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| import os | |
| import re | |
| import sys | |
| import json | |
| import torch | |
| import codecs | |
| import yt_dlp | |
| import shutil | |
| import zipfile | |
| import logging | |
| import platform | |
| import edge_tts | |
| import requests | |
| import warnings | |
| import threading | |
| import gradio as gr | |
| import pandas as pd | |
| from time import sleep | |
| from datetime import datetime | |
| from pydub import AudioSegment | |
| from subprocess import Popen, run | |
| from collections import OrderedDict | |
| from multiprocessing import cpu_count | |
| now_dir = os.getcwd() | |
| sys.path.append(now_dir) | |
| from main.configs.config import Config | |
| from main.tools import gdown, meganz, mediafire, pixeldrain | |
| logging.getLogger("wget").setLevel(logging.WARNING) | |
| logging.getLogger("httpx").setLevel(logging.WARNING) | |
| logging.getLogger("uvicorn").setLevel(logging.WARNING) | |
| logging.getLogger("httpcore").setLevel(logging.WARNING) | |
| logging.getLogger("gradio").setLevel(logging.ERROR) | |
| warnings.filterwarnings("ignore", category=UserWarning) | |
| warnings.filterwarnings("ignore", category=FutureWarning) | |
| config = Config() | |
| python = sys.executable | |
| translations = config.translations | |
| model_name = [] | |
| index_path = [] | |
| pretrainedD = [] | |
| pretrainedG = [] | |
| models = {} | |
| model_options = {} | |
| miku_image = codecs.decode("uggcf://uhttvatsnpr.pb/NauC/Pbyno_EIP_Cebwrpg_2/erfbyir/znva/zvxh.cat", "rot13") | |
| model_search_csv = codecs.decode("uggcf://qbpf.tbbtyr.pbz/fcernqfurrgf/q/1gNHnDeRULtEfz1Yieaw14USUQjWJy0Oq9k0DrCrjApb/rkcbeg?sbezng=pfi&tvq=1977693859", "rot13") | |
| model_search_api = codecs.decode("rlWuoTpvBvWVHmV1AvVfVaE5pPV6VxcKIPW9.rlWcp3ZvBvWmqKOuLzSmMFVfVaWyMvV6VzAdqTMkrzczMTygM3O2pUqbrzk2Vvjvpz9fMFV6VzSho24vYPWcLKDvBwR3ZwL5ZwLkZmDfVzI4pPV6ZwN0ZwHjZwRmAU0.BlQKyuiU6Q-VfUvJuCNTHgfCTTHiJDlaskHrDjsLGbR", "rot13") | |
| pretrained_json = codecs.decode("uggcf://uhttvatsnpr.pb/NauC/Pbyno_EIP_Cebwrpg_2/enj/znva/cergenva_pubvprf.wfba", "rot13") | |
| hugging_face_codecs = codecs.decode("uggcf://uhttvatsnpr.pb", "rot13") | |
| pretrained_v1_link = codecs.decode("uggcf://uhttvatsnpr.pb/VNUvfcnab/Nccyvb/erfbyir/znva/Erfbheprf/cergenvarq_i1/", "rot13") | |
| pretrained_v2_link = codecs.decode("uggcf://uhttvatsnpr.pb/yw1995/IbvprPbairefvbaJroHV/erfbyir/znva/cergenvarq_i2/", "rot13") | |
| configs_json = os.path.join("main", "configs", "config.json") | |
| with open(configs_json, "r") as f: | |
| configs = json.load(f) | |
| theme = configs["theme"] | |
| server_name = configs["server_name"] | |
| port = configs["app_port"] | |
| show_error = configs["app_show_error"] | |
| share = configs["share"] | |
| tts_voice = configs["tts_voice"] | |
| if not theme: theme = "NoCrypt/miku" | |
| if not server_name: server_name = "0.0.0.0" | |
| if not port: port = 7860 | |
| if not tts_voice: tts_voice = ["vi-VN-HoaiMyNeural", "vi-VN-NamMinhNeural"] | |
| if not os.path.exists(os.path.join("assets", "miku.png")): run(["wget", "-q", "--show-progress", "--no-check-certificate", miku_image, "-P", os.path.join("assets")], check=True) | |
| for model in os.listdir(os.path.join("assets", "weights")): | |
| if model.endswith(".pth") and not model.startswith("G_") and not model.startswith("D_"): model_name.append(model) | |
| for root, _, files in os.walk(os.path.join("assets", "logs"), topdown=False): | |
| for name in files: | |
| if name.endswith(".index"): index_path.append(os.path.join(root, name)) | |
| for model in os.listdir(os.path.join("assets", "model", "pretrained_custom")): | |
| if model.endswith(".pth") and "D" in model: pretrainedD.append(model) | |
| if model.endswith(".pth") and "G" in model: pretrainedG.append(model) | |
| if os.path.exists("spreadsheet.csv"): cached_data = pd.read_csv("spreadsheet.csv") | |
| else: | |
| cached_data = pd.read_csv(model_search_csv) | |
| cached_data.to_csv("spreadsheet.csv", index=False) | |
| for _, row in cached_data.iterrows(): | |
| filename = row['Filename'] | |
| url = None | |
| for value in row.values: | |
| if isinstance(value, str) and "huggingface" in value: | |
| url = value | |
| break | |
| if url: models[filename] = url | |
| def get_number_of_gpus(): | |
| return "-".join(map(str, range(torch.cuda.device_count()))) if torch.cuda.is_available() else "-" | |
| def get_gpu_info(): | |
| ngpu = torch.cuda.device_count() | |
| gpu_infos = [] | |
| if torch.cuda.is_available() or ngpu != 0: | |
| for i in range(ngpu): | |
| gpu_name = torch.cuda.get_device_name(i) | |
| mem = int(torch.cuda.get_device_properties(i).total_memory / 1024 / 1024 / 1024 + 0.4) | |
| gpu_infos.append(f"{i}: {gpu_name} ({mem} GB)") | |
| return "\n".join(gpu_infos) if len(gpu_infos) > 0 else translations["no_support_gpu"] | |
| def change_choices_pretrained(): | |
| pretrainedD = [] | |
| pretrainedG = [] | |
| for model in os.listdir(os.path.join("assets", "model", "pretrained_custom")): | |
| if model.endswith(".pth") and "D" in model: pretrainedD.append(model) | |
| for model in os.listdir(os.path.join("assets", "model", "pretrained_custom")): | |
| if model.endswith(".pth") and "G" in model: pretrainedG.append(model) | |
| return [{"choices": sorted(pretrainedD), "__type__": "update"}, {"choices": sorted(pretrainedG), "__type__": "update"}] | |
| def change_choices(): | |
| model_name = [] | |
| index_path = [] | |
| for name in os.listdir(os.path.join("assets", "weights")): | |
| if name.endswith(".pth"): model_name.append(name) | |
| for root, _, files in os.walk(os.path.join("assets", "logs"), topdown=False): | |
| for name in files: | |
| if name.endswith(".index"): index_path.append(f"{root}/{name}") | |
| return [{"choices": sorted(model_name), "__type__": "update"}, {"choices": sorted(index_path), "__type__": "update"}] | |
| def get_index(model): | |
| return {"value": next((f for f in [os.path.join(root, name) for root, _, files in os.walk(os.path.join("assets", "logs"), topdown=False) for name in files if name.endswith(".index")] if model.split(".")[0] in f), ""), "__type__": "update"} | |
| def visible_1(value): | |
| return {"visible": value, "__type__": "update"} | |
| def valueFalse_interactive1(inp): | |
| return {"value": False, "interactive": inp, "__type__": "update"} | |
| def valueFalse_interactive2(inp1, inp2): | |
| return {"value": False, "interactive": inp1 and inp2, "__type__": "update"} | |
| def valueFalse_visible1(inp1): | |
| return {"value": False, "visible": inp1, "__type__": "update"} | |
| def valueEmpty_visible1(inp1): | |
| return {"value": "", "visible": inp1, "__type__": "update"} | |
| def refesh_audio(): | |
| paths_for_files = [os.path.abspath(os.path.join("audios", f)) for f in os.listdir("audios") if os.path.splitext(f)[1] in ('.mp3', '.wav', '.flac', '.ogg', '.m4a')] | |
| return {"value": "" if len(list(f for f in os.listdir("audios") if os.path.splitext(f)[1] in ('.mp3', '.wav', '.flac', '.ogg', '.m4a'))) < 1 else paths_for_files[0], "choices": [] if len(list(f for f in os.listdir("audios") if os.path.splitext(f)[1] in ('.mp3', '.wav', '.flac', '.ogg', '.m4a'))) < 1 else paths_for_files, "__type__": "update"} | |
| def backing_change(backing, merge): | |
| if backing or merge: return {"value": False, "interactive": False, "__type__": "update"} | |
| elif not backing or not merge: return {"interactive": True, "__type__": "update"} | |
| def model_separator_change(mdx): | |
| if not mdx: choices = ["HT-Normal", "HT-Tuned", "HD_MMI", "HT_6S"] | |
| else: choices = ["Main_340", "Main_390", "Main_406", "Main_427", "Main_438", "Inst_full_292", "Inst_HQ_1", "Inst_HQ_2", "Inst_HQ_3", "Inst_HQ_4", "Kim_Vocal_1", "Kim_Vocal_2", "Kim_Inst", "Inst_187_beta", "Inst_82_beta", "Inst_90_beta", "Voc_FT", "Crowd_HQ", "Inst_1", "Inst_2", "Inst_3", "MDXNET_1_9703", "MDXNET_2_9682", "MDXNET_3_9662", "Inst_Main", "MDXNET_Main", "MDXNET_9482"] | |
| return {"value": choices[0], "choices": choices, "__type__": "update"} | |
| def hoplength_show(method, hybrid_method=None): | |
| if method in ["crepe-tiny", "crepe", "fcpe"]: visible = True | |
| elif method == "hybrid": | |
| methods_str = re.search("hybrid\[(.+)\]", hybrid_method) | |
| if methods_str: methods = [method.strip() for method in methods_str.group(1).split("+")] | |
| visible = methods[0] in ["crepe-tiny", "crepe", "fcpe"] or methods[1] in ["crepe-tiny", "crepe", "fcpe"] | |
| else: visible = False | |
| return {"visible": visible, "__type__": "update"} | |
| def process_input(file_path): | |
| with open(file_path, "r", encoding="utf-8") as file: | |
| file_contents = file.read() | |
| gr.Info(translations["upload_success"].format(name=translations["text"])) | |
| return file_contents | |
| def download_change(select): | |
| selects = [False]*10 | |
| if select == translations["download_url"]: selects[0] = selects[1] = selects[2] = True | |
| elif select == translations["download_from_csv"]: selects[3] = selects[4] = True | |
| elif select == translations["download_from_applio"]: selects[5] = selects[6] = True | |
| elif select == translations["upload"]: selects[9] = True | |
| else: gr.Warning(translations["option_not_valid"]) | |
| return [{"visible": selects[i], "__type__": "update"} for i in range(len(selects))] | |
| def fetch_pretrained_data(): | |
| response = requests.get(pretrained_json) | |
| response.raise_for_status() | |
| return response.json() | |
| def download_pretrained_change(select): | |
| selects = [False]*8 | |
| if select == translations["download_url"]: selects[0] = selects[1] = selects[2] = True | |
| elif select == translations["list_model"]: selects[3] = selects[4] = selects[5] = True | |
| elif select == translations["upload"]: selects[6] = selects[7] = True | |
| else: gr.Warning(translations["option_not_valid"]) | |
| return [{"visible": selects[i], "__type__": "update"} for i in range(len(selects))] | |
| def update_sample_rate_dropdown(model): | |
| data = fetch_pretrained_data() | |
| if model != translations["success"]: return {"choices": list(data[model].keys()), "value": list(data[model].keys())[0], "__type__": "update"} | |
| def if_done(done, p): | |
| while 1: | |
| if p.poll() is None: sleep(0.5) | |
| else: break | |
| done[0] = True | |
| def restart_app(): | |
| global app | |
| gr.Info(translations["30s"]) | |
| if platform.system() == "Windows": os.system("cls") | |
| else: os.system("clear") | |
| app.close() | |
| os.system(f"{python} {os.path.join(now_dir, 'main', 'app', 'app.py')}") | |
| def change_language(lang): | |
| with open(configs_json, "r") as f: | |
| configs = json.load(f) | |
| configs["language"] = lang | |
| with open(configs_json, "w") as f: | |
| json.dump(configs, f, indent=4) | |
| def change_theme(theme): | |
| with open(configs_json, "r") as f: | |
| configs = json.load(f) | |
| configs["theme"] = theme | |
| with open(configs_json, "w") as f: | |
| json.dump(configs, f, indent=4) | |
| def change_fp(fp): | |
| gr.Info(translations["fp_select"]) | |
| config.set_precision(fp) | |
| gr.Info(translations["fp_select_2"].format(fp=fp)) | |
| def pretrained_selector(pitch_guidance): | |
| if pitch_guidance: | |
| return { | |
| 32000: ( | |
| "f0G32k.pth", | |
| "f0D32k.pth", | |
| ), | |
| 40000: ( | |
| "f0G40k.pth", | |
| "f0D40k.pth", | |
| ), | |
| 48000: ( | |
| "f0G48k.pth", | |
| "f0D48k.pth", | |
| ), | |
| } | |
| else: | |
| return { | |
| 32000: ( | |
| "G32k.pth", | |
| "D32k.pth", | |
| ), | |
| 40000: ( | |
| "G40k.pth", | |
| "D40k.pth", | |
| ), | |
| 48000: ( | |
| "G48k.pth", | |
| "D48k.pth", | |
| ), | |
| } | |
| def zip_file(name, pth, index): | |
| pth_path = os.path.join("assets", "weights", pth) | |
| if not pth or not os.path.exists(pth_path) or not pth.endswith(".pth"): return gr.Warning(translations["provide_file"].format(filename=translations["model"])) | |
| if not index or not os.path.exists(index) or not index.endswith(".pth"): return gr.Warning(translations["provide_file"].format(filename=translations["index"])) | |
| zip_file_path = os.path.join("assets", name + ".zip") | |
| gr.Info(translations["start"].format(start=translations["zip"])) | |
| with zipfile.ZipFile(zip_file_path, 'w') as zipf: | |
| zipf.write(pth_path, os.path.basename(pth_path)) | |
| zipf.write(index, os.path.basename(index)) | |
| gr.Info(translations["success"]) | |
| return zip_file_path | |
| def search_models(name): | |
| gr.Info(translations["start"].format(start="search")) | |
| url = f"https://cjtfqzjfdimgpvpwhzlv.supabase.co/rest/v1/models?name=ilike.%25{name}%25&order=created_at.desc&limit=15" | |
| response = requests.get(url, headers={"apikey": model_search_api}) | |
| data = response.json() | |
| if len(data) == 0: | |
| gr.Info(translations["not_found"].format(name=name)) | |
| return [None]*2 | |
| else: | |
| model_options.clear() | |
| model_options.update({item["name"] + " " + item["epochs"] + "e": item["link"] for item in data}) | |
| gr.Info(translations["found"].format(results=len(model_options))) | |
| return [{"value": "", "choices": model_options, "interactive": True, "visible": True, "__type__": "update"}, {"value": translations["downloads"], "visible": True, "__type__": "update"}] | |
| def move_files_from_directory(src_dir, dest_weights, dest_logs, model_name): | |
| for root, _, files in os.walk(src_dir): | |
| for file in files: | |
| file_path = os.path.join(root, file) | |
| if file.endswith(".index"): | |
| model_log_dir = os.path.join(dest_logs, model_name) | |
| os.makedirs(model_log_dir, exist_ok=True) | |
| filepath = os.path.join(model_log_dir, file.replace(' ', '_').replace('(', '').replace(')', '').replace('[', '').replace(']', '').strip()) | |
| if os.path.exists(filepath): os.remove(filepath) | |
| shutil.move(file_path, filepath) | |
| elif file.endswith(".pth") and "G_" not in file and "D_" not in file: | |
| pth_path = os.path.join(dest_weights, model_name + ".pth") | |
| if os.path.exists(pth_path): os.remove(pth_path) | |
| shutil.move(file_path, pth_path) | |
| def download_url(url): | |
| if not url: return gr.Warning(translations["provide_url"]) | |
| if not os.path.exists("audios"): os.makedirs("audios", exist_ok=True) | |
| audio_output = os.path.join("audios", "audio.wav") | |
| if os.path.exists(audio_output): os.remove(audio_output) | |
| with warnings.catch_warnings(): | |
| warnings.simplefilter("ignore") | |
| ydl_opts = { | |
| 'format': 'bestaudio/best', | |
| 'outtmpl': os.path.join("audios", "audio"), | |
| 'postprocessors': [{ | |
| 'key': 'FFmpegExtractAudio', | |
| 'preferredcodec': 'wav', | |
| 'preferredquality': '192', | |
| }], | |
| 'noplaylist': True, | |
| 'verbose': False, | |
| } | |
| gr.Info(translations["start"].format(start=translations["download_music"])) | |
| with yt_dlp.YoutubeDL(ydl_opts) as ydl: | |
| ydl.download([url]) | |
| gr.Info(translations["success"]) | |
| return [audio_output, audio_output, translations["success"]] | |
| def download_model(url=None, model=None): | |
| if not url: return gr.Warning(translations["provide_url"]) | |
| if not model: return gr.Warning(translations["provide_name_is_save"]) | |
| model = model.replace('.pth', '').replace('.index', '').replace('.zip', '').replace(' ', '_').replace('(', '').replace(')', '').replace('[', '').replace(']', '').strip() | |
| url = url.replace('/blob/', '/resolve/').replace('?download=true', '').strip() | |
| download_dir = os.path.join("download_model") | |
| weights_dir = os.path.join("assets", "weights") | |
| logs_dir = os.path.join("assets", "logs") | |
| if not os.path.exists(download_dir): os.makedirs(download_dir, exist_ok=True) | |
| if not os.path.exists(weights_dir): os.makedirs(weights_dir, exist_ok=True) | |
| if not os.path.exists(logs_dir): os.makedirs(logs_dir, exist_ok=True) | |
| try: | |
| gr.Info(translations["start"].format(start=translations["download"])) | |
