import os import re import sys import json import torch import codecs import yt_dlp import shutil import zipfile import logging import platform import edge_tts import requests import warnings import threading import gradio as gr import pandas as pd from time import sleep from datetime import datetime from pydub import AudioSegment from subprocess import Popen, run from collections import OrderedDict from multiprocessing import cpu_count now_dir = os.getcwd() sys.path.append(now_dir) from main.configs.config import Config from main.tools import gdown, meganz, mediafire, pixeldrain logging.getLogger("wget").setLevel(logging.WARNING) logging.getLogger("httpx").setLevel(logging.WARNING) logging.getLogger("uvicorn").setLevel(logging.WARNING) logging.getLogger("httpcore").setLevel(logging.WARNING) logging.getLogger("gradio").setLevel(logging.ERROR) warnings.filterwarnings("ignore", category=UserWarning) warnings.filterwarnings("ignore", category=FutureWarning) config = Config() python = sys.executable translations = config.translations model_name = [] index_path = [] pretrainedD = [] pretrainedG = [] models = {} model_options = {} miku_image = codecs.decode("uggcf://uhttvatsnpr.pb/NauC/Pbyno_EIP_Cebwrpg_2/erfbyir/znva/zvxh.cat", "rot13") model_search_csv = codecs.decode("uggcf://qbpf.tbbtyr.pbz/fcernqfurrgf/q/1gNHnDeRULtEfz1Yieaw14USUQjWJy0Oq9k0DrCrjApb/rkcbeg?sbezng=pfi&tvq=1977693859", "rot13") model_search_api = codecs.decode("rlWuoTpvBvWVHmV1AvVfVaE5pPV6VxcKIPW9.rlWcp3ZvBvWmqKOuLzSmMFVfVaWyMvV6VzAdqTMkrzczMTygM3O2pUqbrzk2Vvjvpz9fMFV6VzSho24vYPWcLKDvBwR3ZwL5ZwLkZmDfVzI4pPV6ZwN0ZwHjZwRmAU0.BlQKyuiU6Q-VfUvJuCNTHgfCTTHiJDlaskHrDjsLGbR", "rot13") pretrained_json = codecs.decode("uggcf://uhttvatsnpr.pb/NauC/Pbyno_EIP_Cebwrpg_2/enj/znva/cergenva_pubvprf.wfba", "rot13") hugging_face_codecs = codecs.decode("uggcf://uhttvatsnpr.pb", "rot13") pretrained_v1_link = codecs.decode("uggcf://uhttvatsnpr.pb/VNUvfcnab/Nccyvb/erfbyir/znva/Erfbheprf/cergenvarq_i1/", "rot13") pretrained_v2_link = codecs.decode("uggcf://uhttvatsnpr.pb/yw1995/IbvprPbairefvbaJroHV/erfbyir/znva/cergenvarq_i2/", "rot13") configs_json = os.path.join("main", "configs", "config.json") with open(configs_json, "r") as f: configs = json.load(f) theme = configs["theme"] server_name = configs["server_name"] port = configs["app_port"] show_error = configs["app_show_error"] share = configs["share"] tts_voice = configs["tts_voice"] if not theme: theme = "NoCrypt/miku" if not server_name: server_name = "0.0.0.0" if not port: port = 7860 if not tts_voice: tts_voice = ["vi-VN-HoaiMyNeural", "vi-VN-NamMinhNeural"] if not os.path.exists(os.path.join("assets", "miku.png")): run(["wget", "-q", "--show-progress", "--no-check-certificate", miku_image, "-P", os.path.join("assets")], check=True) for model in os.listdir(os.path.join("assets", "weights")): if model.endswith(".pth") and not model.startswith("G_") and not model.startswith("D_"): model_name.append(model) for root, _, files in os.walk(os.path.join("assets", "logs"), topdown=False): for name in files: if name.endswith(".index"): index_path.append(os.path.join(root, name)) for model in os.listdir(os.path.join("assets", "model", "pretrained_custom")): if model.endswith(".pth") and "D" in model: pretrainedD.append(model) if model.endswith(".pth") and "G" in model: pretrainedG.append(model) if os.path.exists("spreadsheet.csv"): cached_data = pd.read_csv("spreadsheet.csv") else: cached_data = pd.read_csv(model_search_csv) cached_data.to_csv("spreadsheet.csv", 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 get_number_of_gpus(): return "-".join(map(str, range(torch.cuda.device_count()))) if torch.cuda.is_available() else "-" def get_gpu_info(): ngpu = torch.cuda.device_count() gpu_infos = [] if torch.cuda.is_available() or ngpu != 0: for i in range(ngpu): gpu_name = torch.cuda.get_device_name(i) mem = int(torch.cuda.get_device_properties(i).total_memory / 1024 / 1024 / 1024 + 0.4) gpu_infos.append(f"{i}: {gpu_name} ({mem} GB)") return "\n".join(gpu_infos) if len(gpu_infos) > 0 else translations["no_support_gpu"] def change_choices_pretrained(): pretrainedD = [] pretrainedG = [] for model in os.listdir(os.path.join("assets", "model", "pretrained_custom")): if model.endswith(".pth") and "D" in model: pretrainedD.append(model) for model in os.listdir(os.path.join("assets", "model", "pretrained_custom")): if model.endswith(".pth") and "G" in model: pretrainedG.append(model) return [{"choices": sorted(pretrainedD), "__type__": "update"}, {"choices": sorted(pretrainedG), "__type__": "update"}] def change_choices(): model_name = [] index_path = [] for name in os.listdir(os.path.join("assets", "weights")): if name.endswith(".pth"): model_name.append(name) for root, _, files in os.walk(os.path.join("assets", "logs"), topdown=False): for name in files: if name.endswith(".index"): index_path.append(f"{root}/{name}") return [{"choices": sorted(model_name), "__type__": "update"}, {"choices": sorted(index_path), "__type__": "update"}] def get_index(model): 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")] if model.split(".")[0] in f), ""), "__type__": "update"} def visible_1(value): return {"visible": value, "__type__": "update"} def valueFalse_interactive1(inp): return {"value": False, "interactive": inp, "__type__": "update"} def valueFalse_interactive2(inp1, inp2): return {"value": False, "interactive": inp1 and inp2, "__type__": "update"} def valueFalse_visible1(inp1): return {"value": False, "visible": inp1, "__type__": "update"} def valueEmpty_visible1(inp1): return {"value": "", "visible": inp1, "__type__": "update"} def refesh_audio(): paths_for_files = [os.path.abspath(os.path.join("audios", f)) for f in os.listdir("audios") if os.path.splitext(f)[1] in ('.mp3', '.wav', '.flac', '.ogg', '.m4a')] return {"value": "" if len(list(f for f in os.listdir("audios") if os.path.splitext(f)[1] in ('.mp3', '.wav', '.flac', '.ogg', '.m4a'))) < 1 else paths_for_files[0], "choices": [] if len(list(f for f in os.listdir("audios") if os.path.splitext(f)[1] in ('.mp3', '.wav', '.flac', '.ogg', '.m4a'))) < 1 else paths_for_files, "__type__": "update"} def backing_change(backing, merge): if backing or merge: return {"value": False, "interactive": False, "__type__": "update"} elif not backing or not merge: return {"interactive": True, "__type__": "update"} def model_separator_change(mdx): if not mdx: choices = ["HT-Normal", "HT-Tuned", "HD_MMI", "HT_6S"] else: choices = ["Main_340", "Main_390", "Main_406", "Main_427", "Main_438", "Inst_full_292", "Inst_HQ_1", "Inst_HQ_2", "Inst_HQ_3", "Inst_HQ_4", "Kim_Vocal_1", "Kim_Vocal_2", "Kim_Inst", "Inst_187_beta", "Inst_82_beta", "Inst_90_beta", "Voc_FT", "Crowd_HQ", "Inst_1", "Inst_2", "Inst_3", "MDXNET_1_9703", "MDXNET_2_9682", "MDXNET_3_9662", "Inst_Main", "MDXNET_Main", "MDXNET_9482"] return {"value": choices[0], "choices": choices, "__type__": "update"} def hoplength_show(method, hybrid_method=None): if method in ["crepe-tiny", "crepe", "fcpe"]: 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("+")] visible = methods[0] in ["crepe-tiny", "crepe", "fcpe"] or methods[1] in ["crepe-tiny", "crepe", "fcpe"] else: visible = False return {"visible": visible, "__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 download_change(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["download_from_applio"]: 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 fetch_pretrained_data(): response = requests.get(pretrained_json) response.raise_for_status() return response.json() def download_pretrained_change(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 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["30s"]) if platform.system() == "Windows": os.system("cls") else: os.system("clear") app.close() os.system(f"{python} {os.path.join(now_dir, 'main', 'app', 'app.py')}") 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) 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) def change_fp(fp): gr.Info(translations["fp_select"]) config.set_precision(fp) gr.Info(translations["fp_select_2"].format(fp=fp)) def pretrained_selector(pitch_guidance): if pitch_guidance: return { 32000: ( "f0G32k.pth", "f0D32k.pth", ), 40000: ( "f0G40k.pth", "f0D40k.pth", ), 48000: ( "f0G48k.pth", "f0D48k.pth", ), } else: return { 32000: ( "G32k.pth", "D32k.pth", ), 40000: ( "G40k.pth", "D40k.pth", ), 48000: ( "G48k.pth", "D48k.pth", ), } def zip_file(name, pth, index): pth_path = os.path.join("assets", "weights", pth) if not pth or not os.path.exists(pth_path) or not pth.endswith(".pth"): return gr.Warning(translations["provide_file"].format(filename=translations["model"])) if not index or not os.path.exists(index) or not index.endswith(".pth"): return gr.Warning(translations["provide_file"].format(filename=translations["index"])) zip_file_path = os.path.join("assets", name + ".zip") gr.Info(translations["start"].format(start=translations["zip"])) with zipfile.ZipFile(zip_file_path, 'w') as zipf: zipf.write(pth_path, os.path.basename(pth_path)) zipf.write(index, os.path.basename(index)) gr.Info(translations["success"]) return zip_file_path def search_models(name): gr.Info(translations["start"].format(start="search")) url = f"https://cjtfqzjfdimgpvpwhzlv.supabase.co/rest/v1/models?name=ilike.