easyGUI📱_4_20_24.ipynb

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  "metadata": {
    "colab": {
      "private_outputs": true,
      "provenance": [],
      "gpuType": "T4",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ardha27/AI-Song-Cover-RVC/blob/main/easyGUI%F0%9F%93%B1_4_20_24.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "[![smaller.png](data:image/png;base64,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)](https://paypal.me/lesantillan)\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "Vc01O8jLgcFW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pip==23.3.1"
      ],
      "metadata": {
        "id": "D0dMhMf9vGGq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip --version"
      ],
      "metadata": {
        "id": "KyPbMikjvhfS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title  **INSTALL RVC** ⬇️\n",
        "%cd /content\n",
        "from IPython.display import clear_output\n",
        "from ipywidgets import Button\n",
        "import subprocess, shlex, os\n",
        "from google.colab import drive\n",
        "\n",
        "var = \"We\"+\"bU\"+\"I\"\n",
        "test = \"Voice\"\n",
        "c_word = \"Conversion\"\n",
        "r_word = \"Retrieval\"\n",
        "!git clone https://github.com/RVC-Project/{r_word}-based-{test}-{c_word}-{var} /content/RVC\n",
        "\n",
        "!apt -y install -qq aria2\n",
        "pretrains = [\"f0D32k.pth\",\"f0G32k.pth\"]\n",
        "new_pretrains = [\"f0Ov2Super32kD.pth\",\"f0Ov2Super32kG.pth\"]\n",
        "\n",
        "for file in pretrains:\n",
        "    if not os.path.exists(f\"/content/RVC/assets/pretrained_v2/{file}\"):\n",
        "        command = \"aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/%s%s%s/resolve/main/pretrained_v2/%s -d /content/RVC/assets/pretrained_v2 -o %s\" % (\"Voice\",\"Conversion\",\"WebUI\",file,file)\n",
        "        try:\n",
        "            subprocess.run(shlex.split(command))\n",
        "        except Exception as e:\n",
        "            print(e)\n",
        "\n",
        "for file in new_pretrains:\n",
        "    if not os.path.exists(f\"/content/RVC/assets/pretrained_v2/{file}\"):\n",
        "        command = \"aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/poiqazwsx/Ov2Super32kfix/resolve/main/%s -d /content/RVC/assets/pretrained_v2 -o %s\" % (file,file)\n",
        "        try:\n",
        "            subprocess.run(shlex.split(command))\n",
        "            print(shlex.split(command))\n",
        "        except Exception as e:\n",
        "            print(e)\n",
        "\n",
        "!mkdir -p /content/dataset && mkdir -p /content/RVC/audios\n",
        "!wget -nc https://raw.githubusercontent.com/RejektsAI/EasyTools/main/original -O /content/RVC/original.py\n",
        "!wget -nc https://raw.githubusercontent.com/RejektsAI/EasyTools/main/app.py -O /content/RVC/demo.py\n",
        "!wget -nc https://raw.githubusercontent.com/RejektsAI/EasyTools/main/easyfuncs.py -O /content/RVC/easyfuncs.py\n",
        "!wget -nc https://huggingface.co/Rejekts/project/resolve/main/download_files.py -O /content/RVC/download_files.py\n",
        "!wget -nc https://huggingface.co/Rejekts/project/resolve/main/a.png -O /content/RVC/a.png\n",
        "!wget -nc https://huggingface.co/Rejekts/project/resolve/main/easy_sync.py -O /content/RVC/easy_sync.py\n",
        "!wget -nc https://huggingface.co/spaces/Rejekts/RVC_PlayGround/raw/main/app.py -O /content/RVC/playground.py\n",
        "!wget -nc https://huggingface.co/spaces/Rejekts/RVC_PlayGround/raw/main/tools/useftools.py -O /content/RVC/tools/useftools.py\n",
