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{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "#@title # Установка (3 мин без претрейнов)\n",
        "%cd /content\n",
        "\n",
        "from google.colab import files\n",
        "import zipfile\n",
        "import os\n",
        "\n",
        "# Загружаем ZIP-файл с устройства\n",
        "online = True # @param {\"type\":\"boolean\"}\n",
        "if not online:\n",
        "    uploaded = files.upload()\n",
        "else:\n",
        "    uploaded = \"mvsepless.zip\"\n",
        "    !wget -O {uploaded} https://huggingface.co/noblebarkrr/mvsepless_updates_zip/resolve/main/mvsepless_alpha_2_ru.zip?download=true\n",
        "\n",
        "# Проверяем, что файл был загружен\n",
        "if not uploaded:\n",
        "    print(\"Не был загружен ни один файл.\")\n",
        "else:\n",
        "    if not online:\n",
        "        # Получаем имя загруженного файла (предполагаем, что загружен только один файл)\n",
        "        zip_filename = next(iter(uploaded))\n",
        "    else:\n",
        "        zip_filename = uploaded\n",
        "    # Путь для распаковки\n",
        "    extract_to = '/content/mvsepless'\n",
        "\n",
        "    # Создаем папку, если она не существует\n",
        "    os.makedirs(extract_to, exist_ok=True)\n",
        "\n",
        "    # Распаковываем архив с перезаписью существующих файлов\n",
        "    with zipfile.ZipFile(zip_filename, 'r') as zip_ref:\n",
        "        zip_ref.extractall(extract_to)\n",
        "\n",
        "    print(f\"Файл {zip_filename} был успешно распакован в {extract_to} с перезаписью существующих файлов.\")\n",
        "\n",
        "!cd mvsepless && pip install -r requirements.txt\n",
        "!pip install audio-separator[gpu]==0.32.0\n",
        "!wget -O mvsepless/models/medley_vox/pretrained_models/xlsr_53_56k.pt https://dl.fbaipublicfiles.com/fairseq/wav2vec/xlsr_53_56k.pt\n",
        "!cd mvsepless && python download_models.py"
      ],
      "metadata": {
        "cellView": "form",
        "id": "9DpDNzJllHCJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "615f1b2d-48fb-4675-c94c-9ce0960135fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n",
            "--2025-06-03 11:40:42--  https://huggingface.co/noblebarkrr/mvsepless_updates_zip/resolve/main/mvsepless_alpha_2_ru.zip?download=true\n",
            "Resolving huggingface.co (huggingface.co)... 13.35.202.34, 13.35.202.40, 13.35.202.121, ...\n",
            "Connecting to huggingface.co (huggingface.co)|13.35.202.34|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://cdn-lfs-us-1.hf.co/repos/73/59/7359eecc07125fe5d27b22c3fbd8410831156571ee0e8b37bf38b7bff3648cb5/d55570c15c7dc76509d03c201ec0116a5c6135e374eac8b714b332746e0ecd5e?response-content-disposition=attachment%3B+filename*%3DUTF-8%27%27mvsepless_alpha_2_ru.zip%3B+filename%3D%22mvsepless_alpha_2_ru.zip%22%3B&response-content-type=application%2Fzip&Expires=1748954442&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTc0ODk1NDQ0Mn19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy11cy0xLmhmLmNvL3JlcG9zLzczLzU5LzczNTllZWNjMDcxMjVmZTVkMjdiMjJjM2ZiZDg0MTA4MzExNTY1NzFlZTBlOGIzN2JmMzhiN2JmZjM2NDhjYjUvZDU1NTcwYzE1YzdkYzc2NTA5ZDAzYzIwMWVjMDExNmE1YzYxMzVlMzc0ZWFjOGI3MTRiMzMyNzQ2ZTBlY2Q1ZT9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPSoifV19&Signature=Z28T2ki2K34gRyEOcgmxda1uOk-GJZLEMwWDZwOQH25kIEdd4-Vr4V6OlLJ70jQDbwl8jmw0XhcLBwDfO7zkSpOSX6i-P5%7EJYsmMf9WeyG%7EFJgnV%7EscRQWjNS5xUehscr0ukUr3vP7VKchUc-8rdlnFhkeA41lxsC09CmDliFul1k56fbK9t8wjbJCnmoWOMCJW00n9AKV1MK7te6MRhsb2-xOfja08ga08d5LQmZnb58btUxGiEJqMhC2PeHKI0iyjOkF5m%7EIgneH6vVfm4Pn3-v-nC3nRsXFV80TMFQbT-NTR8epkw%7EigGWq2xMT-K2UTIscrJ7Iut2PJZJ4fr-w__&Key-Pair-Id=K24J24Z295AEI9 [following]\n",
