Copy_of_Vocal_Remover_5_arch.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Copy of Vocal Remover 5_arch.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/melonjack/API-Playgrounds/blob/master/Copy_of_Vocal_Remover_5_arch.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mgJIlzkaph9y"
      },
      "source": [
        "#<font color=\"Green\">Vocal Remover 5.0.1</font>\n",
        "<br>Trained models provided in this notebook are by [Anjok07](https://github.com/Anjok07), and [aufr33](https://github.com/aufr33).</br>\n",
        "<br>OFFICIAL UVR GITHUB PAGE: [here](https://github.com/Anjok07/ultimatevocalremovergui).</br>\n",
        "<br>OFFICIAL CLI Version: [here](https://github.com/tsurumeso/vocal-remover).</br>\n",
        "<sup><br>Powered by [tsurumeso](http://github.com/tsurumeso/).\n",
        "<br>Colaboratory version by [AudioHacker](https://www.youtube.com/channel/UC0NiSV1jLMH-9E09wiDVFYw).</br></sup>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4G6899OCFMNF",
        "cellView": "form"
      },
      "source": [
        "# ------------VSC REWRITE\n",
        "# pyright: reportMissingImports=false, reportUnusedVariable=warning, reportUntypedBaseClass=error\n",
        "#@title Mount to Drive\n",
        "\n",
        "#update channels\n",
        "ai = 'https://github.com/NaJeongMo/Colaboratory-Notebook-for-Ultimate-Vocal-Remover'\n",
        "\n",
        "model_ver = 'https://vocalremover501.tzuyuhv.repl.co/model_ver'\n",
        "\n",
        "vercheck = 'https://VocalRemover501.tzuyuhv.repl.co/v501_/check_ver'\n",
        "\n",
        "from IPython.display import Audio, display\n",
        "from google.colab import output\n",
        "from google.colab import drive\n",
        "from google.colab import files\n",
        "from sys import exit\n",
        "import zipfile\n",
        "import hashlib\n",
        "import os.path\n",
        "import shutil\n",
        "import psutil\n",
        "import random\n",
        "import glob\n",
        "import time\n",
        "import zlib\n",
        "import sys\n",
        "import os\n",
        "#@markdown Uncheck if you want to use VocalRemover5 without mounting to drive.\n",
        "MountDrive = True #@param{type:\"boolean\"}\n",
        "#@markdown Mounting path; don't touch this if you don't know what you're doing\n",
        "mounting_path = '/content/drive/MyDrive' #@param [\"snippets:\",\"/content/drive/MyDrive\",\"/content/drive/Shareddrives/<your shared drive name>\", \"/content/drive/Shareddrives/Shared Drive\"]{allow-input: true}\n",
        "#@markdown Force trigger update\n",
        "ForceUpdate = False #@param{type:\"boolean\"}\n",
        "## @markdown Reinstall VR\n",
        "# flush = False #@param{type:\"boolean\"}\n",
        "## output.clear()\n",
        "class hide_opt: # hide outputs\n",
        "    def __enter__(self):\n",
        "        self._original_stdout = sys.stdout\n",
        "        sys.stdout = open(os.devnull, 'w')\n",
        "    def __exit__(self, exc_type, exc_val, exc_tb):\n",
        "        sys.stdout.close()\n",
        "        sys.stdout = self._original_stdout\n",
        "\n",
        "def get_size(bytes, suffix='B'): # read ram\n",
        "    global svmem\n",
        "    factor = 1024\n",
        "    for unit in [\"\", \"K\", \"M\", \"G\", \"T\", \"P\"]:\n",
        "        if bytes < factor:\n",
        "            return f'{bytes:.2f}{unit}{suffix}'\n",
        "        bytes /= factor\n",
        "    svmem = psutil.virtual_memory()\n",
        "\n",
        "def validateModelLinks():\n",
        "    with hide_opt():\n",
        "        get_ipython().system('wget {} -O model_ver'.format(model_ver))\n",
        "    model_ver_ = open(\"model_ver\", \"r\")\n",
        "    model_ver_ = model_ver_.read()\n",
        "    with hide_opt():\n",
        "        get_ipython().system('wget {} -O model_list'.format(model_ver_))\n",
        "    model_list = open(\"model_list\", \"r\")\n",
        "    model_list = model_list.readlines()\n",
        "    models = []\n",
        "    for i in model_list:\n",
        "        models.append(i)\n",
        "    os.remove('model_ver')\n",
        "    os.remove('model_list')\n",
        "    return models\n",
        "\n",
        "def installAI():\n",
        "    print('Installing ai...', end=' ')\n",
        "    with hide_opt():\n",
        "        get_ipython().system('git clone {} VocalRemover5-COLAB_arch'.format(ai))\n",
        "        os.chdir('VocalRemover5-COLAB_arch')\n",
        "    print('done')\n",
        "\n",
        "    print('Downloading models...', end=' ')\n",
        "    for i in validateModelLinks():\n",
        "        with hide_opt():\n",
        "            zname = sanitize_filename(os.path.basename(i))\n",
        "            get_ipython().system(\"wget {}\".format(i))\n",
        "            get_ipython().system('unzip -o {}'.format(zname))\n",
        "            os.remove(zname)\n",
        "    print('done')\n",
        "\n",
        "def mount_to_colab(): # cd to drive\n",
        "    os.chdir(f'{mounting_path}/VocalRemover5-COLAB_arch')\n",
        "    print('Success!')\n",
        "\n",
        "def vrmount(): # install ai and or cd to drive\n",
        "    drive.mount(\"/content/drive\", force_remount=True)\n",
        "    os.chdir(mounting_path + '/')\n",
        "    if os.path.isdir(f'{mounting_path}/VocalRemover5-COLAB_arch'):\n",
        "        check_update()\n",
        "        mount_to_colab()\n",
        "    else:\n",
        "        print(\"Uh oh! It looks like it's your first time using VocalRemover5!\")\n",