| if url.endswith('.pth'): | |
| run(["wget", "-q", "--show-progress", "--no-check-certificate", url, "-O", os.path.join(weights_dir, f"{model}.pth")], check=True) | |
| elif url.endswith('.index'): | |
| model_log_dir = os.path.join(logs_dir, model) | |
| os.makedirs(model_log_dir, exist_ok=True) | |
| run(["wget", "-q", "--show-progress", "--no-check-certificate", url, "-O", os.path.join(model_log_dir, f"{model}.index")], check=True) | |
| elif url.endswith('.zip'): | |
| dest_path = os.path.join(download_dir, model + ".zip") | |
| run(["wget", "-q", "--show-progress", "--no-check-certificate", url, "-O", dest_path], check=True) | |
| shutil.unpack_archive(dest_path, download_dir) | |
| move_files_from_directory(download_dir, weights_dir, logs_dir, model) | |
| else: | |
| if 'drive.google.com' in url: | |
| file_id = None | |
| if '/file/d/' in url: file_id = url.split('/d/')[1].split('/')[0] | |
| elif 'open?id=' in url: file_id = url.split('open?id=')[1].split('/')[0] | |
| if file_id: | |
| file = gdown.gdown_download(id=file_id, output_dir=download_dir) | |
| if file.endswith('.zip'): shutil.unpack_archive(os.path.join(download_dir, file), download_dir) | |
| move_files_from_directory(download_dir, weights_dir, logs_dir, model) | |
| elif 'mega.nz' in url: | |
| meganz.mega_download_url(url, download_dir) | |
| file_download = next((f for f in os.listdir(download_dir)), None) | |
| if file_download.endswith(".zip"): shutil.unpack_archive(os.path.join(download_dir, file_download), download_dir) | |
| move_files_from_directory(download_dir, weights_dir, logs_dir, model) | |
| elif 'mediafire.com' in url: | |
| file = mediafire.Mediafire_Download(url, download_dir) | |
| if file.endswith('.zip'): shutil.unpack_archive(file, download_dir) | |
| move_files_from_directory(download_dir, weights_dir, logs_dir, model) | |
| elif 'pixeldrain.com' in url: | |
| file = pixeldrain.pixeldrain(url, download_dir) | |
| if file.endswith('.zip'): shutil.unpack_archive(file, download_dir) | |
| move_files_from_directory(download_dir, weights_dir, logs_dir, model) | |
| else: | |
| gr.Warning(translations["not_support_url"]) | |
| return translations["not_support_url"] | |
| gr.Info(translations["success"]) | |
| return translations["success"] | |
| except Exception as e: | |
| gr.Error(message=translations["error_occurred"].format(e=e)) | |
| print(translations["error_occurred"].format(e=e)) | |
| return translations["error_occurred"].format(e=e) | |
| finally: | |
| shutil.rmtree(download_dir, ignore_errors=True) | |
| def extract_name_model(filename): | |
| match = re.search(r"([A-Za-z]+)(?=_v|\.|$)", filename) | |
| return match.group(1) if match else None | |
| def save_drop_model(dropbox): | |
| weight_folder = os.path.join("assets", "weights") | |
| logs_folder = os.path.join("assets", "logs") | |
| save_model_temp = os.path.join("save_model_temp") | |
| if not os.path.exists(weight_folder): os.makedirs(weight_folder, exist_ok=True) | |
| if not os.path.exists(logs_folder): os.makedirs(logs_folder, exist_ok=True) | |
| if not os.path.exists(save_model_temp): os.makedirs(save_model_temp, exist_ok=True) | |
| shutil.move(dropbox, save_model_temp) | |
| try: | |
| file_name = os.path.basename(dropbox) | |
| if file_name.endswith(".pth") and file_name.endswith(".index"): gr.Warning(translations["not_model"]) | |
| else: | |
| if file_name.endswith(".zip"): | |
| shutil.unpack_archive(os.path.join(save_model_temp, file_name), save_model_temp) | |
| move_files_from_directory(save_model_temp, weight_folder, logs_folder, file_name.replace(".zip", "")) | |
| elif file_name.endswith(".pth"): | |
| output_file = os.path.join(weight_folder, file_name) | |
| if os.path.exists(output_file): os.remove(output_file) | |
| shutil.move(os.path.join(save_model_temp, file_name), output_file) | |
| elif file_name.endswith(".index"): | |
| model_logs = os.path.join(logs_folder, extract_name_model(file_name)) | |
| if not os.path.exists(model_logs): os.makedirs(model_logs, exist_ok=True) | |
| shutil.move(os.path.join(save_model_temp, file_name), model_logs) | |
| else: | |
| gr.Warning(translations["unable_analyze_model"]) | |
| return None | |
| gr.Info(translations["upload_success"].format(name=translations["model"])) | |
| return None | |
| except Exception as e: | |
| gr.Error(message=translations["error_occurred"].format(e=e)) | |
| print(translations["error_occurred"].format(e=e)) | |
| return None | |
| finally: | |
| shutil.rmtree(save_model_temp, ignore_errors=True) | |
| def download_pretrained_model(choices, model, sample_rate): | |
| if choices == translations["list_model"]: | |
| data = fetch_pretrained_data() | |
| paths = data[model][sample_rate] | |
| pretraineds_custom_path = os.path.join("assets", "model", "pretrained_custom") | |
| if not os.path.exists(pretraineds_custom_path): os.makedirs(pretraineds_custom_path, exist_ok=True) | |
| d_url = hugging_face_codecs + f"/{paths['D']}" | |
| g_url = hugging_face_codecs + f"/{paths['G']}" | |
| gr.Info(translations["download_pretrain"]) | |
| run(["wget", "-q", "--show-progress", "--no-check-certificate", d_url.replace('/blob/', '/resolve/').replace('?download=true', '').strip(), "-P", os.path.join(pretraineds_custom_path)], check=True) | |
| run(["wget", "-q", "--show-progress", "--no-check-certificate", g_url.replace('/blob/', '/resolve/').replace('?download=true', '').strip(), "-P", os.path.join(pretraineds_custom_path)], check=True) | |
| gr.Info(translations["success"]) | |
| return translations["success"] | |
| elif choices == translations["download_url"]: | |
| if not model: return gr.Warning(translations["provide_pretrain"].format(dg="D")) | |
| if not sample_rate: return gr.Warning(translations["provide_pretrain"].format(dg="G")) | |
| gr.Info(translations["download_pretrain"]) | |
| run(["wget", "-q", "--show-progress", "--no-check-certificate", model, "-P", os.path.join(pretraineds_custom_path)], check=True) | |
| run(["wget", "-q", "--show-progress", "--no-check-certificate", sample_rate, "-P", os.path.join(pretraineds_custom_path)], check=True) | |
| gr.Info(translations["success"]) | |
| return translations["success"] | |
| def hubert_download(hubert): | |
| if not hubert: | |
| gr.Warning(translations["provide_hubert"]) | |
| return translations["provide_hubert"] | |
| run(["wget", "-q", "--show-progress", "--no-check-certificate", hubert.replace('/blob/', '/resolve/').replace('?download=true', '').strip(), "-P", os.path.join("assets", "model", "embedders")], check=True) | |
| gr.Info(translations["success"]) | |
| return translations["success"] | |
| def fushion_model(name, pth_1, pth_2, ratio): | |
| if not name: | |
| gr.Warning(translations["provide_name_is_save"]) | |
| return [translations["provide_name_is_save"], None] | |
| if not name.endswith(".pth"): name = name + ".pth" | |
| if not pth_1 or not os.path.exists(pth_1) or not pth_1.endswith(".pth"): | |
| gr.Warning(translations["provide_file"].format(filename=translations["model"] + " 1")) | |
| return [translations["provide_file"].format(filename=translations["model"] + " 1"), None] | |
| if not pth_2 or not os.path.exists(pth_2) or not pth_1.endswith(".pth"): | |
| gr.Warning(translations["provide_file"].format(filename=translations["model"] + " 2")) | |
| return [translations["provide_file"].format(filename=translations["model"] + " 2"), None] | |
| def extract(ckpt): | |
| a = ckpt["model"] | |
| opt = OrderedDict() | |
| opt["weight"] = {} | |
| for key in a.keys(): | |
| if "enc_q" in key: continue | |
| opt["weight"][key] = a[key] | |
| return opt | |
| try: | |
| ckpt1 = torch.load(pth_1, map_location="cpu") | |
| ckpt2 = torch.load(pth_2, map_location="cpu") | |
| if ckpt1["sr"] != ckpt2["sr"]: | |
| gr.Warning(translations["sr_not_same"]) | |
| return [translations["sr_not_same"], None] | |
| cfg = ckpt1["config"] | |
| cfg_f0 = ckpt1["f0"] | |
| cfg_version = ckpt1["version"] | |
| cfg_sr = ckpt1["sr"] | |
| ckpt1 = extract(ckpt1) if "model" in ckpt1 else ckpt1["weight"] | |
| ckpt2 = extract(ckpt2) if "model" in ckpt2 else ckpt2["weight"] | |
| if sorted(list(ckpt1.keys())) != sorted(list(ckpt2.keys())): | |
| gr.Warning(translations["architectures_not_same"]) | |
| return [translations["architectures_not_same"], None] | |
| gr.Info(translations["start"].format(start=translations["fushion_model"])) | |
| opt = OrderedDict() | |
| opt["weight"] = {} | |
| for key in ckpt1.keys(): | |
| if key == "emb_g.weight" and ckpt1[key].shape != ckpt2[key].shape: | |
| min_shape0 = min(ckpt1[key].shape[0], ckpt2[key].shape[0]) | |
| opt["weight"][key] = (ratio * (ckpt1[key][:min_shape0].float()) + (1 - ratio) * (ckpt2[key][:min_shape0].float())).half() | |
| else: opt["weight"][key] = (ratio * (ckpt1[key].float()) + (1 - ratio) * (ckpt2[key].float())).half() | |
| opt["config"] = cfg | |
| opt["sr"] = cfg_sr | |
| opt["f0"] = cfg_f0 | |
| opt["version"] = cfg_version | |
| opt["infos"] = translations["model_fushion_info"].format(name=name, pth_1=pth_1, pth_2=pth_2, ratio=ratio) | |
| output_model = os.path.join("assets", "weights") | |
| if not os.path.exists(output_model): os.makedirs(output_model, exist_ok=True) | |
| torch.save(opt, os.path.join(output_model, name)) | |
| gr.Info(translations["success"]) | |
| return [translations["success"], os.path.join(output_model, name)] | |
| except Exception as e: | |
| gr.Error(message=translations["error_occurred"].format(e=e)) | |
| print(translations["error_occurred"].format(e=e)) | |
| return [e, None] | |
| def model_info(path): | |
| if not path or not os.path.exists(path) or os.path.isdir(path) or not path.endswith(".pth"): return gr.Warning(translations["provide_file"].format(filename=translations["model"])) | |
| def prettify_date(date_str): | |
| if date_str == translations["not_found_create_time"]: return None | |
| try: | |
| return datetime.strptime(date_str, "%Y-%m-%dT%H:%M:%S.%f").strftime("%Y-%m-%d %H:%M:%S") | |
| except ValueError: | |
| return translations["format_not_valid"] | |
| model_data = torch.load(path, map_location=torch.device("cpu")) | |
| gr.Info(translations["read_info"]) | |
| epochs = model_data.get("epoch", None) | |
| if epochs is None: | |
| epochs = model_data.get("info", None) | |
| epoch = epochs.replace("epoch", "").replace("e", "").isdigit() | |
| if epoch and epochs is None: epochs = translations["not_found"].format(name=translations["epoch"]) | |
| steps = model_data.get("step", translations["not_found"].format(name=translations["step"])) | |
| sr = model_data.get("sr", translations["not_found"].format(name=translations["sr"])) | |
| f0 = model_data.get("f0", translations["not_found"].format(name=translations["f0"])) | |
| version = model_data.get("version", translations["not_found"].format(name=translations["version"])) | |
| creation_date = model_data.get("creation_date", translations["not_found_create_time"]) | |
| model_hash = model_data.get("model_hash", translations["not_found"].format(name="model_hash")) | |
| pitch_guidance = translations["trained_f0"] if f0 else translations["not_f0"] | |
| creation_date_str = prettify_date(creation_date) if creation_date else translations["not_found_create_time"] | |
| model_name = model_data.get("model_name", translations["unregistered"]) | |
| model_author = model_data.get("author", translations["not_author"]) | |
| gr.Info(translations["success"]) | |
| return translations["model_info"].format(model_name=model_name, model_author=model_author, epochs=epochs, steps=steps, version=version, sr=sr, pitch_guidance=pitch_guidance, model_hash=model_hash, creation_date_str=creation_date_str) | |
| def audio_effects(input_path, output_path, resample, resample_sr, chorus_depth, chorus_rate, chorus_mix, chorus_delay, chorus_feedback, distortion_drive, reverb_room_size, reverb_damping, reverb_wet_level, reverb_dry_level, reverb_width, reverb_freeze_mode, pitch_shift, delay_seconds, delay_feedback, delay_mix, compressor_threshold, compressor_ratio, compressor_attack_ms, compressor_release_ms, limiter_threshold, limiter_release, gain_db, bitcrush_bit_depth, clipping_threshold, phaser_rate_hz, phaser_depth, phaser_centre_frequency_hz, phaser_feedback, phaser_mix, bass_boost_db, bass_boost_frequency, treble_boost_db, treble_boost_frequency, fade_in_duration, fade_out_duration, export_format, chorus, distortion, reverb, delay, compressor, limiter, gain, bitcrush, clipping, phaser, treble_bass_boost, fade_in_out): | |
| if not input_path or not os.path.exists(input_path) or os.path.isdir(input_path): | |
| gr.Warning(translations["input_not_valid"]) | |
| return None | |
| if not output_path: | |
| gr.Warning(translations["output_not_valid"]) | |
| return None | |
| if os.path.isdir(output_path): output_path = os.path.join(output_path, f"audio_effects.{export_format}") | |
| output_dir = os.path.dirname(output_path) | |
| output_dir = output_path if not output_dir else output_dir | |
| if not os.path.exists(output_dir): os.makedirs(output_dir, exist_ok=True) | |
| if os.path.exists(output_path): os.remove(output_path) | |
| gr.Info(translations["start"].format(start=translations["apply_effect"])) | |
| pitchshift = pitch_shift != 0 | |
| cmd = f'{python} main/inference/audio_effects.py --input_path "{input_path}" --output_path "{output_path}" --resample {resample} --resample_sr {resample_sr} --chorus_depth {chorus_depth} --chorus_rate {chorus_rate} --chorus_mix {chorus_mix} --chorus_delay {chorus_delay} --chorus_feedback {chorus_feedback} --drive_db {distortion_drive} --reverb_room_size {reverb_room_size} --reverb_damping {reverb_damping} --reverb_wet_level {reverb_wet_level} --reverb_dry_level {reverb_dry_level} --reverb_width {reverb_width} --reverb_freeze_mode {reverb_freeze_mode} --pitch_shift {pitch_shift} --delay_seconds {delay_seconds} --delay_feedback {delay_feedback} --delay_mix {delay_mix} --compressor_threshold {compressor_threshold} --compressor_ratio {compressor_ratio} --compressor_attack_ms {compressor_attack_ms} --compressor_release_ms {compressor_release_ms} --limiter_threshold {limiter_threshold} --limiter_release {limiter_release} --gain_db {gain_db} --bitcrush_bit_depth {bitcrush_bit_depth} --clipping_threshold {clipping_threshold} --phaser_rate_hz {phaser_rate_hz} --phaser_depth {phaser_depth} --phaser_centre_frequency_hz {phaser_centre_frequency_hz} --phaser_feedback {phaser_feedback} --phaser_mix {phaser_mix} --bass_boost_db {bass_boost_db} --bass_boost_frequency {bass_boost_frequency} --treble_boost_db {treble_boost_db} --treble_boost_frequency {treble_boost_frequency} --fade_in_duration {fade_in_duration} --fade_out_duration {fade_out_duration} --export_format {export_format} --chorus {chorus} --distortion {distortion} --reverb {reverb} --pitchshift {pitchshift} --delay {delay} --compressor {compressor} --limiter {limiter} --gain {gain} --bitcrush {bitcrush} --clipping {clipping} --phaser {phaser} --treble_bass_boost {treble_bass_boost} --fade_in_out {fade_in_out}' | |
| os.system(cmd) | |
| gr.Info(translations["success"]) | |
| return output_path | |
| async def TTS(prompt, voice, speed, output): | |
| if not prompt: | |
| gr.Warning(translations["enter_the_text"]) | |
| return None | |
| if not voice: | |
| gr.Warning(translations["choose_voice"]) | |
| return None | |
| if not output: | |
| gr.Warning(translations["output_not_valid"]) | |
| return None | |
| if os.path.isdir(output): output = os.path.join(output, f"output_tts.wav") | |
| gr.Info(translations["convert"].format(name=translations["text"])) | |
| output_dir = os.path.dirname(output) | |
| output_dir = output if not output_dir else output_dir | |
| if not os.path.exists(output_dir): os.makedirs(output_dir, exist_ok=True) | |
| await edge_tts.Communicate(text=prompt, voice=voice, rate=f"+{speed}%" if speed >= 0 else f"{speed}%").save(output) | |
