index_path"], choices=index_path, value=index_path[0] if len(index_path) >= 1 else "", 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):
+ with gr.Group():
+ onnx_f0_mode1 = gr.Checkbox(label=translations["f0_onnx_mode"], info=translations["f0_onnx_mode_info"], value=False, interactive=True)
+ method0 = gr.Radio(label=translations["f0_method"], info=translations["f0_method_info"], choices=method_f0+["hybrid"], 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["f0_file"], open=False):
+ upload_f0_file0 = gr.File(label=translations["upload_f0"], file_types=[".txt"])
+ f0_file_dropdown0 = gr.Dropdown(label=translations["f0_file_2"], value="", choices=f0_file, allow_custom_value=True, interactive=True)
+ refesh_f0_file0 = gr.Button(translations["refesh"])
+ with gr.Accordion(translations["hubert_model"], open=False):
+ onnx_embed_mode1 = gr.Checkbox(label=translations["embed_onnx"], info=translations["embed_onnx_info"], value=False, interactive=True)
+ embedders0 = gr.Radio(label=translations["hubert_model"], info=translations["hubert_info"], choices=embedders_model, 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():
+ formant_shifting1 = gr.Checkbox(label=translations["formantshift"], value=False, interactive=True)
+ split_audio0 = gr.Checkbox(label=translations["split_audio"], value=False, interactive=True)
+ cleaner1 = gr.Checkbox(label=translations["clear_audio"], value=False, interactive=True)
+ 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():
+ formant_qfrency1 = gr.Slider(value=1.0, label=translations["formant_qfrency"], info=translations["formant_qfrency"], minimum=0.0, maximum=16.0, step=0.1, interactive=True, visible=False)
+ formant_timbre1 = gr.Slider(value=1.0, label=translations["formant_timbre"], info=translations["formant_timbre"], minimum=0.0, maximum=16.0, step=0.1, interactive=True, visible=False)
+ 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():
+ upload_f0_file0.upload(fn=lambda inp: shutil.move(inp.name, os.path.join("assets", "f0")), inputs=[upload_f0_file0], outputs=[f0_file_dropdown0])
+ refesh_f0_file0.click(fn=change_f0_choices, inputs=[], outputs=[f0_file_dropdown0])
+ 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])
+ formant_shifting1.change(fn=lambda a: [visible(a)]*2, inputs=[formant_shifting1], outputs=[formant_qfrency1, formant_timbre1])
+ 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,
+ onnx_f0_mode1,
+ formant_shifting1,
+ formant_qfrency1,
+ formant_timbre1,
+ f0_file_dropdown0,
+ onnx_embed_mode1
+ ],
+ 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)
+ preprocess_cut = gr.Checkbox(label=translations["split_audio"], value=True, interactive=True)
+ process_effects = gr.Checkbox(label=translations["preprocess_effect"], value=False, interactive=True)
+ checkpointing1 = gr.Checkbox(label=translations["memory_efficient_training"], value=False, interactive=True)
+ training_f0 = gr.Checkbox(label=translations["training_pitch"], value=True, interactive=True)
+ upload = gr.Checkbox(label=translations["upload_dataset"], 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):
+ with gr.Group():
+ onnx_f0_mode2 = gr.Checkbox(label=translations["f0_onnx_mode"], info=translations["f0_onnx_mode_info"], value=False, interactive=True)
+ extract_method = gr.Radio(label=translations["f0_method"], info=translations["f0_method_info"], choices=method_f0, 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):
+ with gr.Group():
+ onnx_embed_mode2 = gr.Checkbox(label=translations["embed_onnx"], info=translations["embed_onnx_info"], value=False, interactive=True)
+ extract_embedders = gr.Radio(label=translations["hubert_model"], info=translations["hubert_info"], choices=embedders_model, 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=pretrainedD, value=pretrainedD[0] if len(pretrainedD) > 0 else '', interactive=True, allow_custom_value=True)
+ pretrained_G = gr.Dropdown(label=translations["pretrain_file"].format(dg="G"), choices=pretrainedG, value=pretrainedG[0] if len(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=model_name, value=model_name[0] if len(model_name) >= 1 else "", interactive=True, allow_custom_value=True)
+ index_file = gr.Dropdown(label=translations["index_path"], choices=index_path, value=index_path[0] if len(index_path) >= 1 else "", 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,
+ onnx_f0_mode2,
+ onnx_embed_mode2
+ ],
+ 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", ".onnx"])
+ model_b = gr.File(label=f"{translations['model_name']} 2", file_types=[".pth", ".onnx"])
+ 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"], file_types=[".pth", ".onnx"], interactive=False, 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", ".onnx"])
+ 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="", placeholder="assets/weights/Model.pth", 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["convert_model"], visible=configs.get("onnx_tab", True)):