%25{name}%25&order=created_at.desc&limit=15" response = requests.get(url, headers={"apikey": model_search_api}) data = response.json() if len(data) == 0: gr.Info(translations["not_found"].format(name=name)) return [None]*2 else: model_options.clear() model_options.update({item["name"] + " " + item["epochs"] + "e": item["link"] for item in data}) 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(']', '').strip()) if os.path.exists(filepath): os.remove(filepath) shutil.move(file_path, filepath) elif file.endswith(".pth") and "G_" not in file and "D_" not in file: pth_path = os.path.join(dest_weights, model_name + ".pth") if os.path.exists(pth_path): os.remove(pth_path) shutil.move(file_path, pth_path) def download_url(url): if not url: return gr.Warning(translations["provide_url"]) if not os.path.exists("audios"): os.makedirs("audios", exist_ok=True) audio_output = os.path.join("audios", "audio.wav") if os.path.exists(audio_output): os.remove(audio_output) with warnings.catch_warnings(): warnings.simplefilter("ignore") ydl_opts = { 'format': 'bestaudio/best', 'outtmpl': os.path.join("audios", "audio"), 'postprocessors': [{ 'key': 'FFmpegExtractAudio', 'preferredcodec': 'wav', 'preferredquality': '192', }], 'noplaylist': True, 'verbose': False, } gr.Info(translations["start"].format(start=translations["download_music"])) with yt_dlp.YoutubeDL(ydl_opts) as ydl: ydl.download([url]) gr.Info(translations["success"]) return [audio_output, audio_output, translations["success"]] def download_model(url=None, model=None): if not url: return gr.Warning(translations["provide_url"]) if not model: return gr.Warning(translations["provide_name_is_save"]) model = model.replace('.pth', '').replace('.index', '').replace('.zip', '').replace(' ', '_').replace('(', '').replace(')', '').replace('[', '').replace(']', '').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'): run(["wget", "-q", "--show-progress", "--no-check-certificate", url, "-O", os.path.join(weights_dir, f"{model}.pth")], check=True) elif url.endswith('.index'): model_log_dir = os.path.join(logs_dir, model) os.makedirs(model_log_dir, exist_ok=True) run(["wget", "-q", "--show-progress", "--no-check-certificate", url, "-O", os.path.join(model_log_dir, f"{model}.index")], check=True) elif url.endswith('.zip'): dest_path = os.path.join(download_dir, model + ".zip") run(["wget", "-q", "--show-progress", "--no-check-certificate", url, "-O", dest_path], check=True) shutil.unpack_archive(dest_path, download_dir) move_files_from_directory(download_dir, weights_dir, logs_dir, model) else: if 'drive.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] if file_id: file = gdown.gdown_download(id=file_id, output_dir=download_dir) if file.endswith('.zip'): shutil.unpack_archive(os.path.join(download_dir, 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)) print(translations["error_occurred"].format(e=e)) return translations["error_occurred"].format(e=e) finally: shutil.rmtree(download_dir, ignore_errors=True) def extract_name_model(filename): match = re.search(r"([A-Za-z]+)(?=_v|\.|$)", filename) return match.group(1) if match else None def save_drop_model(dropbox): weight_folder = os.path.join("assets", "weights") logs_folder = os.path.join("assets", "logs") save_model_temp = os.path.join("save_model_temp") if not os.path.exists(weight_folder): os.makedirs(weight_folder, exist_ok=True) if not os.path.exists(logs_folder): os.makedirs(logs_folder, exist_ok=True) if not os.path.exists(save_model_temp): os.makedirs(save_model_temp, exist_ok=True) shutil.move(dropbox, save_model_temp) try: file_name = os.path.basename(dropbox) if file_name.endswith(".pth") and file_name.endswith(".index"): gr.Warning(translations["not_model"]) else: if file_name.endswith(".zip"): shutil.unpack_archive(os.path.join(save_model_temp, file_name), save_model_temp) move_files_from_directory(save_model_temp, weight_folder, logs_folder, file_name.replace(".zip", "")) elif file_name.endswith(".pth"): output_file = os.path.join(weight_folder, file_name) if os.path.exists(output_file): os.remove(output_file) shutil.move(os.path.join(save_model_temp, file_name), output_file) elif file_name.endswith(".index"): 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)) print(translations["error_occurred"].format(e=e)) return None finally: shutil.rmtree(save_model_temp, ignore_errors=True) def download_pretrained_model(choices, model, sample_rate): if choices == translations["list_model"]: data = fetch_pretrained_data() paths = data[model][sample_rate] pretraineds_custom_path = os.path.join("assets", "model", "pretrained_custom") if not os.path.exists(pretraineds_custom_path): os.makedirs(pretraineds_custom_path, exist_ok=True) d_url = hugging_face_codecs + f"/{paths['D']}" g_url = hugging_face_codecs + f"/{paths['G']}" gr.Info(translations["download_pretrain"]) run(["wget", "-q", "--show-progress", "--no-check-certificate", d_url.replace('/blob/', '/resolve/').replace('?download=true', '').strip(), "-P", os.path.join(pretraineds_custom_path)], check=True) run(["wget", "-q", "--show-progress", "--no-check-certificate", g_url.replace('/blob/', '/resolve/').replace('?download=true', '').strip(), "-P", os.path.join(pretraineds_custom_path)], check=True) 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"]) run(["wget", "-q", "--show-progress", "--no-check-certificate", model, "-P", os.path.join(pretraineds_custom_path)], check=True) run(["wget", "-q", "--show-progress", "--no-check-certificate", sample_rate, "-P", os.path.join(pretraineds_custom_path)], check=True) gr.Info(translations["success"]) return translations["success"] def hubert_download(hubert): if not hubert: gr.Warning(translations["provide_hubert"]) return translations["provide_hubert"] run(["wget", "-q", "--show-progress", "--no-check-certificate", hubert.replace('/blob/', '/resolve/').replace('?download=true', '').strip(), "-P", os.path.join("assets", "model", "embedders")], check=True) gr.Info(translations["success"]) return translations["success"] def fushion_model(name, pth_1, pth_2, ratio): if not name: gr.Warning(translations["provide_name_is_save"]) return [translations["provide_name_is_save"], None] if not name.endswith(".pth"): name = name + ".pth" if not pth_1 or not os.path.exists(pth_1) or not pth_1.endswith(".pth"): gr.Warning(translations["provide_file"].format(filename=translations["model"] + " 1")) return [translations["provide_file"].format(filename=translations["model"] + " 1"), None] if not pth_2 or not os.path.exists(pth_2) or not pth_1.endswith(".pth"): gr.Warning(translations["provide_file"].format(filename=translations["model"] + " 2")) return [translations["provide_file"].format(filename=translations["model"] + " 2"), None] def extract(ckpt): a = ckpt["model"] opt = OrderedDict() opt["weight"] = {} for key in a.keys(): if "enc_q" in key: continue opt["weight"][key] = a[key] return opt try: ckpt1 = torch.load(pth_1, map_location="cpu") ckpt2 = torch.load(pth_2, map_location="cpu") if ckpt1["sr"] != ckpt2["sr"]: gr.Warning(translations["sr_not_same"]) return [translations["sr_not_same"], None] cfg = ckpt1["config"] cfg_f0 = ckpt1["f0"] cfg_version = ckpt1["version"] cfg_sr = ckpt1["sr"] ckpt1 = extract(ckpt1) if "model" in ckpt1 else ckpt1["weight"] ckpt2 = extract(ckpt2) if "model" in ckpt2 else ckpt2["weight"] if sorted(list(ckpt1.keys())) != sorted(list(ckpt2.keys())): gr.Warning(translations["architectures_not_same"]) return [translations["architectures_not_same"], None] gr.Info(translations["start"].format(start=translations["fushion_model"])) opt = OrderedDict() opt["weight"] = {} for key in ckpt1.keys(): if key == "emb_g.weight" and ckpt1[key].shape != ckpt2[key].shape: min_shape0 = min(ckpt1[key].shape[0], ckpt2[key].shape[0]) opt["weight"][key] = (ratio * (ckpt1[key][:min_shape0].float()) + (1 - ratio) * (ckpt2[key][:min_shape0].float())).half() else: opt["weight"][key] = (ratio * (ckpt1[key].float()) + (1 - ratio) * (ckpt2[key].float())).half() opt["config"] = cfg opt["sr"] = cfg_sr opt["f0"] = cfg_f0 opt["version"] = cfg_version opt["infos"] = translations["model_fushion_info"].format(name=name, pth_1=pth_1, pth_2=pth_2, ratio=ratio) output_model = os.path.join("assets", "weights") if not os.path.exists(output_model): os.makedirs(output_model, exist_ok=True) torch.save(opt, os.path.join(output_model, name)) gr.Info(translations["success"]) return [translations["success"], os.path.join(output_model, name)] except Exception as e: gr.Error(message=translations["error_occurred"].format(e=e)) print(translations["error_occurred"].format(e=e)) return [e, None] def model_info(path): if not path or not os.path.exists(path) or os.path.isdir(path) or not path.endswith(".pth"): return gr.Warning(translations["provide_file"].format(filename=translations["model"])) def prettify_date(date_str): if date_str == translations["not_found_create_time"]: return None try: return datetime.strptime(date_str, "%Y-%m-%dT%H:%M:%S.