| gr.Info(translations["success"]) | |
| return output | |
| def separator_music(input, output_audio, format, shifts, segments_size, overlap, clean_audio, clean_strength, backing_denoise, separator_model, kara_model, backing, mdx, mdx_denoise, reverb, reverb_denoise, backing_reverb, hop_length, batch_size): | |
| output = os.path.dirname(output_audio) | |
| output = output_audio if not output else output | |
| if not input or not os.path.exists(input) or os.path.isdir(input): | |
| gr.Warning(translations["input_not_valid"]) | |
| return [None]*4 | |
| if not os.path.exists(output): | |
| gr.Warning(translations["output_not_valid"]) | |
| return [None]*4 | |
| gr.Info(translations["start"].format(start=translations["separator_music"])) | |
| cmd = f'{python} main/inference/separator_music.py --input_path "{input}" --output_path "{output}" --format {format} --shifts {shifts} --segments_size {segments_size} --overlap {overlap} --mdx_hop_length {hop_length} --mdx_batch_size {batch_size} --clean_audio {clean_audio} --clean_strength {clean_strength} --backing_denoise {backing_denoise} --kara_model {kara_model} --backing {backing} --mdx {mdx} --mdx_denoise {mdx_denoise} --reverb {reverb} --reverb_denoise {reverb_denoise} --backing_reverb {backing_reverb}' | |
| if separator_model == "HT-Normal" or separator_model == "HT-Tuned" or separator_model == "HD_MMI" or separator_model == "HT_6S": cmd += f' --demucs_model {separator_model}' | |
| else: cmd += f' --mdx_model {separator_model}' | |
| os.system(cmd) | |
| gr.Info(translations["success"]) | |
| if not os.path.exists(output): os.makedirs(output) | |
| original_output = os.path.join(output, f"Original_Vocals_No_Reverb.{format}") if reverb else os.path.join(output, f"Original_Vocals.{format}") | |
| instrument_output = os.path.join(output, f"Instruments.{format}") | |
| main_output = os.path.join(output, f"Main_Vocals_No_Reverb.{format}") if reverb else os.path.join(output, f"Main_Vocals.{format}") | |
| backing_output = os.path.join(output, f"Backing_Vocals_No_Reverb.{format}") if backing_reverb else os.path.join(output, f"Backing_Vocals.{format}") | |
| if backing: return [original_output, instrument_output, main_output, backing_output] | |
| else: return [original_output, instrument_output, None, None] | |
| def convert(pitch, filter_radius, index_rate, volume_envelope, protect, hop_length, f0_method, input_path, output_path, pth_path, index_path, f0_autotune, clean_audio, clean_strength, export_format, embedder_model, upscale_audio, resample_sr, batch_process, batch_size, split_audio, f0_autotune_strength): | |
| cmd = f'{python} main/inference/convert.py --pitch {pitch} --filter_radius {filter_radius} --index_rate {index_rate} --volume_envelope {volume_envelope} --protect {protect} --hop_length {hop_length} --f0_method {f0_method} --input_path "{input_path}" --output_path "{output_path}" --pth_path {pth_path} --index_path {index_path} --f0_autotune {f0_autotune} --clean_audio {clean_audio} --clean_strength {clean_strength} --export_format {export_format} --embedder_model {embedder_model} --upscale_audio {upscale_audio} --resample_sr {resample_sr} --batch_process {batch_process} --batch_size {batch_size} --split_audio {split_audio} --f0_autotune_strength {f0_autotune_strength}' | |
| os.system(cmd) | |
| def convert_audio(clean, upscale, autotune, use_audio, use_original, convert_backing, not_merge_backing, merge_instrument, pitch, clean_strength, model, index, index_rate, input, output, format, method, hybrid_method, hop_length, embedders, custom_embedders, resample_sr, filter_radius, volume_envelope, protect, batch_process, batch_size, split_audio, f0_autotune_strength): | |
| def get_audio_file(label): | |
| matching_files = [f for f in os.listdir("audios") if label in f] | |
| if not matching_files: return translations["notfound"] | |
| return os.path.join("audios", matching_files[0]) | |
| model_path = os.path.join("assets", "weights", model) | |
| if not use_audio: | |
| if merge_instrument or not_merge_backing or convert_backing or use_original: | |
| gr.Warning(translations["turn_on_use_audio"]) | |
| return [None]*5 | |
| if use_original: | |
| if convert_backing: | |
| gr.Warning(translations["turn_off_convert_backup"]) | |
| return [None]*5 | |
| elif not_merge_backing: | |
| gr.Warning(translations["turn_off_merge_backup"]) | |
| return [None]*5 | |
| if not model or not os.path.exists(model_path) or os.path.isdir(model_path) or not model.endswith(".pth"): | |
| gr.Warning(translations["provide_file"].format(filename=translations["model"])) | |
| return [None]*5 | |
| if not index or not os.path.exists(index) or os.path.isdir(index) or not index.endswith(".index"): | |
| gr.Warning(translations["provide_file"].format(filename=translations["index"])) | |
| return [None]*5 | |
| f0method = method if method != "hybrid" else hybrid_method | |
| embedder_model = embedders if embedders != "custom" else custom_embedders | |
| output_path = os.path.join("audios", f"Convert_Vocals.{format}") | |
| output_backing = os.path.join("audios", f"Convert_Backing.{format}") | |
| output_merge_backup = os.path.join("audios", f"Vocals+Backing.{format}") | |
| output_merge_instrument = os.path.join("audios", f"Vocals+Instruments.{format}") | |
| if use_audio: | |
| if os.path.exists("audios"): os.makedirs("audios", exist_ok=True) | |
| if os.path.exists(output_path): os.remove(output_path) | |
| if use_original: | |
| original_vocal = get_audio_file('Original_Vocals_No_Reverb.') | |
| if original_vocal == translations["notfound"]: original_vocal = get_audio_file('Original_Vocals.') | |
| if original_vocal == translations["notfound"]: | |
| gr.Warning(translations["not_found_original_vocal"]) | |
| return [None]*5 | |
| input_path = original_vocal | |
| else: | |
| main_vocal = get_audio_file('Main_Vocals_No_Reverb.') | |
| backing_vocal = get_audio_file('Backing_Vocals_No_Reverb.') | |
| if main_vocal == translations["notfound"]: main_vocal = get_audio_file('Main_Vocals.') | |
| if not not_merge_backing and backing_vocal == translations["notfound"]: backing_vocal = get_audio_file('Backing_Vocals.') | |
| if main_vocal == translations["notfound"]: | |
| gr.Warning(translations["not_found_main_vocal"]) | |
| return [None]*5 | |
| if not not_merge_backing and backing_vocal == translations["notfound"]: | |
| gr.Warning(translations["not_found_backing_vocal"]) | |
| return [None]*5 | |
| input_path = main_vocal | |
| backing_path = backing_vocal | |
| gr.Info(translations["convert_vocal"]) | |
| convert(pitch, filter_radius, index_rate, volume_envelope, protect, hop_length, f0method, input_path, output_path, model_path, index, autotune, clean, clean_strength, format, embedder_model, upscale, resample_sr, batch_process, batch_size, split_audio, f0_autotune_strength) | |
| gr.Info(translations["convert_success"]) | |
| if convert_backing: | |
| if os.path.exists(output_backing): os.remove(output_backing) | |
| gr.Info(translations["convert_backup"]) | |
| convert(pitch, filter_radius, index_rate, volume_envelope, protect, hop_length, f0method, backing_path, output_backing, model_path, index, autotune, clean, clean_strength, format, embedder_model, upscale, resample_sr, batch_process, batch_size, split_audio, f0_autotune_strength) | |
| gr.Info(translations["convert_backup_success"]) | |
| if not not_merge_backing and not use_original: | |
| backing_source = output_backing if convert_backing else backing_vocal | |
| if os.path.exists(output_merge_backup): os.remove(output_merge_backup) | |
| gr.Info(translations["merge_backup"]) | |
| AudioSegment.from_file(output_path).overlay(AudioSegment.from_file(backing_source)).export(output_merge_backup, format=format) | |
| gr.Info(translations["merge_success"]) | |
| if merge_instrument: | |
| vocals = output_merge_backup if not not_merge_backing and not use_original else output_path | |
| if os.path.exists(output_merge_instrument): os.remove(output_merge_instrument) | |
| gr.Info(translations["merge_instruments_process"]) | |
| instruments = get_audio_file('Instruments.') | |
| if instruments == translations["notfound"]: | |
| gr.Warning(translations["not_found_instruments"]) | |
| output_merge_instrument = None | |
| else: AudioSegment.from_file(instruments).overlay(AudioSegment.from_file(vocals)).export(output_merge_instrument, format=format) | |
| gr.Info(translations["merge_success"]) | |
| return [(None if use_original else output_path), output_backing, (None if not_merge_backing and use_original else output_merge_backup), (output_path if use_original else None), (output_merge_instrument if merge_instrument else None)] | |
| else: | |
| if not input or not os.path.exists(input): | |
| gr.Warning(translations["input_not_valid"]) | |
| return [None]*5 | |
| if not output: | |
| gr.Warning(translations["output_not_valid"]) | |
| return [None]*5 | |
| if os.path.isdir(input): | |
| gr.Info(translations["is_folder"]) | |
| if not [f for f in os.listdir(input) if f.lower().endswith(("wav", "mp3", "flac", "ogg", "opus", "m4a", "mp4", "aac", "alac", "wma", "aiff", "webm", "ac3"))]: | |
| gr.Warning(translations["not_found_in_folder"]) | |
| return [None]*5 | |
| gr.Info(translations["batch_convert"]) | |
| output_dir = os.path.dirname(output) | |
| output_dir = output if not output_dir else output_dir | |
| convert(pitch, filter_radius, index_rate, volume_envelope, protect, hop_length, f0method, input, output_dir, model_path, index, autotune, clean, clean_strength, format, embedder_model, upscale, resample_sr, batch_process, batch_size, split_audio, f0_autotune_strength) | |
| gr.Info(translations["batch_convert_success"]) | |
| return [None]*5 | |
| else: | |
| output_dir = os.path.dirname(output) | |
| output_dir = output if not output_dir else output_dir | |
| if not os.path.exists(output_dir): os.makedirs(output_dir, exist_ok=True) | |
| if os.path.exists(output): os.remove(output) | |
| gr.Info(translations["convert_vocal"]) | |
| convert(pitch, filter_radius, index_rate, volume_envelope, protect, hop_length, f0method, input, output, model_path, index, autotune, clean, clean_strength, format, embedder_model, upscale, resample_sr, batch_process, batch_size, split_audio, f0_autotune_strength) | |
| gr.Info(translations["convert_success"]) | |
| return [output, None, None, None, None] | |
| def convert_tts(clean, upscale, autotune, pitch, clean_strength, model, index, index_rate, input, output, format, method, hybrid_method, hop_length, embedders, custom_embedders, resample_sr, filter_radius, volume_envelope, protect, batch_process, batch_size, split_audio, f0_autotune_strength): | |
| model_path = os.path.join("assets", "weights", model) | |
| if not model_path or not os.path.exists(model_path) or os.path.isdir(model_path) or not model.endswith(".pth"): | |
| gr.Warning(translations["provide_file"].format(filename=translations["model"])) | |
| return None | |
| if not index or not os.path.exists(index) or os.path.isdir(index) or not index.endswith(".index"): | |
| gr.Warning(translations["provide_file"].format(filename=translations["index"])) | |
| return None | |
| if not input or not os.path.exists(input): | |
| gr.Warning(translations["input_not_valid"]) | |
| return None | |
| if os.path.isdir(input): | |
| input_audio = [f for f in os.listdir(input) if "output_tts" in f and f.lower().endswith(("wav", "mp3", "flac", "ogg", "opus", "m4a", "mp4", "aac", "alac", "wma", "aiff", "webm", "ac3"))] | |
| if not input_audio: | |
| gr.Warning(translations["not_found_in_folder"]) | |
| return None | |
| input = os.path.join(input, input_audio[0]) | |
| if not output: | |
| gr.Warning(translations["output_not_valid"]) | |
| return None | |
| if os.path.isdir(output): output = os.path.join(output, f"output_tts-convert.{format}") | |
| output_dir = os.path.dirname(output) | |
| if not os.path.exists(output_dir): os.makedirs(output_dir, exist_ok=True) | |
| if os.path.exists(output): os.remove(output) | |
| f0method = method if method != "hybrid" else hybrid_method | |
| embedder_model = embedders if embedders != "custom" else custom_embedders | |
| gr.Info(translations["convert_vocal"]) | |
| convert(pitch, filter_radius, index_rate, volume_envelope, protect, hop_length, f0method, input, output, model_path, index, autotune, clean, clean_strength, format, embedder_model, upscale, resample_sr, batch_process, batch_size, split_audio, f0_autotune_strength) | |
| gr.Info(translations["convert_success"]) | |
| return output | |
| def create_dataset(input_audio, output_dataset, resample, resample_sr, clean_dataset, clean_strength, separator_music, separator_reverb, kim_vocals_version, overlap, segments_size, denoise_mdx, skip, skip_start, skip_end, hop_length, batch_size): | |
| version = 1 if kim_vocals_version == "Version-1" else 2 | |
| cmd = f'{python} main/inference/create_dataset.py --input_audio "{input_audio}" --output_dataset "{output_dataset}" --resample {resample} --resample_sr {resample_sr} --clean_dataset {clean_dataset} --clean_strength {clean_strength} --separator_music {separator_music} --separator_reverb {separator_reverb} --kim_vocal_version {version} --overlap {overlap} --segments_size {segments_size} --mdx_hop_length {hop_length} --mdx_batch_size {batch_size} --denoise_mdx {denoise_mdx} --skip {skip} --skip_start_audios "{skip_start}" --skip_end_audios "{skip_end}"' | |
| gr.Info(translations["start"].format(start=translations["create"])) | |
| p = Popen(cmd, shell=True) | |
| done = [False] | |
| threading.Thread(target=if_done, args=(done, p)).start() | |
| create_dataset_log = os.path.join("assets", "logs", "create_dataset.log") | |
| f = open(create_dataset_log, "w", encoding="utf-8") | |
| f.close() | |
| while 1: | |
| with open(create_dataset_log, "r", encoding='utf-8') as f: | |
| yield (f.read()) | |
| sleep(1) | |
| if done[0]: break | |
| with open(create_dataset_log, "r", encoding='utf-8') as f: | |
| log = f.read() | |
| yield log | |
| def preprocess(model_name, sample_rate, cpu_core, cut_preprocess, process_effects, path, clean_dataset, clean_strength): | |
| dataset = os.path.join(path) | |
| sr = int(sample_rate.rstrip("k")) * 1000 | |
| if not model_name: return gr.Warning(translations["provide_name"]) | |
| if len([f for f in os.listdir(os.path.join(dataset)) if os.path.isfile(os.path.join(dataset, f)) and f.lower().endswith((".wav", ".mp3", ".flac", ".ogg"))]) < 1: return gr.Warning(translations["not_found_data"]) | |
| cmd = f'{python} main/inference/preprocess.py --model_name "{model_name}" --dataset_path "{dataset}" --sample_rate {sr} --cpu_cores {cpu_core} --cut_preprocess {cut_preprocess} --process_effects {process_effects} --clean_dataset {clean_dataset} --clean_strength {clean_strength}' | |
| p = Popen(cmd, shell=True) | |
| done = [False] | |
| threading.Thread(target=if_done, args=(done, p)).start() | |
| model_dir = os.path.join("assets", "logs", model_name) | |
| preprocess_log = os.path.join(model_dir, "preprocess.log") | |
| os.makedirs(model_dir, exist_ok=True) | |
| f = open(preprocess_log, "w", encoding="utf-8") | |
| f.close() | |
| while 1: | |
| with open(preprocess_log, "r", encoding='utf-8') as f: | |
| yield (f.read()) | |
| sleep(1) | |
| if done[0]: break | |
| with open(preprocess_log, "r", encoding='utf-8') as f: | |
| log = f.read() | |
| yield log | |
| def extract(model_name, version, method, pitch_guidance, hop_length, cpu_cores, gpu, sample_rate, embedders, custom_embedders): | |
| embedder_model = embedders if embedders != "custom" else custom_embedders | |
| model_dir = os.path.join("assets", "logs", model_name) | |
| sr = int(sample_rate.rstrip("k")) * 1000 | |
| if not model_name: return gr.Warning(translations["provide_name"]) | |