            "--2025-06-03 11:40:42--  https://cdn-lfs-us-1.hf.co/repos/73/59/7359eecc07125fe5d27b22c3fbd8410831156571ee0e8b37bf38b7bff3648cb5/d55570c15c7dc76509d03c201ec0116a5c6135e374eac8b714b332746e0ecd5e?response-content-disposition=attachment%3B+filename*%3DUTF-8%27%27mvsepless_alpha_2_ru.zip%3B+filename%3D%22mvsepless_alpha_2_ru.zip%22%3B&response-content-type=application%2Fzip&Expires=1748954442&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTc0ODk1NDQ0Mn19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy11cy0xLmhmLmNvL3JlcG9zLzczLzU5LzczNTllZWNjMDcxMjVmZTVkMjdiMjJjM2ZiZDg0MTA4MzExNTY1NzFlZTBlOGIzN2JmMzhiN2JmZjM2NDhjYjUvZDU1NTcwYzE1YzdkYzc2NTA5ZDAzYzIwMWVjMDExNmE1YzYxMzVlMzc0ZWFjOGI3MTRiMzMyNzQ2ZTBlY2Q1ZT9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPSoifV19&Signature=Z28T2ki2K34gRyEOcgmxda1uOk-GJZLEMwWDZwOQH25kIEdd4-Vr4V6OlLJ70jQDbwl8jmw0XhcLBwDfO7zkSpOSX6i-P5%7EJYsmMf9WeyG%7EFJgnV%7EscRQWjNS5xUehscr0ukUr3vP7VKchUc-8rdlnFhkeA41lxsC09CmDliFul1k56fbK9t8wjbJCnmoWOMCJW00n9AKV1MK7te6MRhsb2-xOfja08ga08d5LQmZnb58btUxGiEJqMhC2PeHKI0iyjOkF5m%7EIgneH6vVfm4Pn3-v-nC3nRsXFV80TMFQbT-NTR8epkw%7EigGWq2xMT-K2UTIscrJ7Iut2PJZJ4fr-w__&Key-Pair-Id=K24J24Z295AEI9\n",
            "Resolving cdn-lfs-us-1.hf.co (cdn-lfs-us-1.hf.co)... 13.33.45.103, 13.33.45.80, 13.33.45.64, ...\n",
            "Connecting to cdn-lfs-us-1.hf.co (cdn-lfs-us-1.hf.co)|13.33.45.103|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 24353846 (23M) [application/zip]\n",
            "Saving to: ‘mvsepless.zip’\n",
            "\n",
            "mvsepless.zip       100%[===================>]  23.23M  --.-KB/s    in 0.1s    \n",
            "\n",
            "2025-06-03 11:40:42 (228 MB/s) - ‘mvsepless.zip’ saved [24353846/24353846]\n",
            "\n",
            "Файл mvsepless.zip был успешно распакован в /content/mvsepless с перезаписью существующих файлов.\n",
            "Processing ./fixed/fairseq_fixed-0.13.0-cp311-cp311-linux_x86_64.whl (from -r requirements.txt (line 36))\n",
            "Requirement already satisfied: torch==2.6.0 in /usr/local/lib/python3.11/dist-packages (from -r requirements.txt (line 2)) (2.6.0+cu124)\n",
            "Requirement already satisfied: torchvision==0.21.0 in /usr/local/lib/python3.11/dist-packages (from -r requirements.txt (line 3)) (0.21.0+cu124)\n",
            "Requirement already satisfied: torchaudio==2.6.0 in /usr/local/lib/python3.11/dist-packages (from -r requirements.txt (line 4)) (2.6.0+cu124)\n",
            "Collecting torchcrepe==0.0.23 (from -r requirements.txt (line 5))\n",
            "  Downloading torchcrepe-0.0.23-py3-none-any.whl.metadata (7.8 kB)\n",
            "Requirement already satisfied: numpy==2.0.2 in /usr/local/lib/python3.11/dist-packages (from -r requirements.txt (line 6)) (2.0.2)\n",
            "Requirement already satisfied: pandas==2.2.2 in /usr/local/lib/python3.11/dist-packages (from -r requirements.txt (line 7)) (2.2.2)\n",
            "Requirement already satisfied: scipy==1.15.3 in /usr/local/lib/python3.11/dist-packages (from -r requirements.txt (line 8)) (1.15.3)\n",
            "Collecting librosa==0.9.1 (from -r requirements.txt (line 9))\n",
            "  Downloading librosa-0.9.1-py3-none-any.whl.metadata (6.9 kB)\n",
            "Collecting matplotlib==3.9.0 (from -r requirements.txt (line 10))\n",
