{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "ymhGfgFSR17k"
},
"source": [
"## **Applio**\n",
"A simple, high-quality voice conversion tool focused on ease of use and performance.\n",
"\n",
"[Support](https://discord.gg/urxFjYmYYh) — [GitHub](https://github.com/IAHispano/Applio) — [Terms of Use](https://github.com/IAHispano/Applio/blob/main/TERMS_OF_USE.md)\n",
"\n",
"
\n",
"\n",
"---\n",
"\n",
"
\n",
"\n",
"#### **Acknowledgments**\n",
"\n",
"To all external collaborators for their special help in the following areas: Hina (Encryption method), Poopmaster (Extra section), Shirou (UV installer)\n",
"\n",
"#### **Disclaimer**\n",
"By using Applio, you agree to comply with ethical and legal standards, respect intellectual property and privacy rights, avoid harmful or prohibited uses, and accept full responsibility for any outcomes, while Applio disclaims liability and reserves the right to amend these terms."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "vtON700qokuQ"
},
"outputs": [],
"source": [
"# @title **Install Applio**\n",
"import sys\n",
"from IPython.display import clear_output\n",
"import codecs\n",
"import os\n",
"\n",
"encoded_url = \"uggcf://tvguho.pbz/VNUvfcnab/Nccyvb/\"\n",
"decoded_url = codecs.decode(encoded_url, \"rot_13\")\n",
"\n",
"repo_name_encoded = \"Nccyvb\"\n",
"repo_name = codecs.decode(repo_name_encoded, \"rot_13\")\n",
"\n",
"!pip install -q uv\n",
"\n",
"!git clone --depth 1 {decoded_url} --branch 3.2.8-bugfix --single-branch\n",
"%cd {repo_name}\n",
"clear_output()\n",
"\n",
"print(\"Installing requirements...\")\n",
"!uv pip install -r requirements.txt -q --prerelease if-necessary-or-explicit\n",
"!uv pip install torch==2.3.1 torchvision==0.18.1 torchaudio==2.3.1 \\\n",
" --index-url https://download.pytorch.org/whl/cu121 \\\n",
" -q --prerelease if-necessary-or-explicit\n",
"!uv pip install numpy==1.26.4 gradio==5.23.1 pyngrok -q --prerelease if-necessary-or-explicit\n",
"\n",
"clear_output()\n",
"print(\"✅ Finished installing requirements!\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "nAlXiNYnFH9F"
},
"outputs": [],
"source": [
"# @title **Start Applio**\n",
"# @markdown ### Choose a sharing method:\n",
"\n",
"import codecs\n",
"import threading\n",
"import urllib.request\n",
"import time\n",
"import ipywidgets as widgets\n",
"from IPython.display import display\n",
"import os\n",
"from pyngrok import ngrok\n",
"os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n",
"\n",
"method = \"gradio\" # @param [\"gradio\", \"localtunnel\", \"ngrok\"]\n",
"ngrok_token = \"If you selected the 'ngrok' method, obtain your auth token here: https://dashboard.ngrok.com/get-started/your-authtoken\" # @param {type:\"string\"}\n",
"tensorboard = True #@param {type: \"boolean\"}\n",
"\n",
"def start_gradio():\n",
" !python app.py --listen --share\n",
"\n",
"def start_localtunnel():\n",
" !npm install -g localtunnel &>/dev/null\n",
" with open('url.txt', 'w') as file:\n",
" file.write('')\n",
" get_ipython().system_raw('lt --port 6969 >> url.txt 2>&1 &')\n",
" time.sleep(2)\n",
" endpoint_ip = urllib.request.urlopen('https://ipv4.icanhazip.com').read().decode('utf8').strip(\"\\n\")\n",
"\n",
" with open('url.txt', 'r') as file:\n",
" tunnel_url = file.read()\n",
" tunnel_url = tunnel_url.replace(\"your url is: \", \"\")\n",
"\n",
" print(f\"Share Link: \\033[0m\\033[93m{tunnel_url}\\033[0m\", end=\"\\033[0m\\n\")\n",
"\n",
" password_endpoint_widget = widgets.Text(\n",
" value=endpoint_ip,\n",
" description='Password IP:',\n",
" disabled=True\n",
" )\n",
" display(password_endpoint_widget)\n",
" !python app.py --listen\n",
"\n",
"\n",
"def start_ngrok():\n",
" try:\n",
" ngrok.set_auth_token(ngrok_token)\n",
" ngrok.kill()\n",
" tunnel = ngrok.connect(6969)\n",
" print(f\"Ngrok URL: \\033[0m\\033[93m{tunnel.public_url}\\033[0m\", end=\"\\033[0m\\n\")\n",
" !python app.py --listen\n",
" except Exception as e:\n",
" print(f\"Error starting ngrok: {e}\")\n",
"\n",
"def start_applio():\n",
" if method == 'gradio':\n",
" start_gradio()\n",
" elif method == 'localtunnel':\n",
" start_localtunnel()\n",
" elif method == 'ngrok':\n",
" start_ngrok()\n",
"\n",
"if tensorboard:\n",
" %load_ext tensorboard\n",
" %reload_ext tensorboard\n",
" %tensorboard --logdir logs --bind_all\n",
"\n",
"if \"autobackups\" not in globals():\n",
" autobackups = False\n",
"\n",
"if autobackups:\n",
" thread = threading.Thread(target=backup_files)\n",
" thread.start()\n",
"\n",
"thread_applio = threading.Thread(target=start_applio)\n",
"thread_applio.start()\n",
"\n",
"\n",
"while True:\n",
" time.sleep(5)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3b59-2x-qEnX"
},
"source": [
"### **Extra**\n",
"Enjoy extra options that can make it easier for you to use Applio\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "19LNv6iYqF6_"