        "!wget -nc https://huggingface.co/Rejekts/project/resolve/main/astronauts.mp3 -O /content/RVC/audios/astronauts.mp3\n",
        "!wget -nc https://huggingface.co/Rejekts/project/resolve/main/somegirl.mp3 -O /content/RVC/audios/somegirl.mp3\n",
        "!wget -nc https://huggingface.co/Rejekts/project/resolve/main/someguy.mp3 -O /content/RVC/audios/someguy.mp3\n",
        "!wget -nc https://huggingface.co/Rejekts/project/resolve/main/unchico.mp3 -O /content/RVC/audios/unchico.mp3\n",
        "!wget -nc https://huggingface.co/Rejekts/project/resolve/main/unachica.mp3 -O /content/RVC/audios/unachica.mp3\n",
        "!cd /content/RVC && python /content/RVC/download_files.py\n",
        "\n",
        "if not \"installed\" in locals():\n",
        "    !cd /content/RVC && pip install -r requirements.txt\n",
        "    !pip install mega.py gdown==4.6.0 pytube pydub  gradio==3.42.0\n",
        "installed=True\n",
        "\n",
        "save_to_drive=False#@param {type:\"boolean\"}\n",
        "\n",
        "if save_to_drive:\n",
        "    try:\n",
        "        from google.colab import auth\n",
        "        from pydrive2.auth import GoogleAuth\n",
        "        from oauth2client.client import GoogleCredentials\n",
        "        from pydrive2.drive import GoogleDrive\n",
        "        auth.authenticate_user()\n",
        "        gauth = GoogleAuth()\n",
        "        gauth.credentials = GoogleCredentials.get_application_default()\n",
        "        my_drive = GoogleDrive(gauth)\n",
        "        drive.mount('/content/drive')\n",
        "        drive_trash = my_drive.ListFile({'q': \"trashed = true\"}).GetList()\n",
        "        from RVC.easy_sync import GarbageMan\n",
        "        kevin = GarbageMan()\n",
        "        kevin.start(path=drive_trash,every=40,pattern=\"[GD]_*.pth\")\n",
        "    except Exception as e:\n",
        "        print(e)\n",
        "from RVC.easy_sync import Channel\n",
        "logs_folder ='/content/drive/MyDrive/project-main/logs'\n",
        "weights_folder = '/content/drive/MyDrive/project-main/assets/weights'\n",
        "if not \"logs_backup\" in locals(): logs_backup = Channel('/content/RVC/logs',logs_folder,every=30,exclude=\"mute\")\n",
        "if not \"weights_backup\" in locals(): weights_backup = Channel('/content/RVC/assets/weights',weights_folder,every=30)\n",
        "\n",
        "if os.path.exists('/content/drive/MyDrive'):\n",
        "    if not os.path.exists(logs_folder): os.makedirs(logs_folder)\n",
        "    if not os.path.exists(weights_folder): os.makedirs(weights_folder)\n",
        "    logs_backup.start()\n",
        "    weights_backup.start()\n",
        "\n",
        "clear_output()\n",
        "Button(description=\"\\u2714 Success\", button_style=\"success\")"
      ],
      "metadata": {
        "id": "Sb5fzhzEXK8X",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Open the **easyGUI** 📱\n",
        "import shutil\n",
        "%cd /content/RVC\n",
        "load_models_from_drive = False #@param{type:\"boolean\"}\n",
        "open_tensorboard = True #@param{type:\"boolean\"}\n",
        "py = \"demo.py\"\n",
        "if load_models_from_drive:\n",
        "    if os.path.exists('/content/drive/MyDrive/project-main'):\n",
        "        for file in os.listdir('/content/drive/MyDrive/project-main/assets/weights'):\n",
        "            try: shutil.copy2(f'/content/drive/MyDrive/project-main/assets/weights/{file}','/content/RVC/assets/weights/')\n",
        "            except: print(f\"Error loading {file}\")\n",
        "        for file in os.listdir('/content/drive/MyDrive/project-main/logs'):\n",
