{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Jit example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install -q ipython # For jupyter audio display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-15T13:09:54.623434Z",
     "start_time": "2020-12-15T13:09:54.241855Z"
    }
   },
   "outputs": [],
   "source": [
    "# dependencies\n",
    "import glob\n",
    "import torch\n",
    "torch.set_num_threads(1)\n",
    "from IPython.display import Audio\n",
    "\n",
    "model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad',\n",
    "                              model='silero_vad')\n",
    "\n",
    "\n",
    "(get_speech_ts,\n",
    " save_audio,\n",
    " read_audio,\n",
    " state_generator,\n",
    " single_audio_stream,\n",
    " collect_speeches) = utils"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Full audio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-15T13:09:56.879818Z",
     "start_time": "2020-12-15T13:09:56.864765Z"
    }
   },
   "outputs": [],
   "source": [
    "wav = read_audio('files/en.wav')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-15T13:09:58.876034Z",
     "start_time": "2020-12-15T13:09:57.139254Z"
    }
   },
   "outputs": [],
   "source": [
    "speech_timestamps = get_speech_ts(wav, model, num_steps=4) # get speech timestamps from full audio file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-15T13:09:58.885802Z",
     "start_time": "2020-12-15T13:09:58.877327Z"
    }
   },
   "outputs": [],
   "source": [
    "speech_timestamps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-15T13:09:58.941063Z",
     "start_time": "2020-12-15T13:09:58.887006Z"
    }
   },
   "outputs": [],
   "source": [
    "save_audio('only_speech.wav', collect_speeches(speech_timestamps, wav), 16000) # merge all speech chunks to one audio\n",
    "Audio('only_speech.wav')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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"
    }
   },
   "outputs": [],
   "source": [
    "wav = 'files/en.wav'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-15T13:10:03.585644Z",
     "start_time": "2020-12-15T13:09:59.429757Z"
    }
   },
   "outputs": [],
   "source": [
    "for batch in single_audio_stream(model, wav):\n",
    "    if batch:\n",
    "        print(batch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiple audio stream"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-15T13:10:03.590358Z",
     "start_time": "2020-12-15T13:10:03.587071Z"
    }
   },
   "outputs": [],
   "source": [
    "audios_for_stream = glob.glob('files/*.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"
    }
   },
   "outputs": [],
   "source": [
    "for batch in state_generator(model, audios_for_stream, audios_in_stream=2): # 2 audio stream\n",
    "    if batch:\n",
    "        print(batch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Onnx example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install -q ipython # For jupyter audio display\n",
    "!pip install -q onnxruntime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-15T13:09:05.932256Z",
     "start_time": "2020-12-15T13:09:05.043659Z"
    }
   },
   "outputs": [],
   "source": [
    "# dependencies\n",
    "import glob\n",
    "import torch\n",
    "from IPython.display import Audio\n",
    "torch.set_num_threads(1)\n",
    "import onnxruntime\n",
    "\n",
    "from utils import (get_speech_ts, save_audio, read_audio, \n",
    "                   state_generator, single_audio_stream, collect_speeches)\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": {},
   "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"
    }
   },
   "outputs": [],
   "source": [
    "model = init_onnx_model('files/model.onnx')\n",
    "wav = read_audio('files/en.wav')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-15T13:09:08.094414Z",
     "start_time": "2020-12-15T13:09:07.073253Z"
    }
   },
   "outputs": [],
   "source": [
    "speech_timestamps = get_speech_ts(wav, model, num_steps=4, run_function=validate_onnx) # get speech timestamps from full audio file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-15T13:09:08.107584Z",
     "start_time": "2020-12-15T13:09:08.096550Z"
    }
   },
   "outputs": [],
   "source": [
    "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"
    }
   },
   "outputs": [],
   "source": [
    "save_audio('only_speech.wav', collect_speeches(speech_timestamps, wav), 16000) # merge all speech chunks to one audio\n",
    "Audio('only_speech.wav')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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"
    }
   },
   "outputs": [],
   "source": [
    "model = init_onnx_model('files/model.onnx')\n",
    "wav = 'files/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"
    }
   },
   "outputs": [],
   "source": [
    "for batch in single_audio_stream(model, wav, run_function=validate_onnx):\n",
    "    if batch:\n",
    "        print(batch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiple audio stream"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-15T13:09:11.540423Z",
     "start_time": "2020-12-15T13:09:11.455706Z"
    }
   },
   "outputs": [],
   "source": [
    "model = init_onnx_model('files/model.onnx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-15T13:09:11.550815Z",
     "start_time": "2020-12-15T13:09:11.542954Z"
    }
   },
   "outputs": [],
   "source": [
    "audios_for_stream = glob.glob('files/*.wav')\n",
    "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"
    }
   },
   "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",
    "        print(batch)"
   ]
  }
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