        "        print('About 4 gb of Google Drive will be used.')\n",
        "        print('Make sure to clear your trash bin!!')\n",
        "        confirmation = input('Do you want to continue? (y/n): ')\n",
        "        if confirmation.lower() == 'y':\n",
        "            print('')\n",
        "            installAI()\n",
        "            mount_to_colab()\n",
        "        else:\n",
        "            print('Please uncheck MountDrive')\n",
        "            exit()\n",
        "\n",
        "def dlvr(): # download vr to colab only\n",
        "    print('Warning: changes will not be saved.')\n",
        "    print('Downloading VR5...')\n",
        "    with hide_opt():\n",
        "        os.chdir('/content')\n",
        "    installAI()\n",
        "    #os.chdir('/content/VocalRemover5-COLAB_arch')\n",
        "    print (\"Success!\")\n",
        "\n",
        "def check_update():\n",
        "    if os.path.isdir(f'{mounting_path}/VocalRemover5-COLAB_arch'): # check update if ai installed is True\n",
        "        os.chdir(f'{mounting_path}/VocalRemover5-COLAB_arch')\n",
        "        print(\"Checking for updates...\", end=\" \")\n",
        "        with hide_opt():\n",
        "            get_ipython().system('wget {} -O check_ver'.format(vercheck))\n",
        "        f = open(\"check_ver\", \"r\")\n",
        "        nver = f.read()\n",
        "        f = open(\"v\", \"r\")\n",
        "        cver = f.read()\n",
        "        if cver != nver or ForceUpdate:\n",
        "            print('New update found! {}'.format(nver))\n",
        "            os.chdir('../')\n",
        "            print('Updating ai...',end=' ')\n",
        "            with hide_opt():\n",
        "                get_ipython().system('git clone {} temp_VocalRemover5-COLAB_arch'.format(ai))\n",
        "            get_ipython().system('cp -a temp_VocalRemover5-COLAB_arch/* VocalRemover5-COLAB_arch/')\n",
        "            get_ipython().system('rm -rf temp_VocalRemover5-COLAB_arch')\n",
        "            print('done')\n",
        "\n",
        "            print('Downloading models...', end=' ')\n",
        "            os.chdir('VocalRemover5-COLAB_arch')\n",
        "            for i in validateModelLinks():\n",
        "                with hide_opt():\n",
        "                    zname = sanitize_filename(os.path.basename(i))\n",
        "                    get_ipython().system(\"wget {}\".format(i))\n",
        "                    get_ipython().system('unzip -o {}'.format(zname))\n",
        "                    os.remove(zname)\n",
        "            print('done')\n",
        "            output.clear()\n",
        "            os.remove(f'{mounting_path}/VocalRemover5-COLAB_arch/v')\n",
        "            os.rename(f'{mounting_path}/VocalRemover5-COLAB_arch/check_ver',f'{mounting_path}/VocalRemover5-COLAB_arch/v')\n",
        "            os.chdir(f'{mounting_path}/VocalRemover5-COLAB_arch') # just to make sure\n",
        "        else:\n",
        "            os.remove('check_ver')\n",
        "            print('No update found.')\n",
        "    else:\n",
        "        if MountDrive:\n",
        "            vrmount() # colab\n",
        "        elif MountDrive == False:\n",
        "            if os.path.isdir('/content/VocalRemover5-COLAB_arch'):\n",
        "                print(\"Success!\")\n",
        "            else:\n",
        "                dlvr()\n",
        "# Import other modules for cell-inference\n",
        "with hide_opt():\n",
        "    ! pip install pathvalidate\n",
        "    ! pip install youtube-dl\n",
        "    ! pip install samplerate\n",
        "\n",
        "    ! pip install librosa==0.8.0\n",
        "    ! pip install numba==0.48.0\n",
        "    ! pip install resampy==0.2.2\n",
        "\n",
        "    from pathvalidate import sanitize_filename # for inference\n",
        "    import youtube_dl # for inference\n",
        "#----------DEFINE INFERENCE REQ------------\n",
        "def crc32(fileName):\n",
        "    with open(fileName, 'rb') as fh:\n",
        "        hash = 0\n",
        "        while True:\n",
        "            s = fh.read(65536)\n",
        "            if not s:\n",
        "                break\n",
        "            hash = zlib.crc32(s, hash)\n",
        "        return \"%08X\" % (hash & 0xFFFFFFFF)\n",
        "def YouTube(link, dl=True):\n",
        "    inputsha = hashlib.sha1(bytes(link, encoding='utf8')).hexdigest() + '.wav'\n",
        "    fmt = '251/140/250/139'\n",
        "    opt = {'format': fmt, 'outtmpl': inputsha, 'updatetime': False, 'nocheckcertificate': True}\n",
        "    if dl == True:\n",
        "        print('YouTube link detected')\n",
        "        print('Downloading...', end=' ')\n",
        "    with hide_opt():\n",
        "        with youtube_dl.YoutubeDL(opt) as ydl:\n",
        "            global desc\n",
        "            if dl == True:\n",
        "                desc = ydl.extract_info(link, download=not os.path.isfile(inputsha))\n",
        "            else:\n",
        "                desc = ydl.extract_info(link, download=not True)\n",
        "    if dl == True:\n",
        "        print('done')\n",
        "    titlename = sanitize_filename(desc['title'])\n",
        "    if dl == True:\n",
        "        print(titlename)\n",
        "    if dl==False:\n",
        "        return titlename\n",
        "    else:\n",
        "        return titlename, inputsha\n",
        "def zipdir(folder, zipname): # LINUX CALL MODIFIED!!!\n",