        "            try: shutil.copytree(f'/content/drive/MyDrive/project-main/logs/{file}',f'/content/RVC/logs/{file}')\n",
        "            except: print(f\"Error loading {file}\")\n",
        "    else:\n",
        "        print(\"Google Drive not connected...\")\n",
        "if open_tensorboard:\n",
        "    %load_ext tensorboard\n",
        "    %tensorboard --logdir ./logs --port=8888\n",
        "!python {py} --colab"
      ],
      "metadata": {
        "id": "DZDKirCM0F9g",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## If you want to train/infer **without a UI**, expand this tab! 😉"
      ],
      "metadata": {
        "id": "p6oK9M6hHdSq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title ⛏️ **CREATE TRANING FILES** - This will process the data, extract the features and create your index file for you!\n",
        "import os\n",
        "from pytube import YouTube\n",
        "from IPython.display import clear_output\n",
        "\n",
        "def calculate_audio_duration(file_path):\n",
        "    duration_seconds = len(AudioSegment.from_file(file_path)) / 1000.0\n",
        "    return duration_seconds\n",
        "\n",
        "def youtube_to_wav(url,dataset_folder=\"/content/dataset\"):\n",
        "    try:\n",
        "        yt = YouTube(url).streams.get_audio_only().download(output_path=dataset_folder)\n",
        "        mp4_path = os.path.join(dataset_folder,'audio.mp4')\n",
        "        wav_path = os.path.join(dataset_folder,'audio.wav')\n",
        "        os.rename(yt,mp4_path)\n",
        "        !ffmpeg -i {mp4_path} -acodec pcm_s16le -ar 44100 {wav_path}\n",
        "        os.remove(mp4_path)\n",
        "    except Exception as e:\n",
        "        print(e)\n",
        "\n",
        "%cd /content/RVC\n",
        "#@markdown <small> Give your AI Voice Model a name you won't forget 😉 Avoid weird characters, spaces, symbols, etc.\n",
        "model_name = 'My-Voice' #@param {type:\"string\"}\n",
        "dataset_folder = '/content/dataset' #@param {type:\"string\"}\n",
        "or_paste_a_youtube_link=\"\"#@param {type:\"string\"}\n",
        "if or_paste_a_youtube_link !=\"\":\n",
        "    youtube_to_wav(or_paste_a_youtube_link)\n",
        "\n",
        "from pydub import AudioSegment\n",
        "file_path = dataset_folder\n",
        "try:\n",
        "    duration = calculate_audio_duration(file_path)\n",
        "    if duration < 600:\n",
        "        cache = True\n",
        "    else:\n",
        "        cache = False\n",
        "except:\n",
        "    cache = False\n",
        "\n",
        "while len(os.listdir(dataset_folder)) < 1:\n",
        "    input(\"Your dataset folder is empty.\")\n",
        "!mkdir -p ./logs/{model_name}\n",
        "with open(f'./logs/{model_name}/preprocess.log','w') as f:\n",
        "    print(\"Starting...\")\n",
        "!python infer/modules/train/preprocess.py {dataset_folder} 32000 2 ./logs/{model_name} False 3.0 > /dev/null 2>&1\n",
        "with open(f'./logs/{model_name}/preprocess.log','r') as f:\n",
        "    if 'end preprocess' in f.read():\n",
        "        clear_output()\n",
        "        display(Button(description=\"\\u2714 Success\", button_style=\"success\"))\n",
        "    else:\n",
        "        print(\"Error preprocessing data... Make sure your dataset folder is correct.\")\n",
        "\n",
        "f0method = \"rmvpe_gpu\" # @param [\"pm\", \"harvest\", \"rmvpe\", \"rmvpe_gpu\"]\n",
        "%cd /content/RVC\n",
        "with open(f'./logs/{model_name}/extract_f0_feature.log','w') as f:\n",
        "    print(\"Starting...\")\n",