%f").strftime("%Y-%m-%d %H:%M:%S") except ValueError: return translations["format_not_valid"] model_data = torch.load(path, map_location=torch.device("cpu")) gr.Info(translations["read_info"]) epochs = model_data.get("epoch", None) if epochs is None: epochs = model_data.get("info", None) epoch = epochs.replace("epoch", "").replace("e", "").isdigit() if epoch and epochs is None: epochs = translations["not_found"].format(name=translations["epoch"]) steps = model_data.get("step", translations["not_found"].format(name=translations["step"])) sr = model_data.get("sr", translations["not_found"].format(name=translations["sr"])) f0 = model_data.get("f0", translations["not_found"].format(name=translations["f0"])) version = model_data.get("version", translations["not_found"].format(name=translations["version"])) creation_date = model_data.get("creation_date", translations["not_found_create_time"]) model_hash = model_data.get("model_hash", translations["not_found"].format(name="model_hash")) pitch_guidance = translations["trained_f0"] if f0 else translations["not_f0"] creation_date_str = prettify_date(creation_date) if creation_date else translations["not_found_create_time"] model_name = model_data.get("model_name", translations["unregistered"]) model_author = model_data.get("author", translations["not_author"]) gr.Info(translations["success"]) return translations["model_info"].format(model_name=model_name, model_author=model_author, epochs=epochs, steps=steps, version=version, sr=sr, pitch_guidance=pitch_guidance, model_hash=model_hash, creation_date_str=creation_date_str) def audio_effects(input_path, output_path, resample, resample_sr, chorus_depth, chorus_rate, chorus_mix, chorus_delay, chorus_feedback, distortion_drive, reverb_room_size, reverb_damping, reverb_wet_level, reverb_dry_level, reverb_width, reverb_freeze_mode, pitch_shift, delay_seconds, delay_feedback, delay_mix, compressor_threshold, compressor_ratio, compressor_attack_ms, compressor_release_ms, limiter_threshold, limiter_release, gain_db, bitcrush_bit_depth, clipping_threshold, phaser_rate_hz, phaser_depth, phaser_centre_frequency_hz, phaser_feedback, phaser_mix, bass_boost_db, bass_boost_frequency, treble_boost_db, treble_boost_frequency, fade_in_duration, fade_out_duration, export_format, chorus, distortion, reverb, delay, compressor, limiter, gain, bitcrush, clipping, phaser, treble_bass_boost, fade_in_out): 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) output_dir = output_path if not output_dir else output_dir 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"])) pitchshift = pitch_shift != 0 cmd = 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 {pitchshift} --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}' os.system(cmd) gr.Info(translations["success"]) return output_path async def TTS(prompt, voice, speed, output): 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"output_tts.wav") gr.Info(translations["convert"].format(name=translations["text"])) output_dir = os.path.dirname(output) output_dir = output if not output_dir else output_dir if not os.path.exists(output_dir): os.makedirs(output_dir, exist_ok=True) await edge_tts.Communicate(text=prompt, voice=voice, rate=f"+{speed}%" if speed >= 0 else f"{speed}%").save(output) gr.Info(translations["success"]) return output def separator_music(input, output_audio, format, shifts, segments_size, overlap, clean_audio, clean_strength, backing_denoise, separator_model, kara_model, backing, mdx, mdx_denoise, reverb, reverb_denoise, backing_reverb, hop_length, batch_size): output = os.path.dirname(output_audio) output = output_audio if not output else output 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 gr.Info(translations["start"].format(start=translations["separator_music"])) cmd = 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} --backing_denoise {backing_denoise} --kara_model {kara_model} --backing {backing} --mdx {mdx} --mdx_denoise {mdx_denoise} --reverb {reverb} --reverb_denoise {reverb_denoise} --backing_reverb {backing_reverb}' if separator_model == "HT-Normal" or separator_model == "HT-Tuned" or separator_model == "HD_MMI" or separator_model == "HT_6S": cmd += f' --demucs_model {separator_model}' else: cmd += f' --mdx_model {separator_model}' os.system(cmd) gr.Info(translations["success"]) if not os.path.exists(output): os.makedirs(output) original_output = os.path.join(output, f"Original_Vocals_No_Reverb.{format}") if reverb else os.path.join(output, f"Original_Vocals.{format}") instrument_output = os.path.join(output, f"Instruments.{format}") main_output = os.path.join(output, f"Main_Vocals_No_Reverb.{format}") if reverb else os.path.join(output, f"Main_Vocals.{format}") backing_output = os.path.join(output, f"Backing_Vocals_No_Reverb.{format}") if backing_reverb else os.path.join(output, f"Backing_Vocals.{format}") if backing: return [original_output, instrument_output, main_output, backing_output] else: return [original_output, instrument_output, None, None] def convert(pitch, filter_radius, index_rate, volume_envelope, protect, hop_length, f0_method, input_path, output_path, pth_path, index_path, f0_autotune, clean_audio, clean_strength, export_format, embedder_model, upscale_audio, resample_sr, batch_process, batch_size, split_audio, f0_autotune_strength): cmd = 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} --upscale_audio {upscale_audio} --resample_sr {resample_sr} --batch_process {batch_process} --batch_size {batch_size} --split_audio {split_audio} --f0_autotune_strength {f0_autotune_strength}' os.system(cmd) def convert_audio(clean, upscale, 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, batch_process, batch_size, split_audio, f0_autotune_strength): def get_audio_file(label): matching_files = [f for f in os.listdir("audios") if label in f] if not matching_files: return translations["notfound"] return os.path.join("audios", matching_files[0]) model_path = os.path.join("assets", "weights", model) 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 [None]*5 if use_original: if convert_backing: gr.Warning(translations["turn_off_convert_backup"]) return [None]*5 elif not_merge_backing: gr.Warning(translations["turn_off_merge_backup"]) return [None]*5 if not model or not os.path.exists(model_path) or os.path.isdir(model_path) or not model.endswith(".pth"): gr.Warning(translations["provide_file"].format(filename=translations["model"])) return [None]*5 if not index or not os.path.exists(index) or os.path.isdir(index) or not index.endswith(".index"): gr.Warning(translations["provide_file"].format(filename=translations["index"])) return [None]*5 f0method = method if method != "hybrid" else hybrid_method embedder_model = embedders if embedders != "custom" else custom_embedders output_path = os.path.join("audios", f"Convert_Vocals.{format}") output_backing = os.path.join("audios", f"Convert_Backing.{format}") output_merge_backup = os.path.join("audios", f"Vocals+Backing.{format}") output_merge_instrument = os.path.join("audios", f"Vocals+Instruments.{format}") if use_audio: if os.path.exists("audios"): os.makedirs("audios", 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 [None]*5 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 [None]*5 if not not_merge_backing and backing_vocal == translations["notfound"]: gr.Warning(translations["not_found_backing_vocal"]) return [None]*5 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, upscale, resample_sr, batch_process, batch_size, split_audio, f0_autotune_strength) 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, upscale, resample_sr, batch_process, batch_size, split_audio, f0_autotune_strength) gr.Info(translations["convert_backup_success"]) 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"]) AudioSegment.from_file(output_path).overlay(AudioSegment.from_file(backing_source)).export(output_merge_backup, format=format) gr.Info(translations["merge_success"]) if merge_instrument: vocals = output_merge_backup if not not_merge_backing and not use_original else output_path if os.path.exists(output_merge_instrument): os.remove(output_merge_instrument) gr.Info(translations["merge_instruments_process"]) instruments = get_audio_file('Instruments.') if instruments == translations["notfound"]: gr.Warning(translations["not_found_instruments"]) output_merge_instrument = None else: AudioSegment.from_file(instruments).overlay(AudioSegment.from_file(vocals)).export(output_merge_instrument, format=format) gr.Info(translations["merge_success"]) 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)] else: if not input or not os.path.exists(input): gr.Warning(translations["input_not_valid"]) return [None]*5 if not output: gr.Warning(translations["output_not_valid"]) return [None]*5 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 [None]*5 gr.Info(translations["batch_convert"]) output_dir = os.path.dirname(output) output_dir = output if not output_dir else output_dir 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, upscale, resample_sr, batch_process, batch_size, split_audio, f0_autotune_strength) gr.Info(translations["batch_convert_success"]) return [None]*5 else: output_dir = os.path.dirname(output) output_dir = output if not output_dir else output_dir 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, upscale, resample_sr, batch_process, batch_size, split_audio, f0_autotune_strength) gr.Info(translations["convert_success"]) return [output, None, None, None, None] def convert_tts(clean, upscale, 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, batch_process, batch_size, split_audio, f0_autotune_strength): model_path = os.path.join("assets", "weights", model) if not model_path or not os.path.exists(model_path) or os.path.isdir(model_path) or not model.endswith(".pth"): gr.Warning(translations["provide_file"].format(filename=translations["model"])) return None if not index or not os.path.exists(index) or os.path.isdir(index) or not index.endswith(".index"): gr.Warning(translations["provide_file"].format(filename=translations["index"])) 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 "output_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"output_tts-convert.