        "    if '.zip' in zipname:\n",
        "        zipname = zipname.strip('.zip')\n",
        "    get_ipython().system('zip -r {}.zip {}'.format(zipname,folder))\n",
        "\n",
        "def dlFile(track,pretrained_model,isYouTube=False,export_as_mp3=False):\n",
        "    modelname = os.path.splitext(os.path.basename(pretrained_model))[0]\n",
        "    stems = [f'_{modelname}_Instruments',f'_{modelname}_Vocals']\n",
        "    filename = os.path.splitext(os.path.basename(track))[0]\n",
        "    if isYouTube:\n",
        "        filename = YouTube(track,dl=False)\n",
        "    if os.path.isdir(filename):\n",
        "        shutil.rmtree(filename)\n",
        "    else:\n",
        "        os.mkdir(filename)\n",
        "    if os.path.isfile(f'{filename}.zip'):\n",
        "        os.remove(f'{filename}.zip')\n",
        "    if export_as_mp3:\n",
        "        os.chdir('separated/')\n",
        "        for i in stems:\n",
        "            wav_path = filename + i + '.wav'\n",
        "            mp3_path = filename + i + '.mp3'\n",
        "            get_ipython().system(f'ffmpeg -y -i \"{wav_path}\" -vn -ar 44100 -ac 2 -b:a 320k \"{mp3_path}\" -loglevel quiet')\n",
        "        os.chdir('../')\n",
        "        for move in stems:\n",
        "            shutil.move('separated/' + filename + move + '.mp3',filename)\n",
        "    else:\n",
        "        for move in stems:\n",
        "            shutil.move('separated/' + filename + move + '.wav',filename)\n",
        "    with hide_opt():\n",
        "        get_ipython().system(f'zip -r \"{filename}.zip\" \"{filename}\"')\n",
        "    shutil.rmtree(filename)\n",
        "    files.download(f'{filename}.zip')\n",
        "\n",
        "#-------------Script begin-------------\n",
        "\n",
        "if MountDrive:\n",
        "    vrmount()\n",
        "else:\n",
        "    if os.path.isdir('/content/VocalRemover5-COLAB_arch') == False:\n",
        "        if os.path.isdir('/content/VocalRemover5-COLAB_arch'):\n",
        "            print('Success!')\n",
        "        else:\n",
        "            dlvr()\n",
        "\n",
        "\n",
        "gpu_info = !nvidia-smi\n",
        "gpu_info = '\\n'.join(gpu_info)\n",
        "\n",
        "if gpu_info.find('failed') >= 0:\n",
        "    svmem = psutil.virtual_memory()\n",
        "    print(f'GPU runtime is disabled. You have {get_size(svmem.total)} RAM.')\n",
        "if gpu_info.find('Tesla T4') >= 0:\n",
        "    print('You got a Tesla T4 GPU. (Speeds are around 10-25 it/s)')\n",
        "elif gpu_info.find('Tesla K80') >= 0:\n",
        "    print('You got a Tesla K80 GPU. (Speeds are around 2-10 it/s)')\n",
        "elif gpu_info.find('Tesla P100') >= 0:\n",
        "    print('You got a Tesla P100 GPU. (Speeds are around 15-42 it/s) Lucky!')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sMC2obPy0F7U"
      },
      "source": [
        "#<font color=\"Blue\">Print a list of tracks and models</font>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SHpEMYmWXC8d",
        "cellView": "form"
      },
      "source": [
        "if os.path.isfile('main.py') == False:\n",
        "  if MountDrive:\n",
        "    os.chdir(mounting_path + '/VocalRemover5-COLAB_arch')\n",
        "  else:\n",
        "    os.chdir('/content/VocalRemover5-COLAB_arch')\n",
        "#@title <- Press me\n",
        "print(\"Here's a list of files in your tracks folder:\")\n",
        "print(\"\")\n",
        "! ls tracks/\n",
        "print(\"\")\n",
        "print(\"---------------------------------------------------\")\n",
        "print(\"\")\n",
        "print(\"Usable models:\")\n",
        "print(\"\")\n",
        "print(\"   v5 (new arch):\")\n",
        "! ls models/v5_new\n",
        "print(\"   v5:\")\n",
        "! ls models/v5\n",
        "print(\"---------------------------------------------------\")\n",
        "print(\"\")\n",
        "print(\"Usable parameters:\")\n",
        "print(\"\")\n",
        "! ls modelparams/\n",
        "print(\"---------------------------------------------------\")\n",
        "print(\"\")\n",
        "print(\"Your separated files:\")\n",
        "print(\"\")\n",
        "! ls separated/"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1lj-5wN6XWXC"
      },
      "source": [
        "#Inference"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CT8TuXWLBrXF",
        "cellView": "form"
      },
      "source": [
        "# ------------VSC REWRITE\n",
        "#type: ignore\n",
        "if os.path.isfile('main.py') == False:\n",
        "    if MountDrive:\n",
        "        os.chdir(mounting_path + '/VocalRemover5-COLAB_arch')\n",
        "    else:\n",
        "        os.chdir('/content/VocalRemover5-COLAB_arch')\n",
        "\n",
        "#parameter markdowns-----------------\n",
        "\n",
        "#@markdown ### Input files\n",
        "#@markdown Track name / any link (Upload your songs in tracks folder)\n",
        "input = \"Enter any link or filename\" #@param {type:\"string\"}\n",
        "#@markdown Convert all files in your tracks folder\n",
        "convertAll = False #@param {type:\"boolean\"}\n",
        "if convertAll:\n",
        "    convertAll = '--convert_all'\n",
        "else:\n",
        "    convertAll = ''\n",
        "#@markdown Model name (Upload your models in models folder)\n",
