Notebook improvements from @xxNessuxX

notebooks/piper_multilingual_training_notebook.ipynb

@@ -5,7 +5,6 @@
     "colab": {
       "provenance": [],
       "gpuType": "T4",
-      "authorship_tag": "ABX9TyN5orawFZNYEhhbtJPe+1fd",
       "include_colab_link": true
     },
     "kernelspec": {
@@ -32,10 +31,13 @@
     {
       "cell_type": "markdown",
       "source": [
-        "# [Piper](https://github.com/rhasspy/piper) training notebook\n",
-        "## ![Piper](https://contribute.rhasspy.org/img/logo.png)\n",
+        "# <font color=\"pink\"> **[Piper](https://github.com/rhasspy/piper) training notebook.**\n",
+        "## ![Piper logo](https://contribute.rhasspy.org/img/logo.png)\n",
         "\n",
-        "Notebook made by [rmcpantoja](http://github.com/rmcpantoja)"
+        "---\n",
+        "\n",
+        "- Notebook made by [rmcpantoja](http://github.com/rmcpantoja)\n",
+        "- Collaborator: [Xx_Nessu_xX](https://fakeyou.com/profile/Xx_Nessu_xX)"
       ],
       "metadata": {
         "id": "eK3nmYDB6C1a"
@@ -44,7 +46,7 @@
     {
       "cell_type": "markdown",
       "source": [
-        "# First steps"
+        "# <font color=\"pink\">🔧 ***First steps.*** 🔧"
       ],
       "metadata": {
         "id": "AICh6p5OJybj"
@@ -53,7 +55,7 @@
     {
       "cell_type": "code",
       "source": [
-        "#@markdown ## <font color=\"pink\"> **Google Colab Anti-Disconnect.**\n",
+        "#@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",
@@ -77,7 +79,7 @@
     {
       "cell_type": "code",
       "source": [
-        "#@markdown ## Check GPU type\n",
+        "#@markdown ## <font color=\"pink\"> **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"
@@ -98,7 +100,7 @@
       },
       "outputs": [],
       "source": [
-        "#@markdown ## mount Google Drive\n",
+        "#@markdown # <font color=\"pink\"> **Mount Google Drive.** 📂\n",
         "from google.colab import drive\n",
         "drive.mount('/content/drive', force_remount=True)"
       ]
@@ -106,10 +108,10 @@
     {
       "cell_type": "code",
       "source": [
-        "#@markdown ## Install software\n",
-        "#@markdown In this cell the synthesizer and its necessary dependencies to execute the training will be installed. (this may take a while)\n",
+        "#@markdown # <font color=\"pink\"> **Install software.** 📦\n",
+        "#@markdown ####In this cell the synthesizer and its necessary dependencies to execute the training will be installed. (this may take a while)\n",
         "\n",
-        "#@markdown **Note: Please restart the runtime environment when the cell execution is finished. Then you can continue with the training section.**\n",
+        "#@markdown <font color=\"orange\">**Note: Please restart the runtime environment when the cell execution is finished. Then you can continue with the training section.**\n",
         "\n",
         "# clone:\n",
         "!git clone https://github.com/rmcpantoja/piper\n",
@@ -133,7 +135,7 @@
     {
       "cell_type": "markdown",
       "source": [
-        "# Training"
+        "# <font color=\"pink\"> 🤖 ***Training.*** 🤖"
       ],
       "metadata": {
         "id": "A3bMzEE0V5Ma"
@@ -142,8 +144,8 @@
     {
       "cell_type": "code",
       "source": [
-        "#@markdown ## 1. Extract dataset\n",
-        "#@markdown <font color=\"orange\">**Important: the audios must be in wav format, (16000 or 22050hz, 16-bits, mono), and, for convenience, numbered. Example:**\n",
+        "#@markdown # <font color=\"pink\"> **1. Extract dataset.** 📥\n",
+        "#@markdown ####Important: the audios must be in <font color=\"orange\">**wav format, (16000 or 22050hz, 16-bits, mono), and, for convenience, numbered. Example:**\n",
         "\n",
         "#@markdown * <font color=\"orange\">**1.wav**</font>\n",
         "#@markdown * <font color=\"orange\">**2.wav**</font>\n",
@@ -171,14 +173,15 @@
     {
       "cell_type": "code",
       "source": [
-        "#@markdown ## 2. Upload the transcript file\n",
-        "#@markdown Important: the transcription means writing what the character says in each of the audios, and it must have the following structure:\n",
+        "#@markdown # <font color=\"pink\"> **2. Upload the transcript file.** 📝\n",
+        "#@markdown ---\n",
+        "#@markdown ####Important: the transcription means writing what the character says in each of the audios, and it must have the following structure:\n",
         "\n",
         "#@markdown * wavs/1.wav|This is what my character says in audio 1.\n",
         "#@markdown * wavs/2.wav|This, the text that the character says in audio 2.\n",
-        "#@markdown * ...............\n",
+        "#@markdown * ...\n",
         "\n",
-        "#@markdown And so on. In addition, the transcript must be in a .csv format (UTF8 without bom)\n",