{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, upscale, resample_sr, batch_process, batch_size, split_audio, f0_autotune_strength) gr.Info(translations["convert_success"]) return output def create_dataset(input_audio, output_dataset, resample, resample_sr, clean_dataset, clean_strength, separator_music, separator_reverb, kim_vocals_version, overlap, segments_size, denoise_mdx, skip, skip_start, skip_end, hop_length, batch_size): version = 1 if kim_vocals_version == "Version-1" else 2 cmd = f'{python} main/inference/create_dataset.py --input_audio "{input_audio}" --output_dataset "{output_dataset}" --resample {resample} --resample_sr {resample_sr} --clean_dataset {clean_dataset} --clean_strength {clean_strength} --separator_music {separator_music} --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}"' gr.Info(translations["start"].format(start=translations["create"])) p = Popen(cmd, shell=True) done = [False] threading.Thread(target=if_done, args=(done, p)).start() create_dataset_log = os.path.join("assets", "logs", "create_dataset.log") f = open(create_dataset_log, "w", encoding="utf-8") f.close() while 1: with open(create_dataset_log, "r", encoding='utf-8') as f: yield (f.read()) sleep(1) if done[0]: break with open(create_dataset_log, "r", encoding='utf-8') as f: log = f.read() 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(sample_rate.rstrip("k")) * 1000 if not model_name: return gr.Warning(translations["provide_name"]) if len([f for f in os.listdir(os.path.join(dataset)) if os.path.isfile(os.path.join(dataset, f)) and f.lower().endswith((".wav", ".mp3", ".flac", ".ogg"))]) < 1: return gr.Warning(translations["not_found_data"]) cmd = 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}' p = Popen(cmd, shell=True) done = [False] threading.Thread(target=if_done, args=(done, p)).start() model_dir = os.path.join("assets", "logs", model_name) preprocess_log = os.path.join(model_dir, "preprocess.log") os.makedirs(model_dir, exist_ok=True) f = open(preprocess_log, "w", encoding="utf-8") f.close() while 1: with open(preprocess_log, "r", encoding='utf-8') as f: yield (f.read()) sleep(1) if done[0]: break with open(preprocess_log, "r", encoding='utf-8') as f: log = f.read() yield log def extract(model_name, version, method, pitch_guidance, hop_length, cpu_cores, gpu, sample_rate, embedders, custom_embedders): embedder_model = embedders if embedders != "custom" else custom_embedders model_dir = os.path.join("assets", "logs", model_name) sr = int(sample_rate.rstrip("k")) * 1000 if not model_name: return gr.Warning(translations["provide_name"]) if len([f for f in os.listdir(os.path.join(model_dir, "sliced_audios")) if os.path.isfile(os.path.join(model_dir, "sliced_audios", f))]) < 1 or len([f for f in os.listdir(os.path.join(model_dir, "sliced_audios_16k")) if os.path.isfile(os.path.join(model_dir, "sliced_audios_16k", f))]) < 1: return gr.Warning(translations["not_found_data_preprocess"]) cmd = 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}' p = Popen(cmd, shell=True) done = [False] threading.Thread(target=if_done, args=(done, p)).start() extract_log = os.path.join(model_dir, model_name, "extract.log") os.makedirs(model_dir, exist_ok=True) f = open(extract_log, "w", encoding="utf-8") f.close() while 1: with open(extract_log, "r", encoding='utf-8') as f: yield (f.read()) sleep(1) if done[0]: break with open(extract_log, "r", encoding='utf-8') as f: log = f.read() 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 len([f for f in os.listdir(os.path.join(model_dir, f"{rvc_version}_extracted")) if os.path.isfile(os.path.join(model_dir, f"{rvc_version}_extracted", f))]) < 1: return gr.Warning(translations["not_found_data_extract"]) cmd = f'{python} main/inference/create_index.py --model_name "{model_name}" --rvc_version {rvc_version} --index_algorithm {index_algorithm}' p = Popen(cmd, shell=True) done = [False] threading.Thread(target=if_done, args=(done, p)).start() create_index_log = os.path.join(model_dir, "create_index.log") os.makedirs(model_dir, exist_ok=True) f = open(create_index_log, "w", encoding="utf-8") f.close() while 1: with open(create_index_log, "r", encoding='utf-8') as f: yield (f.read()) sleep(1) if done[0]: break with open(create_index_log, "r", encoding='utf-8') as f: log = f.read() 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, sync_graph, cache, model_author): sr = int(sample_rate.rstrip("k")) * 1000 model_dir = os.path.join("assets", "logs", model_name) if not model_name: return gr.Warning(translations["provide_name"]) if len([f for f in os.listdir(os.path.join(model_dir, f"{rvc_version}_extracted")) if os.path.isfile(os.path.join(model_dir, f"{rvc_version}_extracted", f))]) < 1: return gr.Warning(translations["not_found_data_extract"]) cmd = 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} --sync_graph {sync_graph} --cache_data_in_gpu {cache}' if not not_pretrain: if not custom_pretrained: 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 = pretrain_g pd = pretrain_d if not custom_pretrained: pretrained_G = os.path.join("assets", "model", f"pretrained_{rvc_version}", pg) pretrained_D = os.path.join("assets", "model", f"pretrained_{rvc_version}", pd) else: pretrained_G = os.path.join("assets", "model", f"pretrained_custom", pg) pretrained_D = os.path.join("assets", "model", f"pretrained_custom", pd) download_version = pretrained_v2_link if rvc_version == "v2" else pretrained_v1_link if not custom_pretrained: if not os.path.exists(pretrained_G): gr.Info(translations["download_pretrained"].format(dg="G", rvc_version=rvc_version)) run(["wget", "-q", "--show-progress", "-q", "--show-progress", "--no-check-certificate", f"{download_version}{pg}", "-P", os.path.join("assets", "model", f"pretrained_{rvc_version}")], check=True) if not os.path.exists(pretrained_D): gr.Info(translations["download_pretrained"].format(dg="D", rvc_version=rvc_version)) run(["wget", "-q", "--show-progress", "-q", "--show-progress", "--no-check-certificate", f"{download_version}{pd}", "-P", os.path.join("assets", "model", f"pretrained_{rvc_version}")], check=True) 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")) cmd += f" --g_pretrained_path {pretrained_G} --d_pretrained_path {pretrained_D}" else: gr.Warning(translations["not_use_pretrain"]) if model_author: cmd += f'--model_author {model_author}' gr.Info(translations["start"].format(start=translations["training"])) p = Popen(cmd, 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) train_log = os.path.join(model_dir, "train.log") f = open(train_log, "w", encoding="utf-8") f.close() while 1: with open(train_log, "r", encoding='utf-8') as f: yield (f.read()) sleep(1) if done[0]: break with open(train_log, "r", encoding='utf-8') as f: log = f.read() yield log with gr.Blocks(title="📱 RVC GUI BY ANH", theme=theme) as app: gr.HTML(translations["display_title"]) with gr.Row(): gr.Markdown(translations["rick_roll"].format(rickroll=codecs.decode('uggcf://jjj.lbhghor.pbz/jngpu?i=qDj4j9JtKpD', 'rot13'))) with gr.Row(): gr.Markdown(translations["terms_of_use"]) with gr.Row(): gr.Markdown(translations["exemption"]) with gr.Row(): gr.Markdown(f"Use full: [Colab](https://colab.research.google.com/drive/18Ed5HbwcX0di6aJymX0EaUNz-xXU5uUc?hl=vi#scrollTo=DZDKirCM0F9g)") with gr.Tabs(): paths_for_files = lambda path: [os.path.abspath(os.path.join(path, f)) for f in os.listdir(path) if os.path.splitext(f)[1] in ('.mp3', '.wav', '.flac', '.ogg', '.m4a')] with gr.TabItem(translations["separator_tab"], visible=configs["separator_tab"]): 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) backing = gr.Checkbox(label=translations["separator_backing"], value=False, interactive=True) denoise = gr.Checkbox(label=translations["denoise_backing"], value=False, interactive=False) separator_denoise = gr.Checkbox(label=translations["denoise_mdx"], value=False, interactive=False) mdx_model = gr.Checkbox(label=translations["use_mdx"], value=False, interactive=True) reverb = gr.Checkbox(label=translations["dereveb_audio"], value=False, interactive=True) backing_reverb = gr.Checkbox(label=translations["dereveb_backing"], value=False, interactive=False) reverb_denoise = gr.Checkbox(label=translations["denoise_dereveb"], value=False, interactive=False) with gr.Row(): separator_model = gr.Dropdown(label=translations["separator_model"], value="HT-Normal", choices=["HT-Normal", "HT-Tuned", "HD_MMI", "HT_6S"], interactive=True, visible=True) separator_backing_model = gr.Dropdown(label=translations["separator_backing_model"], value="Version-1", choices=["Version-1", "Version-2"], interactive=True, visible=False) with gr.Column(): separator_button = gr.Button(translations["separator_tab"], variant="primary", scale=2) 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=4000, value=256, step=8, 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=False) 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) format = gr.Radio(label=translations["export_format"], info=translations["export_info"], choices=["wav", "mp3", "flac"], value="wav", 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=False) with gr.Row(): with gr.Column(): input = gr.File(label=translations["drop_audio"], file_types=['audio']) 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(): 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=False) with gr.Accordion(translations["input_output"]): input_audio = gr.Dropdown(label=translations["audio_path"], value="" if len(list(f for f in os.listdir("audios") if os.path.splitext(f)[1] in ('.mp3', '.wav', '.flac', '.ogg', '.m4a'))) < 1 else paths_for_files("audios")[0], choices=[] if len(list(f for f in os.listdir("audios") if os.path.splitext(f)[1] in ('.mp3', '.wav', '.flac', '.ogg', '.m4a'))) < 1 else paths_for_files("audios"), 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=False) backing_vocals = gr.Audio(show_download_button=True, interactive=False, label=translations["backing_vocal"], visible=False) with gr.Row(): backing.change(fn=lambda a, b, c: [visible_1(a or b or c), visible_1(a or b or c)], inputs=[backing, mdx_model, reverb], outputs=[mdx_batch_size, mdx_hop_length]) mdx_model.change(fn=lambda a, b, c: [visible_1(a or b or c), visible_1(a or b or c)], inputs=[backing, mdx_model, reverb], outputs=[mdx_batch_size, mdx_hop_length]) reverb.change(fn=lambda a, b, c: [visible_1(a or b or c), visible_1(a or b or c)], inputs=[backing, mdx_model, reverb], outputs=[mdx_batch_size, mdx_hop_length]) with gr.Row(): backing.change(fn=visible_1, inputs=[backing], outputs=[separator_backing_model]) backing.change(fn=visible_1, inputs=[backing], outputs=[main_vocals]) backing.change(fn=visible_1, inputs=[backing], outputs=[backing_vocals]) with gr.Row(): backing.change(fn=valueFalse_interactive1, inputs=[backing], outputs=[denoise]) backing.change(fn=valueFalse_interactive2, inputs=[backing, reverb], outputs=[backing_reverb]) with