        "pretrained_model = \"HighPrecison_4band_arch-124m_1.pth\" #@param [\"HP_4BAND_3090_arch-124m.pth\",\"HP2-4BAND-3090_4band_arch-500m_1.pth\",\"HP2-4BAND-3090_4band_arch-500m_2.pth\",\"Vocal_HP_4BAND_3090_AGG_arch-124m.pth\",\"Vocal_HP_4BAND_3090_arch-124m.pth\",\"HP_4BAND_arch-124m_3090.pth\",\"HighPrecison_4band_arch-124m_1.pth\",\"HighPrecison_4band_arch-124m_2.pth\",\"LOFI_2band-1_arch-34m.pth\",\"LOFI_2band-2_arch-34m.pth\",\"NewLayer_4band_arch-130m_1.pth\",\"NewLayer_4band_arch-130m_2.pth\",\"NewLayer_4band_arch-130m_3.pth\",\"MGM-v5-2Band-32000-_arch-default-BETA1.pth\",\"MGM-v5-2Band-32000-_arch-default-BETA2.pth\",\"MGM-v5-3Band-44100-_arch-default-BETA.pth\",\"MGM-v5-4Band-44100-_arch-default-BETA1.pth\",\"MGM-v5-4Band-44100-_arch-default-BETA2.pth\",\"MGM-v5-KAROKEE-32000-_arch-default-BETA1.pth\",\"MGM-v5-KAROKEE-32000-_arch-default-BETA2-AGR.pth\",\"MGM-v5-MIDSIDE-44100-_arch-default-BETA1.pth\",\"MGM-v5-MIDSIDE-44100-_arch-default-BETA2.pth\",\"MGM-v5-Vocal_2Band-32000-_arch-default-BETA1.pth\",\"MGM-v5-Vocal_2Band-32000-_arch-default-BETA2.pth\"]{allow-input: true}\n",
        "#@markdown ---\n",
        "#@markdown ### Arguments\n",
        "window_size =  320#@param {type:\"integer\"}\n",
        "if window_size < 320:\n",
        "    print('Warning: window_size lower than 320.')\n",
        "if window_size < 272:\n",
        "    window_size = 272\n",
        "#1band_sr32000_hl512.json  2band_44100_lofi.json  3band_44100_mid.json\n",
        "#1band_sr44100_hl512.json  2band_48000.json\t 4band_44100.json\n",
        "#2band_32000.json\t  3band_44100.json\t ensemble.json\n",
        "parameter = \"Auto detect\" #@param [\"Auto detect\",\"1band_sr32000_hl512.json\",\"1band_sr44100_hl512.json\", \"2band_32000.json\" , \"2band_44100_lofi.json\", \"2band_48000.json\", \"3band_44100.json\", \"3band_44100_mid.json\", \"4band_44100.json\"]\n",
        "parameter = \"modelparams/\" + parameter\n",
        "high_end_process = 'none' #@param [\"none\",\"mirroring\", \"mirroring2\" , \"bypass\"]\n",
        "aggressiveness = '0.325' #@param {type:\"string\"}\n",
        "aggressiveness = float(aggressiveness)\n",
        "#@markdown Mute low volume vocals\n",
        "postprocess = False #@param {type: \"boolean\"}\n",
        "if postprocess:\n",
        "    threshold = 0.2 #@param {type:\"number\"}\n",
        "    min_range = 64 #@param {type:\"integer\"}\n",
        "    fade_size = 32 #@param {type:\"integer\"}\n",
        "    if min_range < fade_size * 2:\n",
        "        print('min_range must be greater than or equal fade_size * 2')\n",
        "        print('Using default instead. (except threshold)')\n",
        "        min_range = 32\n",
        "        fade_size = 64\n",
        "    postprocess = f\"-p -thres {threshold} -mrange {min_range} -fsize {fade_size}\"\n",
        "else:\n",
        "    postprocess = \"\"\n",
        "#@markdown ### Architecture\n",
        "nn_architecture = 'Auto detect' #@param [\"Auto detect\",\"default\", \"34 MB\", \"124 MB\", \"130 MB\", \"500 MB\"]\n",
        "if nn_architecture == '34 MB':\n",
        "    nn_architecture = '33966KB'\n",
        "elif nn_architecture == '124 MB':\n",
        "    nn_architecture = '123821KB'\n",
        "elif nn_architecture == '130 MB':\n",
        "    nn_architecture = '129605KB'\n",
        "elif nn_architecture == '500 MB':\n",
        "    nn_architecture = '537238KB'\n",
        "elif nn_architecture == 'default':\n",
        "    pass\n",
        "#@markdown ---\n",
        "#@markdown ### Checkboxes\n",
        "#@markdown Use GPU for faster conversion\n",
        "gpu = True #@param {type: \"boolean\"}\n",
        "if gpu == True:\n",
        "    gpu = 0\n",
        "else:\n",
        "    gpu = -1\n",
        "#@markdown Aggressively remove vocals from Instrumental\n",
        "deepExtraction = False #@param {type:\"boolean\"}\n",
        "if deepExtraction:\n",
        "    deepExtraction = \"-D\"\n",
        "else:\n",
        "    deepExtraction = \"\"\n",
        "#@markdown Flip Instruments and Vocals output (Only for Vocal Models)\n",
        "isVocal = False #@param {type:\"boolean\"}\n",
        "if isVocal:\n",
        "    isVocal = '--isVocal'\n",
        "else:\n",
        "    isVocal = ''\n",
        "#@markdown Hide warnings\n",
        "suppress = True #@param {type:\"boolean\"}\n",
        "if suppress:\n",
        "    suppress = '--suppress'\n",
        "else:\n",
        "    suppress = ''\n",
        "#@markdown Export spectogram image\n",
        "output_image = False #@param {type: \"boolean\"}\n",
        "if output_image:\n",
        "    output_image = \"-I\"\n",
        "else:\n",
        "    output_image = \"\"\n",
        "#@markdown perform Test Time Augmentation to improve the separation quality\n",
        "tta = False #@param {type: \"boolean\"}\n",
        "if tta:\n",
        "    tta = \"-t\"\n",
        "else:\n",
        "    tta = \"\"\n",
        "#@markdown Use custom arguments\n",
        "useCustomArguments = False #@param {type: \"boolean\"}\n",
        "CustomArguments = \"-h\" #@param {type:\"string\"}\n",
        "#@markdown Download files\n",
        "download = False #@param {type:\"boolean\"}\n",
        "export_as_mp3 = True #@param {type:\"boolean\"}\n",
        "#@markdown Use all model\n",
        "model_version = \"Don't use all model\" #@param [\"Don't use all model\",\"v5_new\", \"v5\", \"all\"]\n",
        "# {none,v5,v5_new,all}\n",
        "# automation\n",
        "if nn_architecture == 'Auto detect':\n",
        "    if 'arch-default' in pretrained_model:\n",
        "        nn_architecture = 'default'\n",
        "    elif 'arch-34m' in pretrained_model:\n",
        "        nn_architecture = '33966KB'\n",
        "    elif 'arch-124m' in pretrained_model:\n",