        "if f0method != \"rmvpe_gpu\":\n",
        "    !python infer/modules/train/extract/extract_f0_print.py ./logs/{model_name} 2 {f0method}\n",
        "else:\n",
        "    !python infer/modules/train/extract/extract_f0_rmvpe.py 1 0 0 ./logs/{model_name} True\n",
        "#!python infer/modules/train/extract_feature_print.py cuda:0 1 0 0 ./logs/{model_name} v2\n",
        "!python infer/modules/train/extract_feature_print.py cuda:0 1 0 ./logs/{model_name} v2 True\n",
        "with open(f'./logs/{model_name}/extract_f0_feature.log','r') as f:\n",
        "    if 'all-feature-done' in f.read():\n",
        "        clear_output()\n",
        "    else:\n",
        "        print(\"Error preprocessing data... Make sure your data was preprocessed.\")\n",
        "import numpy as np\n",
        "import faiss\n",
        "%cd /content/RVC\n",
        "def train_index(exp_dir1, version19):\n",
        "    exp_dir = \"logs/%s\" % (exp_dir1)\n",
        "    os.makedirs(exp_dir, exist_ok=True)\n",
        "    feature_dir = (\n",
        "        \"%s/3_feature256\" % (exp_dir)\n",
        "        if version19 == \"v1\"\n",
        "        else \"%s/3_feature768\" % (exp_dir)\n",
        "    )\n",
        "    if not os.path.exists(feature_dir):\n",
        "        return \"请先进行特征提取!\"\n",
        "    listdir_res = list(os.listdir(feature_dir))\n",
        "    if len(listdir_res) == 0:\n",
        "        return \"请先进行特征提取！\"\n",
        "    infos = []\n",
        "    npys = []\n",
        "    for name in sorted(listdir_res):\n",
        "        phone = np.load(\"%s/%s\" % (feature_dir, name))\n",
        "        npys.append(phone)\n",
        "    big_npy = np.concatenate(npys, 0)\n",
        "    big_npy_idx = np.arange(big_npy.shape[0])\n",
        "    np.random.shuffle(big_npy_idx)\n",
        "    big_npy = big_npy[big_npy_idx]\n",
        "    if big_npy.shape[0] > 2e5:\n",
        "        infos.append(\"Trying doing kmeans %s shape to 10k centers.\" % big_npy.shape[0])\n",
        "        yield \"\\n\".join(infos)\n",
        "        try:\n",
        "            big_npy = (\n",
        "                MiniBatchKMeans(\n",
        "                    n_clusters=10000,\n",
        "                    verbose=True,\n",
        "                    batch_size=256 * config.n_cpu,\n",
        "                    compute_labels=False,\n",
        "                    init=\"random\",\n",
        "                )\n",
        "                .fit(big_npy)\n",
        "                .cluster_centers_\n",
        "            )\n",
        "        except:\n",
        "            info = traceback.format_exc()\n",
        "            logger.info(info)\n",
        "            infos.append(info)\n",
        "            yield \"\\n\".join(infos)\n",
        "\n",
        "    np.save(\"%s/total_fea.npy\" % exp_dir, big_npy)\n",
        "    n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39)\n",
        "    infos.append(\"%s,%s\" % (big_npy.shape, n_ivf))\n",
        "    yield \"\\n\".join(infos)\n",
        "    index = faiss.index_factory(256 if version19 == \"v1\" else 768, \"IVF%s,Flat\" % n_ivf)\n",
        "    infos.append(\"training\")\n",
        "    yield \"\\n\".join(infos)\n",
        "    index_ivf = faiss.extract_index_ivf(index)  #\n",
        "    index_ivf.nprobe = 1\n",
        "    index.train(big_npy)\n",
        "    faiss.write_index(\n",
        "        index,\n",
        "        \"%s/trained_IVF%s_Flat_nprobe_%s_%s_%s.index\"\n",
        "        % (exp_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19),\n",
        "    )\n",
