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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyPovMyxp8xorYRHeQp1RAP2",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/rmcpantoja/piper/blob/master/notebooks/piper_multilingual_training_notebook.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# [Piper](https://github.com/rhasspy/piper) training notebook\n",
+        "\n",
+        "Notebook made by [rmcpantoja](http://github.com/rmcpantoja)"
+      ],
+      "metadata": {
+        "id": "eK3nmYDB6C1a"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# First steps"
+      ],
+      "metadata": {
+        "id": "AICh6p5OJybj"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#@markdown ## <font color=\"pink\"> **Google Colab Anti-Disconnect.**\n",
+        "#@markdown ---\n",
+        "#@markdown #### Avoid automatic disconnection. Still, it will disconnect after <font color=\"orange\">**6 to 12 hours**</font>.\n",
+        "\n",
+        "import IPython\n",
+        "js_code = '''\n",
+        "function ClickConnect(){\n",
+        "console.log(\"Working\");\n",
+        "document.querySelector(\"colab-toolbar-button#connect\").click()\n",
+        "}\n",
+        "setInterval(ClickConnect,60000)\n",
+        "'''\n",
+        "display(IPython.display.Javascript(js_code))"
+      ],
+      "metadata": {
+        "cellView": "form",
+        "id": "qyxSMuzjfQrz"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#@markdown ## Check GPU type\n",
+        "#@markdown ---\n",
+        "#@markdown #### A higher capable GPU can lead to faster training speeds. By default, you will have a <font color=\"orange\">**Tesla T4**</font>.\n",
+        "!nvidia-smi"
+      ],
+      "metadata": {
+        "cellView": "form",
+        "id": "ygxzp-xHTC7T"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "sUNjId07JfAK"
+      },
+      "outputs": [],
+      "source": [
+        "#@markdown ## mount Google Drive\n",
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive', force_remount=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#@markdown ## Install software\n",
+        "# clone:\n",
+        "!git clone https://github.com/rmcpantoja/piper\n",
+        "%cd piper/src/python\n",
+        "!pip install --upgrade pip\n",
+        "!pip install --upgrade wheel setuptools\n",
+        "!pip install -r requirements.txt\n",
+        "!pip install torchtext==0.12.0\n",
+        "!pip install torchvision==0.12.0\n",
+        "!bash build_monotonic_align.sh\n",
+        "!apt-get install espeak-ng\n",
+        "%cd /content"
+      ],
+      "metadata": {
+        "cellView": "form",
+        "id": "_XwmTVlcUgCh"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Training"
+      ],
+      "metadata": {
+        "id": "A3bMzEE0V5Ma"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#@markdown ## 1. Extract dataset\n",
+        "%cd /content\n",
+        "!mkdir /content/dataset\n",
+        "%cd /content/dataset\n",
+        "!mkdir /content/dataset/wavs\n",
+        "#@markdown ### Audio dataset path to unzip\n",
+        "zip_path = \"/content/drive/MyDrive/Fakeyou/odal_castilian/wavs16k.zip\" #@param {type:\"string\"}\n",
+        "!unzip \"{zip_path}\" -d /content/dataset/wavs\n",
+        "#@markdown ---"
+      ],
+      "metadata": {
+        "cellView": "form",
+        "id": "SvEGjf0aV8eg"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#@markdown ## 2. Upload the transcript file\n",
+        "%cd /content/dataset\n",
+        "from google.colab import files\n",
+        "!rm /content/dataset/metadata.csv\n",
+        "listfn, length = files.upload().popitem()\n",
+        "if listfn != \"metadata.csv\":\n",
+        "  !mv \"$listfn\" metadata.csv\n",
+        "%cd .."
