silero-vad.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sVNOuHQQjsrp"
   },
   "source": [
    "# PyTorch Examples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## VAD"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Install Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-30T17:35:43.397137Z",
     "start_time": "2020-12-30T17:33:10.962078Z"
    },
    "hidden": true
   },
   "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 soundfile\n",
    "\n",
    "import glob\n",
    "import torch\n",
    "torch.set_num_threads(1)\n",
    "\n",
    "from IPython.display import Audio\n",
    "from pprint import pprint\n",
    "\n",
    "model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad',\n",
    "                              model='silero_vad',\n",
    "                              force_reload=True)\n",
    "\n",
    "(get_speech_ts,\n",
    " save_audio,\n",
    " read_audio,\n",
    " state_generator,\n",
    " single_audio_stream,\n",
    " collect_chunks) = utils\n",
    "\n",
    "files_dir = torch.hub.get_dir() + '/snakers4_silero-vad_master/files'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true,
    "id": "fXbbaUO3jsrw"
   },
   "source": [
    "### Full Audio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-30T17:35:44.362860Z",
     "start_time": "2020-12-30T17:35:43.398441Z"
    },
    "hidden": true,
    "id": "aI_eydBPjsrx"
   },
   "outputs": [],
   "source": [
    "wav = read_audio(f'{files_dir}/en.wav')\n",
    "# get speech timestamps from full audio file\n",
    "speech_timestamps = get_speech_ts(wav, model,\n",
    "                                  num_steps=4)\n",
    "pprint(speech_timestamps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-30T17:35:44.419280Z",
     "start_time": "2020-12-30T17:35:44.364175Z"
    },
    "hidden": true,
    "id": "OuEobLchjsry"
   },
   "outputs": [],
   "source": [
    "# merge all speech chunks to one audio\n",
    "save_audio('only_speech.wav',\n",
    "           collect_chunks(speech_timestamps, wav), 16000) \n",
    "Audio('only_speech.wav')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true,
    "id": "iDKQbVr8jsry"
   },
   "source": [
    "### Single Audio Stream"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-15T13:09:59.199321Z",
     "start_time": "2020-12-15T13:09:59.196823Z"
    },
    "hidden": true,
    "id": "q-lql_2Wjsry"
   },
   "outputs": [],
   "source": [
    "wav = f'{files_dir}/en.wav'\n",
    "\n",
    "for batch in single_audio_stream(model, wav):\n",
    "    if batch:\n",
    "        print(batch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true,
    "id": "KBDVybJCjsrz"
   },
   "source": [
    "### Multiple Audio Streams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-15T13:10:03.590358Z",
     "start_time": "2020-12-15T13:10:03.587071Z"
    },
    "hidden": true,
    "id": "BK4tGfWgjsrz"
   },
   "outputs": [],
   "source": [
    "audios_for_stream = glob.glob(f'{files_dir}/*.wav')\n",
    "len(audios_for_stream) # total 4 audios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-15T13:10:15.762491Z",
     "start_time": "2020-12-15T13:10:03.591388Z"
    },
    "hidden": true,
    "id": "v1l8sam1jsrz"
   },
   "outputs": [],
   "source": [
    "for batch in state_generator(model, audios_for_stream, audios_in_stream=2): # 2 audio stream\n",
    "    if batch:\n",
    "        pprint(batch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Number detector"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Install Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "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 soundfile\n",
    "\n",
    "import glob\n",
    "import torch\n",
    "torch.set_num_threads(1)\n",
    "\n",
    "from IPython.display import Audio\n",
    "from pprint import pprint\n",
    "\n",
    "model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad',\n",
    "                              model='silero_number_detector',\n",
    "                              force_reload=True)\n",
    "\n",
    "(get_number_ts,\n",
    " save_audio,\n",
    " read_audio,\n",
    " collect_chunks,\n",
    " drop_chunks) = utils\n",
    "\n",
    "files_dir = torch.hub.get_dir() + '/snakers4_silero-vad_master/files'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Full audio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "wav = read_audio(f'{files_dir}/en_num.wav')\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
   },
   "outputs": [],
   "source": [
    "sample_rate = 16000\n",
    "# convert ms in timestamps to samples\n",
    "for timestamp in number_timestamps:\n",
    "    timestamp['start'] = int(timestamp['start'] * sample_rate / 1000)\n",
    "    timestamp['end'] = int(timestamp['end'] * sample_rate / 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# merge all number chunks to one audio\n",
    "save_audio('only_numbers.wav',\n",
    "           collect_chunks(number_timestamps, wav), sample_rate) \n",
    "Audio('only_numbers.wav')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# drop all number chunks from audio\n",