        "\n",
        "    infos.append(\"adding\")\n",
        "    yield \"\\n\".join(infos)\n",
        "    batch_size_add = 8192\n",
        "    for i in range(0, big_npy.shape[0], batch_size_add):\n",
        "        index.add(big_npy[i : i + batch_size_add])\n",
        "    faiss.write_index(\n",
        "        index,\n",
        "        \"%s/added_IVF%s_Flat_nprobe_%s_%s_%s.index\"\n",
        "        % (exp_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19),\n",
        "    )\n",
        "    infos.append(\n",
        "        \"成功构建索引，added_IVF%s_Flat_nprobe_%s_%s_%s.index\"\n",
        "        % (n_ivf, index_ivf.nprobe, exp_dir1, version19)\n",
        "    )\n",
        "\n",
        "training_log = train_index(model_name, 'v2')\n",
        "\n",
        "for line in training_log:\n",
        "    print(line)\n",
        "    if 'adding' in line:\n",
        "        clear_output()\n",
        "        display(Button(description=\"\\u2714 Success\", button_style=\"success\"))"
      ],
      "metadata": {
        "cellView": "form",
        "id": "w4wXvoez9Rce"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title 🤖 **TRAIN YOUR MODEL** - You can also resume training here!\n",
        "%cd /content/RVC\n",
        "from random import shuffle\n",
        "import json\n",
        "import os\n",
        "import pathlib\n",
        "from subprocess import Popen, PIPE, STDOUT\n",
        "now_dir=os.getcwd()\n",
        "model_name = 'My-Voice'#@param {type:\"string\"}\n",
        "#@markdown <small> Choose how often to save the model and how much training you want it to have\n",
        "save_frequency = 25 # @param {type:\"slider\", min:5, max:50, step:5}\n",
        "epochs = 500 # @param {type:\"slider\", min:10, max:2000, step:10}\n",
        "#@markdown ---\n",
        "#@markdown <small> **Advanced Settings**:\n",
        "\n",
        "# Remove the logging setup\n",
        "OV2=True#@param {type:\"boolean\"}\n",
        "\n",
        "batch_size = 8 # @param {type:\"slider\", min:1, max:20, step:1}\n",
        "sample_rate='32k'\n",
        "if OV2:\n",
        "    G_file=f'assets/pretrained_v2/f0Ov2Super{sample_rate}G.pth'\n",
        "    D_file=f'assets/pretrained_v2/f0Ov2Super{sample_rate}D.pth'\n",
        "else:\n",
        "    G_file=f'assets/pretrained_v2/f0G{sample_rate}.pth'\n",
        "    D_file=f'assets/pretrained_v2/f0D{sample_rate}.pth'\n",
        "def click_train(\n",
        "    exp_dir1,\n",
        "    sr2,\n",
        "    if_f0_3,\n",
        "    spk_id5,\n",
        "    save_epoch10,\n",
        "    total_epoch11,\n",
        "    batch_size12,\n",
        "    if_save_latest13,\n",
        "    pretrained_G14,\n",
        "    pretrained_D15,\n",
        "    gpus16,\n",
        "    if_cache_gpu17,\n",
        "    if_save_every_weights18,\n",
        "    version19,\n",
        "):\n",
        "    # 生成filelist\n",
        "    exp_dir = \"%s/logs/%s\" % (now_dir, exp_dir1)\n",
        "    os.makedirs(exp_dir, exist_ok=True)\n",
        "    gt_wavs_dir = \"%s/0_gt_wavs\" % (exp_dir)\n",
        "    feature_dir = (\n",
        "        \"%s/3_feature256\" % (exp_dir)\n",
        "        if version19 == \"v1\"\n",
        "        else \"%s/3_feature768\" % (exp_dir)\n",
        "    )\n",
        "    if if_f0_3:\n",
        "        f0_dir = \"%s/2a_f0\" % (exp_dir)\n",
        "        f0nsf_dir = \"%s/2b-f0nsf\" % (exp_dir)\n",
        "        names = (\n",
        "            set([name.split(\".\")[0] for name in os.listdir(gt_wavs_dir)])\n",
        "            & set([name.split(\".\")[0] for name in os.listdir(feature_dir)])\n",