| if len([f for f in os.listdir(os.path.join(model_dir, "sliced_audios")) if os.path.isfile(os.path.join(model_dir, "sliced_audios", f))]) < 1 or len([f for f in os.listdir(os.path.join(model_dir, "sliced_audios_16k")) if os.path.isfile(os.path.join(model_dir, "sliced_audios_16k", f))]) < 1: return gr.Warning(translations["not_found_data_preprocess"]) | |
| cmd = f'{python} main/inference/extract.py --model_name "{model_name}" --rvc_version {version} --f0_method {method} --pitch_guidance {pitch_guidance} --hop_length {hop_length} --cpu_cores {cpu_cores} --gpu {gpu} --sample_rate {sr} --embedder_model {embedder_model}' | |
| p = Popen(cmd, shell=True) | |
| done = [False] | |
| threading.Thread(target=if_done, args=(done, p)).start() | |
| extract_log = os.path.join(model_dir, model_name, "extract.log") | |
| os.makedirs(model_dir, exist_ok=True) | |
| f = open(extract_log, "w", encoding="utf-8") | |
| f.close() | |
| while 1: | |
| with open(extract_log, "r", encoding='utf-8') as f: | |
| yield (f.read()) | |
| sleep(1) | |
| if done[0]: break | |
| with open(extract_log, "r", encoding='utf-8') as f: | |
| log = f.read() | |
| yield log | |
| def create_index(model_name, rvc_version, index_algorithm): | |
| if not model_name: return gr.Warning(translations["provide_name"]) | |
| model_dir = os.path.join("assets", "logs", model_name) | |
| if len([f for f in os.listdir(os.path.join(model_dir, f"{rvc_version}_extracted")) if os.path.isfile(os.path.join(model_dir, f"{rvc_version}_extracted", f))]) < 1: return gr.Warning(translations["not_found_data_extract"]) | |
| cmd = f'{python} main/inference/create_index.py --model_name "{model_name}" --rvc_version {rvc_version} --index_algorithm {index_algorithm}' | |
| p = Popen(cmd, shell=True) | |
| done = [False] | |
| threading.Thread(target=if_done, args=(done, p)).start() | |
| create_index_log = os.path.join(model_dir, "create_index.log") | |
| os.makedirs(model_dir, exist_ok=True) | |
| f = open(create_index_log, "w", encoding="utf-8") | |
| f.close() | |
| while 1: | |
| with open(create_index_log, "r", encoding='utf-8') as f: | |
| yield (f.read()) | |
| sleep(1) | |
| if done[0]: break | |
| with open(create_index_log, "r", encoding='utf-8') as f: | |
| log = f.read() | |
| yield log | |
| def training(model_name, rvc_version, save_every_epoch, save_only_latest, save_every_weights, total_epoch, sample_rate, batch_size, gpu, pitch_guidance, not_pretrain, custom_pretrained, pretrain_g, pretrain_d, detector, threshold, sync_graph, cache, model_author): | |
| sr = int(sample_rate.rstrip("k")) * 1000 | |
| model_dir = os.path.join("assets", "logs", model_name) | |
| if not model_name: return gr.Warning(translations["provide_name"]) | |
| if len([f for f in os.listdir(os.path.join(model_dir, f"{rvc_version}_extracted")) if os.path.isfile(os.path.join(model_dir, f"{rvc_version}_extracted", f))]) < 1: return gr.Warning(translations["not_found_data_extract"]) | |
| cmd = f'{python} main/inference/train.py --model_name "{model_name}" --rvc_version {rvc_version} --save_every_epoch {save_every_epoch} --save_only_latest {save_only_latest} --save_every_weights {save_every_weights} --total_epoch {total_epoch} --sample_rate {sr} --batch_size {batch_size} --gpu {gpu} --pitch_guidance {pitch_guidance} --overtraining_detector {detector} --overtraining_threshold {threshold} --sync_graph {sync_graph} --cache_data_in_gpu {cache}' | |
| if not not_pretrain: | |
| if not custom_pretrained: pg, pd = pretrained_selector(pitch_guidance)[sr] | |
| else: | |
| if not pretrain_g: return gr.Warning(translations["provide_pretrained"].format(dg="G")) | |
| if not pretrain_d: return gr.Warning(translations["provide_pretrained"].format(dg="D")) | |
| pg = pretrain_g | |
| pd = pretrain_d | |
| if not custom_pretrained: | |
| pretrained_G = os.path.join("assets", "model", f"pretrained_{rvc_version}", pg) | |
| pretrained_D = os.path.join("assets", "model", f"pretrained_{rvc_version}", pd) | |
| else: | |
| pretrained_G = os.path.join("assets", "model", f"pretrained_custom", pg) | |
| pretrained_D = os.path.join("assets", "model", f"pretrained_custom", pd) | |
| download_version = pretrained_v2_link if rvc_version == "v2" else pretrained_v1_link | |
| if not custom_pretrained: | |
| if not os.path.exists(pretrained_G): | |
| gr.Info(translations["download_pretrained"].format(dg="G", rvc_version=rvc_version)) | |
| run(["wget", "-q", "--show-progress", "-q", "--show-progress", "--no-check-certificate", f"{download_version}{pg}", "-P", os.path.join("assets", "model", f"pretrained_{rvc_version}")], check=True) | |
| if not os.path.exists(pretrained_D): | |
| gr.Info(translations["download_pretrained"].format(dg="D", rvc_version=rvc_version)) | |
| run(["wget", "-q", "--show-progress", "-q", "--show-progress", "--no-check-certificate", f"{download_version}{pd}", "-P", os.path.join("assets", "model", f"pretrained_{rvc_version}")], check=True) | |
| else: | |
| if not os.path.exists(pretrained_G): return gr.Warning(translations["not_found_pretrain"].format(dg="G")) | |
| if not os.path.exists(pretrained_D): return gr.Warning(translations["not_found_pretrain"].format(dg="D")) | |
| cmd += f" --g_pretrained_path {pretrained_G} --d_pretrained_path {pretrained_D}" | |
| else: gr.Warning(translations["not_use_pretrain"]) | |
| if model_author: cmd += f'--model_author {model_author}' | |
| gr.Info(translations["start"].format(start=translations["training"])) | |
| p = Popen(cmd, shell=True) | |
| done = [False] | |
| threading.Thread(target=if_done, args=(done, p)).start() | |
| if not os.path.exists(model_dir): os.makedirs(model_dir, exist_ok=True) | |
| train_log = os.path.join(model_dir, "train.log") | |
| f = open(train_log, "w", encoding="utf-8") | |
| f.close() | |
| while 1: | |
| with open(train_log, "r", encoding='utf-8') as f: | |
| yield (f.read()) | |
| sleep(1) | |
| if done[0]: break | |
| with open(train_log, "r", encoding='utf-8') as f: | |
| log = f.read() | |
| yield log | |
| with gr.Blocks(title="📱 RVC GUI BY ANH", theme=theme) as app: | |
| gr.HTML(translations["display_title"]) | |
| with gr.Row(): | |
| gr.Markdown(translations["rick_roll"].format(rickroll=codecs.decode('uggcf://jjj.lbhghor.pbz/jngpu?i=qDj4j9JtKpD', 'rot13'))) | |
| with gr.Row(): | |
| gr.Markdown(translations["terms_of_use"]) | |
| with gr.Row(): | |
| gr.Markdown(translations["exemption"]) | |
| with gr.Row(): | |
| gr.Markdown(f"Use full: [Colab](https://colab.research.google.com/drive/18Ed5HbwcX0di6aJymX0EaUNz-xXU5uUc?hl=vi#scrollTo=DZDKirCM0F9g)") | |
| with gr.Tabs(): | |
| paths_for_files = lambda path: [os.path.abspath(os.path.join(path, f)) for f in os.listdir(path) if os.path.splitext(f)[1] in ('.mp3', '.wav', '.flac', '.ogg', '.m4a')] | |
| with gr.TabItem(translations["separator_tab"], visible=configs["separator_tab"]): | |
| gr.Markdown(f"## {translations['separator_tab']}") | |
| with gr.Row(): | |
| gr.Markdown(translations["4_part"]) | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Group(): | |
| with gr.Row(): | |
| cleaner = gr.Checkbox(label=translations["clear_audio"], value=False, interactive=True) | |
| backing = gr.Checkbox(label=translations["separator_backing"], value=False, interactive=True) | |
| denoise = gr.Checkbox(label=translations["denoise_backing"], value=False, interactive=False) | |
| separator_denoise = gr.Checkbox(label=translations["denoise_mdx"], value=False, interactive=False) | |
| mdx_model = gr.Checkbox(label=translations["use_mdx"], value=False, interactive=True) | |
| reverb = gr.Checkbox(label=translations["dereveb_audio"], value=False, interactive=True) | |
| backing_reverb = gr.Checkbox(label=translations["dereveb_backing"], value=False, interactive=False) | |
| reverb_denoise = gr.Checkbox(label=translations["denoise_dereveb"], value=False, interactive=False) | |
| with gr.Row(): | |
| separator_model = gr.Dropdown(label=translations["separator_model"], value="HT-Normal", choices=["HT-Normal", "HT-Tuned", "HD_MMI", "HT_6S"], interactive=True, visible=True) | |
| separator_backing_model = gr.Dropdown(label=translations["separator_backing_model"], value="Version-1", choices=["Version-1", "Version-2"], interactive=True, visible=False) | |
| with gr.Column(): | |
| separator_button = gr.Button(translations["separator_tab"], variant="primary", scale=2) | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Group(): | |
| with gr.Row(): | |
| shifts = gr.Slider(label=translations["shift"], info=translations["shift_info"], minimum=1, maximum=20, value=2, step=1, interactive=True) | |
| segment_size = gr.Slider(label=translations["segments_size"], info=translations["segments_size_info"], minimum=32, maximum=4000, value=256, step=8, interactive=True) | |
| with gr.Row(): | |
| mdx_batch_size = gr.Slider(label=translations["batch_size"], info=translations["mdx_batch_size_info"], minimum=1, maximum=64, value=1, step=1, interactive=True, visible=False) | |
| with gr.Column(): | |
| with gr.Group(): | |
| with gr.Row(): | |
| overlap = gr.Radio(label=translations["overlap"], info=translations["overlap_info"], choices=["0.25", "0.5", "0.75", "0.99"], value="0.25", interactive=True) | |
| format = gr.Radio(label=translations["export_format"], info=translations["export_info"], choices=["wav", "mp3", "flac"], value="wav", interactive=True) | |
| with gr.Row(): | |
| mdx_hop_length = gr.Slider(label="Hop length", info=translations["hop_length_info"], minimum=1, maximum=8192, value=1024, step=1, interactive=True, visible=False) | |
| with gr.Row(): | |
| with gr.Column(): | |
| input = gr.File(label=translations["drop_audio"], file_types=['audio']) | |
| with gr.Accordion(translations["use_url"], open=False): | |
| url = gr.Textbox(label=translations["url_audio"], value="", placeholder="https://www.youtube.com/...", scale=6) | |
| download_button = gr.Button(translations["downloads"]) | |
| with gr.Column(): | |
| clean_strength = gr.Slider(label=translations["clean_strength"], info=translations["clean_strength_info"], minimum=0, maximum=1, value=0.5, step=0.1, interactive=True, visible=False) | |
| with gr.Accordion(translations["input_output"]): | |
| input_audio = gr.Dropdown(label=translations["audio_path"], value="" if len(list(f for f in os.listdir("audios") if os.path.splitext(f)[1] in ('.mp3', '.wav', '.flac', '.ogg', '.m4a'))) < 1 else paths_for_files("audios")[0], choices=[] if len(list(f for f in os.listdir("audios") if os.path.splitext(f)[1] in ('.mp3', '.wav', '.flac', '.ogg', '.m4a'))) < 1 else paths_for_files("audios"), allow_custom_value=True, interactive=True) | |
| refesh_separator = gr.Button(translations["refesh"]) | |
| output_separator = gr.Textbox(label=translations["output_folder"], value="audios", placeholder="audios", info=translations["output_folder_info"], interactive=True) | |
| audio_input = gr.Audio(show_download_button=True, interactive=False, label=translations["input_audio"]) | |
| with gr.Row(): | |
| gr.Markdown(translations["output_separator"]) | |
| with gr.Row(): | |
| instruments_audio = gr.Audio(show_download_button=True, interactive=False, label=translations["instruments"]) | |
| original_vocals = gr.Audio(show_download_button=True, interactive=False, label=translations["original_vocal"]) | |
| main_vocals = gr.Audio(show_download_button=True, interactive=False, label=translations["main_vocal"], visible=False) | |
| backing_vocals = gr.Audio(show_download_button=True, interactive=False, label=translations["backing_vocal"], visible=False) | |
| with gr.Row(): | |
| backing.change(fn=lambda a, b, c: [visible_1(a or b or c), visible_1(a or b or c)], inputs=[backing, mdx_model, reverb], outputs=[mdx_batch_size, mdx_hop_length]) | |
| mdx_model.change(fn=lambda a, b, c: [visible_1(a or b or c), visible_1(a or b or c)], inputs=[backing, mdx_model, reverb], outputs=[mdx_batch_size, mdx_hop_length]) | |
| reverb.change(fn=lambda a, b, c: [visible_1(a or b or c), visible_1(a or b or c)], inputs=[backing, mdx_model, reverb], outputs=[mdx_batch_size, mdx_hop_length]) | |
| with gr.Row(): | |
| backing.change(fn=visible_1, inputs=[backing], outputs=[separator_backing_model]) | |
| backing.change(fn=visible_1, inputs=[backing], outputs=[main_vocals]) | |
| backing.change(fn=visible_1, inputs=[backing], outputs=[backing_vocals]) | |
| with gr.Row(): | |
| backing.change(fn=valueFalse_interactive1, inputs=[backing], outputs=[denoise]) | |
| backing.change(fn=valueFalse_interactive2, inputs=[backing, reverb], outputs=[backing_reverb]) | |
| with gr.Row(): | |
| reverb.change(fn=valueFalse_interactive1, inputs=[reverb], outputs=[reverb_denoise]) | |
| reverb.change(fn=valueFalse_interactive2, inputs=[backing, reverb], outputs=[backing_reverb]) | |
| with gr.Row(): | |
| mdx_model.change(fn=valueFalse_interactive1, inputs=[mdx_model], outputs=[separator_denoise]) | |
| mdx_model.change(fn=model_separator_change, inputs=[mdx_model], outputs=[separator_model]) | |
| mdx_model.change(fn=lambda inp: visible_1(not inp), inputs=[mdx_model], outputs=[shifts]) | |
| with gr.Row(): | |
| input_audio.change(fn=lambda audio: audio if audio else None, inputs=[input_audio], outputs=[audio_input]) | |
| cleaner.change(fn=visible_1, inputs=[cleaner], outputs=[clean_strength]) | |
| input.upload(fn=lambda audio_in: shutil.move(audio_in.name, os.path.join("audios")), inputs=[input], outputs=[input_audio]) | |
| refesh_separator.click(fn=refesh_audio, inputs=[], outputs=[input_audio]) | |
| with gr.Row(): | |
| download_button.click( | |
| fn=download_url, | |
| inputs=[url], | |
| outputs=[input_audio, audio_input, url], | |
| api_name='download_url' | |
| ) | |
| separator_button.click( | |
| fn=separator_music, | |
| inputs=[ | |
| input_audio, | |
| output_separator, | |
| format, | |
| shifts, | |
| segment_size, | |
| overlap, | |
| cleaner, | |
| clean_strength, | |
| denoise, | |
| separator_model, | |
| separator_backing_model, | |
| backing, | |
| mdx_model, | |
| separator_denoise, | |
| reverb, | |
| reverb_denoise, | |
| backing_reverb, | |
| mdx_hop_length, | |
| mdx_batch_size | |
| ], | |
| outputs=[original_vocals, instruments_audio, main_vocals, backing_vocals], | |
| api_name='separator_music' | |
| ) | |
| with gr.TabItem(translations["convert_audio"], visible=configs["convert_tab"]): | |
| gr.Markdown(f"## {translations['convert_audio']}") | |
| with gr.Row(): | |
| gr.Markdown(translations["convert_info"]) | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Group(): | |
| with gr.Row(): | |
| cleaner0 = gr.Checkbox(label=translations["clear_audio"], value=False, interactive=True) | |
| upscale = gr.Checkbox(label=translations["upscale_audio"], value=False, interactive=True) | |
| autotune = gr.Checkbox(label=translations["autotune"], value=False, interactive=True) | |
| use_audio = gr.Checkbox(label=translations["use_audio"], value=False, interactive=True) | |
| use_original = gr.Checkbox(label=translations["convert_original"], value=False, interactive=True, visible=False) | |
| convert_backing = gr.Checkbox(label=translations["convert_backing"], value=False, interactive=True, visible=False) | |
| not_merge_backing = gr.Checkbox(label=translations["not_merge_backing"], value=False, interactive=True, visible=False) | |
| merge_instrument = gr.Checkbox(label=translations["merge_instruments"], value=False, interactive=True, visible=False) | |
| with gr.Row(): | |
| pitch = gr.Slider(minimum=-20, maximum=20, step=1, info=translations["pitch_info"], label=translations["pitch"], value=0, interactive=True) | |
| clean_strength0 = gr.Slider(label=translations["clean_strength"], info=translations["clean_strength_info"], minimum=0, maximum=1, value=0.5, step=0.1, interactive=True, visible=False) | |
| with gr.Column(): | |
| convert_button = gr.Button(translations["convert_audio"], variant="primary", scale=4) | |
| with gr.Row(): | |
| with gr.Column(): | |
| input0 = gr.File(label=translations["drop_audio"], file_types=['audio']) | |
| play_audio = gr.Audio(show_download_button=True, interactive=False, label=translations["input_audio"]) | |