gr.Row(): reverb.change(fn=valueFalse_interactive1, inputs=[reverb], outputs=[reverb_denoise]) reverb.change(fn=valueFalse_interactive2, inputs=[backing, reverb], outputs=[backing_reverb]) with gr.Row(): mdx_model.change(fn=valueFalse_interactive1, inputs=[mdx_model], outputs=[separator_denoise]) mdx_model.change(fn=model_separator_change, inputs=[mdx_model], outputs=[separator_model]) mdx_model.change(fn=lambda inp: visible_1(not inp), inputs=[mdx_model], outputs=[shifts]) with gr.Row(): input_audio.change(fn=lambda audio: audio if audio else None, inputs=[input_audio], outputs=[audio_input]) cleaner.change(fn=visible_1, inputs=[cleaner], outputs=[clean_strength]) input.upload(fn=lambda audio_in: shutil.move(audio_in.name, os.path.join("audios")), inputs=[input], outputs=[input_audio]) refesh_separator.click(fn=refesh_audio, 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, mdx_model, separator_denoise, reverb, reverb_denoise, backing_reverb, mdx_hop_length, mdx_batch_size ], outputs=[original_vocals, instruments_audio, main_vocals, backing_vocals], api_name='separator_music' ) with gr.TabItem(translations["convert_audio"], visible=configs["convert_tab"]): 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) upscale = gr.Checkbox(label=translations["upscale_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) use_original = gr.Checkbox(label=translations["convert_original"], value=False, interactive=True, visible=False) convert_backing = gr.Checkbox(label=translations["convert_backing"], value=False, interactive=True, visible=False) not_merge_backing = gr.Checkbox(label=translations["not_merge_backing"], value=False, interactive=True, visible=False) merge_instrument = gr.Checkbox(label=translations["merge_instruments"], value=False, interactive=True, visible=False) 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=False) with gr.Column(): convert_button = gr.Button(translations["convert_audio"], variant="primary", scale=4) with gr.Row(): with gr.Column(): input0 = gr.File(label=translations["drop_audio"], file_types=['audio']) play_audio = gr.Audio(show_download_button=True, interactive=False, label=translations["input_audio"]) with gr.Column(): with gr.Accordion(translations["model_accordion"], open=True): with gr.Row(): model_pth = gr.Dropdown(label=translations["model_name"], choices=sorted(model_name), value=sorted(model_name)[0] if len(sorted(model_name)) > 0 else '', interactive=True, allow_custom_value=True) model_index = gr.Dropdown(label=translations["index_path"], choices=sorted(index_path), value=sorted(index_path)[0] if len(sorted(index_path)) > 0 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) 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", "m4a"], value="wav", interactive=True) input_audio0 = gr.Dropdown(label=translations["audio_path"], value="" if len(list(f for f in os.listdir("audios") if os.path.splitext(f)[1] in ('.mp3', '.wav', '.flac', '.ogg', '.m4a'))) < 1 else paths_for_files("audios")[0], choices=[] if len(list(f for f in os.listdir("audios") if os.path.splitext(f)[1] in ('.mp3', '.wav', '.flac', '.ogg', '.m4a'))) < 1 else paths_for_files("audios"), info="Nhập đường dẫn đến tệp âm thanh", allow_custom_value=True, interactive=True) output_audio = gr.Textbox(label=translations["output_path"], value="audios/output.wav", placeholder="audios/output.wav", info=translations["output_path_info"], interactive=True) with gr.Column(): refesh0 = gr.Button(translations["refesh"]) with gr.Accordion(translations["setting"], open=False): with gr.Accordion(translations["f0_method"], open=False): method = gr.Radio(label=translations["f0_method"], info=translations["f0_method_info"], choices=["pm", "dio", "crepe-tiny", "crepe", "fcpe", "rmvpe", "harvest", "hybrid"], value="rmvpe", interactive=True) hybrid_method = gr.Radio(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[dio+crepe-tiny]", "hybrid[dio+crepe]", "hybrid[dio+fcpe]", "hybrid[dio+rmvpe]", "hybrid[dio+harvest]", "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[fcpe+rmvpe]", "hybrid[fcpe+harvest]", "hybrid[rmvpe+harvest]"], value="hybrid[pm+dio]", interactive=True, visible=False) hop_length = gr.Slider(label="Hop length", info=translations["hop_length_info"], minimum=1, maximum=512, value=128, step=1, interactive=True, visible=False) with gr.Accordion(translations["hubert_model"], open=False): embedders = gr.Radio(label=translations["hubert_model"], info=translations["hubert_info"], choices=["contentvec_base", "hubert_base", "japanese_hubert_base", "korean_hubert_base", "chinese_hubert_base", "custom"], value="contentvec_base", interactive=True) custom_embedders = gr.Textbox(label=translations["modelname"], info=translations["modelname_info"], value="", placeholder="hubert_base", interactive=True, visible=False) with gr.Column(): with gr.Group(): with gr.Row(): split_audio = gr.Checkbox(label=translations["split_audio"], info=translations["split_audio_info"], value=False, interactive=True) batch_process = gr.Checkbox(label=translations["batch_process"], info=translations["batch_process_info"], value=False, interactive=True, visible=False) with gr.Row(): batch_size = gr.Slider(minimum=1, maximum=10, label=translations["batch_size"], info=translations["batch_size_info"], value=1, step=1, interactive=True, visible=False) 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=False) resample_sr = gr.Slider(minimum=0, maximum=48000, label=translations["resample"], info=translations["resample_info"], value=0, step=1, interactive=True) filter_radius = gr.Slider(minimum=0, maximum=7, label=translations["filter_radius"], info=translations["filter_radius_info"], value=3, step=1, interactive=True) volume_envelope = gr.Slider(minimum=0, maximum=1, label=translations["volume_envelope"], info=translations["volume_envelope_info"], value=1, step=0.1, interactive=True) protect = gr.Slider(minimum=0, maximum=1, label=translations["protect"], info=translations["protect_info"], value=0.33, step=0.01, interactive=True) with gr.Row(): gr.Markdown(translations["output_convert"]) with gr.Row(): main_convert = gr.Audio(show_download_button=True, interactive=False, label=translations["main_convert"]) backing_convert = gr.Audio(show_download_button=True, interactive=False, label=translations["convert_backing"], visible=False) main_backing = gr.Audio(show_download_button=True, interactive=False, label=translations["main_or_backing"], visible=False) with gr.Row(): original_convert = gr.Audio(show_download_button=True, interactive=False, label=translations["convert_original"], visible=False) vocal_instrument = gr.Audio(show_download_button=True, interactive=False, label=translations["voice_or_instruments"], visible=False) with gr.Row(): split_audio.change(fn=valueFalse_visible1, inputs=[split_audio], outputs=[batch_process]) batch_process.change(fn=visible_1, inputs=[batch_process], outputs=[batch_size]) autotune.change(fn=visible_1, inputs=[autotune], outputs=[f0_autotune_strength]) with gr.Row(): use_audio.change(fn=visible_1, inputs=[use_audio], outputs=[main_backing]) use_audio.change(fn=lambda audio: [valueFalse_interactive1(audio), valueFalse_interactive1(audio), valueFalse_interactive1(audio), valueFalse_interactive1(audio)], inputs=[use_audio], outputs=[use_original, convert_backing, not_merge_backing, merge_instrument]) with gr.Row(): use_audio.change(fn=visible_1, inputs=[use_audio], outputs=[use_original]); use_audio.change(fn=visible_1, inputs=[use_audio], outputs=[convert_backing]) use_audio.change(fn=visible_1, inputs=[use_audio], outputs=[not_merge_backing]); use_audio.change(fn=visible_1, inputs=[use_audio], outputs=[merge_instrument]) use_audio.change(fn=lambda audio: [visible_1(not audio), visible_1(not audio), visible_1(not audio), visible_1(not audio)], inputs=[use_audio], outputs=[input_audio0, output_audio, input0, play_audio]) with gr.Row(): convert_backing.change(fn=visible_1, inputs=[convert_backing], outputs=[backing_convert]) convert_backing.change(fn=backing_change, inputs=[convert_backing, not_merge_backing], outputs=[use_original]) with gr.Row(): use_original.change(fn=lambda original: [valueFalse_interactive1(not original), valueFalse_interactive1(not original)], inputs=[use_original], outputs=[convert_backing, not_merge_backing]) use_original.change(fn=lambda audio, original: [visible_1(original), visible_1(not original), visible_1(audio and not original)], inputs=[use_audio, use_original], outputs=[original_convert, main_convert, main_backing]) with gr.Row(): cleaner0.change(fn=visible_1, inputs=[cleaner0], outputs=[clean_strength0]) merge_instrument.change(fn=visible_1, inputs=[merge_instrument], outputs=[vocal_instrument]) with gr.Row(): not_merge_backing.change(fn=lambda audio, merge: visible_1(audio and not merge), inputs=[use_audio, not_merge_backing], outputs=[main_backing]) not_merge_backing.change(fn=backing_change, inputs=[convert_backing, not_merge_backing], outputs=[use_original]) with gr.Row(): method.change(fn=lambda method: visible_1(True if method == "hybrid" else False), inputs=[method], outputs=[hybrid_method]) method.change(fn=hoplength_show, inputs=[method, hybrid_method], outputs=[hop_length]) with gr.Row(): hybrid_method.change(fn=hoplength_show, inputs=[method, hybrid_method], outputs=[hop_length]) refesh.click(fn=change_choices, inputs=[], outputs=[model_pth, model_index]) model_pth.change(fn=get_index, inputs=[model_pth], outputs=[model_index]) with gr.Row(): input0.upload(fn=lambda audio_in: shutil.move(audio_in.name, os.path.join("audios")), inputs=[input0], outputs=[input_audio0]) input_audio0.change(fn=lambda audio: audio if audio else None, inputs=[input_audio0], outputs=[play_audio]) with gr.Row(): embedders.change(fn=lambda embedders: visible_1(True if embedders == "custom" else False), inputs=[embedders], outputs=[custom_embedders]) refesh0.click(fn=refesh_audio, inputs=[], outputs=[input_audio0]) with gr.Row(): convert_button.click( fn=convert_audio, inputs=[ cleaner0, upscale, 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, batch_process, batch_size, split_audio, f0_autotune_strength ], outputs=[main_convert, backing_convert, main_backing, original_convert, vocal_instrument], api_name="convert_audio" ) with gr.TabItem(translations["convert_text"], visible=configs["tts_tab"]): gr.Markdown(translations["convert_text_markdown"]) with gr.Row(): gr.Markdown(translations["convert_text_markdown_2"]) with gr.Row(): with gr.Column(): use_txt = gr.Checkbox(label=translations["input_txt"], value=False, interactive=True) prompt = gr.Textbox(label=translations["text_to_speech"], value="", placeholder="Hello Words", lines=2) with gr.Row(): 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.Column(): 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(): tts_voice = gr.Dropdown(label=translations["voice"], choices=tts_voice, interactive=True, value="vi-VN-NamMinhNeural") txt_input = gr.File(label=translations["drop_text"], file_types=['txt'], visible=False) with gr.Column(): with gr.Accordion(translations["model_accordion"], open=True): with gr.Row(): model_pth0 = gr.Dropdown(label=translations["model_name"], choices=sorted(model_name), value=sorted(model_name)[0] if len(sorted(model_name)) > 0 else '', interactive=True, allow_custom_value=True) model_index0 = gr.Dropdown(label=translations["index_path"], choices=sorted(index_path), value=sorted(index_path)[0] if len(sorted(index_path)) > 0 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) 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", "m4a"], value="wav", interactive=True) output_audio0 = gr.Textbox(label=translations["output_tts"], value="audios/tts.wav", placeholder="audios/tts.wav", info=translations["tts_output"], interactive=True) output_audio1 = gr.Textbox(label=translations["output_tts_convert"], value="audios/tts-convert.wav", placeholder="audios/tts-convert.wav", info=translations["tts_output"], interactive=True) with gr.Accordion(translations["setting"], open=False): with gr.Accordion(translations["f0_method"], open=False): method0 = gr.Radio(label=translations["f0_method"], info=translations["f0_method_info"], choices=["pm", "dio", "crepe-tiny", "crepe", "fcpe", "rmvpe", "harvest", "hybrid"], value="rmvpe", interactive=True) hybrid_method0 = gr.Radio(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[dio+crepe-tiny]", "hybrid[dio+crepe]", "hybrid[dio+fcpe]", "hybrid[dio+rmvpe]", "hybrid[dio+harvest]", "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[fcpe+rmvpe]", "hybrid[fcpe+harvest]", "hybrid[rmvpe+harvest]"], value="hybrid[pm+dio]", interactive=True, visible=False) hop_length0 = gr.Slider(label="Hop length", info=translations["hop_length_info"], minimum=1, maximum=512, value=128, step=1, interactive=True, visible=False) with gr.Accordion(translations["hubert_model"], open=False): embedders0 = gr.Radio(label=translations["hubert_model"], info=translations["hubert_info"], choices=["contentvec_base", "hubert_base", "japanese_hubert_base", "korean_hubert_base", "chinese_hubert_base", "custom"], value="contentvec_base", interactive=True) custom_embedders0 = gr.Textbox(label=translations["modelname"], info=translations["modelname_info"], value="", placeholder="hubert_base", interactive=True, visible=False) with gr.Group(): with gr.Row(): split_audio0 = gr.Checkbox(label=translations["split_audio"], info=translations["split_audio_info"], value=False, interactive=True) batch_process0 = gr.Checkbox(label=translations["batch_process"], info=translations["batch_process_info"], value=False, interactive=True, visible=False) with gr.Row(): batch_size0 = gr.Slider(minimum=1, maximum=10, label=translations["batch_size"], info=translations["batch_size_info"], value=1, step=1, interactive=True, visible=False) with gr.Row(): cleaner1 = gr.Checkbox(label=translations["clear_audio"], value=False, interactive=True) upscale2 = gr.Checkbox(label=translations["upscale_audio"], value=False, interactive=True) autotune3 = gr.Checkbox(label=translations["autotune"], 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=False) 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=False) resample_sr0 = gr.Slider(minimum=0, maximum=48000, label=translations["resample"], info=translations["resample_info"], value=0, step=1, interactive=True) filter_radius0 = gr.Slider(minimum=0, maximum=7, label=translations["filter_radius"], info=translations["filter_radius_info"], value=3, step=1, interactive=True) volume_envelope0 = gr.Slider(minimum=0, maximum=1, label=translations["volume_envelope"], info=translations["volume_envelope_info"], value=1, step=0.1, interactive=True) protect0 = gr.Slider(minimum=0, maximum=1, label=translations["protect"], info=translations["protect_info"], value=0.33, step=0.01, interactive=True) with gr.Row(): gr.Markdown(translations["output_tts_markdown"]) with gr.Row(): tts_voice_audio = gr.Audio(show_download_button=True, interactive=False, label=translations["output_text_to_speech"]) tts_voice_convert = gr.Audio(show_download_button=True, interactive=False, label=translations["output_file_tts_convert"]) with gr.Row(): batch_process0.change(fn=visible_1, inputs=[batch_process0], outputs=[batch_size0]) split_audio0.change(fn=valueFalse_visible1, inputs=[split_audio0], outputs=[batch_process0]) autotune3.change(fn=visible_1, inputs=[autotune3], outputs=[f0_autotune_strength0]) with gr.Row(): cleaner1.change(fn=visible_1, inputs=[cleaner1], outputs=[clean_strength1]) method0.change(fn=lambda method: visible_1(True if method == "hybrid" else False), inputs=[method0], outputs=[hybrid_method0]) method0.change(fn=hoplength_show, inputs=[method0, hybrid_method0], outputs=[hop_length0]) hybrid_method0.change(fn=hoplength_show, inputs=[method0, hybrid_method0], outputs=[hop_length0]) with gr.Row(): refesh1.click(fn=change_choices, inputs=[], outputs=[model_pth0, model_index0]) model_pth0.change(fn=get_index, inputs=[model_pth0], outputs=[model_index0]) embedders0.change(fn=lambda embedders: visible_1(True if embedders == "custom" else False), inputs=[embedders0], outputs=[custom_embedders0]) with gr.Row(): txt_input.upload(fn=process_input, inputs=[txt_input], outputs=[prompt]) use_txt.change(fn=visible_1, inputs=[use_txt], outputs=[txt_input]) with gr.Row(): tts_button.click( fn=TTS, inputs=[ prompt, tts_voice, speed, output_audio0 ], outputs=[tts_voice_audio], api_name="text-to-speech" ) convert_button0.click( fn=convert_tts, inputs=[ cleaner1, upscale2, 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, batch_process0, batch_size0, split_audio0, f0_autotune_strength0 ], outputs=[tts_voice_convert], api_name="convert_tts" ) with gr.TabItem(translations["audio_effects"], visible=configs["effects_tab"]): gr.Markdown(translations["apply_audio_effects"]) with gr.Row(): gr.Markdown(translations["audio_effects_edit"]) with gr.Row(): with gr.Column(): with gr.Group(): 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) with gr.Row(): more_options = gr.Checkbox(label=translations["more_option"], 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) with gr.Column(): apply_effects_button = gr.Button(translations["apply"], variant="primary", scale=2) with gr.Row(): with gr.Row(): with gr.Accordion(translations["input_output"], open=False): with gr.Row(): upload_audio = gr.File(label=translations["drop_audio"], file_types=['audio']) with gr.Row(): audio_in_path = gr.Dropdown(label=translations["input_audio"], value="" if len(list(f for f in os.listdir("audios") if os.path.splitext(f)[1] in ('.mp3', '.wav', '.flac', '.ogg', '.m4a'))) < 1 else paths_for_files("audios")[0], choices=[] if len(list(f for f in os.listdir("audios") if os.path.splitext(f)[1] in ('.mp3', '.wav', '.flac', '.ogg', '.m4a'))) < 1 else paths_for_files("audios"), info="Nhập đường dẫn đầu vào âm thanh", 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(): audio_output_format = gr.Radio(label=translations["export_format"], info=translations["export_info"], choices=["wav", "mp3", "flac"], value="wav", interactive=True) audio_effects_refesh = gr.Button(translations["refesh"]) with gr.Row(): with gr.Column(): with gr.Row(): with gr.Accordion(translations["reverb"], open=False, visible=False) 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=False) 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=False) 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=False) 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=False) 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=False) 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=False) 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=48000, step=1, value=0, label=translations["resample"], info=translations["resample_info"], interactive=True, visible=False) distortion_drive_db = gr.Slider(minimum=0, maximum=50, step=1, value=20, label=translations["distortion"], info=translations["distortion_info"], interactive=True, visible=False) gain_db = gr.Slider(minimum=-60, maximum=60, step=1, value=0, label=translations["gain"], info=translations["gain_info"], interactive=True, visible=False) 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=False) 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=False) with gr.Row(): with gr.Accordion(translations["phaser"], open=False, visible=False) 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=False) 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_1, inputs=[reverb_check_box], outputs=[reverb_accordion]) chorus_check_box.change(fn=visible_1, inputs=[chorus_check_box], outputs=[chorus_accordion]) delay_check_box.change(fn=visible_1, inputs=[delay_check_box], outputs=[delay_accordion]) with