diff --git "a/main/app/app.py" "b/main/app/app.py" --- "a/main/app/app.py" +++ "b/main/app/app.py" @@ -1,2400 +1,2400 @@ -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="📱 Vietnamese-RVC GUI BY ANH", theme=theme) as app: - gr.HTML(translations["display_title"]) - with gr.Row(): - gr.Markdown(translations["rick_roll"].format(rickroll=codecs.decode('uggcf://jjj.lbhghor.pbz/jngpu?i=qDj4j9JtKpD', 'rot13'))) - with gr.Row(): - gr.Markdown(translations["terms_of_use"]) - with gr.Row(): - gr.Markdown(translations["exemption"]) - - with gr.Tabs(): - 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: +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="📱 Vietnamese-RVC GUI BY ANH", theme=theme) as app: + gr.HTML(translations["display_title"]) + with gr.Row(): + gr.Markdown(translations["rick_roll"].format(rickroll=codecs.decode('uggcf://jjj.lbhghor.pbz/jngpu?i=qDj4j9JtKpD', 'rot13'))) + with gr.Row(): + gr.Markdown(translations["terms_of_use"]) + with gr.Row(): + gr.Markdown(translations["exemption"]) + + with gr.Tabs(): + 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)) \ No newline at end of file