        "            & set([name.split(\".\")[0] for name in os.listdir(f0_dir)])\n",
        "            & set([name.split(\".\")[0] for name in os.listdir(f0nsf_dir)])\n",
        "        )\n",
        "    else:\n",
        "        names = set([name.split(\".\")[0] for name in os.listdir(gt_wavs_dir)]) & set(\n",
        "            [name.split(\".\")[0] for name in os.listdir(feature_dir)]\n",
        "        )\n",
        "    opt = []\n",
        "    for name in names:\n",
        "        if if_f0_3:\n",
        "            opt.append(\n",
        "                \"%s/%s.wav|%s/%s.npy|%s/%s.wav.npy|%s/%s.wav.npy|%s\"\n",
        "                % (\n",
        "                    gt_wavs_dir.replace(\"\\\\\", \"\\\\\\\\\"),\n",
        "                    name,\n",
        "                    feature_dir.replace(\"\\\\\", \"\\\\\\\\\"),\n",
        "                    name,\n",
        "                    f0_dir.replace(\"\\\\\", \"\\\\\\\\\"),\n",
        "                    name,\n",
        "                    f0nsf_dir.replace(\"\\\\\", \"\\\\\\\\\"),\n",
        "                    name,\n",
        "                    spk_id5,\n",
        "                )\n",
        "            )\n",
        "        else:\n",
        "            opt.append(\n",
        "                \"%s/%s.wav|%s/%s.npy|%s\"\n",
        "                % (\n",
        "                    gt_wavs_dir.replace(\"\\\\\", \"\\\\\\\\\"),\n",
        "                    name,\n",
        "                    feature_dir.replace(\"\\\\\", \"\\\\\\\\\"),\n",
        "                    name,\n",
        "                    spk_id5,\n",
        "                )\n",
        "            )\n",
        "    fea_dim = 256 if version19 == \"v1\" else 768\n",
        "    if if_f0_3:\n",
        "        for _ in range(2):\n",
        "            opt.append(\n",
        "                \"%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s/logs/mute/2a_f0/mute.wav.npy|%s/logs/mute/2b-f0nsf/mute.wav.npy|%s\"\n",
        "                % (now_dir, sr2, now_dir, fea_dim, now_dir, now_dir, spk_id5)\n",
        "            )\n",
        "    else:\n",
        "        for _ in range(2):\n",
        "            opt.append(\n",
        "                \"%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s\"\n",
        "                % (now_dir, sr2, now_dir, fea_dim, spk_id5)\n",
        "            )\n",
        "    shuffle(opt)\n",
        "    with open(\"%s/filelist.txt\" % exp_dir, \"w\") as f:\n",
        "        f.write(\"\\n\".join(opt))\n",
        "\n",
        "    # Replace logger.debug, logger.info with print statements\n",
        "    print(\"Write filelist done\")\n",
        "    print(\"Use gpus:\", str(gpus16))\n",
        "    if pretrained_G14 == \"\":\n",
        "        print(\"No pretrained Generator\")\n",
        "    if pretrained_D15 == \"\":\n",
        "        print(\"No pretrained Discriminator\")\n",
        "    if version19 == \"v1\" or sr2 == \"40k\":\n",
        "        config_path = \"configs/v1/%s.json\" % sr2\n",
        "    else:\n",
        "        config_path = \"configs/v2/%s.json\" % sr2\n",
        "    config_save_path = os.path.join(exp_dir, \"config.json\")\n",
        "    if not pathlib.Path(config_save_path).exists():\n",
        "        with open(config_save_path, \"w\", encoding=\"utf-8\") as f:\n",
        "            with open(config_path, \"r\") as config_file:\n",
        "                config_data = json.load(config_file)\n",
        "                json.dump(\n",