            "  Downloading matplotlib-3.9.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (11 kB)\n",
            "Requirement already satisfied: tqdm==4.67.1 in /usr/local/lib/python3.11/dist-packages (from -r requirements.txt (line 11)) (4.67.1)\n",
            "Requirement already satisfied: einops==0.8.1 in /usr/local/lib/python3.11/dist-packages (from -r requirements.txt (line 12)) (0.8.1)\n",
            "Requirement already satisfied: protobuf==5.29.4 in /usr/local/lib/python3.11/dist-packages (from -r requirements.txt (line 13)) (5.29.4)\n",
            "Requirement already satisfied: soundfile==0.13.1 in /usr/local/lib/python3.11/dist-packages (from -r requirements.txt (line 16)) (0.13.1)\n",
            "Collecting pydub==0.25.1 (from -r requirements.txt (line 17))\n",
            "  Downloading pydub-0.25.1-py2.py3-none-any.whl.metadata (1.4 kB)\n",
            "Collecting pyloudnorm==0.1.1 (from -r requirements.txt (line 18))\n",
            "  Downloading pyloudnorm-0.1.1-py3-none-any.whl.metadata (5.6 kB)\n",
            "Collecting praat-parselmouth==0.4.5 (from -r requirements.txt (line 19))\n",
            "  Downloading praat_parselmouth-0.4.5-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (2.9 kB)\n",
            "Collecting webrtcvad==2.0.10 (from -r requirements.txt (line 20))\n",
            "  Downloading webrtcvad-2.0.10.tar.gz (66 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m66.2/66.2 kB\u001b[0m \u001b[31m6.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting edge-tts==7.0.2 (from -r requirements.txt (line 21))\n",
            "  Downloading edge_tts-7.0.2-py3-none-any.whl.metadata (5.5 kB)\n",
            "Collecting audiomentations==0.24.0 (from -r requirements.txt (line 22))\n",
            "  Downloading audiomentations-0.24.0-py3-none-any.whl.metadata (35 kB)\n",
            "Collecting pedalboard==0.8.1 (from -r requirements.txt (line 23))\n",
            "  Downloading pedalboard-0.8.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (15 kB)\n",
            "Collecting ffmpeg-python==0.2.0 (from -r requirements.txt (line 24))\n",
            "  Downloading ffmpeg_python-0.2.0-py3-none-any.whl.metadata (1.7 kB)\n",
            "Collecting faiss-cpu==1.11 (from -r requirements.txt (line 27))\n",
            "  Downloading faiss_cpu-1.11.0-cp311-cp311-manylinux_2_28_x86_64.whl.metadata (4.8 kB)\n",
            "Collecting ml_collections==1.1.0 (from -r requirements.txt (line 28))\n",
            "  Downloading ml_collections-1.1.0-py3-none-any.whl.metadata (22 kB)\n",
            "Requirement already satisfied: timm==1.0.15 in /usr/local/lib/python3.11/dist-packages (from -r requirements.txt (line 29)) (1.0.15)\n",
            "Requirement already satisfied: wandb==0.19.11 in /usr/local/lib/python3.11/dist-packages (from -r requirements.txt (line 30)) (0.19.11)\n",
            "Requirement already satisfied: accelerate==1.7.0 in /usr/local/lib/python3.11/dist-packages (from -r requirements.txt (line 31)) (1.7.0)\n",
            "Collecting bitsandbytes==0.46.0 (from -r requirements.txt (line 32))\n",
            "  Downloading bitsandbytes-0.46.0-py3-none-manylinux_2_24_x86_64.whl.metadata (10 kB)\n",
            "Collecting tokenizers==0.19 (from -r requirements.txt (line 33))\n",
            "  Downloading tokenizers-0.19.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.7 kB)\n",
            "Collecting huggingface-hub==0.28.1 (from -r requirements.txt (line 34))\n",
            "  Downloading huggingface_hub-0.28.1-py3-none-any.whl.metadata (13 kB)\n",