    "save_audio('no_numbers.wav',\n",
    "           drop_chunks(number_timestamps, wav), sample_rate) \n",
    "Audio('no_numbers.wav')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Language detector"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Install Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "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 soundfile\n",
    "\n",
    "import glob\n",
    "import torch\n",
    "torch.set_num_threads(1)\n",
    "\n",
    "from IPython.display import Audio\n",
    "from pprint import pprint\n",
    "\n",
    "model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad',\n",
    "                              model='silero_lang_detector',\n",
    "                              force_reload=True)\n",
    "\n",
    "(get_language,\n",
    " read_audio) = utils\n",
    "\n",
    "files_dir = torch.hub.get_dir() + '/snakers4_silero-vad_master/files'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Full audio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "wav = read_audio(f'{files_dir}/en.wav')\n",
    "lang = get_language(wav, model)\n",
    "print(lang)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "57avIBd6jsrz"
   },
   "source": [
    "# ONNX Example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## VAD"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true,
    "id": "bL4kn4KJrlyL"
   },
   "source": [
    "### Install Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cellView": "form",
    "hidden": true,
    "id": "Q4QIfSpprnkI"
   },
   "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 soundfile onnxruntime\n",
    "\n",
    "import glob\n",
    "import onnxruntime\n",
    "from pprint import pprint\n",
    "\n",
    "from IPython.display import Audio\n",
    "\n",
    "_, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad',\n",
    "                              model='silero_vad',\n",
    "                              force_reload=True)\n",
    "\n",
    "(get_speech_ts,\n",
    " save_audio,\n",
    " read_audio,\n",
    " state_generator,\n",
    " single_audio_stream,\n",
    " collect_speeches) = utils\n",
    "\n",
    "files_dir = torch.hub.get_dir() + '/snakers4_silero-vad_master/files'\n",
    "\n",
    "def init_onnx_model(model_path: str):\n",
    "    return onnxruntime.InferenceSession(model_path)\n",
    "\n",
    "def validate_onnx(model, inputs):\n",
    "    with torch.no_grad():\n",
    "        ort_inputs = {'input': inputs.cpu().numpy()}\n",
    "        outs = model.run(None, ort_inputs)\n",
    "        outs = [torch.Tensor(x) for x in outs]\n",
    "    return outs[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true,
    "id": "5JHErdB7jsr0"
   },
   "source": [
    "### Full Audio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-15T13:09:06.643812Z",
     "start_time": "2020-12-15T13:09:06.473386Z"
    },
    "hidden": true,
    "id": "krnGoA6Kjsr0"
   },
   "outputs": [],
   "source": [
    "model = init_onnx_model(f'{files_dir}/model.onnx')\n",
    "wav = read_audio(f'{files_dir}/en.wav')\n",
    "\n",
    "# get speech timestamps from full audio file\n",
    "speech_timestamps = get_speech_ts(wav, model, num_steps=4, run_function=validate_onnx) \n",
    "pprint(speech_timestamps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-15T13:09:08.862421Z",
     "start_time": "2020-12-15T13:09:08.820014Z"
    },
    "hidden": true,
    "id": "B176Lzfnjsr1"
   },
   "outputs": [],
   "source": [
    "# merge all speech chunks to one audio\n",
    "save_audio('only_speech.wav', collect_chunks(speech_timestamps, wav), 16000)\n",
    "Audio('only_speech.wav')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true,
    "id": "Rio9W50gjsr1"
   },
   "source": [
    "### Single Audio Stream"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-15T13:09:09.606031Z",
     "start_time": "2020-12-15T13:09:09.504239Z"
    },
    "hidden": true,
    "id": "IPkl8Yy1jsr1"
   },
   "outputs": [],
   "source": [
    "model = init_onnx_model(f'{files_dir}/model.onnx')\n",
    "wav = f'{files_dir}/en.wav'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-15T13:09:11.453171Z",
     "start_time": "2020-12-15T13:09:09.633435Z"
    },
    "hidden": true,
    "id": "NC6Jim0hjsr1"
   },
   "outputs": [],
   "source": [
    "for batch in single_audio_stream(model, wav, run_function=validate_onnx):\n",
    "    if batch:\n",
    "        pprint(batch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true,
    "id": "WNZ42u0ajsr1"
   },
   "source": [
    "### Multiple Audio Streams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-15T13:09:11.540423Z",
     "start_time": "2020-12-15T13:09:11.455706Z"
    },
    "hidden": true,
    "id": "XjhGQGppjsr1"
   },
   "outputs": [],
   "source": [
    "model = init_onnx_model(f'{files_dir}/model.onnx')\n",
    "audios_for_stream = glob.glob(f'{files_dir}/*.wav')\n",
    "pprint(len(audios_for_stream)) # total 4 audios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-15T13:09:19.565434Z",
     "start_time": "2020-12-15T13:09:11.552097Z"
    },
    "hidden": true,
    "id": "QI7-arlqjsr2"
   },
