diff --git a/silero-vad.ipynb b/silero-vad.ipynb
index d9c62e8..f3b521d 100644
--- a/silero-vad.ipynb
+++ b/silero-vad.ipynb
@@ -1,14 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "FpMplOCA2Fwp"
-   },
-   "source": [
-    "#VAD"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -170,238 +161,6 @@
     "\n",
     "print(speech_probs[:10]) # first 10 chunks predicts"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "heading_collapsed": true,
-    "id": "36jY0niD2Fww"
-   },
-   "source": [
-    "# Number detector"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "heading_collapsed": true,
-    "hidden": true,
-    "id": "scd1DlS42Fwx"
-   },
-   "source": [
-    "## Install Dependencies"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "hidden": true,
-    "id": "Kq5gQuYq2Fwx"
-   },
-   "outputs": [],
-   "source": [
-    "#@title Install and Import Dependencies\n",
-    "\n",
-    "# this assumes that you have a relevant version of PyTorch installed\n",
-    "!pip install -q torchaudio\n",
-    "\n",
-    "SAMPLING_RATE = 16000\n",
-    "\n",
-    "import torch\n",
-    "torch.set_num_threads(1)\n",
-    "\n",
-    "from IPython.display import Audio\n",
-    "from pprint import pprint\n",
-    "# download example\n",
-    "torch.hub.download_url_to_file('https://models.silero.ai/vad_models/en_num.wav', 'en_number_example.wav')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "dPwCFHmFycUF"
-   },
-   "outputs": [],
-   "source": [
-    "USE_ONNX = False # change this to True if you want to test onnx model\n",
-    "if USE_ONNX:\n",
-    "    !pip install -q onnxruntime\n",
-    "  \n",
-    "model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad',\n",
-    "                              model='silero_number_detector',\n",
-    "                              force_reload=True,\n",
-    "                              onnx=USE_ONNX)\n",
-    "\n",
-    "(get_number_ts,\n",
-    " save_audio,\n",
-    " read_audio,\n",
-    " collect_chunks,\n",
-    " drop_chunks) = utils\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "heading_collapsed": true,
-    "hidden": true,
-    "id": "qhPa30ij2Fwy"
-   },
-   "source": [
-    "## Full audio"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "hidden": true,
-    "id": "EXpau6xq2Fwy"
-   },
-   "outputs": [],
-   "source": [
-    "wav = read_audio('en_number_example.wav', sampling_rate=SAMPLING_RATE)\n",
-    "# get number timestamps from full audio file\n",
-    "number_timestamps = get_number_ts(wav, model)\n",
-    "pprint(number_timestamps)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "hidden": true,
-    "id": "u-KfXRhZ2Fwy"
-   },
-   "outputs": [],
-   "source": [
-    "# convert ms in timestamps to samples\n",
-    "for timestamp in number_timestamps:\n",
-    "    timestamp['start'] = int(timestamp['start'] * SAMPLING_RATE / 1000)\n",
-    "    timestamp['end'] = int(timestamp['end'] * SAMPLING_RATE / 1000)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "hidden": true,
-    "id": "iwYEC4aZ2Fwy"
-   },
-   "outputs": [],
-   "source": [
-    "# merge all number chunks to one audio\n",
-    "save_audio('only_numbers.wav',\n",
-    "           collect_chunks(number_timestamps, wav), SAMPLING_RATE) \n",
-    "Audio('only_numbers.wav')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "hidden": true,
-    "id": "fHaYejX12Fwy"
-   },
-   "outputs": [],
-   "source": [
-    "# drop all number chunks from audio\n",
-    "save_audio('no_numbers.wav',\n",
-    "           drop_chunks(number_timestamps, wav), SAMPLING_RATE) \n",
-    "Audio('no_numbers.wav')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "heading_collapsed": true,
-    "id": "PnKtJKbq2Fwz"
-   },
-   "source": [
-    "# Language detector"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "heading_collapsed": true,
-    "hidden": true,
-    "id": "F5cAmMbP2Fwz"
-   },
-   "source": [
-    "## Install Dependencies"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "hidden": true,
-    "id": "Zu9D0t6n2Fwz"
-   },
-   "outputs": [],
-   "source": [
-    "#@title Install and Import Dependencies\n",
-    "\n",
-    "# this assumes that you have a relevant version of PyTorch installed\n",
-    "!pip install -q torchaudio\n",
-    "\n",
-    "SAMPLING_RATE = 16000\n",
-    "\n",
-    "import torch\n",
-    "torch.set_num_threads(1)\n",
-    "\n",
-    "from IPython.display import Audio\n",
-    "from pprint import pprint\n",
-    "# download example\n",
-    "torch.hub.download_url_to_file('https://models.silero.ai/vad_models/en.wav', 'en_example.wav')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "JfRKDZiRztFe"
-   },
-   "outputs": [],
-   "source": [
-    "USE_ONNX = False # change this to True if you want to test onnx model\n",
-    "if USE_ONNX:\n",
-    "    !pip install -q onnxruntime\n",
-    "  \n",
-    "model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad',\n",
-    "                              model='silero_lang_detector',\n",
-    "                              force_reload=True,\n",
-    "                              onnx=USE_ONNX)\n",
-    "\n",
-    "get_language, read_audio = utils"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "heading_collapsed": true,
-    "hidden": true,
-    "id": "iC696eMX2Fwz"
-   },
-   "source": [
-    "## Full audio"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "hidden": true,
-    "id": "c8UYnYBF2Fw0"
-   },
-   "outputs": [],
-   "source": [
-    "wav = read_audio('en_example.wav', sampling_rate=SAMPLING_RATE)\n",
-    "lang = get_language(wav, model)\n",
-    "print(lang)"
-   ]
   }
  ],
  "metadata": {