            "Collecting transformers==4.41 (from -r requirements.txt (line 35))\n",
            "  Downloading transformers-4.41.0-py3-none-any.whl.metadata (43 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.8/43.8 kB\u001b[0m \u001b[31m3.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting segmentation_models_pytorch==0.5.0 (from -r requirements.txt (line 39))\n",
            "  Downloading segmentation_models_pytorch-0.5.0-py3-none-any.whl.metadata (17 kB)\n",
            "Collecting torchseg==0.0.1a4 (from -r requirements.txt (line 40))\n",
            "  Downloading torchseg-0.0.1a4-py3-none-any.whl.metadata (12 kB)\n",
            "Collecting demucs==4.0.0 (from -r requirements.txt (line 42))\n",
            "  Downloading demucs-4.0.0.tar.gz (1.2 MB)\n",
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HyxHY762qmyE"
      },
      "source": [
        "# NON-CLI"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "18XSjQzyyl5E"
      },
      "outputs": [],
      "source": [
        "# @title Mvsepless & Vbach (напрямую в ячейке)\n",
        "#@markdown Разделение / замена музыки и голоса\n",
        "\n",
        "%cd /content/mvsepless\n",
        "\n",
        "import gradio as gr\n",
        "from multi_infer import theme, mvsepless_non_cli\n",
        "\n",
        "with gr.Blocks(title=\"Разделение музыки и голоса\", theme=theme) as demo:\n",
        "    gr.HTML(\"<h1><center> MVSEPLESS </center></h1>\")\n",
        "    mvsepless_non_cli(True)\n",
        "    demo.launch(share=True, allowed_paths=[\"/content\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "JMZh5sYvoeuq"
      },
      "outputs": [],
      "source": [
        "# @title VbachGen\n",
        "#@markdown Быстрая генерация сурсов для ИИ-каверов\n",
        "%cd /content/mvsepless\n",
        "!python gencover.py"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "VNW0Op_Nsbql"
      },
      "outputs": [],
      "source": [
        "# @title Mvsepless\n",
        "#@markdown Разделение музыки и голоса\n",
        "\n",
        "%cd /content/mvsepless\n",
        "!python multi_infer.py -gr"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "TmxdFPBXXC-c"
      },
      "outputs": [],
      "source": [
        "# @title Mvsepless & Vbach\n",
        "#@markdown Разделение / Замена музыки и голоса\n",
        "\n",
        "%cd /content/mvsepless\n",
        "!python multi_infer.py -grvc"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n3PP3N-spH_0"
      },
      "source": [
        "# CLI"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "sOrwMYdj_cuF"
      },
      "outputs": [],
      "source": [
        "# @title Объединить аудио файлы в ансамбль\n",
        "\n",
        "import os\n",
        "import numpy\n",
        "\n",
        "%cd /content/mvsepless\n",
        "\n",
        "from ensem import ensemble_audio_files\n",
        "from infer_utils.audio_processing.invert import invert_and_overlay_wav\n",
        "\n",
        "\n",
        "input = \"\" # @param {\"type\":\"string\",\"placeholder\":\"Введите путь к папке с результатами моделей\"}\n",
        "input_orig = \"\" # @param {\"type\":\"string\",\"placeholder\":\"Введите путь к оригинальному аудио\"}\n",
        "output = \"\" # @param {\"type\":\"string\",\"placeholder\":\"Введите путь к папке сохранения результатов\"}\n",
        "\n",
        "type = \"max_fft\" # @param [\"max_fft\",\"min_fft\",\"median_fft\",\"max_wave\",\"avg_fft\"]\n",
        "\n",
        "# Создаем выходную директорию, если ее нет\n",
        "os.makedirs(output, exist_ok=True)\n",