+        "#@markdown And so on. In addition, the transcript must be in a .csv format. (UTF-8 without BOM)\n",
         "\n",
         "%cd /content/dataset\n",
         "from google.colab import files\n",
@@ -198,7 +201,7 @@
     {
       "cell_type": "code",
       "source": [
-        "#@markdown ## 3. Preprocess dataset\n",
+        "#@markdown # <font color=\"pink\"> **3. Preprocess dataset.** 🔄\n",
         "\n",
         "import os\n",
         "#@markdown ### First of all, select the language of your dataset.\n",
@@ -238,7 +241,7 @@
         "model_name = \"Test\" #@param {type:\"string\"}\n",
         "#@markdown ---\n",
         "# output:\n",
-        "#@markdown ### Choose the working folder. (recommended to save to Drive)\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_path = \"/content/drive/MyDrive/colab/piper\" #@param {type:\"string\"}\n",
@@ -246,7 +249,7 @@
         "if not os.path.exists(output_dir):\n",
         "  os.makedirs(output_dir)\n",
         "#@markdown ---\n",
-        "#@markdown ### Choose dataset format\n",
+        "#@markdown ### Choose dataset format:\n",
         "dataset_format = \"ljspeech\" #@param [\"ljspeech\", \"mycroft\"]\n",
         "#@markdown ---\n",
         "#@markdown ### Is this a single speaker dataset? Otherwise, uncheck:\n",
@@ -256,7 +259,7 @@
         "else:\n",
         "  force_sp = \"\"\n",
         "#@markdown ---\n",
-        "#@markdown ### Select the sample rate of the dataset\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",
@@ -278,7 +281,7 @@
     {
       "cell_type": "code",
       "source": [
-        "#@markdown ## 4. Settings\n",
+        "#@markdown # <font color=\"pink\"> **4. Settings.** 🧰\n",
         "import json\n",
         "import ipywidgets as widgets\n",
         "from IPython.display import display\n",
@@ -309,10 +312,10 @@
         "        raise Exception(\"The pretrained_models.json file was not found.\")\n",
         "else:\n",
         "    ft_command = \"\"\n",
-        "#@markdown ### Choose batch size based on this dataset\n",
+        "#@markdown ### Choose batch size based on this dataset:\n",
         "batch_size = 16 #@param {type:\"integer\"}\n",
         "#@markdown ---\n",
-        "#@markdown ### Validation split\n",
+        "#@markdown ### Validation split:\n",
         "validation_split = 0.03 #@param {type:\"number\"}\n",
         "#@markdown ---\n",
         "#@markdown ### Choose the quality for this model:\n",
@@ -326,10 +329,10 @@
         "#@markdown ### For how many epochs to save training checkpoints?\n",
         "checkpoint_epochs = 25 #@param {type:\"integer\"}\n",
         "#@markdown ---\n",
-        "#@markdown ### Step interval to generate model samples\n",
+        "#@markdown ### Step interval to generate model samples:\n",
         "log_every_n_steps = 250 #@param {type:\"integer\"}\n",
         "#@markdown ---\n",
-        "#@markdown ### training epochs\n",
+        "#@markdown ### Training epochs:\n",
         "max_epochs = 10000 #@param {type:\"integer\"}\n",
         "#@markdown ---"
       ],
@@ -343,9 +346,9 @@
     {
       "cell_type": "code",
       "source": [
-        "#@markdown ## 5. Run the tensorboard extension\n",
+        "#@markdown # <font color=\"pink\"> **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",
+        "#@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}"
       ],
@@ -359,9 +362,9 @@
     {
       "cell_type": "code",
       "source": [
-        "#@markdown ## 6. Train\n",
+        "#@markdown # <font color=\"pink\"> **6. Train.** 🏋️‍♂️\n",
         "\n",
-        "#@markdown **Note: Remember to empty the trash of your Drive from time to time to avoid a lot of space consumption when saving the models.**\n",
+        "#@markdown <font color=\"orange\">**Note: Remember to empty the trash of your Drive from time to time to avoid a lot of space consumption when saving the models.**\n",
         "!python -m piper_train \\\n",
         "    --dataset-dir \"{output_dir}\" \\\n",
         "    --accelerator 'gpu' \\\n",
@@ -371,8 +374,7 @@
         "    --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",
+        "    --check_val_every_n_epoch {checkpoint_epochs} \\\n",
         "    --log_every_n_steps {log_every_n_steps} \\\n",
         "    --max_epochs {max_epochs} \\\n",
         "    {ft_command}\\\n",
@@ -384,6 +386,17 @@
       },
       "execution_count": null,
       "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Have you finished training and want to test the model?\n",
+        "\n",
+        "Export your model using the [model exporter notebook](https://colab.research.google.com/github/rmcpantoja/piper/blob/master/notebooks/piper_model_exporter.ipynb)!"
+      ],
+      "metadata": {
+        "id": "6ISG085SYn85"
+      }
     }
   ]
 }