},
"outputs": [],
"source": [
"# @title Mount Drive\n",
"# @markdown Mount the files from Google Drive to the Colab.\n",
"from google.colab import drive\n",
"\n",
"drive.mount(\"/content/drive\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "I5o6MlpFouiG"
},
"outputs": [],
"source": [
"# @title Auto Backup\n",
"# @markdown When running it, it will be activated or deactivated previously to start up together with Applio.\n",
"import os\n",
"import shutil\n",
"import time\n",
"\n",
"LOGS_FOLDER = \"/content/Applio/logs/\"\n",
"GOOGLE_DRIVE_PATH = \"/content/drive/MyDrive/ApplioBackup\"\n",
"\n",
"if \"autobackups\" not in globals():\n",
" autobackups = False\n",
"\n",
"cooldown = 15 # @param {type:\"slider\", min:0, max:100, step:0}\n",
"def backup_files():\n",
" print(\"\\nStarting backup loop...\")\n",
" last_backup_timestamps_path = os.path.join(\n",
" LOGS_FOLDER, \"last_backup_timestamps.txt\"\n",
" )\n",
" fully_updated = False\n",
"\n",
" while True:\n",
" try:\n",
" updated_files = 0\n",
" deleted_files = 0\n",
" new_files = 0\n",
" last_backup_timestamps = {}\n",
"\n",
" try:\n",
" with open(last_backup_timestamps_path, \"r\") as f:\n",
" last_backup_timestamps = dict(line.strip().split(\":\") for line in f)\n",
" except FileNotFoundError:\n",
" pass\n",
"\n",
" for root, dirs, files in os.walk(LOGS_FOLDER):\n",
" # Excluding \"zips\" and \"mute\" directories\n",
" if \"zips\" in dirs:\n",
" dirs.remove(\"zips\")\n",
" if \"mute\" in dirs:\n",
" dirs.remove(\"mute\")\n",
"\n",
" for filename in files:\n",
" if filename != \"last_backup_timestamps.txt\":\n",
" filepath = os.path.join(root, filename)\n",
" if os.path.isfile(filepath):\n",
" backup_filepath = os.path.join(\n",
" GOOGLE_DRIVE_PATH,\n",
" os.path.relpath(filepath, LOGS_FOLDER),\n",
" )\n",
" backup_folderpath = os.path.dirname(backup_filepath)\n",
" if not os.path.exists(backup_folderpath):\n",
" os.makedirs(backup_folderpath)\n",
" last_backup_timestamp = last_backup_timestamps.get(filepath)\n",
" current_timestamp = os.path.getmtime(filepath)\n",
" if (\n",
" last_backup_timestamp is None\n",
" or float(last_backup_timestamp) < current_timestamp\n",
" ):\n",
" shutil.copy2(filepath, backup_filepath)\n",
" last_backup_timestamps[filepath] = str(current_timestamp)\n",
" if last_backup_timestamp is None:\n",
" new_files += 1\n",
" else:\n",
" updated_files += 1\n",
"\n",
"\n",
" for filepath in list(last_backup_timestamps.keys()):\n",
" if not os.path.exists(filepath):\n",
" backup_filepath = os.path.join(\n",
" GOOGLE_DRIVE_PATH, os.path.relpath(filepath, LOGS_FOLDER)\n",
" )\n",
" if os.path.exists(backup_filepath):\n",
" os.remove(backup_filepath)\n",
" deleted_files += 1\n",
" del last_backup_timestamps[filepath]\n",
"\n",
"\n",
" if updated_files > 0 or deleted_files > 0 or new_files > 0:\n",
" print(f\"Backup Complete: {new_files} new, {updated_files} updated, {deleted_files} deleted.\")\n",
" fully_updated = False\n",
" elif not fully_updated:\n",
" print(\"Files are up to date.\")\n",
" fully_updated = True\n",
"\n",
" with open(last_backup_timestamps_path, \"w\") as f:\n",
" for filepath, timestamp in last_backup_timestamps.items():\n",
" f.write(f\"{filepath}:{timestamp}\\n\")\n",
"\n",
" time.sleep(cooldown if fully_updated else 0.1)\n",
"\n",
"\n",
" except Exception as error:\n",
" print(f\"An error occurred during backup: {error}\")\n",
"\n",
"\n",
"if autobackups:\n",
" autobackups = False\n",
" print(\"Autobackup Disabled\")\n",
"else:\n",
" autobackups = True\n",
" print(\"Autobackup Enabled\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "ifV_vc4h4Uvx"
},
"outputs": [],
"source": [
"# @title Load a Backup\n",
"import os\n",
"import shutil\n",
"\n",
"# @markdown Put the exact name you put as your Model Name in Applio.\n",
"modelname = \"My-Project\" # @param {type:\"string\"}\n",
"source_path = \"/content/drive/MyDrive/ApplioBackup/\" + modelname\n",
"destination_path = \"/content/Applio/logs/\" + modelname\n",
"backup_timestamps_file = \"last_backup_timestamps.txt\"\n",
"if not os.path.exists(source_path):\n",
" print(\n",
" \"The model folder does not exist. Please verify the name is correct or check your Google Drive.\"\n",
" )\n",
"else:\n",
" time_ = os.path.join(\"/content/drive/MyDrive/ApplioBackup/\", backup_timestamps_file)\n",
" time__ = os.path.join(\"/content/Applio/logs/\", backup_timestamps_file)\n",
" if os.path.exists(time_):\n",
" shutil.copy(time_, time__)\n",
" shutil.copytree(source_path, destination_path)\n",
" print(\"Model backup loaded successfully.\")"
]
}
],
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"accelerator": "GPU",
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"3b59-2x-qEnX"
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