+ gr.Markdown(translations["pytorch2onnx"])
+ with gr.Row():
+ gr.Markdown(translations["pytorch2onnx_markdown"])
+ with gr.Row():
+ model_pth_upload = gr.File(label=translations["drop_model"], file_types=[".pth"])
+ with gr.Row():
+ convert_onnx = gr.Button(translations["convert_model"], variant="primary", scale=2)
+ with gr.Row():
+ model_pth_path = gr.Textbox(label=translations["model_path"], value="", placeholder="assets/weights/Model.pth", info=translations["model_path_info"], interactive=True)
+ with gr.Row():
+ output_model2 = gr.File(label=translations["output_model_path"], file_types=[".pth", ".onnx"], interactive=False, visible=False)
+ with gr.Row():
+ model_pth_upload.upload(fn=lambda model_pth_upload: shutil.move(model_pth_upload.name, os.path.join("assets", "weights")), inputs=[model_pth_upload], outputs=[model_pth_path])
+ convert_onnx.click(
+ fn=onnx_export,
+ inputs=[model_pth_path],
+ outputs=[output_model2, output_info],
+ api_name="model_onnx_export"
+ )
+ convert_onnx.click(fn=lambda: visible(True), inputs=[], outputs=[output_model2])
+
+ 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", ".onnx", ".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["f0_extractor_tab"], visible=configs.get("f0_extractor_tab", True)):
+ gr.Markdown(translations["f0_extractor_markdown"])
+ with gr.Row():
+ gr.Markdown(translations["f0_extractor_markdown_2"])
+ with gr.Row():
+ extractor_button = gr.Button(translations["extract_button"].replace("2. ", ""), variant="primary")
+ with gr.Row():
+ with gr.Column():
+ upload_audio_file = gr.File(label=translations["drop_audio"], file_types=[".wav", ".mp3", ".flac", ".ogg", ".opus", ".m4a", ".mp4", ".aac", ".alac", ".wma", ".aiff", ".webm", ".ac3"])
+ audioplay = gr.Audio(show_download_button=True, interactive=False, label=translations["input_audio"])
+ with gr.Column():
+ with gr.Accordion(translations["f0_method"], open=False):
+ with gr.Group():
+ onnx_f0_mode3 = gr.Checkbox(label=translations["f0_onnx_mode"], info=translations["f0_onnx_mode_info"], value=False, interactive=True)
+ f0_method_extract = gr.Radio(label=translations["f0_method"], info=translations["f0_method_info"], choices=method_f0, value="rmvpe", interactive=True)
+ with gr.Accordion(translations["input_output"], open=True):
+ input_audio_path = gr.Dropdown(label=translations["audio_path"], value="", choices=paths_for_files, allow_custom_value=True, interactive=True)
+ refesh_audio_button = gr.Button(translations["refesh"])
+ with gr.Row():
+ gr.Markdown("___")
+ with gr.Row():
+ file_output = gr.File(label="", file_types=[".txt"], interactive=False)
+ image_output = gr.Image(label="", interactive=False, show_download_button=True)
+ with gr.Row():
+ upload_audio_file.upload(fn=lambda audio_in: shutil.move(audio_in.name, os.path.join("audios")), inputs=[upload_audio_file], outputs=[input_audio_path])
+ input_audio_path.change(fn=lambda audio: audio if os.path.isfile(audio) else None, inputs=[input_audio_path], outputs=[audioplay])
+ refesh_audio_button.click(fn=change_audios_choices, inputs=[], outputs=[input_audio_path])
+ with gr.Row():
+ extractor_button.click(
+ fn=f0_extract,
+ inputs=[
+ input_audio_path,
+ f0_method_extract,
+ onnx_f0_mode3
+ ],
+ outputs=[file_output, image_output],
+ api_name="f0_extract"
+ )
+
+ 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=model_name, value="", interactive=True, allow_custom_value=True)
+ index_select = gr.Dropdown(label=translations["index_path"], choices=delete_index, value=delete_index[0] if len(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=Allpretrained, value=Allpretrained[0] if len(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=separate_model, value=separate_model[0] if len(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=presets_file, value=presets_file[0] if len(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")
+ clean_f0_file = gr.Button(translations["clean_f0_file"], 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=[])
+ clean_f0_file.click(fn=clean_f0_files, 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=[])
+
+ 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"])
+
+ 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(
+ 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 and not app_mode,
+ share="--share" in sys.argv and not app_mode,
+ allowed_paths=allow_disk,
+ prevent_thread_lock=app_mode
+ )
+ break
+ except OSError:
+ logger.debug(translations["port"].format(port=port))
+ port -= 1
+ except Exception as e:
+ logger.error(translations["error_occurred"].format(e=e))
+ sys.exit(1)
+
+if app_mode:
+ import webview
+
+ def on_closed():
+ logger.info(translations["close"])
+ sys.exit(0)
+
+ window = webview.create_window("Vietnamese RVC", f"localhost:{port}", width=1600, height=900, min_size=(800, 600))
+ window.events.closed += on_closed
+
+ webview.start(debug=False)
\ No newline at end of file