        "                    config_data,\n",
        "                    f,\n",
        "                    ensure_ascii=False,\n",
        "                    indent=4,\n",
        "                    sort_keys=True,\n",
        "                )\n",
        "            f.write(\"\\n\")\n",
        "\n",
        "    cmd = (\n",
        "        'python infer/modules/train/train.py -e \"%s\" -sr %s -f0 %s -bs %s -g %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s'\n",
        "        % (\n",
        "            exp_dir1,\n",
        "            sr2,\n",
        "            1 if if_f0_3 else 0,\n",
        "            batch_size12,\n",
        "            gpus16,\n",
        "            total_epoch11,\n",
        "            save_epoch10,\n",
        "            \"-pg %s\" % pretrained_G14 if pretrained_G14 != \"\" else \"\",\n",
        "            \"-pd %s\" % pretrained_D15 if pretrained_D15 != \"\" else \"\",\n",
        "            1 if if_save_latest13 == True else 0,\n",
        "            1 if if_cache_gpu17 == True else 0,\n",
        "            1 if if_save_every_weights18 == True else 0,\n",
        "            version19,\n",
        "        )\n",
        "    )\n",
        "    # Use PIPE to capture the output and error streams\n",
        "    p = Popen(cmd, shell=True, cwd=now_dir, stdout=PIPE, stderr=STDOUT, bufsize=1, universal_newlines=True)\n",
        "\n",
        "    # Print the command's output as it runs\n",
        "    for line in p.stdout:\n",
        "        print(line.strip())\n",
        "\n",
        "    # Wait for the process to finish\n",
        "    p.wait()\n",
        "    return \"训练结束, 您可查看控制台训练日志或实验文件夹下的train.log\"\n",
        "%load_ext tensorboard\n",
        "%tensorboard --logdir ./logs --port=8888\n",
        "if \"cache\" not in locals():\n",
        "    cache = False\n",
        "training_log = click_train(\n",
        "    model_name,\n",
        "    sample_rate,\n",
        "    True,\n",
        "    0,\n",
        "    save_frequency,\n",
        "    epochs,\n",
        "    batch_size,\n",
        "    True,\n",
        "    G_file,\n",
        "    D_file,\n",
        "    0,\n",
        "    cache,\n",
        "    True,\n",
        "    'v2',\n",
        ")\n",
        "print(training_log)"
      ],
      "metadata": {
        "id": "FFfC9x239kC1",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title 🔊 **LISTEN TO YOUR MODEL**\n",
        "import os\n",
        "import IPython.display as ipd\n",
        "%cd /content/RVC\n",
        "model_path = \"/content/RVC/assets/weights/My-Voice.pth\"#@param {type:\"string\"}\n",
        "index_path = \"/content/RVC/logs/My-Voice/added_IVF439_Flat_nprobe_1_My-Voice_v2.index\"#@param {type:\"string\"}\n",
        "from colorama import Fore\n",
        "print(Fore.GREEN + f\"{index_path} was found\") if os.path.exists(index_path) else print(Fore.RED + f\"{index_path} was not found\")\n",
        "pitch = 0 # @param {type:\"slider\", min:-12, max:12, step:1}\n",
        "input_path = \"/content/RVC/audios/astronauts.mp3\"#@param {type:\"string\"}\n",
        "if not os.path.exists(input_path):\n",
        "    raise ValueError(f\"{input_path} was not found in your RVC folder.\")\n",
        "os.environ['index_root']  = os.path.dirname(index_path)\n",
        "index_path = os.path.basename(index_path)\n",
        "f0_method = \"rmvpe\" # @param [\"rmvpe\", \"pm\", \"harvest\"]\n",
        "save_as = \"/content/RVC/audios/cli_output.wav\"#@param {type:\"string\"}\n",
        "model_name = os.path.basename(model_path)\n",
        "os.environ['weight_root'] = os.path.dirname(model_path)\n",