+      ],
+      "metadata": {
+        "cellView": "form",
+        "id": "E0W0OCvXXvue"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#@markdown ## 3. Preprocess dataset\n",
+        "\n",
+        "import os\n",
+        "#@markdown ### First of all, select the language of your dataset.\n",
+        "language = \"English (U.S.)\" #@param [\"Català\", \"Dansk\", \"Deutsch\", \"Ελληνικά\", \"English (British)\", \"English (U.S.)\", \"Español\", \"Suomi\", \"Français\", \"Icelandic\", \"Italiano\", \"қазақша\", \"नेपाली\", \"Nederlands\", \"Norsk\", \"Polski\", \"Português (Brasil)\", \"Русский\", \"Svenska\", \"украї́нська\", \"Tiếng Việt\", \"简体中文\"]\n",
+        "#@markdown ---\n",
+        "# language definition:\n",
+        "languages = {\n",
+        "    \"Català\": \"ca\",\n",
+        "    \"Dansk\": \"da\",\n",
+        "    \"Deutsch\": \"de\",\n",
+        "    \"Ελληνικά\": \"grc\",\n",
+        "    \"English (British)\": \"en\",\n",
+        "    \"English (U.S.)\": \"en-us\",\n",
+        "    \"Español\": \"es\",\n",
+        "    \"Suomi\": \"fi\",\n",
+        "    \"Français\": \"fr\",\n",
+        "    \"Icelandic\": \"is\",\n",
+        "    \"Italiano\": \"it\",\n",
+        "    \"қазақша\": \"kk\",\n",
+        "    \"नेपाली\": \"ne\",\n",
+        "    \"Nederlands\": \"nl\",\n",
+        "    \"Norsk\": \"nb\",\n",
+        "    \"Polski\": \"pl\",\n",
+        "    \"Português (Brasil)\": \"pt-br\",\n",
+        "    \"Русский\": \"ru\",\n",
+        "    \"Svenska\": \"sv\",\n",
+        "    \"украї́нська\": \"uk\",\n",
+        "    \"Tiếng Việt\": \"vi-vn-x-central\",\n",
+        "    \"简体中文\": \"yue\"\n",
+        "}\n",
+        "\n",
+        "def _get_language(code):\n",
+        "    return languages[code]\n",
+        "\n",
+        "final_language = _get_language(language)\n",
+        "# output:\n",
+        "#@markdown ### Choose the working folder. (recommended to save to Drive)\n",
+        "\n",
+        "#@markdown The working folder will be used in preprocessing, but also in training the model.\n",
+        "output_dir = \"/content/drive/MyDrive/colab/piper\" #@param {type:\"string\"}\n",
+        "if not os.path.exists(output_dir):\n",
+        "  os.makedirs(output_dir)\n",
+        "#@markdown ---\n",
+        "#@markdown ### Choose dataset format\n",
+        "dataset_format = \"ljspeech\" #@param [\"ljspeech\", \"mycroft\"]\n",
+        "#@markdown ---\n",
+        "#@markdown ### Select the sample rate of the dataset\n",
+        "sample_rate = \"16000\" #@param [\"16000\", \"22050\"]\n",
+        "#@markdown ---\n",
+        "%cd /content/piper/src/python\n",
+        "!python -m piper_train.preprocess \\\n",
+        "  --language {final_language} \\\n",
+        "  --input-dir /content/dataset \\\n",
+        "  --output-dir {output_dir} \\\n",
+        "  --dataset-format {dataset_format} \\\n",
+        "  --sample-rate {sample_rate}"
+      ],
+      "metadata": {
+        "cellView": "form",
+        "id": "dOyx9Y6JYvRF"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#@markdown ## 4. Settings\n",
+        "import json\n",
+        "import ipywidgets as widgets\n",
+        "from IPython.display import display\n",
+        "\n",
+        "#@markdown ### Fine-tune this dataset?\n",
+        "finetune = True #@param {type:\"boolean\"}\n",
+        "#@markdown ---\n",
+        "if finetune:\n",
+        "    ft_command = '--resume_from_checkpoint \"/content/pretrained.ckpt\" '\n",
+        "    try:\n",
+        "        with open('/CONTENT/PIPER/NOTEBOOKS/pretrained_models.json') as f:\n",
+        "            pretrained_models = json.load(f)\n",
+        "        if final_language in pretrained_models:\n",