        "        nn_architecture = '123821KB'\n",
        "    elif 'arch-130m' in pretrained_model:\n",
        "        nn_architecture = '129605KB'\n",
        "    elif 'arch-500m' in pretrained_model:\n",
        "        nn_architecture = '537238KB'\n",
        "    else:\n",
        "        print('Error! autoDetect_arch')\n",
        "        print('Using 124 MB instead.')\n",
        "        nn_architecture = '123821KB'\n",
        "if parameter == 'modelparams/Auto detect':\n",
        "    if '4band' in pretrained_model:\n",
        "        parameter = 'modelparams/4band_44100.json'\n",
        "    elif '3Band' in pretrained_model:\n",
        "        parameter = 'modelparams/3band_44100.json'\n",
        "    elif 'MIDSIDE' in pretrained_model:\n",
        "        parameter = 'modelparams/3band_44100_mid.json'\n",
        "    elif '2Band' in pretrained_model:\n",
        "        parameter = 'modelparams/2band_48000.json'\n",
        "    elif 'LOFI' in pretrained_model:\n",
        "        parameter = 'modelparams/2band_44100_lofi.json'\n",
        "    else:\n",
        "        parameter = 'modelparams/1band_sr44100_hl512.json'\n",
        "if '34m' in pretrained_model or '124m' in pretrained_model or '130m' in pretrained_model or '500m' in pretrained_model:\n",
        "    pretrained_model = \"models/v5_new/\" + pretrained_model\n",
        "else:\n",
        "    pretrained_model = \"models/v5/\" + pretrained_model\n",
        "    if os.path.isfile(pretrained_model) == False:\n",
        "        print('========================================================')\n",
        "        print('                Error model not found.')\n",
        "        print('Custom models should be uploaded in \"models/v5/\" folder!')\n",
        "        print('========================================================')\n",
        "if 'https://' not in input:\n",
        "    input = 'tracks/' + input\n",
        "    \"\"\"if '∕' in input:\n",
        "        input = input.split('∕')\n",
        "        c = 0\n",
        "        for i in input:\n",
        "            input[c] = 'tracks/' + i\n",
        "            c += 1\n",
        "        input[:] = ['∕'.join(input[:])]\n",
        "        input = input[0]\n",
        "        input = input.split('∕')\n",
        "        print(len(input))\n",
        "        input[:] = [' '.join(input[:])]\n",
        "        print(input)\n",
        "    else:\n",
        "        \"\"\"\n",
        "   \n",
        "if model_version == \"Don't use all model\":\n",
        "    model_version = 'none'\n",
        "\n",
        "# --------------AI----------------\n",
        "start_time = time.time()\n",
        "if useCustomArguments == False:\n",
        "    get_ipython().system(f'python3 main.py -i \"{input}\" {convertAll} --useAllModel \"{model_version}\" --model_params \"{parameter}\" -P \"{pretrained_model}\" -w {window_size} -H \"{high_end_process}\" --aggressiveness {aggressiveness} -n \"{nn_architecture}\" -g {gpu} {deepExtraction} {isVocal} {suppress} {output_image} {postprocess} {tta}')\n",
        "    if download and convertAll:\n",
        "        sys.exit(\"No no, this is not an error but downloading with convertAll is not yet possible. Please DON'T report this to me (Hv) or the server\")\n",
        "    if download:\n",
        "        dlFile(input,pretrained_model,isYouTube='http://' in input,export_as_mp3=export_as_mp3)\n",
        "if useCustomArguments:\n",
        "    get_ipython().system(f'python3 main.py {CustomArguments}')\n",
        "print('Notebook took: {0:.{1}f}s'.format(time.time() - start_time, 1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z6IO9kog67S6"
      },
      "source": [
        "---\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UY_aMvgsKhKk",
        "cellView": "form"
      },
      "source": [
        "#@markdown ### Custom Model Ensemble\n",
        "#@markdown Arguments\n",
        "input = \"Enter any link or filename\" #@param {type:\"string\"}\n",
        "window_size =  320#@param {type:\"integer\"}\n",
        "if window_size < 320:\n",
        "  print('Warning: window_size lower than 320.')\n",
        "if window_size < 272:\n",
        "  window_size = 272\n",
        "high_end_process = 'none' #@param [\"none\",\"mirroring\", \"mirroring2\" , \"bypass (Recommended for ensembling!)\"]\n",
        "if high_end_process == 'bypass (Recommended for ensembling!)':\n",
        "    high_end_process = 'bypass'\n",
        "aggressiveness = '0.3' #@param {type:\"string\"}\n",
        "aggressiveness = float(aggressiveness)\n",
        "FlipVocalModels = True #@param {type:\"boolean\"}\n",
        "if FlipVocalModels:\n",
        "    FlipVocalModels = '--flipVocals'\n",
        "else:\n",
        "    FlipVocalModels = ''\n",
        "tta = False #@param {type:\"boolean\"}\n",
        "if tta:\n",
        "    tta = '--tta'\n",
        "else:\n",
        "    tta = ''\n",
        "postprocess = False #@param{type:\"boolean\"}\n",
        "if postprocess:\n",
        "    postprocess = '--postprocess'\n",
        "else:\n",
        "    postprocess = ''\n",
        "#@markdown Algorithm\n",
        "instrumental = 'min_mag' #@param [\"min_mag\",\"max_mag\",\"comb_norm\",\"np_min\",\"np_max\",\"invert\"]\n",
        "vocal = 'max_mag' #@param [\"min_mag\",\"max_mag\",\"comb_norm\",\"np_min\",\"np_max\",\"invert\"]\n",
        "#@markdown Model cocktail ( ͡° ͜ʖ ͡°)\n",
        "# just a quick note, you can add more model markdowns if you want.\n",
        "def ensYouTube(link, dl=True):\n",