   "outputs": [],
   "source": [
    "for batch in state_generator(model, audios_for_stream, audios_in_stream=2, run_function=validate_onnx): # 2 audio stream\n",
    "    if batch:\n",
    "        pprint(batch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Number detector"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true,
    "id": "bL4kn4KJrlyL"
   },
   "source": [
    "### Install Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-30T17:25:19.107534Z",
     "start_time": "2020-12-30T17:24:51.853293Z"
    },
    "cellView": "form",
    "hidden": true,
    "id": "Q4QIfSpprnkI"
   },
   "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 soundfile onnxruntime\n",
    "\n",
    "import glob\n",
    "import torch\n",
    "import onnxruntime\n",
    "from pprint import pprint\n",
    "\n",
    "from IPython.display import Audio\n",
    "\n",
    "_, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad',\n",
    "                              model='silero_number_detector',\n",
    "                              force_reload=True)\n",
    "\n",
    "(get_number_ts,\n",
    " save_audio,\n",
    " read_audio,\n",
    " collect_chunks,\n",
    " drop_chunks) = utils\n",
    "\n",
    "files_dir = torch.hub.get_dir() + '/snakers4_silero-vad_master/files'\n",
    "\n",
    "def init_onnx_model(model_path: str):\n",
    "    return onnxruntime.InferenceSession(model_path)\n",
    "\n",
    "def validate_onnx(model, inputs):\n",
    "    with torch.no_grad():\n",
    "        ort_inputs = {'input': inputs.cpu().numpy()}\n",
    "        outs = model.run(None, ort_inputs)\n",
    "        outs = [torch.Tensor(x) for x in outs]\n",
    "    return outs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true,
    "id": "5JHErdB7jsr0"
   },
   "source": [
    "### Full Audio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-15T13:09:06.643812Z",
     "start_time": "2020-12-15T13:09:06.473386Z"
    },
    "hidden": true,
    "id": "krnGoA6Kjsr0"
   },
   "outputs": [],
   "source": [
    "model = init_onnx_model(f'{files_dir}/number_detector.onnx')\n",
    "wav = read_audio(f'{files_dir}/en_num.wav')\n",
    "\n",
    "# get number timestamps from full audio file\n",
    "number_timestamps = get_number_ts(wav, model, run_function=validate_onnx)\n",
    "pprint(number_timestamps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "sample_rate = 16000\n",
    "# convert ms in timestamps to samples\n",
    "for timestamp in number_timestamps:\n",
    "    timestamp['start'] = int(timestamp['start'] * sample_rate / 1000)\n",
    "    timestamp['end'] = int(timestamp['end'] * sample_rate / 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-15T13:09:08.862421Z",
     "start_time": "2020-12-15T13:09:08.820014Z"
    },
    "hidden": true,
    "id": "B176Lzfnjsr1"
   },
   "outputs": [],
   "source": [
    "# merge all number chunks to one audio\n",
    "save_audio('only_numbers.wav',\n",
    "           collect_chunks(number_timestamps, wav), 16000) \n",
    "Audio('only_numbers.wav')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# drop all number chunks from audio\n",
    "save_audio('no_numbers.wav',\n",
    "           drop_chunks(number_timestamps, wav), 16000) \n",
    "Audio('no_numbers.wav')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Language detector"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true,
    "id": "bL4kn4KJrlyL"
   },
   "source": [
    "### Install Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-30T17:25:19.107534Z",
     "start_time": "2020-12-30T17:24:51.853293Z"
    },
    "cellView": "form",
    "hidden": true,
    "id": "Q4QIfSpprnkI"
   },
   "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 soundfile onnxruntime\n",
    "\n",
    "import glob\n",
    "import torch\n",
    "import onnxruntime\n",
    "from pprint import pprint\n",
    "\n",
    "from IPython.display import Audio\n",
    "\n",
    "_, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad',\n",
    "                              model='silero_lang_detector',\n",
    "                              force_reload=True)\n",
    "\n",
    "(get_language,\n",
    " read_audio) = utils\n",
    "\n",
    "files_dir = torch.hub.get_dir() + '/snakers4_silero-vad_master/files'\n",
    "\n",
    "def init_onnx_model(model_path: str):\n",
    "    return onnxruntime.InferenceSession(model_path)\n",
    "\n",
    "def validate_onnx(model, inputs):\n",
    "    with torch.no_grad():\n",
    "        ort_inputs = {'input': inputs.cpu().numpy()}\n",
    "        outs = model.run(None, ort_inputs)\n",
    "        outs = [torch.Tensor(x) for x in outs]\n",
    "    return outs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true,
    "id": "5JHErdB7jsr0"
   },
   "source": [
    "### Full Audio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "model = init_onnx_model(f'{files_dir}/number_detector.onnx')\n",
    "wav = read_audio(f'{files_dir}/en.wav')\n",
    "\n",
    "lang = get_language(wav, model, run_function=validate_onnx)\n",
    "print(lang)"
   ]
  }
 ],
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