Update basic_training_notebook.ipynb

basic_training_notebook.ipynb

@@ -501,36 +501,38 @@
     "id": "WoEXJBaiC9mf"
    },
    "outputs": [],
-   "source": [
-    "# Trains a model. When finished, it will quantize and convert the model to a\n",
-    "# streaming version suitable for on-device detection.\n",
-    "# It will resume if stopped, but it will start over at the configured training\n",
-    "# steps in the yaml file.\n",
-    "# Change --train 0 to only convert and test the best-weighted model.\n",
-    "# On Google colab, it doesn't print the mini-batch results, so it may appear\n",
-    "# stuck for several minutes! Additionally, it is very slow compared to training\n",
-    "# on a local GPU.\n",
-    "\n",
-    "!python -m microwakeword.model_train_eval \\\n",
-    "--training_config='training_parameters.yaml' \\\n",
-    "--train 1 \\\n",
-    "--restore_checkpoint 1 \\\n",
-    "--test_tf_nonstreaming 0 \\\n",
-    "--test_tflite_nonstreaming 0 \\\n",
-    "--test_tflite_nonstreaming_quantized 0 \\\n",
-    "--test_tflite_streaming 0 \\\n",
-    "--test_tflite_streaming_quantized 1 \\\n",
-    "--use_weights \"best_weights\" \\\n",
-    "mixednet \\\n",
-    "--pointwise_filters \"64,64,64,64\" \\\n",
-    "--repeat_in_block  \"1, 1, 1, 1\" \\\n",
-    "--mixconv_kernel_sizes '[5], [7,11], [9,15], [23]' \\\n",
-    "--residual_connection \"0,0,0,0\" \\\n",
-    "--first_conv_filters 32 \\\n",
-    "--first_conv_kernel_size 5 \\\n",
-    "--stride 3"
-   ]
-  },
+    "source": [
+     "# Trains a model. When finished, it will quantize and convert the model to a\n",
+     "# streaming version suitable for on-device detection.\n",
+     "# It will resume if stopped, but it will start over at the configured training\n",
+     "# steps in the yaml file.\n",
+     "# Change --train 0 to only convert and test the best-weighted model.\n",
+     "# On Google colab, it doesn't print the mini-batch results, so it may appear\n",
+     "# stuck for several minutes! Additionally, it is very slow compared to training\n",
+     "# on a local GPU.\n",
+     "import os\n",
+     "os.environ['LD_LIBRARY_PATH'] = \"/usr/lib/x86_64-linux-gnu:\" + os.environ.get('LD_LIBRARY_PATH', '')\n",
+     "\n",
+     "!\"{sys.executable}\" -m microwakeword.model_train_eval \\\n",
+     "--training_config='training_parameters.yaml' \\\n",
+     "--train 1 \\\n",
+     "--restore_checkpoint 1 \\\n",
+     "--test_tf_nonstreaming 0 \\\n",
+     "--test_tflite_nonstreaming 0 \\\n",
+     "--test_tflite_nonstreaming_quantized 0 \\\n",
+     "--test_tflite_streaming 0 \\\n",
+     "--test_tflite_streaming_quantized 1 \\\n",
+     "--use_weights \"best_weights\" \\\n",
+     "mixednet \\\n",
+     "--pointwise_filters \"64,64,64,64\" \\\n",
+     "--repeat_in_block  \"1, 1, 1, 1\" \\\n",
+     "--mixconv_kernel_sizes '[5], [7,11], [9,15], [23]' \\\n",
+     "--residual_connection \"0,0,0,0\" \\\n",
+     "--first_conv_filters 32 \\\n",
+     "--first_conv_kernel_size 5 \\\n",
+     "--stride 3"
+    ]
+   },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -539,20 +541,21 @@
    },
    "outputs": [],
    "source": [
-    "# Downloads the tflite model file. To use on the device, you need to write a\n",
-    "# Model JSON file. See https://esphome.io/components/micro_wake_word for the\n",
-    "# documentation and\n",
-    "# https://github.com/esphome/micro-wake-word-models/tree/main/models/v2 for\n",
-    "# examples. Adjust the probability threshold based on the test results obtained\n",
-    "# after training is finished. You may also need to increase the Tensor arena\n",
-    "# model size if the model fails to load.\n",
+    "import shutil\n",
+    "from IPython.display import FileLink\n",
     "\n",
-    "from google.colab import files\n",
+    "# Define the source path and desired download location\n",
+    "source_path = \"trained_models/wakeword/tflite_stream_state_internal_quant/stream_state_internal_quant.tflite\"\n",
+    "destination_path = \"./stream_state_internal_quant.tflite\"\n",
     "\n",
-    "files.download(f\"trained_models/wakeword/tflite_stream_state_internal_quant/stream_state_internal_quant.tflite\")"
+    "# Copy the file to the current working directory\n",
+    "shutil.copy(source_path, destination_path)\n",
+    "\n",
+    "# Generate a link to download the file\n",
+    "print(\"Download your file:\")\n",
+    "FileLink(destination_path)"
    ]
-  }
- ],
+  },
  "metadata": {
   "accelerator": "GPU",
   "colab": {