        "    inputsha = hashlib.sha1(bytes(link, encoding='utf8')).hexdigest() + '.wav'\n",
        "    fmt = '251/140/250/139'\n",
        "    opt = {'format': fmt, 'outtmpl': inputsha, 'updatetime': False, 'nocheckcertificate': True}\n",
        "    if dl == True:\n",
        "        print('YouTube link detected')\n",
        "        print('Downloading...', end=' ')\n",
        "    with hide_opt():\n",
        "        with youtube_dl.YoutubeDL(opt) as ydl:\n",
        "            global desc\n",
        "            if dl == True:\n",
        "                desc = ydl.extract_info(link, download=not os.path.isfile(inputsha))\n",
        "            else:\n",
        "                desc = ydl.extract_info(link, download=not True)\n",
        "    if dl == True:\n",
        "        print('done')\n",
        "    titlename = sanitize_filename(desc['title'])\n",
        "    if dl == True:\n",
        "        print(titlename)\n",
        "    if dl==False:\n",
        "        return titlename\n",
        "    else:\n",
        "        return titlename, inputsha\n",
        "\n",
        "def modelPath(model):\n",
        "    if model == 'slot':\n",
        "        model = ''\n",
        "    if '34m' in model or '124m' in model or '130m' in model or '500m' in model:\n",
        "        model = \"models/v5_new/\" + model\n",
        "    elif model == '':\n",
        "        pass\n",
        "    else:\n",
        "        model = \"models/v5/\" + model\n",
        "    return model\n",
        "def quot():\n",
        "    return '\"'\n",
        "model_1 = \"HP2-4BAND-3090_4band_arch-500m_1.pth\" #@param [\"slot\",\"HP_4BAND_3090_arch-124m.pth\",\"HP2-4BAND-3090_4band_arch-500m_1.pth\",\"HP2-4BAND-3090_4band_arch-500m_2.pth\",\"Vocal_HP_4BAND_3090_AGG_arch-124m.pth\",\"Vocal_HP_4BAND_3090_arch-124m.pth\",\"HP_4BAND_arch-124m_3090.pth\",\"HighPrecison_4band_arch-124m_1.pth\",\"HighPrecison_4band_arch-124m_2.pth\",\"LOFI_2band-1_arch-34m.pth\",\"LOFI_2band-2_arch-34m.pth\",\"NewLayer_4band_arch-130m_1.pth\",\"NewLayer_4band_arch-130m_2.pth\",\"NewLayer_4band_arch-130m_3.pth\",\"MGM-v5-2Band-32000-_arch-default-BETA1.pth\",\"MGM-v5-2Band-32000-_arch-default-BETA2.pth\",\"MGM-v5-3Band-44100-_arch-default-BETA.pth\",\"MGM-v5-4Band-44100-_arch-default-BETA1.pth\",\"MGM-v5-4Band-44100-_arch-default-BETA2.pth\",\"MGM-v5-KAROKEE-32000-_arch-default-BETA1.pth\",\"MGM-v5-KAROKEE-32000-_arch-default-BETA2-AGR.pth\",\"MGM-v5-MIDSIDE-44100-_arch-default-BETA1.pth\",\"MGM-v5-MIDSIDE-44100-_arch-default-BETA2.pth\",\"MGM-v5-Vocal_2Band-32000-_arch-default-BETA1.pth\",\"MGM-v5-Vocal_2Band-32000-_arch-default-BETA2.pth\"]{allow-input: true}\n",
        "model_2 = \"HP2-4BAND-3090_4band_arch-500m_2.pth\" #@param [\"slot\",\"HP_4BAND_3090_arch-124m.pth\",\"HP2-4BAND-3090_4band_arch-500m_1.pth\",\"HP2-4BAND-3090_4band_arch-500m_2.pth\",\"Vocal_HP_4BAND_3090_AGG_arch-124m.pth\",\"Vocal_HP_4BAND_3090_arch-124m.pth\",\"HP_4BAND_arch-124m_3090.pth\",\"HighPrecison_4band_arch-124m_1.pth\",\"HighPrecison_4band_arch-124m_2.pth\",\"LOFI_2band-1_arch-34m.pth\",\"LOFI_2band-2_arch-34m.pth\",\"NewLayer_4band_arch-130m_1.pth\",\"NewLayer_4band_arch-130m_2.pth\",\"NewLayer_4band_arch-130m_3.pth\",\"MGM-v5-2Band-32000-_arch-default-BETA1.pth\",\"MGM-v5-2Band-32000-_arch-default-BETA2.pth\",\"MGM-v5-3Band-44100-_arch-default-BETA.pth\",\"MGM-v5-4Band-44100-_arch-default-BETA1.pth\",\"MGM-v5-4Band-44100-_arch-default-BETA2.pth\",\"MGM-v5-KAROKEE-32000-_arch-default-BETA1.pth\",\"MGM-v5-KAROKEE-32000-_arch-default-BETA2-AGR.pth\",\"MGM-v5-MIDSIDE-44100-_arch-default-BETA1.pth\",\"MGM-v5-MIDSIDE-44100-_arch-default-BETA2.pth\",\"MGM-v5-Vocal_2Band-32000-_arch-default-BETA1.pth\",\"MGM-v5-Vocal_2Band-32000-_arch-default-BETA2.pth\"]{allow-input: true}\n",
        "model_3 = \"slot\" #@param [\"slot\",\"HP_4BAND_3090_arch-124m.pth\",\"HP2-4BAND-3090_4band_arch-500m_1.pth\",\"HP2-4BAND-3090_4band_arch-500m_2.pth\",\"Vocal_HP_4BAND_3090_AGG_arch-124m.pth\",\"Vocal_HP_4BAND_3090_arch-124m.pth\",\"HP_4BAND_arch-124m_3090.pth\",\"HighPrecison_4band_arch-124m_1.pth\",\"HighPrecison_4band_arch-124m_2.pth\",\"LOFI_2band-1_arch-34m.pth\",\"LOFI_2band-2_arch-34m.pth\",\"NewLayer_4band_arch-130m_1.pth\",\"NewLayer_4band_arch-130m_2.pth\",\"NewLayer_4band_arch-130m_3.pth\",\"MGM-v5-2Band-32000-_arch-default-BETA1.pth\",\"MGM-v5-2Band-32000-_arch-default-BETA2.pth\",\"MGM-v5-3Band-44100-_arch-default-BETA.pth\",\"MGM-v5-4Band-44100-_arch-default-BETA1.pth\",\"MGM-v5-4Band-44100-_arch-default-BETA2.pth\",\"MGM-v5-KAROKEE-32000-_arch-default-BETA1.pth\",\"MGM-v5-KAROKEE-32000-_arch-default-BETA2-AGR.pth\",\"MGM-v5-MIDSIDE-44100-_arch-default-BETA1.pth\",\"MGM-v5-MIDSIDE-44100-_arch-default-BETA2.pth\",\"MGM-v5-Vocal_2Band-32000-_arch-default-BETA1.pth\",\"MGM-v5-Vocal_2Band-32000-_arch-default-BETA2.pth\"]{allow-input: true}\n",
        "model_4 = \"slot\" #@param [\"slot\",\"HP_4BAND_3090_arch-124m.pth\",\"HP2-4BAND-3090_4band_arch-500m_1.pth\",\"HP2-4BAND-3090_4band_arch-500m_2.pth\",\"Vocal_HP_4BAND_3090_AGG_arch-124m.pth\",\"Vocal_HP_4BAND_3090_arch-124m.pth\",\"HP_4BAND_arch-124m_3090.pth\",\"HighPrecison_4band_arch-124m_1.pth\",\"HighPrecison_4band_arch-124m_2.pth\",\"LOFI_2band-1_arch-34m.pth\",\"LOFI_2band-2_arch-34m.pth\",\"NewLayer_4band_arch-130m_1.pth\",\"NewLayer_4band_arch-130m_2.pth\",\"NewLayer_4band_arch-130m_3.pth\",\"MGM-v5-2Band-32000-_arch-default-BETA1.pth\",\"MGM-v5-2Band-32000-_arch-default-BETA2.pth\",\"MGM-v5-3Band-44100-_arch-default-BETA.pth\",\"MGM-v5-4Band-44100-_arch-default-BETA1.pth\",\"MGM-v5-4Band-44100-_arch-default-BETA2.pth\",\"MGM-v5-KAROKEE-32000-_arch-default-BETA1.pth\",\"MGM-v5-KAROKEE-32000-_arch-default-BETA2-AGR.pth\",\"MGM-v5-MIDSIDE-44100-_arch-default-BETA1.pth\",\"MGM-v5-MIDSIDE-44100-_arch-default-BETA2.pth\",\"MGM-v5-Vocal_2Band-32000-_arch-default-BETA1.pth\",\"MGM-v5-Vocal_2Band-32000-_arch-default-BETA2.pth\"]{allow-input: true}\n",