gr.Row(): compressor_check_box.change(fn=visible_1, inputs=[compressor_check_box], outputs=[compressor_accordion]) phaser_check_box.change(fn=visible_1, inputs=[phaser_check_box], outputs=[phaser_accordion]) more_options.change(fn=visible_1, inputs=[more_options], outputs=[more_accordion]) with gr.Row(): fade.change(fn=visible_1, inputs=[fade], outputs=[fade_accordion]) bass_or_treble.change(fn=visible_1, inputs=[bass_or_treble], outputs=[bass_treble_accordion]) limiter.change(fn=visible_1, inputs=[limiter], outputs=[limiter_accordion]) resample_checkbox.change(fn=visible_1, inputs=[resample_checkbox], outputs=[audio_effect_resample_sr]) with gr.Row(): distortion_checkbox.change(fn=visible_1, inputs=[distortion_checkbox], outputs=[distortion_drive_db]) gain_checkbox.change(fn=visible_1, inputs=[gain_checkbox], outputs=[gain_db]) clipping_checkbox.change(fn=visible_1, inputs=[clipping_checkbox], outputs=[clipping_threashold_db]) bitcrush_checkbox.change(fn=visible_1, 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=refesh_audio, inputs=[], outputs=[audio_in_path]) with gr.Row(): more_options.change(fn=lambda: [False]*4, inputs=[], outputs=[fade, bass_or_treble, limiter, resample_checkbox]) more_options.change(fn=lambda: [False]*4, inputs=[], outputs=[distortion_checkbox, gain_checkbox, clipping_checkbox, bitcrush_checkbox]) 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 ], outputs=[audio_play_output], api_name="audio_effects" ) with gr.TabItem(translations["createdataset"], visible=configs["create_dataset_tab"]): 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) with gr.Row(): with gr.Row(): with gr.Column(): with gr.Group(): with gr.Row(): separator_audio = gr.Checkbox(label=translations["separator_tab"], value=False, interactive=True) separator_reverb = gr.Checkbox(label=translations["dereveb_audio"], value=False, interactive=False) denoise_mdx = gr.Checkbox(label=translations["denoise"], value=False, interactive=False) with gr.Row(): clean_audio = gr.Checkbox(label=translations["clear_audio"], value=False, interactive=True) resample = gr.Checkbox(label=translations["resample"], value=False, interactive=True) skip = gr.Checkbox(label=translations["skip"], value=False, interactive=True) with gr.Row(): resample_sample_rate = gr.Slider(minimum=0, maximum=48000, step=1, value=0, label=translations["resample"], info=translations["resample_info"], interactive=True, visible=False) 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=False) with gr.Column(): create_button = gr.Button(translations["createdataset"], variant="primary", scale=2) with gr.Row(): with gr.Column(): with gr.Group(visible=False) as separator_dataset: with gr.Row() as kim_vocal_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, visible=False) 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, visible=False) with gr.Row() as kim_vocal_row_2: kim_vocal_segments_size = gr.Slider(label=translations["segments_size"], info=translations["segments_size_info"], minimum=32, maximum=4000, value=256, step=8, interactive=True, visible=False) kim_vocal_hop_length = gr.Slider(label="Hop length", info=translations["hop_length_info"], minimum=1, maximum=8192, value=1024, step=1, interactive=True, visible=False) with gr.Row() as kim_vocal_row_3: 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, visible=False) with gr.Row(): create_dataset_info = gr.Textbox(label=translations["create_dataset_info"], value="", interactive=False) with gr.Row(): with gr.Column(): output_dataset = gr.Textbox(label=translations["output_data"], info=translations["output_data_info"], value="dataset", placeholder="dataset", interactive=True) with gr.Row(): skip_start = gr.Textbox(label=translations["skip_start"], info=translations["skip_start_info"], value="", placeholder="0,...", interactive=True, visible=False) skip_end = gr.Textbox(label=translations["skip_end"], info=translations["skip_end_info"], value="", placeholder="0,...", interactive=True, visible=False) with gr.Row(): separator_audio.change(fn=valueFalse_interactive1, inputs=[separator_audio], outputs=[separator_reverb]) separator_audio.change(fn=valueFalse_interactive1, inputs=[separator_audio], outputs=[denoise_mdx]) separator_audio.change(fn=visible_1, inputs=[separator_audio], outputs=[separator_dataset]) with gr.Row(): separator_audio.change(fn=visible_1, inputs=[separator_audio], outputs=[kim_vocal_row]) separator_audio.change(fn=visible_1, inputs=[separator_audio], outputs=[kim_vocal_row_2]) separator_audio.change(fn=visible_1, inputs=[separator_audio], outputs=[kim_vocal_row_3]) with gr.Row(): resample.change(fn=visible_1, inputs=[resample], outputs=[resample_sample_rate]) clean_audio.change(fn=visible_1, inputs=[clean_audio], outputs=[dataset_clean_strength]) with gr.Row(): skip.change(fn=valueEmpty_visible1, inputs=[skip], outputs=[skip_start]) skip.change(fn=valueEmpty_visible1, inputs=[skip], outputs=[skip_end]) with gr.Row(): create_button.click( fn=create_dataset, inputs=[ dataset_url, output_dataset, resample, resample_sample_rate, clean_audio, dataset_clean_strength, separator_audio, 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 ], outputs=[create_dataset_info], api_name="create_dataset" ) with gr.TabItem(translations["training_model"], visible=configs["training_tab"]): 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", "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(): training_f0 = gr.Checkbox(label=translations["training_pitch"], info=translations["training_pitch_info"], value=True, interactive=True) upload = gr.Checkbox(label=translations["upload"], info=translations["upload_dataset"], value=False, interactive=True) preprocess_cut = gr.Checkbox(label=translations["split_audio"], info=translations["preprocess_split"], value=False, interactive=True) process_effects = gr.Checkbox(label=translations["preprocess_effect"], info=translations["preprocess_effect_info"], value=False, interactive=True) with gr.Column(): clean_dataset = gr.Checkbox(label=translations["clear_dataset"], info=translations["clear_dataset_info"], value=False, interactive=True) 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=False) with gr.Column(): preprocess_button = gr.Button(translations["preprocess_button"], scale=2) upload_dataset = gr.Files(label=translations["drop_audio"], file_types=['audio'], visible=False) preprocess_info = gr.Textbox(label=translations["preprocess_info"], value="", interactive=False) with gr.Column(): with gr.Row(): with gr.Column(): extract_method = gr.Radio(label=translations["f0_method"], info=translations["f0_method"], choices=["pm", "dio", "crepe", "crepe-tiny", "fcpe", "rmvpe", "harvest"], value="pm", interactive=True) extract_hop_length = gr.Slider(label="Hop length", info=translations["hop_length_info"], minimum=1, maximum=512, value=128, step=1, interactive=True, visible=False) with gr.Accordion(label=translations["hubert_model"], open=False): extract_embedders = gr.Radio(label=translations["hubert_model"], info=translations["hubert_info"], choices=["contentvec_base", "hubert_base", "japanese_hubert_base", "korean_hubert_base", "chinese_hubert_base", "custom"], value="contentvec_base", interactive=True) with gr.Row(): extract_embedders_custom = gr.Textbox(label=translations["modelname"], info=translations["modelname_info"], value="", placeholder="hubert_base", interactive=True, visible=False) 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) sync_graph = gr.Checkbox(label=translations["sync_graph"], info=translations["sync_graph_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=False) with gr.Column(): threshold = gr.Slider(minimum=1, maximum=100, value=50, step=1, label=translations["threshold"], interactive=True, visible=False) with gr.Accordion(translations["setting_cpu_gpu"], open=False): with gr.Column(): gpu_number = gr.Textbox(label=translations["gpu_number"], value=str(get_number_of_gpus()), 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(): 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(): 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=False) as pretrain_setting: pretrained_D = gr.Dropdown(label=translations["pretrain_file"].format(dg="D"), choices=sorted(pretrainedD), value=sorted(pretrainedD)[0] if len(sorted(pretrainedD)) > 0 else '', interactive=True, allow_custom_value=True, visible=False) pretrained_G = gr.Dropdown(label=translations["pretrain_file"].format(dg="G"), choices=sorted(pretrainedG), value=sorted(pretrainedG)[0] if len(sorted(pretrainedG)) > 0 else '', interactive=True, allow_custom_value=True, visible=False) refesh_pretrain = gr.Button(translations["refesh_pretrain"], scale=2, visible=False) with gr.Row(): training_info = gr.Textbox(label=translations["train_info"], value="", interactive=False) with gr.Row(): with gr.Column(): with gr.Accordion(translations["export_model"], open=False): with gr.Row(): model_file= gr.Dropdown(label=translations["model_name"], choices=sorted(model_name), value=sorted(model_name)[0] if len(sorted(model_name)) > 0 else '', interactive=True, allow_custom_value=True) index_file = gr.Dropdown(label=translations["index_path"], choices=sorted(index_path), value=sorted(index_path)[0] if len(sorted(index_path)) > 0 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_choices, inputs=[], outputs=[model_file, index_file]) zip_model.click(fn=lambda: visible_1(True), inputs=[], outputs=[zip_output]) zip_model.click(fn=zip_file, inputs=[training_name, model_file, index_file], outputs=[zip_output]) with gr.Row(): dataset_path.change( fn=lambda folder: os.makedirs(folder, exist_ok=True), inputs=[dataset_path], outputs=[], api_name="create_folder" ) upload.change(fn=visible_1, inputs=[upload], outputs=[upload_dataset]) overtraining_detector.change(fn=visible_1, inputs=[overtraining_detector], outputs=[threshold]) clean_dataset.change(fn=visible_1, inputs=[clean_dataset], outputs=[clean_dataset_strength]) with