| with gr.Column(): | |
| with gr.Accordion(translations["model_accordion"], open=True): | |
| with gr.Row(): | |
| model_pth = gr.Dropdown(label=translations["model_name"], choices=sorted(model_name), value=sorted(model_name)[0] if len(sorted(model_name)) > 0 else '', interactive=True, allow_custom_value=True) | |
| model_index = gr.Dropdown(label=translations["index_path"], choices=sorted(index_path), value=sorted(index_path)[0] if len(sorted(index_path)) > 0 else '', interactive=True, allow_custom_value=True) | |
| with gr.Row(): | |
| refesh = gr.Button(translations["refesh"]) | |
| with gr.Row(): | |
| index_strength = gr.Slider(label=translations["index_strength"], info=translations["index_strength_info"], minimum=0, maximum=1, value=0.5, step=0.01, interactive=True) | |
| with gr.Accordion(translations["input_output"], open=False): | |
| with gr.Column(): | |
| export_format = gr.Radio(label=translations["export_format"], info=translations["export_info"], choices=["wav", "mp3", "flac", "ogg", "m4a"], value="wav", interactive=True) | |
| input_audio0 = gr.Dropdown(label=translations["audio_path"], value="" if len(list(f for f in os.listdir("audios") if os.path.splitext(f)[1] in ('.mp3', '.wav', '.flac', '.ogg', '.m4a'))) < 1 else paths_for_files("audios")[0], choices=[] if len(list(f for f in os.listdir("audios") if os.path.splitext(f)[1] in ('.mp3', '.wav', '.flac', '.ogg', '.m4a'))) < 1 else paths_for_files("audios"), info="Nhập đường dẫn đến tệp âm thanh", allow_custom_value=True, interactive=True) | |
| output_audio = gr.Textbox(label=translations["output_path"], value="audios/output.wav", placeholder="audios/output.wav", info=translations["output_path_info"], interactive=True) | |
| with gr.Column(): | |
| refesh0 = gr.Button(translations["refesh"]) | |
| with gr.Accordion(translations["setting"], open=False): | |
| with gr.Accordion(translations["f0_method"], open=False): | |
| method = gr.Radio(label=translations["f0_method"], info=translations["f0_method_info"], choices=["pm", "dio", "crepe-tiny", "crepe", "fcpe", "rmvpe", "harvest", "hybrid"], value="rmvpe", interactive=True) | |
| hybrid_method = gr.Radio(label=translations["f0_method_hybrid"], info=translations["f0_method_hybrid_info"], choices=["hybrid[pm+dio]", "hybrid[pm+crepe-tiny]", "hybrid[pm+crepe]", "hybrid[pm+fcpe]", "hybrid[pm+rmvpe]", "hybrid[pm+harvest]", "hybrid[dio+crepe-tiny]", "hybrid[dio+crepe]", "hybrid[dio+fcpe]", "hybrid[dio+rmvpe]", "hybrid[dio+harvest]", "hybrid[crepe-tiny+crepe]", "hybrid[crepe-tiny+fcpe]", "hybrid[crepe-tiny+rmvpe]", "hybrid[crepe-tiny+harvest]", "hybrid[crepe+fcpe]", "hybrid[crepe+rmvpe]", "hybrid[crepe+harvest]", "hybrid[fcpe+rmvpe]", "hybrid[fcpe+harvest]", "hybrid[rmvpe+harvest]"], value="hybrid[pm+dio]", interactive=True, visible=False) | |
| hop_length = gr.Slider(label="Hop length", info=translations["hop_length_info"], minimum=1, maximum=512, value=128, step=1, interactive=True, visible=False) | |
| with gr.Accordion(translations["hubert_model"], open=False): | |
| embedders = gr.Radio(label=translations["hubert_model"], info=translations["hubert_info"], choices=["contentvec_base", "hubert_base", "japanese_hubert_base", "korean_hubert_base", "chinese_hubert_base", "custom"], value="contentvec_base", interactive=True) | |
| custom_embedders = gr.Textbox(label=translations["modelname"], info=translations["modelname_info"], value="", placeholder="hubert_base", interactive=True, visible=False) | |
| with gr.Column(): | |
| with gr.Group(): | |
| with gr.Row(): | |
| split_audio = gr.Checkbox(label=translations["split_audio"], info=translations["split_audio_info"], value=False, interactive=True) | |
| batch_process = gr.Checkbox(label=translations["batch_process"], info=translations["batch_process_info"], value=False, interactive=True, visible=False) | |
| with gr.Row(): | |
| batch_size = gr.Slider(minimum=1, maximum=10, label=translations["batch_size"], info=translations["batch_size_info"], value=1, step=1, interactive=True, visible=False) | |
| f0_autotune_strength = gr.Slider(minimum=0, maximum=1, label=translations["autotune_rate"], info=translations["autotune_rate_info"], value=1, step=0.1, interactive=True, visible=False) | |
| resample_sr = gr.Slider(minimum=0, maximum=48000, label=translations["resample"], info=translations["resample_info"], value=0, step=1, interactive=True) | |
| filter_radius = gr.Slider(minimum=0, maximum=7, label=translations["filter_radius"], info=translations["filter_radius_info"], value=3, step=1, interactive=True) | |
| volume_envelope = gr.Slider(minimum=0, maximum=1, label=translations["volume_envelope"], info=translations["volume_envelope_info"], value=1, step=0.1, interactive=True) | |
| protect = gr.Slider(minimum=0, maximum=1, label=translations["protect"], info=translations["protect_info"], value=0.33, step=0.01, interactive=True) | |
| with gr.Row(): | |
| gr.Markdown(translations["output_convert"]) | |
| with gr.Row(): | |
| main_convert = gr.Audio(show_download_button=True, interactive=False, label=translations["main_convert"]) | |
| backing_convert = gr.Audio(show_download_button=True, interactive=False, label=translations["convert_backing"], visible=False) | |
| main_backing = gr.Audio(show_download_button=True, interactive=False, label=translations["main_or_backing"], visible=False) | |
| with gr.Row(): | |
| original_convert = gr.Audio(show_download_button=True, interactive=False, label=translations["convert_original"], visible=False) | |
| vocal_instrument = gr.Audio(show_download_button=True, interactive=False, label=translations["voice_or_instruments"], visible=False) | |
| with gr.Row(): | |
| split_audio.change(fn=valueFalse_visible1, inputs=[split_audio], outputs=[batch_process]) | |
| batch_process.change(fn=visible_1, inputs=[batch_process], outputs=[batch_size]) | |
| autotune.change(fn=visible_1, inputs=[autotune], outputs=[f0_autotune_strength]) | |
| with gr.Row(): | |
| use_audio.change(fn=visible_1, inputs=[use_audio], outputs=[main_backing]) | |
| use_audio.change(fn=lambda audio: [valueFalse_interactive1(audio), valueFalse_interactive1(audio), valueFalse_interactive1(audio), valueFalse_interactive1(audio)], inputs=[use_audio], outputs=[use_original, convert_backing, not_merge_backing, merge_instrument]) | |
| with gr.Row(): | |
| use_audio.change(fn=visible_1, inputs=[use_audio], outputs=[use_original]); use_audio.change(fn=visible_1, inputs=[use_audio], outputs=[convert_backing]) | |
| use_audio.change(fn=visible_1, inputs=[use_audio], outputs=[not_merge_backing]); use_audio.change(fn=visible_1, inputs=[use_audio], outputs=[merge_instrument]) | |
| use_audio.change(fn=lambda audio: [visible_1(not audio), visible_1(not audio), visible_1(not audio), visible_1(not audio)], inputs=[use_audio], outputs=[input_audio0, output_audio, input0, play_audio]) | |
| with gr.Row(): | |
| convert_backing.change(fn=visible_1, inputs=[convert_backing], outputs=[backing_convert]) | |
| convert_backing.change(fn=backing_change, inputs=[convert_backing, not_merge_backing], outputs=[use_original]) | |
| with gr.Row(): | |
| use_original.change(fn=lambda original: [valueFalse_interactive1(not original), valueFalse_interactive1(not original)], inputs=[use_original], outputs=[convert_backing, not_merge_backing]) | |
| use_original.change(fn=lambda audio, original: [visible_1(original), visible_1(not original), visible_1(audio and not original)], inputs=[use_audio, use_original], outputs=[original_convert, main_convert, main_backing]) | |
| with gr.Row(): | |
| cleaner0.change(fn=visible_1, inputs=[cleaner0], outputs=[clean_strength0]) | |
| merge_instrument.change(fn=visible_1, inputs=[merge_instrument], outputs=[vocal_instrument]) | |
| with gr.Row(): | |
| not_merge_backing.change(fn=lambda audio, merge: visible_1(audio and not merge), inputs=[use_audio, not_merge_backing], outputs=[main_backing]) | |
| not_merge_backing.change(fn=backing_change, inputs=[convert_backing, not_merge_backing], outputs=[use_original]) | |
| with gr.Row(): | |
| method.change(fn=lambda method: visible_1(True if method == "hybrid" else False), inputs=[method], outputs=[hybrid_method]) | |
| method.change(fn=hoplength_show, inputs=[method, hybrid_method], outputs=[hop_length]) | |
| with gr.Row(): | |
| hybrid_method.change(fn=hoplength_show, inputs=[method, hybrid_method], outputs=[hop_length]) | |
| refesh.click(fn=change_choices, inputs=[], outputs=[model_pth, model_index]) | |
| model_pth.change(fn=get_index, inputs=[model_pth], outputs=[model_index]) | |
| with gr.Row(): | |
| input0.upload(fn=lambda audio_in: shutil.move(audio_in.name, os.path.join("audios")), inputs=[input0], outputs=[input_audio0]) | |
| input_audio0.change(fn=lambda audio: audio if audio else None, inputs=[input_audio0], outputs=[play_audio]) | |
| with gr.Row(): | |
| embedders.change(fn=lambda embedders: visible_1(True if embedders == "custom" else False), inputs=[embedders], outputs=[custom_embedders]) | |
| refesh0.click(fn=refesh_audio, inputs=[], outputs=[input_audio0]) | |
| with gr.Row(): | |
| convert_button.click( | |
| fn=convert_audio, | |
| inputs=[ | |
| cleaner0, | |
| upscale, | |
| autotune, | |
| use_audio, | |
| use_original, | |
| convert_backing, | |
| not_merge_backing, | |
| merge_instrument, | |
| pitch, | |
| clean_strength0, | |
| model_pth, | |
| model_index, | |
| index_strength, | |
| input_audio0, | |
| output_audio, | |
| export_format, | |
| method, | |
| hybrid_method, | |
| hop_length, | |
| embedders, | |
| custom_embedders, | |
| resample_sr, | |
| filter_radius, | |
| volume_envelope, | |
| protect, | |
| batch_process, | |
| batch_size, | |
| split_audio, | |
| f0_autotune_strength | |
| ], | |
| outputs=[main_convert, backing_convert, main_backing, original_convert, vocal_instrument], | |
| api_name="convert_audio" | |
| ) | |
| with gr.TabItem(translations["convert_text"], visible=configs["tts_tab"]): | |
| gr.Markdown(translations["convert_text_markdown"]) | |
| with gr.Row(): | |
| gr.Markdown(translations["convert_text_markdown_2"]) | |
| with gr.Row(): | |
| with gr.Column(): | |
| use_txt = gr.Checkbox(label=translations["input_txt"], value=False, interactive=True) | |
| prompt = gr.Textbox(label=translations["text_to_speech"], value="", placeholder="Hello Words", lines=2) | |
| with gr.Row(): | |
| speed = gr.Slider(label=translations["voice_speed"], info=translations["voice_speed_info"], minimum=-100, maximum=100, value=0, step=1) | |
| pitch0 = gr.Slider(minimum=-20, maximum=20, step=1, info=translations["pitch_info"], label=translations["pitch"], value=0, interactive=True) | |
| with gr.Column(): | |
| tts_button = gr.Button(translations["tts_1"], variant="primary", scale=2) | |
| convert_button0 = gr.Button(translations["tts_2"], variant="secondary", scale=2) | |
| with gr.Row(): | |
| with gr.Column(): | |
| tts_voice = gr.Dropdown(label=translations["voice"], choices=tts_voice, interactive=True, value="vi-VN-NamMinhNeural") | |
| txt_input = gr.File(label=translations["drop_text"], file_types=['txt'], visible=False) | |
| with gr.Column(): | |
| with gr.Accordion(translations["model_accordion"], open=True): | |
| with gr.Row(): | |
| model_pth0 = gr.Dropdown(label=translations["model_name"], choices=sorted(model_name), value=sorted(model_name)[0] if len(sorted(model_name)) > 0 else '', interactive=True, allow_custom_value=True) | |
| model_index0 = gr.Dropdown(label=translations["index_path"], choices=sorted(index_path), value=sorted(index_path)[0] if len(sorted(index_path)) > 0 else '', interactive=True, allow_custom_value=True) | |
| with gr.Row(): | |
| refesh1 = gr.Button(translations["refesh"]) | |
| with gr.Row(): | |
| index_strength0 = gr.Slider(label=translations["index_strength"], info=translations["index_strength_info"], minimum=0, maximum=1, value=0.5, step=0.01, interactive=True) | |
| with gr.Accordion(translations["output_path"], open=False): | |
| export_format0 = gr.Radio(label=translations["export_format"], info=translations["export_info"], choices=["wav", "mp3", "flac", "ogg", "m4a"], value="wav", interactive=True) | |
| output_audio0 = gr.Textbox(label=translations["output_tts"], value="audios/tts.wav", placeholder="audios/tts.wav", info=translations["tts_output"], interactive=True) | |
| output_audio1 = gr.Textbox(label=translations["output_tts_convert"], value="audios/tts-convert.wav", placeholder="audios/tts-convert.wav", info=translations["tts_output"], interactive=True) | |
| with gr.Accordion(translations["setting"], open=False): | |
| with gr.Accordion(translations["f0_method"], open=False): | |
| method0 = gr.Radio(label=translations["f0_method"], info=translations["f0_method_info"], choices=["pm", "dio", "crepe-tiny", "crepe", "fcpe", "rmvpe", "harvest", "hybrid"], value="rmvpe", interactive=True) | |
| hybrid_method0 = gr.Radio(label=translations["f0_method_hybrid"], info=translations["f0_method_hybrid_info"], choices=["hybrid[pm+dio]", "hybrid[pm+crepe-tiny]", "hybrid[pm+crepe]", "hybrid[pm+fcpe]", "hybrid[pm+rmvpe]", "hybrid[pm+harvest]", "hybrid[dio+crepe-tiny]", "hybrid[dio+crepe]", "hybrid[dio+fcpe]", "hybrid[dio+rmvpe]", "hybrid[dio+harvest]", "hybrid[crepe-tiny+crepe]", "hybrid[crepe-tiny+fcpe]", "hybrid[crepe-tiny+rmvpe]", "hybrid[crepe-tiny+harvest]", "hybrid[crepe+fcpe]", "hybrid[crepe+rmvpe]", "hybrid[crepe+harvest]", "hybrid[fcpe+rmvpe]", "hybrid[fcpe+harvest]", "hybrid[rmvpe+harvest]"], value="hybrid[pm+dio]", interactive=True, visible=False) | |
| hop_length0 = gr.Slider(label="Hop length", info=translations["hop_length_info"], minimum=1, maximum=512, value=128, step=1, interactive=True, visible=False) | |
| with gr.Accordion(translations["hubert_model"], open=False): | |
| embedders0 = gr.Radio(label=translations["hubert_model"], info=translations["hubert_info"], choices=["contentvec_base", "hubert_base", "japanese_hubert_base", "korean_hubert_base", "chinese_hubert_base", "custom"], value="contentvec_base", interactive=True) | |
| custom_embedders0 = gr.Textbox(label=translations["modelname"], info=translations["modelname_info"], value="", placeholder="hubert_base", interactive=True, visible=False) | |
| with gr.Group(): | |
| with gr.Row(): | |
| split_audio0 = gr.Checkbox(label=translations["split_audio"], info=translations["split_audio_info"], value=False, interactive=True) | |
| batch_process0 = gr.Checkbox(label=translations["batch_process"], info=translations["batch_process_info"], value=False, interactive=True, visible=False) | |
| with gr.Row(): | |
| batch_size0 = gr.Slider(minimum=1, maximum=10, label=translations["batch_size"], info=translations["batch_size_info"], value=1, step=1, interactive=True, visible=False) | |
| with gr.Row(): | |
| cleaner1 = gr.Checkbox(label=translations["clear_audio"], value=False, interactive=True) | |
| upscale2 = gr.Checkbox(label=translations["upscale_audio"], value=False, interactive=True) | |
| autotune3 = gr.Checkbox(label=translations["autotune"], value=False, interactive=True) | |
| with gr.Column(): | |
| f0_autotune_strength0 = gr.Slider(minimum=0, maximum=1, label=translations["autotune_rate"], info=translations["autotune_rate_info"], value=1, step=0.1, interactive=True, visible=False) | |
| clean_strength1 = gr.Slider(label=translations["clean_strength"], info=translations["clean_strength_info"], minimum=0, maximum=1, value=0.5, step=0.1, interactive=True, visible=False) | |
| resample_sr0 = gr.Slider(minimum=0, maximum=48000, label=translations["resample"], info=translations["resample_info"], value=0, step=1, interactive=True) | |
| filter_radius0 = gr.Slider(minimum=0, maximum=7, label=translations["filter_radius"], info=translations["filter_radius_info"], value=3, step=1, interactive=True) | |
| volume_envelope0 = gr.Slider(minimum=0, maximum=1, label=translations["volume_envelope"], info=translations["volume_envelope_info"], value=1, step=0.1, interactive=True) | |