        "index_rate = 0.5 # @param {type:\"slider\", min:0, max:1, step:0.01}\n",
        "volume_normalization = 0 #param {type:\"slider\", min:0, max:1, step:0.01}\n",
        "consonant_protection = 0.5 #param {type:\"slider\", min:0, max:1, step:0.01}\n",
        "\n",
        "!rm -f $save_as\n",
        "\n",
        "!python tools/cmd/infer_cli.py --f0up_key $pitch --input_path $input_path --index_path $index_path --f0method $f0_method --opt_path $save_as --model_name $model_name --index_rate $index_rate --device \"cuda:0\" --is_half True --filter_radius 3 --resample_sr 0 --rms_mix_rate $volume_normalization --protect $consonant_protection\n",
        "\n",
        "show_errors = False #@param {type:\"boolean\"}\n",
        "if not show_errors:\n",
        "    ipd.clear_output()\n",
        "ipd.Audio(save_as)"
      ],
      "metadata": {
        "cellView": "form",
        "id": "Fz3XSI8GrXra"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title 🤗 **SHARE YOUR MODEL** <small> - Join thousands of model makers by uploading your model to huggingface! All you need is your token. https://huggingface.co/settings/tokens\n",
        "%cd /content\n",
        "import os\n",
        "from google.colab import userdata\n",
        "from huggingface_hub import HfApi, create_repo, notebook_login\n",
        "\n",
        "try:\n",
        "    HF_TOKEN = userdata.get('HF_TOKEN')\n",
        "except:\n",
        "    notebook_login()\n",
        "\n",
        "api = HfApi()\n",
        "#@markdown Your REPO_ID is the name of your username + whatever you want to name the folder where it will be saved  🤗\n",
        "repo_id = \"Rejekts/My-Voice\" #@param {type:\"string\"}\n",
        "model_path = \"/content/RVC/assets/weights/My-Voice.pth\"#@param {type:\"string\"}\n",
        "model_name = model_path.split(\"/\")[-1].split(\".pth\")[0]\n",
        "index_path = \"/content/RVC/logs/My-Voice/added_IVF175_Flat_nprobe_1_rando_v2.index\"#@param {type:\"string\"}\n",
        "\n",
        "try:\n",
        "    create_repo(repo_id)\n",
        "except:\n",
        "    print(\"Error creating repo, proceeding...\")\n",
        "\n",
        "if os.path.exists(f'/content/{model_name}.zip'):\n",
        "    print(f\"Warning: A file named {model_name}.zip already exists!\")\n",
        "    confirmation = input(\"Type Y if you want to continue anyways...\")\n",
        "    if confirmation.lower() == \"y\":\n",
        "        !rm /content/{model_name}.zip\n",
        "        !zip {model_name}.zip {model_path} {index_path}\n",
        "        api.upload_file(\n",
        "                    path_or_fileobj=f\"/content/{model_name}.zip\",\n",
        "                    path_in_repo=f'{model_name}.zip',\n",
        "                    repo_id=repo_id,\n",
        "                    repo_type = \"model\"\n",
        "                )\n",
        "else:\n",
        "    !zip {model_name}.zip {model_path} {index_path}\n",
        "    api.upload_file(\n",
        "                path_or_fileobj=f\"/content/{model_name}.zip\",\n",
        "                path_in_repo=f'{model_name}.zip',\n",
        "                repo_id=repo_id,\n",
        "                repo_type = \"model\"\n",
        "            )"
      ],
      "metadata": {
        "cellView": "form",
        "id": "yerjlOLvK3hC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### The no UI training/inference is based on https://github.com/ardha27/AI-Song-Cover-RVC\n",
        "\n",
        "## Visit this repo to read more and **SUPPORT**\n"
      ],
      "metadata": {
        "id": "IlPgyIXBL1ue"
      }
    }
  ]
}