{
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    "colab": {
      "name": "silero-vad.ipynb",
      "provenance": []
    },
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sVNOuHQQjsrp"
      },
      "source": [
        "# PyTorch Examples"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FpMplOCA2Fwp"
      },
      "source": [
        "## VAD"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "heading_collapsed": true,
        "id": "62A6F_072Fwq"
      },
      "source": [
        "### Install Dependencies"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "hidden": true,
        "id": "5w5AkskZ2Fwr"
      },
      "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",
        "SAMPLE_RATE = 16000\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_timestamps,\n",
        " save_audio,\n",
        " read_audio,\n",
        " VADIterator,\n",
        " collect_chunks) = utils\n",
        "\n",
        "files_dir = torch.hub.get_dir() + '/snakers4_silero-vad_master/files'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fXbbaUO3jsrw"
      },
      "source": [
        "### Full Audio"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RAfJPb_a-Auj"
      },
      "source": [
        "**Speech timestapms from full audio**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aI_eydBPjsrx"
      },
      "source": [
        "wav = read_audio(f'{files_dir}/en.wav', sampling_rate=SAMPLE_RATE)\n",
        "# get speech timestamps from full audio file\n",
        "speech_timestamps = get_speech_timestamps(wav, model, sampling_rate=SAMPLE_RATE)\n",
        "pprint(speech_timestamps)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OuEobLchjsry"
      },
      "source": [
        "# merge all speech chunks to one audio\n",
        "save_audio('only_speech.wav',\n",
        "           collect_chunks(speech_timestamps, wav), sampling_rate=16000) \n",
        "Audio('only_speech.wav')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iDKQbVr8jsry"
      },
      "source": [
        "**Stream imitation example**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q-lql_2Wjsry"
      },
      "source": [
        "## using VADIterator class\n",
        "\n",
        "vad_iterator = VADiterator(double_model)\n",
        "wav = read_audio((f'{files_dir}/en.wav', sampling_rate=SAMPLE_RATE)\n",
        "\n",
        "window_size_samples = 1536 # number of samples in a single audio chunk\n",
        "for i in range(0, len(wav), window_size_samples):\n",
        "    speech_dict = vad_iterator(wav[i: i+ window_size_samples], return_seconds=True)\n",
        "    if speech_dict:\n",
        "        print(speech_dict, end=' ')\n",
        "vad_iterator.reset_states() # reset model states after each audio"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BX3UgwwB2Fwv"
      },
      "source": [
        "## just probabilities\n",
        "\n",
        "wav = read_audio((f'{files_dir}/en.wav', sampling_rate=SAMPLE_RATE)\n",
        "speech_probs = []\n",
        "window_size_samples = 1536\n",
        "for i in range(0, len(wav), window_size_samples):\n",
        "    speech_prob = model(wav[i: i+ window_size_samples], SAMPLE_RATE).item()\n",
        "    speech_probs.append(speech_prob)\n",
        "\n",
        "pprint(speech_probs[:100])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "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",
      "metadata": {
        "hidden": true,
        "id": "Kq5gQuYq2Fwx"
      },
      "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'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "heading_collapsed": true,
        "hidden": true,
        "id": "qhPa30ij2Fwy"
      },
      "source": [
        "### Full audio"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "hidden": true,
        "id": "EXpau6xq2Fwy"
      },
      "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)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "hidden": true,
        "id": "u-KfXRhZ2Fwy"
      },
      "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)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "hidden": true,
        "id": "iwYEC4aZ2Fwy"
      },
      "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')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "hidden": true,
        "id": "fHaYejX12Fwy"
      },
      "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')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "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",
      "metadata": {
        "hidden": true,
        "id": "Zu9D0t6n2Fwz"
      },
      "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'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "heading_collapsed": true,
        "hidden": true,
        "id": "iC696eMX2Fwz"
      },
      "source": [
        "### Full audio"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "hidden": true,
        "id": "c8UYnYBF2Fw0"
      },
      "source": [
        "wav = read_audio(f'{files_dir}/en.wav')\n",
        "lang = get_language(wav, model)\n",
        "print(lang)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "57avIBd6jsrz"
      },
      "source": [
        "# ONNX Example"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hEhnfORV2Fw0"
      },
      "source": [
        "## VAD"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cy7y-NAyALSe"
      },
      "source": [
        "**TO BE DONE**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "heading_collapsed": true,
        "id": "7QMvUvpg2Fw4"
      },
      "source": [
        "## Number detector"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "heading_collapsed": true,
        "hidden": true,
        "id": "tBPDkpHr2Fw4"
      },
      "source": [
        "### Install Dependencies"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "hidden": true,
        "id": "PdjGd56R2Fw5"
      },
      "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"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "heading_collapsed": true,
        "hidden": true,
        "id": "I9QWSFZh2Fw5"
      },
      "source": [
        "### Full Audio"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "hidden": true,
        "id": "_r6QZiwu2Fw5"
      },
      "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)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "hidden": true,
        "id": "FN4aDwLV2Fw5"
      },
      "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)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "hidden": true,
        "id": "JnvS6WTK2Fw5"
      },
      "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')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "hidden": true,
        "id": "yUxOcOFG2Fw6"
      },
      "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')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "heading_collapsed": true,
        "id": "SR8Bgcd52Fw6"
      },
      "source": [
        "## Language detector"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "heading_collapsed": true,
        "hidden": true,
        "id": "PBnXPtKo2Fw6"
      },
      "source": [
        "### Install Dependencies"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "hidden": true,
        "id": "iNkDWJ3H2Fw6"
      },
      "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"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "hidden": true,
        "id": "G8N8oP4q2Fw6"
      },
      "source": [
        "### Full Audio"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "hidden": true,
        "id": "WHXnh9IV2Fw6"
      },
      "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)"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}