| protect0 = gr.Slider(minimum=0, maximum=1, label=translations["protect"], info=translations["protect_info"], value=0.33, step=0.01, interactive=True) | |
| with gr.Row(): | |
| gr.Markdown(translations["output_tts_markdown"]) | |
| with gr.Row(): | |
| tts_voice_audio = gr.Audio(show_download_button=True, interactive=False, label=translations["output_text_to_speech"]) | |
| tts_voice_convert = gr.Audio(show_download_button=True, interactive=False, label=translations["output_file_tts_convert"]) | |
| with gr.Row(): | |
| batch_process0.change(fn=visible_1, inputs=[batch_process0], outputs=[batch_size0]) | |
| split_audio0.change(fn=valueFalse_visible1, inputs=[split_audio0], outputs=[batch_process0]) | |
| autotune3.change(fn=visible_1, inputs=[autotune3], outputs=[f0_autotune_strength0]) | |
| with gr.Row(): | |
| cleaner1.change(fn=visible_1, inputs=[cleaner1], outputs=[clean_strength1]) | |
| method0.change(fn=lambda method: visible_1(True if method == "hybrid" else False), inputs=[method0], outputs=[hybrid_method0]) | |
| method0.change(fn=hoplength_show, inputs=[method0, hybrid_method0], outputs=[hop_length0]) | |
| hybrid_method0.change(fn=hoplength_show, inputs=[method0, hybrid_method0], outputs=[hop_length0]) | |
| with gr.Row(): | |
| refesh1.click(fn=change_choices, inputs=[], outputs=[model_pth0, model_index0]) | |
| model_pth0.change(fn=get_index, inputs=[model_pth0], outputs=[model_index0]) | |
| embedders0.change(fn=lambda embedders: visible_1(True if embedders == "custom" else False), inputs=[embedders0], outputs=[custom_embedders0]) | |
| with gr.Row(): | |
| txt_input.upload(fn=process_input, inputs=[txt_input], outputs=[prompt]) | |
| use_txt.change(fn=visible_1, inputs=[use_txt], outputs=[txt_input]) | |
| with gr.Row(): | |
| tts_button.click( | |
| fn=TTS, | |
| inputs=[ | |
| prompt, | |
| tts_voice, | |
| speed, | |
| output_audio0 | |
| ], | |
| outputs=[tts_voice_audio], | |
| api_name="text-to-speech" | |
| ) | |
| convert_button0.click( | |
| fn=convert_tts, | |
| inputs=[ | |
| cleaner1, | |
| upscale2, | |
| autotune3, | |
| pitch0, | |
| clean_strength1, | |
| model_pth0, | |
| model_index0, | |
| index_strength0, | |
| output_audio0, | |
| output_audio1, | |
| export_format0, | |
| method0, | |
| hybrid_method0, | |
| hop_length0, | |
| embedders0, | |
| custom_embedders0, | |
| resample_sr0, | |
| filter_radius0, | |
| volume_envelope0, | |
| protect0, | |
| batch_process0, | |
| batch_size0, | |
| split_audio0, | |
| f0_autotune_strength0 | |
| ], | |
| outputs=[tts_voice_convert], | |
| api_name="convert_tts" | |
| ) | |
| with gr.TabItem(translations["audio_effects"], visible=configs["effects_tab"]): | |
| gr.Markdown(translations["apply_audio_effects"]) | |
| with gr.Row(): | |
| gr.Markdown(translations["audio_effects_edit"]) | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Group(): | |
| with gr.Row(): | |
| reverb_check_box = gr.Checkbox(label=translations["reverb"], value=False, interactive=True) | |
| chorus_check_box = gr.Checkbox(label=translations["chorus"], value=False, interactive=True) | |
| delay_check_box = gr.Checkbox(label=translations["delay"], value=False, interactive=True) | |
| with gr.Row(): | |
| more_options = gr.Checkbox(label=translations["more_option"], value=False, interactive=True) | |
| phaser_check_box = gr.Checkbox(label=translations["phaser"], value=False, interactive=True) | |
| compressor_check_box = gr.Checkbox(label=translations["compressor"], value=False, interactive=True) | |
| with gr.Column(): | |
| apply_effects_button = gr.Button(translations["apply"], variant="primary", scale=2) | |
| with gr.Row(): | |
| with gr.Row(): | |
| with gr.Accordion(translations["input_output"], open=False): | |
| with gr.Row(): | |
| upload_audio = gr.File(label=translations["drop_audio"], file_types=['audio']) | |
| with gr.Row(): | |
| audio_in_path = gr.Dropdown(label=translations["input_audio"], value="" if len(list(f for f in os.listdir("audios") if os.path.splitext(f)[1] in ('.mp3', '.wav', '.flac', '.ogg', '.m4a'))) < 1 else paths_for_files("audios")[0], choices=[] if len(list(f for f in os.listdir("audios") if os.path.splitext(f)[1] in ('.mp3', '.wav', '.flac', '.ogg', '.m4a'))) < 1 else paths_for_files("audios"), info="Nhập đường dẫn đầu vào âm thanh", interactive=True, allow_custom_value=True) | |
| audio_out_path = gr.Textbox(label=translations["output_audio"], value="audios/audio_effects.wav", placeholder="audios/audio_effects.wav", info=translations["provide_output"], interactive=True) | |
| with gr.Row(): | |
| audio_output_format = gr.Radio(label=translations["export_format"], info=translations["export_info"], choices=["wav", "mp3", "flac"], value="wav", interactive=True) | |
| audio_effects_refesh = gr.Button(translations["refesh"]) | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Row(): | |
| with gr.Accordion(translations["reverb"], open=False, visible=False) as reverb_accordion: | |
| reverb_freeze_mode = gr.Checkbox(label=translations["reverb_freeze"], info=translations["reverb_freeze_info"], value=False, interactive=True) | |
| reverb_room_size = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.5, label=translations["room_size"], info=translations["room_size_info"], interactive=True) | |
| reverb_damping = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.5, label=translations["damping"], info=translations["damping_info"], interactive=True) | |
| reverb_wet_level = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.3, label=translations["wet_level"], info=translations["wet_level_info"], interactive=True) | |
| reverb_dry_level = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.7, label=translations["dry_level"], info=translations["dry_level_info"], interactive=True) | |
| reverb_width = gr.Slider(minimum=0, maximum=1, step=0.01, value=1, label=translations["width"], info=translations["width_info"], interactive=True) | |
| with gr.Row(): | |
| with gr.Accordion(translations["chorus"], open=False, visible=False) as chorus_accordion: | |
| chorus_depth = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.5, label=translations["chorus_depth"], info=translations["chorus_depth_info"], interactive=True) | |
| chorus_rate_hz = gr.Slider(minimum=0.1, maximum=10, step=0.1, value=1.5, label=translations["chorus_rate_hz"], info=translations["chorus_rate_hz_info"], interactive=True) | |
| chorus_mix = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.5, label=translations["chorus_mix"], info=translations["chorus_mix_info"], interactive=True) | |
| chorus_centre_delay_ms = gr.Slider(minimum=0, maximum=50, step=1, value=10, label=translations["chorus_centre_delay_ms"], info=translations["chorus_centre_delay_ms_info"], interactive=True) | |
| chorus_feedback = gr.Slider(minimum=-1, maximum=1, step=0.01, value=0, label=translations["chorus_feedback"], info=translations["chorus_feedback_info"], interactive=True) | |
| with gr.Row(): | |
| with gr.Accordion(translations["delay"], open=False, visible=False) as delay_accordion: | |
| delay_second = gr.Slider(minimum=0, maximum=5, step=0.01, value=0.5, label=translations["delay_seconds"], info=translations["delay_seconds_info"], interactive=True) | |
| delay_feedback = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.5, label=translations["delay_feedback"], info=translations["delay_feedback_info"], interactive=True) | |
| delay_mix = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.5, label=translations["delay_mix"], info=translations["delay_mix_info"], interactive=True) | |
| with gr.Column(): | |
| with gr.Row(): | |
| with gr.Accordion(translations["more_option"], open=False, visible=False) as more_accordion: | |
| with gr.Row(): | |
| fade = gr.Checkbox(label=translations["fade"], value=False, interactive=True) | |
| bass_or_treble = gr.Checkbox(label=translations["bass_or_treble"], value=False, interactive=True) | |
| limiter = gr.Checkbox(label=translations["limiter"], value=False, interactive=True) | |
| resample_checkbox = gr.Checkbox(label=translations["resample"], value=False, interactive=True) | |
| with gr.Row(): | |
| distortion_checkbox = gr.Checkbox(label=translations["distortion"], value=False, interactive=True) | |
| gain_checkbox = gr.Checkbox(label=translations["gain"], value=False, interactive=True) | |
| bitcrush_checkbox = gr.Checkbox(label=translations["bitcrush"], value=False, interactive=True) | |
| clipping_checkbox = gr.Checkbox(label=translations["clipping"], value=False, interactive=True) | |
| with gr.Accordion(translations["fade"], open=True, visible=False) as fade_accordion: | |
| with gr.Row(): | |
| fade_in = gr.Slider(minimum=0, maximum=10000, step=100, value=0, label=translations["fade_in"], info=translations["fade_in_info"], interactive=True) | |
| fade_out = gr.Slider(minimum=0, maximum=10000, step=100, value=0, label=translations["fade_out"], info=translations["fade_out_info"], interactive=True) | |
| with gr.Accordion(translations["bass_or_treble"], open=True, visible=False) as bass_treble_accordion: | |
| with gr.Row(): | |
| bass_boost = gr.Slider(minimum=0, maximum=20, step=1, value=0, label=translations["bass_boost"], info=translations["bass_boost_info"], interactive=True) | |
| bass_frequency = gr.Slider(minimum=20, maximum=200, step=10, value=100, label=translations["bass_frequency"], info=translations["bass_frequency_info"], interactive=True) | |
| with gr.Row(): | |
| treble_boost = gr.Slider(minimum=0, maximum=20, step=1, value=0, label=translations["treble_boost"], info=translations["treble_boost_info"], interactive=True) | |
| treble_frequency = gr.Slider(minimum=1000, maximum=10000, step=500, value=3000, label=translations["treble_frequency"], info=translations["treble_frequency_info"], interactive=True) | |
| with gr.Accordion(translations["limiter"], open=True, visible=False) as limiter_accordion: | |
| with gr.Row(): | |
| limiter_threashold_db = gr.Slider(minimum=-60, maximum=0, step=1, value=-1, label=translations["limiter_threashold_db"], info=translations["limiter_threashold_db_info"], interactive=True) | |
| limiter_release_ms = gr.Slider(minimum=10, maximum=1000, step=1, value=100, label=translations["limiter_release_ms"], info=translations["limiter_release_ms_info"], interactive=True) | |
| with gr.Column(): | |
| pitch_shift_semitones = gr.Slider(minimum=-20, maximum=20, step=1, value=0, label=translations["pitch"], info=translations["pitch_info"], interactive=True) | |
| audio_effect_resample_sr = gr.Slider(minimum=0, maximum=48000, step=1, value=0, label=translations["resample"], info=translations["resample_info"], interactive=True, visible=False) | |
| distortion_drive_db = gr.Slider(minimum=0, maximum=50, step=1, value=20, label=translations["distortion"], info=translations["distortion_info"], interactive=True, visible=False) | |
| gain_db = gr.Slider(minimum=-60, maximum=60, step=1, value=0, label=translations["gain"], info=translations["gain_info"], interactive=True, visible=False) | |
| clipping_threashold_db = gr.Slider(minimum=-60, maximum=0, step=1, value=-1, label=translations["clipping_threashold_db"], info=translations["clipping_threashold_db_info"], interactive=True, visible=False) | |
| bitcrush_bit_depth = gr.Slider(minimum=1, maximum=24, step=1, value=16, label=translations["bitcrush_bit_depth"], info=translations["bitcrush_bit_depth_info"], interactive=True, visible=False) | |
| with gr.Row(): | |
| with gr.Accordion(translations["phaser"], open=False, visible=False) as phaser_accordion: | |
| phaser_depth = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.5, label=translations["phaser_depth"], info=translations["phaser_depth_info"], interactive=True) | |
| phaser_rate_hz = gr.Slider(minimum=0.1, maximum=10, step=0.1, value=1, label=translations["phaser_rate_hz"], info=translations["phaser_rate_hz_info"], interactive=True) | |
| phaser_mix = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.5, label=translations["phaser_mix"], info=translations["phaser_mix_info"], interactive=True) | |
| phaser_centre_frequency_hz = gr.Slider(minimum=50, maximum=5000, step=10, value=1000, label=translations["phaser_centre_frequency_hz"], info=translations["phaser_centre_frequency_hz_info"], interactive=True) | |
| phaser_feedback = gr.Slider(minimum=-1, maximum=1, step=0.01, value=0, label=translations["phaser_feedback"], info=translations["phaser_feedback_info"], interactive=True) | |
| with gr.Row(): | |
| with gr.Accordion(translations["compressor"], open=False, visible=False) as compressor_accordion: | |
| compressor_threashold_db = gr.Slider(minimum=-60, maximum=0, step=1, value=-20, label=translations["compressor_threashold_db"], info=translations["compressor_threashold_db_info"], interactive=True) | |
| compressor_ratio = gr.Slider(minimum=1, maximum=20, step=0.1, value=1, label=translations["compressor_ratio"], info=translations["compressor_ratio_info"], interactive=True) | |
| compressor_attack_ms = gr.Slider(minimum=0.1, maximum=100, step=0.1, value=10, label=translations["compressor_attack_ms"], info=translations["compressor_attack_ms_info"], interactive=True) | |
| compressor_release_ms = gr.Slider(minimum=10, maximum=1000, step=1, value=100, label=translations["compressor_release_ms"], info=translations["compressor_release_ms_info"], interactive=True) | |
| with gr.Row(): | |
| gr.Markdown(translations["output_audio"]) | |
| with gr.Row(): | |
| audio_play_input = gr.Audio(show_download_button=True, interactive=False, label=translations["input_audio"]) | |
| audio_play_output = gr.Audio(show_download_button=True, interactive=False, label=translations["output_audio"]) | |
| with gr.Row(): | |
| reverb_check_box.change(fn=visible_1, inputs=[reverb_check_box], outputs=[reverb_accordion]) | |
| chorus_check_box.change(fn=visible_1, inputs=[chorus_check_box], outputs=[chorus_accordion]) | |
| delay_check_box.change(fn=visible_1, inputs=[delay_check_box], outputs=[delay_accordion]) | |
| with gr.Row(): | |
| compressor_check_box.change(fn=visible_1, inputs=[compressor_check_box], outputs=[compressor_accordion]) | |
| phaser_check_box.change(fn=visible_1, inputs=[phaser_check_box], outputs=[phaser_accordion]) | |
| more_options.change(fn=visible_1, inputs=[more_options], outputs=[more_accordion]) | |
| with gr.Row(): | |
| fade.change(fn=visible_1, inputs=[fade], outputs=[fade_accordion]) | |
| bass_or_treble.change(fn=visible_1, inputs=[bass_or_treble], outputs=[bass_treble_accordion]) | |
| limiter.change(fn=visible_1, inputs=[limiter], outputs=[limiter_accordion]) | |
| resample_checkbox.change(fn=visible_1, inputs=[resample_checkbox], outputs=[audio_effect_resample_sr]) | |
| with gr.Row(): | |
| distortion_checkbox.change(fn=visible_1, inputs=[distortion_checkbox], outputs=[distortion_drive_db]) | |
| gain_checkbox.change(fn=visible_1, inputs=[gain_checkbox], outputs=[gain_db]) | |
| clipping_checkbox.change(fn=visible_1, inputs=[clipping_checkbox], outputs=[clipping_threashold_db]) | |
| bitcrush_checkbox.change(fn=visible_1, inputs=[bitcrush_checkbox], outputs=[bitcrush_bit_depth]) | |
| with gr.Row(): | |
| upload_audio.upload(fn=lambda audio_in: shutil.move(audio_in.name, os.path.join("audios")), inputs=[upload_audio], outputs=[audio_in_path]) | |
| audio_in_path.change(fn=lambda audio: audio if audio else None, inputs=[audio_in_path], outputs=[audio_play_input]) | |
| audio_effects_refesh.click(fn=refesh_audio, inputs=[], outputs=[audio_in_path]) | |
| with gr.Row(): | |
| more_options.change(fn=lambda: [False]*4, inputs=[], outputs=[fade, bass_or_treble, limiter, resample_checkbox]) | |
| more_options.change(fn=lambda: [False]*4, inputs=[], outputs=[distortion_checkbox, gain_checkbox, clipping_checkbox, bitcrush_checkbox]) | |
| with gr.Row(): | |
| apply_effects_button.click( | |
| fn=audio_effects, | |
| inputs=[ | |
| audio_in_path, | |
