chore: update audio.ipynb to reflect breadth of use cases

Author: rishisurana-labelbox

examples/annotation_import/audio.ipynb

@@ -1,5 +1,76 @@
 {
   "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "137b71f2",
+      "metadata": {},
+      "source": [
+        "## Brief temporal audio examples (Text, Radio, Checklist, Nested)\n",
+        "\n",
+        "- This section shows minimal, class-based examples that serialize to NDJSON:\n",
+        "  - Text: `value` with `frames`\n",
+        "  - Radio: `name` with `frames`\n",
+        "  - Checklist: `name` with `frames`\n",
+        "  - Nested (1 level): child nested under closest containing parent `frames`\n",
+        "\n",
+        "Run this cell and the next one to see the NDJSON output only (no API calls).\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "f58dd5db",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import labelbox.types as lb_types\n",
+        "from labelbox.data.serialization.ndjson.converter import NDJsonConverter\n",
+        "\n",
+        "# Minimal Text temporal example\n",
+        "text_anns = [\n",
+        "    lb_types.AudioClassificationAnnotation(\n",
+        "        start_frame=1000, end_frame=1100, name=\"text_class\", value=lb_types.Text(answer=\"Hello\")\n",
+        "    ),\n",
+        "    lb_types.AudioClassificationAnnotation(\n",
+        "        start_frame=1200, end_frame=1300, name=\"text_class\", value=lb_types.Text(answer=\"World\")\n",
+        "    ),\n",
+        "]\n",
+        "\n",
+        "# Minimal Radio temporal example\n",
+        "radio_anns = [\n",
+        "    lb_types.AudioClassificationAnnotation(\n",
+        "        start_frame=200, end_frame=1500, name=\"radio_class\",\n",
+        "        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name=\"first_radio_answer\")),\n",
+        "    ),\n",
+        "]\n",
+        "\n",
+        "# Minimal Checklist temporal example\n",
+        "checklist_anns = [\n",
+        "    lb_types.AudioClassificationAnnotation(\n",
+        "        start_frame=1200, end_frame=1800, name=\"checklist_class\",\n",
+        "        value=lb_types.Checklist(answer=[lb_types.ClassificationAnswer(name=\"angry\")]),\n",
+        "    ),\n",
+        "]\n",
+        "\n",
+        "# Minimal Nested (1 level) example: nested_text under parent text segment\n",
+        "nested_anns = [\n",
+        "    lb_types.AudioClassificationAnnotation(\n",
+        "        start_frame=1500, end_frame=2400, name=\"text_class\", value=lb_types.Text(answer=\"parent\")\n",
+        "    ),\n",
+        "    lb_types.AudioClassificationAnnotation(\n",
+        "        start_frame=1600, end_frame=2000, name=\"nested_text\", value=lb_types.Text(answer=\"child\")\n",
+        "    ),\n",
+        "]\n",
+        "\n",
+        "label = lb_types.Label(\n",
+        "    data={\"global_key\": \"audio_examples_demo\"},\n",
+        "    annotations=text_anns + radio_anns + checklist_anns + nested_anns,\n",
+        ")\n",
+        "ndjson = list(NDJsonConverter.serialize([label]))\n",
+        "for i, obj in enumerate(ndjson, 1):\n",
+        "    print(f\"{i}. {obj}\")\n"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},