        "model_5 = \"slot\" #@param [\"slot\",\"HP_4BAND_3090_arch-124m.pth\",\"HP2-4BAND-3090_4band_arch-500m_1.pth\",\"HP2-4BAND-3090_4band_arch-500m_2.pth\",\"Vocal_HP_4BAND_3090_AGG_arch-124m.pth\",\"Vocal_HP_4BAND_3090_arch-124m.pth\",\"HP_4BAND_arch-124m_3090.pth\",\"HighPrecison_4band_arch-124m_1.pth\",\"HighPrecison_4band_arch-124m_2.pth\",\"LOFI_2band-1_arch-34m.pth\",\"LOFI_2band-2_arch-34m.pth\",\"NewLayer_4band_arch-130m_1.pth\",\"NewLayer_4band_arch-130m_2.pth\",\"NewLayer_4band_arch-130m_3.pth\",\"MGM-v5-2Band-32000-_arch-default-BETA1.pth\",\"MGM-v5-2Band-32000-_arch-default-BETA2.pth\",\"MGM-v5-3Band-44100-_arch-default-BETA.pth\",\"MGM-v5-4Band-44100-_arch-default-BETA1.pth\",\"MGM-v5-4Band-44100-_arch-default-BETA2.pth\",\"MGM-v5-KAROKEE-32000-_arch-default-BETA1.pth\",\"MGM-v5-KAROKEE-32000-_arch-default-BETA2-AGR.pth\",\"MGM-v5-MIDSIDE-44100-_arch-default-BETA1.pth\",\"MGM-v5-MIDSIDE-44100-_arch-default-BETA2.pth\",\"MGM-v5-Vocal_2Band-32000-_arch-default-BETA1.pth\",\"MGM-v5-Vocal_2Band-32000-_arch-default-BETA2.pth\"]{allow-input: true}\n",
        "model_6 = \"slot\" #@param [\"slot\",\"HP_4BAND_3090_arch-124m.pth\",\"HP2-4BAND-3090_4band_arch-500m_1.pth\",\"HP2-4BAND-3090_4band_arch-500m_2.pth\",\"Vocal_HP_4BAND_3090_AGG_arch-124m.pth\",\"Vocal_HP_4BAND_3090_arch-124m.pth\",\"HP_4BAND_arch-124m_3090.pth\",\"HighPrecison_4band_arch-124m_1.pth\",\"HighPrecison_4band_arch-124m_2.pth\",\"LOFI_2band-1_arch-34m.pth\",\"LOFI_2band-2_arch-34m.pth\",\"NewLayer_4band_arch-130m_1.pth\",\"NewLayer_4band_arch-130m_2.pth\",\"NewLayer_4band_arch-130m_3.pth\",\"MGM-v5-2Band-32000-_arch-default-BETA1.pth\",\"MGM-v5-2Band-32000-_arch-default-BETA2.pth\",\"MGM-v5-3Band-44100-_arch-default-BETA.pth\",\"MGM-v5-4Band-44100-_arch-default-BETA1.pth\",\"MGM-v5-4Band-44100-_arch-default-BETA2.pth\",\"MGM-v5-KAROKEE-32000-_arch-default-BETA1.pth\",\"MGM-v5-KAROKEE-32000-_arch-default-BETA2-AGR.pth\",\"MGM-v5-MIDSIDE-44100-_arch-default-BETA1.pth\",\"MGM-v5-MIDSIDE-44100-_arch-default-BETA2.pth\",\"MGM-v5-Vocal_2Band-32000-_arch-default-BETA1.pth\",\"MGM-v5-Vocal_2Band-32000-_arch-default-BETA2.pth\"]{allow-input: true}\n",
        "model_7 = \"slot\" #@param [\"slot\",\"HP_4BAND_3090_arch-124m.pth\",\"HP2-4BAND-3090_4band_arch-500m_1.pth\",\"HP2-4BAND-3090_4band_arch-500m_2.pth\",\"Vocal_HP_4BAND_3090_AGG_arch-124m.pth\",\"Vocal_HP_4BAND_3090_arch-124m.pth\",\"HP_4BAND_arch-124m_3090.pth\",\"HighPrecison_4band_arch-124m_1.pth\",\"HighPrecison_4band_arch-124m_2.pth\",\"LOFI_2band-1_arch-34m.pth\",\"LOFI_2band-2_arch-34m.pth\",\"NewLayer_4band_arch-130m_1.pth\",\"NewLayer_4band_arch-130m_2.pth\",\"NewLayer_4band_arch-130m_3.pth\",\"MGM-v5-2Band-32000-_arch-default-BETA1.pth\",\"MGM-v5-2Band-32000-_arch-default-BETA2.pth\",\"MGM-v5-3Band-44100-_arch-default-BETA.pth\",\"MGM-v5-4Band-44100-_arch-default-BETA1.pth\",\"MGM-v5-4Band-44100-_arch-default-BETA2.pth\",\"MGM-v5-KAROKEE-32000-_arch-default-BETA1.pth\",\"MGM-v5-KAROKEE-32000-_arch-default-BETA2-AGR.pth\",\"MGM-v5-MIDSIDE-44100-_arch-default-BETA1.pth\",\"MGM-v5-MIDSIDE-44100-_arch-default-BETA2.pth\",\"MGM-v5-Vocal_2Band-32000-_arch-default-BETA1.pth\",\"MGM-v5-Vocal_2Band-32000-_arch-default-BETA2.pth\"]{allow-input: true}\n",
        "model_8 = \"slot\" #@param [\"slot\",\"HP_4BAND_3090_arch-124m.pth\",\"HP2-4BAND-3090_4band_arch-500m_1.pth\",\"HP2-4BAND-3090_4band_arch-500m_2.pth\",\"Vocal_HP_4BAND_3090_AGG_arch-124m.pth\",\"Vocal_HP_4BAND_3090_arch-124m.pth\",\"HP_4BAND_arch-124m_3090.pth\",\"HighPrecison_4band_arch-124m_1.pth\",\"HighPrecison_4band_arch-124m_2.pth\",\"LOFI_2band-1_arch-34m.pth\",\"LOFI_2band-2_arch-34m.pth\",\"NewLayer_4band_arch-130m_1.pth\",\"NewLayer_4band_arch-130m_2.pth\",\"NewLayer_4band_arch-130m_3.pth\",\"MGM-v5-2Band-32000-_arch-default-BETA1.pth\",\"MGM-v5-2Band-32000-_arch-default-BETA2.pth\",\"MGM-v5-3Band-44100-_arch-default-BETA.pth\",\"MGM-v5-4Band-44100-_arch-default-BETA1.pth\",\"MGM-v5-4Band-44100-_arch-default-BETA2.pth\",\"MGM-v5-KAROKEE-32000-_arch-default-BETA1.pth\",\"MGM-v5-KAROKEE-32000-_arch-default-BETA2-AGR.pth\",\"MGM-v5-MIDSIDE-44100-_arch-default-BETA1.pth\",\"MGM-v5-MIDSIDE-44100-_arch-default-BETA2.pth\",\"MGM-v5-Vocal_2Band-32000-_arch-default-BETA1.pth\",\"MGM-v5-Vocal_2Band-32000-_arch-default-BETA2.pth\"]{allow-input: true}\n",
        "model_9 = \"slot\" #@param [\"slot\",\"HP_4BAND_3090_arch-124m.pth\",\"HP2-4BAND-3090_4band_arch-500m_1.pth\",\"HP2-4BAND-3090_4band_arch-500m_2.pth\",\"Vocal_HP_4BAND_3090_AGG_arch-124m.pth\",\"Vocal_HP_4BAND_3090_arch-124m.pth\",\"HP_4BAND_arch-124m_3090.pth\",\"HighPrecison_4band_arch-124m_1.pth\",\"HighPrecison_4band_arch-124m_2.pth\",\"LOFI_2band-1_arch-34m.pth\",\"LOFI_2band-2_arch-34m.pth\",\"NewLayer_4band_arch-130m_1.pth\",\"NewLayer_4band_arch-130m_2.pth\",\"NewLayer_4band_arch-130m_3.pth\",\"MGM-v5-2Band-32000-_arch-default-BETA1.pth\",\"MGM-v5-2Band-32000-_arch-default-BETA2.pth\",\"MGM-v5-3Band-44100-_arch-default-BETA.pth\",\"MGM-v5-4Band-44100-_arch-default-BETA1.pth\",\"MGM-v5-4Band-44100-_arch-default-BETA2.pth\",\"MGM-v5-KAROKEE-32000-_arch-default-BETA1.pth\",\"MGM-v5-KAROKEE-32000-_arch-default-BETA2-AGR.pth\",\"MGM-v5-MIDSIDE-44100-_arch-default-BETA1.pth\",\"MGM-v5-MIDSIDE-44100-_arch-default-BETA2.pth\",\"MGM-v5-Vocal_2Band-32000-_arch-default-BETA1.pth\",\"MGM-v5-Vocal_2Band-32000-_arch-default-BETA2.pth\"]{allow-input: true}\n",