| audio_out_path, | |
| resample_checkbox, | |
| audio_effect_resample_sr, | |
| chorus_depth, | |
| chorus_rate_hz, | |
| chorus_mix, | |
| chorus_centre_delay_ms, | |
| chorus_feedback, | |
| distortion_drive_db, | |
| reverb_room_size, | |
| reverb_damping, | |
| reverb_wet_level, | |
| reverb_dry_level, | |
| reverb_width, | |
| reverb_freeze_mode, | |
| pitch_shift_semitones, | |
| delay_second, | |
| delay_feedback, | |
| delay_mix, | |
| compressor_threashold_db, | |
| compressor_ratio, | |
| compressor_attack_ms, | |
| compressor_release_ms, | |
| limiter_threashold_db, | |
| limiter_release_ms, | |
| gain_db, | |
| bitcrush_bit_depth, | |
| clipping_threashold_db, | |
| phaser_rate_hz, | |
| phaser_depth, | |
| phaser_centre_frequency_hz, | |
| phaser_feedback, | |
| phaser_mix, | |
| bass_boost, | |
| bass_frequency, | |
| treble_boost, | |
| treble_frequency, | |
| fade_in, | |
| fade_out, | |
| audio_output_format, | |
| chorus_check_box, | |
| distortion_checkbox, | |
| reverb_check_box, | |
| delay_check_box, | |
| compressor_check_box, | |
| limiter, | |
| gain_checkbox, | |
| bitcrush_checkbox, | |
| clipping_checkbox, | |
| phaser_check_box, | |
| bass_or_treble, | |
| fade | |
| ], | |
| outputs=[audio_play_output], | |
| api_name="audio_effects" | |
| ) | |
| with gr.TabItem(translations["createdataset"], visible=configs["create_dataset_tab"]): | |
| gr.Markdown(translations["create_dataset_markdown"]) | |
| with gr.Row(): | |
| gr.Markdown(translations["create_dataset_markdown_2"]) | |
| with gr.Row(): | |
| dataset_url = gr.Textbox(label=translations["url_audio"], info=translations["create_dataset_url"], value="", placeholder="https://www.youtube.com/...", interactive=True) | |
| with gr.Row(): | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Group(): | |
| with gr.Row(): | |
| separator_audio = gr.Checkbox(label=translations["separator_tab"], value=False, interactive=True) | |
| separator_reverb = gr.Checkbox(label=translations["dereveb_audio"], value=False, interactive=False) | |
| denoise_mdx = gr.Checkbox(label=translations["denoise"], value=False, interactive=False) | |
| with gr.Row(): | |
| clean_audio = gr.Checkbox(label=translations["clear_audio"], value=False, interactive=True) | |
| resample = gr.Checkbox(label=translations["resample"], value=False, interactive=True) | |
| skip = gr.Checkbox(label=translations["skip"], value=False, interactive=True) | |
| with gr.Row(): | |
| resample_sample_rate = gr.Slider(minimum=0, maximum=48000, step=1, value=0, label=translations["resample"], info=translations["resample_info"], interactive=True, visible=False) | |
| dataset_clean_strength = gr.Slider(minimum=0, maximum=1, step=0.1, value=0.5, label=translations["clean_strength"], info=translations["clean_strength_info"], interactive=True, visible=False) | |
| with gr.Column(): | |
| create_button = gr.Button(translations["createdataset"], variant="primary", scale=2) | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Group(visible=False) as separator_dataset: | |
| with gr.Row() as kim_vocal_row: | |
| kim_vocal_version = gr.Radio(label=translations["model_ver"], info=translations["model_ver_info"], choices=["Version-1", "Version-2"], value="Version-2", interactive=True, visible=False) | |
| kim_vocal_overlap = gr.Radio(label=translations["overlap"], info=translations["overlap_info"], choices=["0.25", "0.5", "0.75", "0.99"], value="0.25", interactive=True, visible=False) | |
| with gr.Row() as kim_vocal_row_2: | |
| kim_vocal_segments_size = gr.Slider(label=translations["segments_size"], info=translations["segments_size_info"], minimum=32, maximum=4000, value=256, step=8, interactive=True, visible=False) | |
| kim_vocal_hop_length = gr.Slider(label="Hop length", info=translations["hop_length_info"], minimum=1, maximum=8192, value=1024, step=1, interactive=True, visible=False) | |
| with gr.Row() as kim_vocal_row_3: | |
| kim_vocal_batch_size = gr.Slider(label=translations["batch_size"], info=translations["mdx_batch_size_info"], minimum=1, maximum=64, value=1, step=1, interactive=True, visible=False) | |
| with gr.Row(): | |
| create_dataset_info = gr.Textbox(label=translations["create_dataset_info"], value="", interactive=False) | |
| with gr.Row(): | |
| with gr.Column(): | |
| output_dataset = gr.Textbox(label=translations["output_data"], info=translations["output_data_info"], value="dataset", placeholder="dataset", interactive=True) | |
| with gr.Row(): | |
| skip_start = gr.Textbox(label=translations["skip_start"], info=translations["skip_start_info"], value="", placeholder="0,...", interactive=True, visible=False) | |
| skip_end = gr.Textbox(label=translations["skip_end"], info=translations["skip_end_info"], value="", placeholder="0,...", interactive=True, visible=False) | |
| with gr.Row(): | |
| separator_audio.change(fn=valueFalse_interactive1, inputs=[separator_audio], outputs=[separator_reverb]) | |
| separator_audio.change(fn=valueFalse_interactive1, inputs=[separator_audio], outputs=[denoise_mdx]) | |
| separator_audio.change(fn=visible_1, inputs=[separator_audio], outputs=[separator_dataset]) | |
| with gr.Row(): | |
| separator_audio.change(fn=visible_1, inputs=[separator_audio], outputs=[kim_vocal_row]) | |
| separator_audio.change(fn=visible_1, inputs=[separator_audio], outputs=[kim_vocal_row_2]) | |
| separator_audio.change(fn=visible_1, inputs=[separator_audio], outputs=[kim_vocal_row_3]) | |
| with gr.Row(): | |
| resample.change(fn=visible_1, inputs=[resample], outputs=[resample_sample_rate]) | |
| clean_audio.change(fn=visible_1, inputs=[clean_audio], outputs=[dataset_clean_strength]) | |
| with gr.Row(): | |
| skip.change(fn=valueEmpty_visible1, inputs=[skip], outputs=[skip_start]) | |
| skip.change(fn=valueEmpty_visible1, inputs=[skip], outputs=[skip_end]) | |
| with gr.Row(): | |
| create_button.click( | |
| fn=create_dataset, | |
| inputs=[ | |
| dataset_url, | |
| output_dataset, | |
| resample, | |
| resample_sample_rate, | |
| clean_audio, | |
| dataset_clean_strength, | |
| separator_audio, | |
| separator_reverb, | |
| kim_vocal_version, | |
| kim_vocal_overlap, | |
| kim_vocal_segments_size, | |
| denoise_mdx, | |
| skip, | |
| skip_start, | |
| skip_end, | |
| kim_vocal_hop_length, | |
| kim_vocal_batch_size | |
| ], | |
| outputs=[create_dataset_info], | |
| api_name="create_dataset" | |
| ) | |
| with gr.TabItem(translations["training_model"], visible=configs["training_tab"]): | |
| gr.Markdown(f"## {translations['training_model']}") | |
| with gr.Row(): | |
| gr.Markdown(translations["training_markdown"]) | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Row(): | |
| with gr.Column(): | |
| training_name = gr.Textbox(label=translations["modelname"], info=translations["training_model_name"], value="", placeholder=translations["modelname"], interactive=True) | |
| training_sr = gr.Radio(label=translations["sample_rate"], info=translations["sample_rate_info"], choices=["32k", "40k", "48k"], value="48k", interactive=True) | |
| training_ver = gr.Radio(label=translations["training_version"], info=translations["training_version_info"], choices=["v1", "v2"], value="v2", interactive=True) | |
| with gr.Row(): | |
| training_f0 = gr.Checkbox(label=translations["training_pitch"], info=translations["training_pitch_info"], value=True, interactive=True) | |
| upload = gr.Checkbox(label=translations["upload"], info=translations["upload_dataset"], value=False, interactive=True) | |
| preprocess_cut = gr.Checkbox(label=translations["split_audio"], info=translations["preprocess_split"], value=False, interactive=True) | |
| process_effects = gr.Checkbox(label=translations["preprocess_effect"], info=translations["preprocess_effect_info"], value=False, interactive=True) | |
| with gr.Column(): | |
| clean_dataset = gr.Checkbox(label=translations["clear_dataset"], info=translations["clear_dataset_info"], value=False, interactive=True) | |
| clean_dataset_strength = gr.Slider(label=translations["clean_strength"], info=translations["clean_strength_info"], minimum=0, maximum=1, value=0.7, step=0.1, interactive=True, visible=False) | |
| with gr.Column(): | |
| preprocess_button = gr.Button(translations["preprocess_button"], scale=2) | |
| upload_dataset = gr.Files(label=translations["drop_audio"], file_types=['audio'], visible=False) | |
| preprocess_info = gr.Textbox(label=translations["preprocess_info"], value="", interactive=False) | |
| with gr.Column(): | |
| with gr.Row(): | |
| with gr.Column(): | |
| extract_method = gr.Radio(label=translations["f0_method"], info=translations["f0_method"], choices=["pm", "dio", "crepe", "crepe-tiny", "fcpe", "rmvpe", "harvest"], value="pm", interactive=True) | |
| extract_hop_length = gr.Slider(label="Hop length", info=translations["hop_length_info"], minimum=1, maximum=512, value=128, step=1, interactive=True, visible=False) | |
| with gr.Accordion(label=translations["hubert_model"], open=False): | |
| extract_embedders = gr.Radio(label=translations["hubert_model"], info=translations["hubert_info"], choices=["contentvec_base", "hubert_base", "japanese_hubert_base", "korean_hubert_base", "chinese_hubert_base", "custom"], value="contentvec_base", interactive=True) | |
| with gr.Row(): | |
| extract_embedders_custom = gr.Textbox(label=translations["modelname"], info=translations["modelname_info"], value="", placeholder="hubert_base", interactive=True, visible=False) | |
| with gr.Column(): | |
| extract_button = gr.Button(translations["extract_button"], scale=2) | |
| extract_info = gr.Textbox(label=translations["extract_info"], value="", interactive=False) | |
| with gr.Column(): | |
| with gr.Row(): | |
| with gr.Column(): | |
| total_epochs = gr.Slider(label=translations["total_epoch"], info=translations["total_epoch_info"], minimum=1, maximum=10000, value=300, step=1, interactive=True) | |
| save_epochs = gr.Slider(label=translations["save_epoch"], info=translations["save_epoch_info"], minimum=1, maximum=10000, value=50, step=1, interactive=True) | |
| with gr.Column(): | |
| index_button = gr.Button(f"3. {translations['create_index']}", variant="primary", scale=2) | |
| training_button = gr.Button(f"4. {translations['training_model']}", variant="primary", scale=2) | |
| with gr.Row(): | |
| with gr.Accordion(label=translations["setting"], open=False): | |
| with gr.Row(): | |
| index_algorithm = gr.Radio(label=translations["index_algorithm"], info=translations["index_algorithm_info"], choices=["Auto", "Faiss", "KMeans"], value="Auto", interactive=True) | |
| with gr.Row(): | |
| custom_dataset = gr.Checkbox(label=translations["custom_dataset"], info=translations["custom_dataset_info"], value=False, interactive=True) | |
| overtraining_detector = gr.Checkbox(label=translations["overtraining_detector"], info=translations["overtraining_detector_info"], value=False, interactive=True) | |
| sync_graph = gr.Checkbox(label=translations["sync_graph"], info=translations["sync_graph_info"], value=False, interactive=True) | |
| cache_in_gpu = gr.Checkbox(label=translations["cache_in_gpu"], info=translations["cache_in_gpu_info"], value=False, interactive=True) | |
| with gr.Column(): | |
| dataset_path = gr.Textbox(label=translations["dataset_folder"], value="dataset", interactive=True, visible=False) | |
| with gr.Column(): | |
| threshold = gr.Slider(minimum=1, maximum=100, value=50, step=1, label=translations["threshold"], interactive=True, visible=False) | |
| with gr.Accordion(translations["setting_cpu_gpu"], open=False): | |
| with gr.Column(): | |
| gpu_number = gr.Textbox(label=translations["gpu_number"], value=str(get_number_of_gpus()), info=translations["gpu_number_info"], interactive=True) | |
| gpu_info = gr.Textbox(label=translations["gpu_info"], value=get_gpu_info(), info=translations["gpu_info_2"], interactive=False) | |
| cpu_core = gr.Slider(label=translations["cpu_core"], info=translations["cpu_core_info"], minimum=0, maximum=cpu_count(), value=cpu_count(), step=1, interactive=True) | |
| train_batch_size = gr.Slider(label=translations["batch_size"], info=translations["batch_size_info"], minimum=1, maximum=64, value=8, step=1, interactive=True) | |
| with gr.Row(): | |
| with gr.Row(): | |
| save_only_latest = gr.Checkbox(label=translations["save_only_latest"], info=translations["save_only_latest_info"], value=True, interactive=True) | |
| save_every_weights = gr.Checkbox(label=translations["save_every_weights"], info=translations["save_every_weights_info"], value=True, interactive=True) | |
| not_use_pretrain = gr.Checkbox(label=translations["not_use_pretrain_2"], info=translations["not_use_pretrain_info"], value=False, interactive=True) | |
| custom_pretrain = gr.Checkbox(label=translations["custom_pretrain"], info=translations["custom_pretrain_info"], value=False, interactive=True) | |
| with gr.Row(): | |
| with gr.Row(): | |
| model_author = gr.Textbox(label=translations["training_author"], info=translations["training_author_info"], value="", placeholder=translations["training_author"], interactive=True) | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Accordion(translations["custom_pretrain_info"], open=False, visible=False) as pretrain_setting: | |
| pretrained_D = gr.Dropdown(label=translations["pretrain_file"].format(dg="D"), choices=sorted(pretrainedD), value=sorted(pretrainedD)[0] if len(sorted(pretrainedD)) > 0 else '', interactive=True, allow_custom_value=True, visible=False) | |
| pretrained_G = gr.Dropdown(label=translations["pretrain_file"].format(dg="G"), choices=sorted(pretrainedG), value=sorted(pretrainedG)[0] if len(sorted(pretrainedG)) > 0 else '', interactive=True, allow_custom_value=True, visible=False) | |
| refesh_pretrain = gr.Button(translations["refesh_pretrain"], scale=2, visible=False) | |
| with gr.Row(): | |
| training_info = gr.Textbox(label=translations["train_info"], value="", interactive=False) | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Accordion(translations["export_model"], open=False): | |
| with gr.Row(): | |
| model_file= gr.Dropdown(label=translations["model_name"], choices=sorted(model_name), value=sorted(model_name)[0] if len(sorted(model_name)) > 0 else '', interactive=True, allow_custom_value=True) | |
| index_file = gr.Dropdown(label=translations["index_path"], choices=sorted(index_path), value=sorted(index_path)[0] if len(sorted(index_path)) > 0 else '', interactive=True, allow_custom_value=True) | |
| with gr.Row(): | |
| refesh_file = gr.Button(f"1. {translations['refesh']}", scale=2) | |
| zip_model = gr.Button(translations["zip_model"], variant="primary", scale=2) | |
| with gr.Row(): | |
| zip_output = gr.File(label=translations["output_zip"], file_types=['zip'], interactive=False, visible=False) | |
| with gr.Row(): | |
| refesh_file.click(fn=change_choices, inputs=[], outputs=[model_file, index_file]) | |
| zip_model.click(fn=lambda: visible_1(True), inputs=[], outputs=[zip_output]) | |
| zip_model.click(fn=zip_file, inputs=[training_name, model_file, index_file], outputs=[zip_output]) | |
| with gr.Row(): | |
| dataset_path.change( | |
| fn=lambda folder: os.makedirs(folder, exist_ok=True), | |
| inputs=[dataset_path], | |
| outputs=[], | |
| api_name="create_folder" | |
| ) | |
| upload.change(fn=visible_1, inputs=[upload], outputs=[upload_dataset]) | |
| overtraining_detector.change(fn=visible_1, inputs=[overtraining_detector], outputs=[threshold]) | |
| clean_dataset.change(fn=visible_1, inputs=[clean_dataset], outputs=[clean_dataset_strength]) | |
| with gr.Row(): | |
| custom_dataset.change(fn=lambda custom_dataset: [visible_1(custom_dataset), "dataset"],inputs=[custom_dataset], outputs=[dataset_path, dataset_path]) | |
| upload_dataset.upload( | |
| fn=lambda files, folder: [shutil.move(f.name, os.path.join(folder, os.path.split(f.name)[1])) for f in files] if folder != "" else gr.Warning('Vui lòng nhập tên thư mục dữ liệu'), | |
