add parallel example

examples/parallel_example.ipynb

@@ -0,0 +1,131 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Install Dependencies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install -q torchaudio\n",
+    "SAMPLING_RATE = 16000\n",
+    "import torch\n",
+    "from pprint import pprint\n",
+    "torch.set_num_threads(1)\n",
+    "# download wav files, make multiple copies\n",
+    "for idx in range(10):\n",
+    "    torch.hub.download_url_to_file('https://models.silero.ai/vad_models/en.wav', f\"en_example{idx}.wav\")\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load VAD model from torch hub"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad',\n",
+    "                              model='silero_vad',\n",
+    "                              force_reload=True,\n",
+    "                              onnx=USE_ONNX)\n",
+    "\n",
+    "(get_speech_timestamps,\n",
+    " save_audio,\n",
+    " read_audio,\n",
+    " VADIterator,\n",
+    " collect_chunks) = utils"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Define a vad process function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def vad_process(audio_file: str):\n",
+    "    with torch.no_grad():\n",
+    "        wav = read_audio(audio_file, sample_rate=SAMPLING_RATE)\n",
+    "        return get_speech_timestamps(\n",
+    "            wav,\n",
+    "            model,\n",
+    "            0.46,  # speech prob threshold\n",
+    "            16000,  # sample rate\n",
+    "            300,  # min speech duration in ms\n",
+    "            20,  # max speech duration in seconds\n",
+    "            600,  # min silence duration\n",
+    "            512,  # window size\n",
+    "            200,  # spech pad ms\n",
+    "        )"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Parallelization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from concurrent.futures import ProcessPoolExecutor, as_completed\n",
+    "\n",
+    "futures = []\n",
+    "with ProcessPoolExecutor(max_workers=4) as ex:\n",
+    "    for i in range(10):\n",
+    "        futures.append(ex.submit(vad_process, f\"en_example{idx}.wav\"))\n",
+    "\n",
+    "for finished in as_completed(futures):\n",
+    "    pprint(finished.result())"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "diarization",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.15"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}