        "\n",
        "# Проверяем существование входной директории\n",
        "if not os.path.exists(input):\n",
        "    raise FileNotFoundError(f\"Input directory {input} does not exist\")\n",
        "\n",
        "# Получаем список файлов\n",
        "temp_ensem_files = [os.path.abspath(os.path.join(input, f))\n",
        "         for f in os.listdir(input)\n",
        "         if os.path.isfile(os.path.join(input, f))]\n",
        "\n",
        "if not temp_ensem_files:\n",
        "    raise ValueError(\"No files found in input directory\")\n",
        "\n",
        "weight_value = 1.0 # @param {\"type\":\"number\"}\n",
        "weights = [weight_value] * len(temp_ensem_files)\n",
        "\n",
        "output_file = os.path.join(output, \"output.wav\")\n",
        "inverted_file = os.path.join(output, \"inverted.wav\")\n",
        "\n",
        "ensemble_audio_files(files=temp_ensem_files, output=output_file, ensemble_type=type, weights=weights)\n",
        "if input_orig != \"\":\n",
        "    invert_and_overlay_wav(output_file, input_orig, inverted_file)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P3tLD2QXqyqp"
      },
      "source": [
        "## Разделить аудио"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "QmK2R6skpvR8"
      },
      "outputs": [],
      "source": [
        "# @title Получить список моделей\n",
        "\n",
        "%cd /content/mvsepless\n",
        "\n",
        "model_type = \"mel_band_roformer\" # @param [\"mel_band_roformer\",\"bs_roformer\",\"mdx23c\",\"vr_arch\",\"htdemucs\",\"scnet\",\"mdx_net\",\"medley_vox\",\"bandit\",\"bandit_v2\"]\n",
        "\n",
        "from model_info import model_info\n",
        "model_info(model_type)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "V8MI-asTQAz4"
      },
      "outputs": [],
      "source": [
        "# @title Инференс\n",
        "\n",
        "%cd /content/mvsepless\n",
        "\n",
        "#@markdown <b>Чтобы получить информацию о моделях, запустите ячейку 'Получить список моделей' с нужным типом модели</b>\n",
        "\n",
        "template = \"NAME_MODEL_STEM\" # @param {\"type\":\"string\",\"placeholder\":\"Шаблон имени стемов, пример: NAME_MODEL_STEM\"}\n",
        "\n",
        "model_type = \"mel_band_roformer\" # @param [\"mel_band_roformer\",\"bs_roformer\",\"mdx23c\",\"vr_arch\",\"htdemucs\",\"scnet\",\"mdx_net\",\"medley_vox\",\"bandit\",\"bandit_v2\"]\n",
        "model_name = \"\" # @param {\"type\":\"string\",\"placeholder\":\"Введите имя модели, которое есть в списке моделей (Можно получить список используя ячейку выше)\"}\n",
        "input = \"\" # @param {\"type\":\"string\",\"placeholder\":\"Введите путь к аудиофайлу/папке с аудиофайлами\"}\n",
        "output = \"\" # @param {\"type\":\"string\",\"placeholder\":\"Введите путь к папке сохранения стемов\"}\n",
        "\n",
        "output_format = \"flac\" # @param [\"wav\",\"mp3\",\"flac\",\"m4a\",\"aac\",\"opus\"]\n",
        "batch = False # @param {\"type\":\"boolean\"}\n",
        "tta = False\n",
        "select_stems = \"\" # @param {\"type\":\"string\",\"placeholder\":\"Введите стемы, которые хотите сохранить, пример: 'vocals drums' или 'male female karaoke''\"}\n",
        "\n",
        "# @markdown <details>\n",
        "# @markdown <summary><b><u>Описание настроек</u></b></summary>\n",
        "# @markdown\n",
        "# @markdown > * <b><u>template</u></b> - Формат имени  результатов разделения.\n",
        "# @markdown >\n",
        "# @markdown >  Существуют три ключа:\n",
        "# @markdown >\n",