| inputs=[upload_dataset, dataset_path], | |
| outputs=[], | |
| api_name="upload_dataset" | |
| ) | |
| with gr.Row(): | |
| not_use_pretrain.change(fn=lambda a, b: [visible_1(a and not b), visible_1(a and not b), visible_1(a and not b), visible_1(a and not b)], inputs=[custom_pretrain, not_use_pretrain], outputs=[pretrained_D, pretrained_G, refesh_pretrain, pretrain_setting]) | |
| custom_pretrain.change(fn=lambda a, b: [visible_1(a and not b), visible_1(a and not b), visible_1(a and not b), visible_1(a and not b)], inputs=[custom_pretrain, not_use_pretrain], outputs=[pretrained_D, pretrained_G, refesh_pretrain, pretrain_setting]) | |
| refesh_pretrain.click(fn=change_choices_pretrained, inputs=[], outputs=[pretrained_D, pretrained_G]) | |
| with gr.Row(): | |
| preprocess_button.click( | |
| fn=preprocess, | |
| inputs=[ | |
| training_name, | |
| training_sr, | |
| cpu_core, | |
| preprocess_cut, | |
| process_effects, | |
| dataset_path, | |
| clean_dataset, | |
| clean_dataset_strength | |
| ], | |
| outputs=[preprocess_info], | |
| api_name="preprocess" | |
| ) | |
| with gr.Row(): | |
| extract_method.change(fn=hoplength_show, inputs=[extract_method], outputs=[extract_hop_length]) | |
| extract_embedders.change(fn=lambda extract_embedders: visible_1(True if extract_embedders == "custom" else False), inputs=[extract_embedders], outputs=[extract_embedders_custom]) | |
| with gr.Row(): | |
| extract_button.click( | |
| fn=extract, | |
| inputs=[ | |
| training_name, | |
| training_ver, | |
| extract_method, | |
| training_f0, | |
| extract_hop_length, | |
| cpu_core, | |
| gpu_number, | |
| training_sr, | |
| extract_embedders, | |
| extract_embedders_custom | |
| ], | |
| outputs=[extract_info], | |
| api_name="extract" | |
| ) | |
| with gr.Row(): | |
| index_button.click( | |
| fn=create_index, | |
| inputs=[ | |
| training_name, | |
| training_ver, | |
| index_algorithm | |
| ], | |
| outputs=[training_info], | |
| api_name="create_index" | |
| ) | |
| with gr.Row(): | |
| training_button.click( | |
| fn=training, | |
| inputs=[ | |
| training_name, | |
| training_ver, | |
| save_epochs, | |
| save_only_latest, | |
| save_every_weights, | |
| total_epochs, | |
| training_sr, | |
| train_batch_size, | |
| gpu_number, | |
| training_f0, | |
| not_use_pretrain, | |
| custom_pretrain, | |
| pretrained_G, | |
| pretrained_D, | |
| overtraining_detector, | |
| threshold, | |
| sync_graph, | |
| cache_in_gpu, | |
| model_author | |
| ], | |
| outputs=[training_info], | |
| api_name="training_model" | |
| ) | |
| with gr.TabItem(translations["fushion"], visible=configs["fushion_tab"]): | |
| gr.Markdown(translations["fushion_markdown"]) | |
| with gr.Row(): | |
| gr.Markdown(translations["fushion_markdown_2"]) | |
| with gr.Row(): | |
| with gr.Column(): | |
| name_to_save = gr.Textbox(label=translations["modelname"], placeholder="Model.pth", value="", max_lines=1, interactive=True) | |
| with gr.Column(): | |
| fushion_button = gr.Button(translations["fushion"], variant="primary", scale=4) | |
| with gr.Column(): | |
| with gr.Row(): | |
| model_a = gr.File(label=f"{translations['model_name']} 1", file_types=['pth']) | |
| model_b = gr.File(label=f"{translations['model_name']} 2", file_types=['pth']) | |
| with gr.Row(): | |
| model_path_a = gr.Textbox(label=f"{translations['model_path']} 1", value="", placeholder="assets/weights/Model_1.pth") | |
| model_path_b = gr.Textbox(label=f"{translations['model_path']} 2", value="", placeholder="assets/weights/Model_2.pth") | |
| with gr.Row(): | |
| ratio = gr.Slider(minimum=0, maximum=1, label=translations["model_ratio"], info=translations["model_ratio_info"], value=0.5, interactive=True) | |
| with gr.Row(): | |
| output_model = gr.File(label=translations["output_model_path"], visible=False) | |
| with gr.Row(): | |
| model_a.upload(fn=lambda model: shutil.move(model.name, os.path.join("assets", "weights")), inputs=[model_a], outputs=[model_path_a]) | |
| model_b.upload(fn=lambda model: shutil.move(model.name, os.path.join("assets", "weights")), inputs=[model_b], outputs=[model_path_b]) | |
| with gr.Row(): | |
| fushion_button.click( | |
| fn=fushion_model, | |
| inputs=[ | |
| name_to_save, | |
| model_path_a, | |
| model_path_b, | |
| ratio | |
| ], | |
| outputs=[name_to_save, output_model], | |
| api_name="fushion_model" | |
| ) | |
| fushion_button.click(fn=lambda: visible_1(True), inputs=[], outputs=[output_model]) | |
| with gr.TabItem(translations["read_model"], visible=configs["read_tab"]): | |
| gr.Markdown(translations["read_model_markdown"]) | |
| with gr.Row(): | |
| gr.Markdown(translations["read_model_markdown_2"]) | |
| with gr.Row(): | |
| with gr.Column(): | |
| model = gr.File(label=translations["drop_model"], file_types=['pth']) | |
| with gr.Column(): | |
| read_button = gr.Button(translations["readmodel"], variant="primary", scale=2) | |
| with gr.Column(): | |
| model_path = gr.Textbox(label=translations["download_url"], value="", info=translations["model_path_info"], interactive=True) | |
| output_info = gr.Textbox(label=translations["modelinfo"], value="", interactive=False, scale=6) | |
| with gr.Row(): | |
| model.upload(fn=lambda model: shutil.move(model.name, os.path.join("assets", "weights")), inputs=[model], outputs=[model_path]) | |
| read_button.click( | |
| fn=model_info, | |
| inputs=[model_path], | |
| outputs=[output_info], | |
| api_name="read_model" | |
| ) | |
| with gr.TabItem(translations["downloads"], visible=configs["downloads_tab"]): | |
| gr.Markdown(translations["download_markdown"]) | |
| with gr.Row(): | |
| gr.Markdown(translations["download_markdown_2"]) | |
| with gr.Row(): | |
| with gr.Accordion(translations["model_download"], open=True): | |
| with gr.Row(): | |
| downloadmodel = gr.Radio(label=translations["model_download_select"], choices=[translations["download_url"], translations["download_from_csv"], translations["download_from_applio"], translations["upload"]], interactive=True, value=translations["download_url"]) | |
| with gr.Row(): | |
| gr.Markdown("___") | |
| with gr.Row(): | |
| url_input = gr.Textbox(label=translations["model_url"], value="", placeholder="https://...", scale=6, visible=True) | |
| model_name = gr.Textbox(label=translations["modelname"], value="", placeholder=translations["modelname"], scale=2, visible=True) | |
| url_download = gr.Button(value=translations["downloads"], scale=2, visible=True) | |
| with gr.Row(): | |
| model_browser = gr.Dropdown(choices=models.keys(), label=translations["model_warehouse"], scale=8, allow_custom_value=True, visible=False) | |
| download_from_browser = gr.Button(value=translations["get_model"], scale=2, variant="primary", visible=False) | |
| with gr.Row(): | |
| model_upload = gr.File(label=translations["drop_model"], file_types=['pth', 'index', 'zip'], visible=False) | |
| with gr.Column(): | |
| with gr.Row(): | |
| search_name = gr.Textbox(label=translations["name_to_search"], placeholder=translations["modelname"], interactive=True, scale=8, visible=False) | |
| search = gr.Button(translations["search_2"], scale=2, visible=False) | |
| with gr.Row(): | |
| search_dropdown = gr.Dropdown(label=translations["select_download_model"], value="", choices=[], allow_custom_value=True, interactive=False, visible=False) | |
| download = gr.Button(translations["downloads"], variant="primary", visible=False) | |
| # with gr.Row(): | |
| # with gr.Accordion(translations["download_pretrainec"], open=False): | |
| # with gr.Row(): | |
| # pretrain_download_choices = gr.Radio(label=translations["model_download_select"], choices=[translations["download_url"], translations["list_model"], translations["upload"]], value=translations["download_url"], interactive=True) | |
| # with gr.Row(): | |
| # gr.Markdown("___") | |
| # with gr.Row(): | |
| # pretrainD = gr.Textbox(label=translations["pretrained_url"].format(dg="D"), value="", info=translations["only_huggingface"], placeholder="https://...", interactive=True, scale=4, visible=True) | |
| # pretrainG = gr.Textbox(label=translations["pretrained_url"].format(dg="G"), value="", info=translations["only_huggingface"], placeholder="https://...", interactive=True, scale=4, visible=True) | |
| # download_pretrain_button = gr.Button(translations["downloads"], scale=2) | |
| # with gr.Row(): | |
| # pretrain_choices = gr.Dropdown(label=translations["select_pretrain"], info=translations["select_pretrain_info"], choices=list(fetch_pretrained_data().keys()), value="Titan_Medium", allow_custom_value=True, interactive=True, scale=6, visible=False) | |
| # sample_rate_pretrain = gr.Dropdown(label=translations["pretrain_sr"], choices=["48k", "40k", "32k"], value="48k", interactive=True, visible=False) | |
| # download_pretrain_choices_button = gr.Button(translations["downloads"], scale=2, variant="primary", visible=False) | |
| # with gr.Row(): | |
| # pretrain_upload_g = gr.File(label=translations["drop_pretrain"].format(dg="G"), file_types=['pth'], visible=False) | |
| # pretrain_upload_d = gr.File(label=translations["drop_pretrain"].format(dg="D"), file_types=['pth'], visible=False) | |
| # with gr.Row(): | |
| # with gr.Accordion(translations["hubert_download"], open=False): | |
| # with gr.Row(): | |
| # hubert_url = gr.Textbox(label=translations["hubert_url"], value="", info=translations["only_huggingface"], placeholder="https://...", interactive=True, scale=8) | |
| # hubert_button = gr.Button(translations["downloads"], scale=2, variant="primary") | |
| # with gr.Row(): | |
| # hubert_input = gr.File(label=translations["drop_hubert"], file_types=['pt']) | |
| with gr.Row(): | |
| url_download.click( | |
| fn=download_model, | |
| inputs=[ | |
| url_input, | |
| model_name | |
| ], | |
| outputs=[url_input], | |
| api_name="download_model" | |
| ) | |
| download_from_browser.click( | |
| fn=lambda model: download_model(models[model], model), | |
| inputs=[model_browser], | |
| outputs=[model_browser], | |
| api_name="download_browser" | |
| ) | |
| with gr.Row(): | |
| downloadmodel.change(fn=download_change, inputs=[downloadmodel], outputs=[url_input, model_name, url_download, model_browser, download_from_browser, search_name, search, search_dropdown, download, model_upload]) | |
| search.click(fn=search_models, inputs=[search_name], outputs=[search_dropdown, download]) | |
| model_upload.upload(fn=save_drop_model, inputs=[model_upload], outputs=[model_upload]) | |
| download.click( | |
| fn=lambda model: download_model(model_options[model], model), | |
| inputs=[search_dropdown], | |
| outputs=[search_dropdown], | |
| api_name="download_applio" | |
| ) | |
| # with gr.Row(): | |
| # pretrain_download_choices.change(fn=download_pretrained_change, inputs=[pretrain_download_choices], outputs=[pretrainD, pretrainG, download_pretrain_button, pretrain_choices, sample_rate_pretrain, download_pretrain_choices_button, pretrain_upload_d, pretrain_upload_g]) | |
| # pretrain_choices.change(fn=update_sample_rate_dropdown, inputs=[pretrain_choices], outputs=[sample_rate_pretrain]) | |
| # with gr.Row(): | |
| # download_pretrain_button.click( | |
| # fn=download_pretrained_model, | |
| # inputs=[ | |
| # pretrain_download_choices, | |
| # pretrainD, | |
| # pretrainG | |
| # ], | |
| # outputs=[pretrainD], | |
| # api_name="download_pretrain_link" | |
| # ) | |
| # download_pretrain_choices_button.click( | |
| # fn=download_pretrained_model, | |
| # inputs=[ | |
| # pretrain_download_choices, | |
| # pretrain_choices, | |
| # sample_rate_pretrain | |
| # ], | |
| # outputs=[pretrain_choices], | |
| # api_name="download_pretrain_choices" | |
| # ) | |
| # pretrain_upload_g.upload( | |
| # fn=lambda pretrain_upload_g: shutil.move(pretrain_upload_g.name, os.path.join("assets", "model", "pretrained_custom")), | |
| # inputs=[pretrain_upload_g], | |
| # outputs=[], | |
| # api_name="upload_pretrain_g" | |
| # ) | |
| # pretrain_upload_d.upload( | |
| # fn=lambda pretrain_upload_d: shutil.move(pretrain_upload_d.name, os.path.join("assets", "model", "pretrained_custom")), | |
| # inputs=[pretrain_upload_d], | |
| # outputs=[], | |
| # api_name="upload_pretrain_d" | |
| # ) | |
| # with gr.Row(): | |
| # hubert_button.click( | |
| # fn=hubert_download, | |
| # inputs=[hubert_url], | |
| # outputs=[hubert_url], | |
| # api_name="hubert_download" | |
| # ) | |
| # hubert_input.upload( | |
| # fn=lambda hubert: shutil.move(hubert.name, os.path.join("assets", "model", "embedders")), | |
| # inputs=[hubert_input], | |
| # outputs=[], | |
| # api_name="upload_hubert" | |
| # ) | |
| with gr.TabItem(translations["settings"], visible=configs["settings_tab"]): | |
| gr.Markdown(translations["settings_markdown"]) | |
| with gr.Row(): | |
| gr.Markdown(translations["settings_markdown_2"]) | |
| with gr.Row(): | |
| with gr.Column(): | |
| language_dropdown = gr.Dropdown(label=translations["lang"], interactive=True, info=translations["lang_restart"], choices=configs["support_language"], value=configs["language"]) | |
| change_lang = gr.Button(translations["change_lang"], variant=["primary"], scale=2) | |
| with gr.Column(): | |
| toggle_button = gr.Button(translations["change_light_dark"], variant=["secondary"], scale=2) | |
| with gr.Row(): | |
| with gr.Column(): | |
| fp_select = gr.Radio(label=translations["fp_train"], info=translations["fp_info"], value="fp32", choices=["fp16", "fp32"], interactive=True) | |
| fp_button = gr.Button(translations["fp_button"], variant=["primary"], scale=2) | |
| with gr.Column(): | |
| theme_dropdown = gr.Dropdown(label=translations["theme"], interactive=True, info=translations["theme_restart"], choices=configs["themes"], value=configs["theme"], allow_custom_value=True) | |
| changetheme = gr.Button(translations["theme_button"], variant=["primary"], scale=2) | |
| with gr.Row(): | |
| toggle_button.click(fn=None, js="""() => {document.body.classList.toggle('dark')}""") | |
| fp_button.click(fn=change_fp, inputs=[fp_select], outputs=[]) | |
| with gr.Row(): | |
| change_lang.click(fn=change_language, inputs=[language_dropdown], outputs=[]) | |
| change_lang.click(fn=restart_app, inputs=[], outputs=[]) | |
| with gr.Row(): | |
| changetheme.click(fn=change_theme, inputs=[theme_dropdown], outputs=[]) | |
| changetheme.click(fn=restart_app, inputs=[], outputs=[]) | |
| with gr.Row(): | |
| change_lang.click(fn=None, js="""setTimeout(function() {location.reload()}, 30000)""", inputs=[], outputs=[]) | |
| changetheme.click(fn=None, js="""setTimeout(function() {location.reload()}, 30000)""", inputs=[], outputs=[]) | |
| with gr.TabItem(translations["source"]): | |
| gr.Markdown(translations["source_info"]) | |
| with gr.Row(): | |
| gr.Markdown("___") | |
| with gr.Row(): | |
| gr.Markdown(translations["credits"].format(author=codecs.decode("uggcf://tvguho.pbz/CunzUhlauNau16", "rot13"), applio=codecs.decode("uggcf://tvguho.pbz/VNUvfcnab/Nccyvb/gerr/znva?gno=ernqzr-bi-svyr", "rot13"), ai_hispano=codecs.decode("uggcf://tvguho.pbz/VNUvfcnab", "rot13"), rvc_webui=codecs.decode("uggcf://tvguho.pbz/EIP-Cebwrpg/Ergevriny-onfrq-Ibvpr-Pbairefvba-JroHV?gno=ernqzr-bi-svyr", "rot13"), rvc_boss=codecs.decode("uggcf://tvguho.pbz/EIP-Obff", "rot13"), python_audio_separator=codecs.decode("uggcf://tvguho.pbz/abznqxnenbxr/clguba-nhqvb-frcnengbe?gno=ernqzr-bi-svyr", "rot13"), andrew_beveridge=codecs.decode("uggcf://tvguho.pbz/orirenqo", "rot13"))) | |
| print(translations["set_lang"].format(lang=configs["language"])) | |
| for i in range(configs["num_of_restart"]): | |
| try: | |
| app.queue().launch( | |
| favicon_path=os.path.join("assets", "miku.png"), | |
| server_name=server_name, | |
| server_port=port, | |
| show_error=show_error, | |
| inbrowser=False, | |
| share=share | |
| ) | |
| break | |
| except OSError: | |
| port -= 1 | |
| except Exception as e: | |
| raise RuntimeError(translations["error_occurred"].format(e=e)) |