        "model_10 = \"slot\" #@param [\"slot\",\"HP_4BAND_3090_arch-124m.pth\",\"HP2-4BAND-3090_4band_arch-500m_1.pth\",\"HP2-4BAND-3090_4band_arch-500m_2.pth\",\"Vocal_HP_4BAND_3090_AGG_arch-124m.pth\",\"Vocal_HP_4BAND_3090_arch-124m.pth\",\"HP_4BAND_arch-124m_3090.pth\",\"HighPrecison_4band_arch-124m_1.pth\",\"HighPrecison_4band_arch-124m_2.pth\",\"LOFI_2band-1_arch-34m.pth\",\"LOFI_2band-2_arch-34m.pth\",\"NewLayer_4band_arch-130m_1.pth\",\"NewLayer_4band_arch-130m_2.pth\",\"NewLayer_4band_arch-130m_3.pth\",\"MGM-v5-2Band-32000-_arch-default-BETA1.pth\",\"MGM-v5-2Band-32000-_arch-default-BETA2.pth\",\"MGM-v5-3Band-44100-_arch-default-BETA.pth\",\"MGM-v5-4Band-44100-_arch-default-BETA1.pth\",\"MGM-v5-4Band-44100-_arch-default-BETA2.pth\",\"MGM-v5-KAROKEE-32000-_arch-default-BETA1.pth\",\"MGM-v5-KAROKEE-32000-_arch-default-BETA2-AGR.pth\",\"MGM-v5-MIDSIDE-44100-_arch-default-BETA1.pth\",\"MGM-v5-MIDSIDE-44100-_arch-default-BETA2.pth\",\"MGM-v5-Vocal_2Band-32000-_arch-default-BETA1.pth\",\"MGM-v5-Vocal_2Band-32000-_arch-default-BETA2.pth\"]{allow-input: true}\n",
        "model_11 = \"slot\" #@param [\"slot\",\"HP_4BAND_3090_arch-124m.pth\",\"HP2-4BAND-3090_4band_arch-500m_1.pth\",\"HP2-4BAND-3090_4band_arch-500m_2.pth\",\"Vocal_HP_4BAND_3090_AGG_arch-124m.pth\",\"Vocal_HP_4BAND_3090_arch-124m.pth\",\"HP_4BAND_arch-124m_3090.pth\",\"HighPrecison_4band_arch-124m_1.pth\",\"HighPrecison_4band_arch-124m_2.pth\",\"LOFI_2band-1_arch-34m.pth\",\"LOFI_2band-2_arch-34m.pth\",\"NewLayer_4band_arch-130m_1.pth\",\"NewLayer_4band_arch-130m_2.pth\",\"NewLayer_4band_arch-130m_3.pth\",\"MGM-v5-2Band-32000-_arch-default-BETA1.pth\",\"MGM-v5-2Band-32000-_arch-default-BETA2.pth\",\"MGM-v5-3Band-44100-_arch-default-BETA.pth\",\"MGM-v5-4Band-44100-_arch-default-BETA1.pth\",\"MGM-v5-4Band-44100-_arch-default-BETA2.pth\",\"MGM-v5-KAROKEE-32000-_arch-default-BETA1.pth\",\"MGM-v5-KAROKEE-32000-_arch-default-BETA2-AGR.pth\",\"MGM-v5-MIDSIDE-44100-_arch-default-BETA1.pth\",\"MGM-v5-MIDSIDE-44100-_arch-default-BETA2.pth\",\"MGM-v5-Vocal_2Band-32000-_arch-default-BETA1.pth\",\"MGM-v5-Vocal_2Band-32000-_arch-default-BETA2.pth\"]{allow-input: true}\n",
        "model_12 = \"slot\" #@param [\"slot\",\"HP_4BAND_3090_arch-124m.pth\",\"HP2-4BAND-3090_4band_arch-500m_1.pth\",\"HP2-4BAND-3090_4band_arch-500m_2.pth\",\"Vocal_HP_4BAND_3090_AGG_arch-124m.pth\",\"Vocal_HP_4BAND_3090_arch-124m.pth\",\"HP_4BAND_arch-124m_3090.pth\",\"HighPrecison_4band_arch-124m_1.pth\",\"HighPrecison_4band_arch-124m_2.pth\",\"LOFI_2band-1_arch-34m.pth\",\"LOFI_2band-2_arch-34m.pth\",\"NewLayer_4band_arch-130m_1.pth\",\"NewLayer_4band_arch-130m_2.pth\",\"NewLayer_4band_arch-130m_3.pth\",\"MGM-v5-2Band-32000-_arch-default-BETA1.pth\",\"MGM-v5-2Band-32000-_arch-default-BETA2.pth\",\"MGM-v5-3Band-44100-_arch-default-BETA.pth\",\"MGM-v5-4Band-44100-_arch-default-BETA1.pth\",\"MGM-v5-4Band-44100-_arch-default-BETA2.pth\",\"MGM-v5-KAROKEE-32000-_arch-default-BETA1.pth\",\"MGM-v5-KAROKEE-32000-_arch-default-BETA2-AGR.pth\",\"MGM-v5-MIDSIDE-44100-_arch-default-BETA1.pth\",\"MGM-v5-MIDSIDE-44100-_arch-default-BETA2.pth\",\"MGM-v5-Vocal_2Band-32000-_arch-default-BETA1.pth\",\"MGM-v5-Vocal_2Band-32000-_arch-default-BETA2.pth\"]{allow-input: true}\n",
        "\n",
        "model_1 = modelPath(model_1)\n",
        "model_2 = modelPath(model_2)\n",
        "model_3 = modelPath(model_3)\n",
        "model_4 = modelPath(model_4)\n",
        "model_5 = modelPath(model_5)\n",
        "model_6 = modelPath(model_6)\n",
        "model_7 = modelPath(model_7)\n",
        "model_8 = modelPath(model_8)\n",
        "model_9 = modelPath(model_9)\n",
        "model_10 = modelPath(model_10)\n",
        "model_11 = modelPath(model_11)\n",
        "model_12 = modelPath(model_12)\n",
        "if 'https://' in input:\n",
        "    titlename,inputsha=ensYouTube(input)\n",
        "    get_ipython().system(f\"python ensemble_colab.py --algo {instrumental} {vocal} {postprocess} {tta} {FlipVocalModels} --aggressiveness {aggressiveness} -w {window_size} --high_end_process {high_end_process} --suppress --input '{inputsha}' --model_ens {model_1} {model_2} {model_3} {model_4} {model_5} {model_6} {model_7} {model_8} {model_9} {model_10} {model_11} {model_12}\")\n",
        "    os.rename(rf'separated/{os.path.splitext(os.path.basename(inputsha))[0]}_Ensembled_Instruments.wav',rf'separated/{titlename}_Ensembled_Instruments.wav')\n",
        "    os.rename(rf'separated/{os.path.splitext(os.path.basename(inputsha))[0]}_Ensembled_Vocals.wav',rf'separated/{titlename}_Ensembled_Vocals.wav')\n",
        "else:\n",
        "    input = 'tracks/' + input\n",
        "    get_ipython().system(f\"python ensemble_colab.py --algo {instrumental} {vocal} {postprocess} {tta} {FlipVocalModels} --aggressiveness {aggressiveness} -w {window_size} --high_end_process {high_end_process} --suppress --input '{input}' --model_ens {model_1} {model_2} {model_3} {model_4} {model_5} {model_6} {model_7} {model_8} {model_9} {model_10} {model_11} {model_12}\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1JUh4ugLHjjP"
      },
      "source": [
        "report errors to `Hv#3868`\n",
        "--"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ut9gbT42iB1L"
      },
      "source": [
        "###**Best settings in my opinon:**\n",
        "<p><sup>Window size: 320</sup></p>\n",
        "<p><sup>Model: HP2-4BAND-3090_4band_arch-500m_1.pth</sup></p>\n",
        "<p><sup>Parameter: 4band_44100.json</sup></p>\n",
        "<p><sup>TTA: ON</sup></p>\n",
        "<p><sup>Aggressiveness: 0.3</sup></p>"
      ]
    }
  ]
}