        "# @markdown >  > NAME - название оригинального аудио файла (без расширения)\n",
        "# @markdown >\n",
        "# @markdown >  > MODEL - название модели, использованной для разделения на стемы\n",
        "# @markdown >\n",
        "# @markdown >  > STEM - название стема\n",
        "# @markdown >\n",
        "# @markdown >  > DATETIME - дата и время создвния файла\n",
        "# @markdown >\n",
        "# @markdown >  Пример:\n",
        "# @markdown >\n",
        "# @markdown >  NAME_STEM --> вход_vocals.mp3\n",
        "# @markdown >\n",
        "# @markdown >  > NAME - 'вход'\n",
        "# @markdown >\n",
        "# @markdown >  > STEM - vocals\n",
        "# @markdown >\n",
        "# @markdown >  MODEL_STEM --> mel_band_roformer_aname_4_stems_large_drums.mp3\n",
        "# @markdown >\n",
        "# @markdown >  > MODEL - 'mel_band_roformer_aname_4_stems_large'\n",
        "# @markdown >\n",
        "# @markdown >  > STEM - drums\n",
        "# @markdown >\n",
        "# @markdown >  mvsepless_STEM --> mvsepless_karaoke.mp3\n",
        "# @markdown >\n",
        "# @markdown >  > mvsepless - mvsepless (кастомное имя)\n",
        "# @markdown >\n",
        "# @markdown >  > STEM - karaoke\n",
        "# @markdown >\n",
        "# @markdown\n",
        "# @markdown > * <b><u>batch</u></b> - Пакетная обработка. Включается при batch = True\n",
        "# @markdown >\n",
        "# @markdown >  (В нашем случае если поставлена галочка напротив batch)\n",
        "# @markdown >\n",
        "# @markdown >  Включено - обрабатывается папка с аудиофайлами\n",
        "# @markdown >\n",
        "# @markdown >  Выключено - обрабатывается один аудиофайл\n",
        "# @markdown\n",
        "# @markdown > * <b><u>select_stems</u></b> - Выбор стемов для разделения\n",
        "# @markdown >\n",
        "# @markdown >  Не работает с:\n",
        "# @markdown >\n",
        "# @markdown >  > моделями на архитектуре Medley-Vox\n",
        "# @markdown >\n",
        "# @markdown >  > моделями с целевым инструментом (Target Instrument is not \"No\")\n",
        "# @markdown >\n",
        "# @markdown >  > моделями на архитектуре MDX-NET и VR ARCH, если выбрано больше одного стема\n",
        "# @markdown\n",
        "# @markdown > * <b><u>model_type</u></b> - Тип модели, то есть её архитектура\n",
        "# @markdown\n",
        "# @markdown > * <b><u>model_name</u></b> - Название модели для разделения вокала\n",
        "\n",
        "run_command = f'python multi_infer.py -i \"{input}\" -o {output} -of {output_format}  {(\"--use_tta\" if tta else \"\")} {(\"--batch\" if batch else \"\")} -inst --model_type {model_type} --model_name {model_name} {(f\"--select {select_stems}\" if select_stems != \"\" else \"\")} --template {template}'\n",
        "!$run_command"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PTzLZWnWq3c8"
      },
      "source": [
        "## Замена вокала"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "TVQeDhtMPwgU"
      },
      "outputs": [],
      "source": [
        "\n",
        "%cd /content/mvsepless\n",
        "\n",
        "import os\n",
        "from datetime import datetime\n",
        "from rvc.scripts.voice_conversion import voice_pipeline\n",
        "\n",
        "\n",
        "def voice_conversion(input, model, pitch, ir, fr, rms, f0, hop, prtct, of, f0_min, f0_max, output, template, batch):\n",
        "      if batch:\n",
        "          for filename in os.listdir(input):\n",
        "              file = os.path.join(input, filename)\n",
        "              if os.path.isfile(file):\n",