gr.Row(): custom_dataset.change(fn=lambda custom_dataset: [visible_1(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('Vui lòng nhập tên thư mục dữ liệu'), inputs=[upload_dataset, dataset_path], outputs=[], api_name="upload_dataset" ) with gr.Row(): not_use_pretrain.change(fn=lambda a, b: [visible_1(a and not b), visible_1(a and not b), visible_1(a and not b), visible_1(a and not b)], inputs=[custom_pretrain, not_use_pretrain], outputs=[pretrained_D, pretrained_G, refesh_pretrain, pretrain_setting]) custom_pretrain.change(fn=lambda a, b: [visible_1(a and not b), visible_1(a and not b), visible_1(a and not b), visible_1(a and not b)], inputs=[custom_pretrain, not_use_pretrain], outputs=[pretrained_D, pretrained_G, refesh_pretrain, pretrain_setting]) refesh_pretrain.click(fn=change_choices_pretrained, 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_1(True if extract_embedders == "custom" else False), inputs=[extract_embedders], outputs=[extract_embedders_custom]) with gr.Row(): extract_button.click( fn=extract, inputs=[ training_name, training_ver, extract_method, training_f0, extract_hop_length, cpu_core, gpu_number, training_sr, extract_embedders, extract_embedders_custom ], outputs=[extract_info], api_name="extract" ) with gr.Row(): index_button.click( fn=create_index, inputs=[ training_name, training_ver, index_algorithm ], outputs=[training_info], api_name="create_index" ) with gr.Row(): training_button.click( fn=training, inputs=[ training_name, training_ver, save_epochs, save_only_latest, save_every_weights, total_epochs, training_sr, train_batch_size, gpu_number, training_f0, not_use_pretrain, custom_pretrain, pretrained_G, pretrained_D, overtraining_detector, threshold, sync_graph, cache_in_gpu, model_author ], outputs=[training_info], api_name="training_model" ) with gr.TabItem(translations["fushion"], visible=configs["fushion_tab"]): gr.Markdown(translations["fushion_markdown"]) with gr.Row(): gr.Markdown(translations["fushion_markdown_2"]) with gr.Row(): with gr.Column(): name_to_save = gr.Textbox(label=translations["modelname"], placeholder="Model.pth", value="", max_lines=1, interactive=True) with gr.Column(): fushion_button = gr.Button(translations["fushion"], variant="primary", scale=4) with gr.Column(): with gr.Row(): model_a = gr.File(label=f"{translations['model_name']} 1", file_types=['pth']) model_b = gr.File(label=f"{translations['model_name']} 2", file_types=['pth']) with gr.Row(): model_path_a = gr.Textbox(label=f"{translations['model_path']} 1", value="", placeholder="assets/weights/Model_1.pth") model_path_b = gr.Textbox(label=f"{translations['model_path']} 2", value="", placeholder="assets/weights/Model_2.pth") with gr.Row(): ratio = gr.Slider(minimum=0, maximum=1, label=translations["model_ratio"], info=translations["model_ratio_info"], value=0.5, interactive=True) with gr.Row(): output_model = gr.File(label=translations["output_model_path"], visible=False) with gr.Row(): model_a.upload(fn=lambda model: shutil.move(model.name, os.path.join("assets", "weights")), inputs=[model_a], outputs=[model_path_a]) model_b.upload(fn=lambda model: shutil.move(model.name, os.path.join("assets", "weights")), inputs=[model_b], outputs=[model_path_b]) with gr.Row(): fushion_button.click( fn=fushion_model, inputs=[ name_to_save, model_path_a, model_path_b, ratio ], outputs=[name_to_save, output_model], api_name="fushion_model" ) fushion_button.click(fn=lambda: visible_1(True), inputs=[], outputs=[output_model]) with gr.TabItem(translations["read_model"], visible=configs["read_tab"]): gr.Markdown(translations["read_model_markdown"]) with gr.Row(): gr.Markdown(translations["read_model_markdown_2"]) with gr.Row(): with gr.Column(): model = gr.File(label=translations["drop_model"], file_types=['pth']) with gr.Column(): read_button = gr.Button(translations["readmodel"], variant="primary", scale=2) with gr.Column(): model_path = gr.Textbox(label=translations["download_url"], value="", info=translations["model_path_info"], interactive=True) output_info = gr.Textbox(label=translations["modelinfo"], value="", interactive=False, scale=6) with gr.Row(): model.upload(fn=lambda model: shutil.move(model.name, os.path.join("assets", "weights")), inputs=[model], outputs=[model_path]) read_button.click( fn=model_info, inputs=[model_path], outputs=[output_info], api_name="read_model" ) with gr.TabItem(translations["downloads"], visible=configs["downloads_tab"]): 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["download_from_applio"], translations["upload"]], interactive=True, value=translations["download_url"]) with gr.Row(): gr.Markdown("___") with gr.Row(): url_input = gr.Textbox(label=translations["model_url"], value="", placeholder="https://...", scale=6, visible=True) model_name = gr.Textbox(label=translations["modelname"], value="", placeholder=translations["modelname"], scale=2, visible=True) url_download = gr.Button(value=translations["downloads"], scale=2, visible=True) with gr.Row(): 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.Row(): model_upload = gr.File(label=translations["drop_model"], file_types=['pth', 'index', 'zip'], visible=False) with gr.Column(): with gr.Row(): 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) with gr.Row(): 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.Row(): # with gr.Accordion(translations["download_pretrainec"], 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.Row(): # pretrainD = gr.Textbox(label=translations["pretrained_url"].format(dg="D"), value="", info=translations["only_huggingface"], placeholder="https://...", interactive=True, scale=4, visible=True) # pretrainG = gr.Textbox(label=translations["pretrained_url"].format(dg="G"), value="", info=translations["only_huggingface"], placeholder="https://...", interactive=True, scale=4, visible=True) # download_pretrain_button = gr.Button(translations["downloads"], scale=2) # 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"], choices=["48k", "40k", "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.Row(): # 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, 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=download_change, inputs=[downloadmodel], outputs=[url_input, 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="download_applio" ) # with gr.Row(): # pretrain_download_choices.change(fn=download_pretrained_change, 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", "model", "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", "model", "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", "model", "embedders")), # inputs=[hubert_input], # outputs=[], # api_name="upload_hubert" # ) with gr.TabItem(translations["settings"], visible=configs["settings_tab"]): gr.Markdown(translations["settings_markdown"]) with gr.Row(): gr.Markdown(translations["settings_markdown_2"]) with gr.Row(): with gr.Column(): language_dropdown = gr.Dropdown(label=translations["lang"], interactive=True, info=translations["lang_restart"], choices=configs["support_language"], value=configs["language"]) change_lang = gr.Button(translations["change_lang"], variant=["primary"], scale=2) with gr.Column(): toggle_button = gr.Button(translations["change_light_dark"], variant=["secondary"], scale=2) with gr.Row(): with gr.Column(): fp_select = gr.Radio(label=translations["fp_train"], info=translations["fp_info"], value="fp32", choices=["fp16", "fp32"], interactive=True) fp_button = gr.Button(translations["fp_button"], variant=["primary"], scale=2) with gr.Column(): theme_dropdown = gr.Dropdown(label=translations["theme"], interactive=True, info=translations["theme_restart"], choices=configs["themes"], value=configs["theme"], allow_custom_value=True) changetheme = gr.Button(translations["theme_button"], variant=["primary"], scale=2) with gr.Row(): toggle_button.click(fn=None, js="""() => {document.body.classList.toggle('dark')}""") fp_button.click(fn=change_fp, inputs=[fp_select], outputs=[]) with gr.Row(): change_lang.click(fn=change_language, inputs=[language_dropdown], outputs=[]) change_lang.click(fn=restart_app, inputs=[], outputs=[]) with gr.Row(): changetheme.click(fn=change_theme, inputs=[theme_dropdown], outputs=[]) changetheme.click(fn=restart_app, inputs=[], outputs=[]) with gr.Row(): change_lang.click(fn=None, js="""setTimeout(function() {location.reload()}, 30000)""", inputs=[], outputs=[]) changetheme.click(fn=None, js="""setTimeout(function() {location.reload()}, 30000)""", inputs=[], outputs=[]) with gr.TabItem(translations["source"]): gr.Markdown(translations["source_info"]) with gr.Row(): gr.Markdown("___") with gr.Row(): gr.Markdown(translations["credits"].format(author=codecs.decode("uggcf://tvguho.pbz/CunzUhlauNau16", "rot13"), applio=codecs.decode("uggcf://tvguho.pbz/VNUvfcnab/Nccyvb/gerr/znva?gno=ernqzr-bi-svyr", "rot13"), ai_hispano=codecs.decode("uggcf://tvguho.pbz/VNUvfcnab", "rot13"), rvc_webui=codecs.decode("uggcf://tvguho.pbz/EIP-Cebwrpg/Ergevriny-onfrq-Ibvpr-Pbairefvba-JroHV?gno=ernqzr-bi-svyr", "rot13"), rvc_boss=codecs.decode("uggcf://tvguho.pbz/EIP-Obff", "rot13"), python_audio_separator=codecs.decode("uggcf://tvguho.pbz/abznqxnenbxr/clguba-nhqvb-frcnengbe?gno=ernqzr-bi-svyr", "rot13"), andrew_beveridge=codecs.decode("uggcf://tvguho.pbz/orirenqo", "rot13"))) print(translations["set_lang"].format(lang=configs["language"])) for i in range(configs["num_of_restart"]): try: app.queue().launch( favicon_path=os.path.join("assets", "miku.png"), server_name=server_name, server_port=port, show_error=show_error, inbrowser=False, share=share ) break except OSError: port -= 1 except Exception as e: raise RuntimeError(translations["error_occurred"].format(e=e))