+        "            models = pretrained_models[final_language]\n",
+        "            model_options = [(model_name, model_url) for model_name, model_url in models.items()]\n",
+        "            model_dropdown = widgets.Dropdown(description = \"Choose pretrained model\", options=model_options)\n",
+        "            download_button = widgets.Button(description=\"Download\")\n",
+        "            def download_model(btn):\n",
+        "                model_name, model_url = model_dropdown.value\n",
+        "                file_id = model_url.split('/')[-2]\n",
+        "                !gdown {file_id} -O \"/content/pretrained.ckpt\"\n",
+        "\n",
+        "            download_button.on_click(download_model)\n",
+        "            display(model_dropdown, download_button)\n",
+        "        else:\n",
+        "            raise Exception(f\"There are no pretrained models available for the language {final_language}\")\n",
+        "    except FileNotFoundError:\n",
+        "        raise Exception(\"The pretrained_models.json file was not found.\")\n",
+        "else:\n",
+        "    ft_command = \"\"\n",
+        "#@makrdown ### Choose batch size based on this dataset\n",
+        "batch_size = 8 #@param {type:\"integer\"}\n",
+        "#@markdown ---\n",
+        "#@markdown ### Validation split\n",
+        "validation_split = 0.03 #@param {type:\"number\"}\n",
+        "#@markdown ---\n",
+        "#@markdown ### Choose the quality for this model:\n",
+        "\n",
+        "#@markdown * x-low - 16Khz audio, 5-7M params\n",
+        "#@markdown * low - 16Khz audio, 15-20M params\n",
+        "#@markdown * medium - 22.05Khz audio, 15-20 params\n",
+        "#@markdown * high - 22.05Khz audio, 28-32M params\n",
+        "quality = \"x-low\" #@param [\"high\", \"low\", \"medium\", \"x-low\"]\n",
+        "#@markdown ---\n",
+        "#@markdown ### For how many steps to save training checkpoints?\n",
+        "checkpoint_epochs = 25 #@param {type:\"integer\"}\n",
+        "#@markdown ---\n",
+        "#@markdown ### Step interval to generate model samples\n",
+        "log_every_n_steps = 1000 #@param {type:\"integer\"}\n",
+        "#@markdown ---\n",
+        "#@markdown ### training epochs\n",
+        "max_epochs = 5000 #@param {type:\"integer\"}\n",
+        "#@markdown ---"
+      ],
+      "metadata": {
+        "cellView": "form",
+        "id": "ickQlOCRjkBL"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#@markdown ## 5. Run the tensorboard extension\n",
+        "\n",
+        "#@markdown The tensorboard is used to visualize the results of the model while it is being trained.\n",
+        "%load_ext tensorboard\n",
+        "%tensorboard --logdir {output_dir}"
+      ],
+      "metadata": {
+        "cellView": "form",
+        "id": "MpKDfhAHjHJ3"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#@markdown ## 6. Train\n",
+        "!python -m piper_train \\\n",
+        "    --dataset-dir \"{output_dir}\" \\\n",
+        "    --accelerator 'gpu' \\\n",
+        "    --devices 1 \\\n",
+        "    --batch-size {batch_size} \\\n",
+        "    --validation-split {validation_split} \\\n",
+        "    --num-test-examples 2 \\\n",
+        "    --quality {quality} \\\n",
+        "    --checkpoint-epochs {checkpoint_epochs} \\\n",
+        "    --check_val_every_n_epoch 25 \\\n",
+        "    --num_sanity_val_steps 1000 \\\n",
+        "    --log_every_n_steps {log_every_n_steps} \\\n",
+        "    --max_epochs {max_epochs} \\\n",
+        "    {ft-command}\\\n",
+        "    --precision 32"
+      ],
+      "metadata": {
+        "cellView": "form",
+        "id": "X4zbSjXg2J3N"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file