        "                  file_name = os.path.basename(file)\n",
        "                  namefile = os.path.splitext(file_name)[0]\n",
        "                  time_create_file = datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n",
        "                  output_name = (\n",
        "                      template\n",
        "                      .replace(\"DATETIME\", time_create_file)\n",
        "                      .replace(\"NAME\", namefile)\n",
        "                      .replace(\"MODEL\", model)\n",
        "                      .replace(\"F0METHOD\", f0)\n",
        "                      .replace(\"PITCH\", f\"{pitch}\")\n",
        "                  )\n",
        "                  voice_pipeline(file, model, pitch, ir, fr, rms, f0, hop, prtct, of, f0_min, f0_max, output, output_name)\n",
        "      else:\n",
        "          time_create_file = datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n",
        "          output_name = (\n",
        "              template\n",
        "              .replace(\"DATETIME\", time_create_file)\n",
        "              .replace(\"NAME\", namefile)\n",
        "              .replace(\"MODEL\", model)\n",
        "              .replace(\"F0METHOD\", f0)\n",
        "              .replace(\"PITCH\", pitch)\n",
        "          )\n",
        "          voice_pipeline(input, model, pitch, ir, fr, rms, f0, hop, prtct, of, f0_min, f0_max, output, output_name)\n",
        "\n",
        "\n",
        "song_input = \"\"  # @param {type:\"string\"}\n",
        "model_name = \"\"  # @param {type:\"string\"}\n",
        "batch = False # @param {\"type\":\"boolean\"}\n",
        "\n",
        "# @markdown #### Основные настройки\n",
        "pitch = 0  # @param {type:\"slider\", min:-48, max:48, step:12}\n",
        "index_rate = 0  # @param {type:\"slider\", min:0, max:1, step:0.01}\n",
        "filter_radius = 3  # @param {type:\"slider\", min:0, max:7, step:1}\n",
        "volume_envelope = 0.25  # @param {type:\"slider\", min:0, max:1, step:0.01}\n",
        "\n",
        "# @markdown #### Настройки F0\n",
        "method = \"rmvpe+\"  # @param [\"rmvpe+\", \"mangio-crepe\", \"fcpe\"]\n",
        "hop_length = 128  # @param {type:\"slider\", min:32, max:512, step:16}\n",
        "protect = 0.33  # @param {type:\"slider\", min:0, max:0.5, step:0.01}\n",
        "f0_min = 50  # @param {type:\"slider\", min:0, max:500, step:1}\n",
        "f0_max = 1100  # @param {type:\"slider\", min:100, max:2000, step:10}\n",
        "\n",
        "# @markdown #### Выходные настройки\n",
        "output_format = \"mp3\"  # @param [\"mp3\", \"wav\", \"flac\"]\n",
        "output_path = \"\"  # @param {type:\"string\"}\n",
        "\n",
        "# Задаем шаблон вручную (можно менять порядок и состав)\n",
        "template = \"DATETIME_NAME_PITCH\"  # @param {type:\"string\"}\n",
        "\n",
        "voice_conversion(song_input, model_name, pitch, index_rate, filter_radius, volume_envelope, method, hop_length, protect, output_format, f0_min, f0_max, output_path, template, batch)"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [
        "n3PP3N-spH_0",
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      "gpuType": "T4",
      "provenance": []
    },
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      "display_name": "Python 3",
      "name": "python3"
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    "language_info": {
      "name": "python"
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