{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Audio Decoder in DALI\n",
    "\n",
    "This tutorial presents, how to set up a simple pipeline, that loads and decodes audio data using DALI. We will use a simple example from Speech Commands Data Set. While this dataset consists of samples in .wav format, the following procedure can be used for most of the well-known digital audio coding formats as well.\n",
    "\n",
    "## Step-by-Step Guide\n",
    "1. Let's start by importing DALI and a handful of utils."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nvidia.dali import pipeline_def\n",
    "import nvidia.dali.fn as fn\n",
    "import nvidia.dali.types as types\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "batch_size = 1\n",
    "audio_files = \"../data/audio\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "used `batch_size` is `1`, to keep things simple.\n",
    "\n",
    "2. Next, let's implement the pipeline. Firstly, we need to load data from disk (or any other source). [readers.file](../../operations/nvidia.dali.fn.readers.file.html) is able to load data, as well as it's labels. For more information, refer to the documentation. Furthermore, similarly to image data, you can use other reader operators that are specific for a given dataset or a dataset format (see [readers.caffe](../../operations/nvidia.dali.fn.readers.caffe.html)). After loading the input data, the pipeline decodes the audio data. As stated above, the [decoders.audio](../../operations/nvidia.dali.fn.decoders.audio.html) operator is able to decode most of the well-known audio formats.\n",
    "   \n",
    "   Note: Please remember that you shall pass proper data type (argument `dtype`) to the operator. Supported data types can be found in the documentation. If you have 24-bit audio data and you set `dtype=INT16`, it will result in loosing some information from the samples. The default `dtype` for this operator is `INT16`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "@pipeline_def\n",
    "def audio_decoder_pipe():\n",
    "    encoded, _ = fn.readers.file(file_root=audio_files)\n",
    "    audio, sr = fn.decoders.audio(encoded, dtype=types.INT16)\n",
    "    return audio, sr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Now let's just build and run the pipeline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe = audio_decoder_pipe(batch_size=batch_size, num_threads=1, device_id=0)\n",
    "pipe.build()\n",
    "cpu_output = pipe.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Outputs from [decoders.audio](../../operations/nvidia.dali.fn.decoders.audio.html) consist of a tensor with the decoded data, as well as some metadata (e.g. sampling rate). To access them just check another output. On top of that, `decoders.audio` returns data in interleaved format, so we need to reshape the output tensor, to properly display it. Here's how to do that:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sampling rate: 16000.0 [Hz]\n",
      "Audio data: [[  -5]\n",
      " [ -95]\n",
      " [-156]\n",
      " ...\n",
      " [ 116]\n",
      " [ 102]\n",
      " [  82]]\n",
      "Audio data flattened: [  -5  -95 -156 ...  116  102   82]\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "audio_data = cpu_output[0].at(0)\n",
    "sampling_rate = cpu_output[1].at(0)\n",
    "print(\"Sampling rate:\", sampling_rate, \"[Hz]\")\n",
    "print(\"Audio data:\", audio_data)\n",
    "audio_data = audio_data.flatten()\n",
    "print(\"Audio data flattened:\", audio_data)\n",
    "plt.plot(audio_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Verification\n",
    "\n",
    "Let's verify, that the decoders.Audio actually works. The presented method can also come in handy for debugging DALI pipeline, in case something doesn't go as planned. \n",
    "\n",
    "We will use external tool to decode used data and compare the results against data decoded by DALI.\n",
    "\n",
    "### Important!\n",
    "\n",
    "Following snippet installs the external dependency (`simpleaudio`). In case you already have it, or don't want to install it, you might want to stop here and not run this one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "\n",
    "!{sys.executable} -m pip install simpleaudio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below is the side-by-side comparision of decoded data. If you have the `simpleaudio` module installed, you can run the snippet and see it for yourself."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "src: simpleaudio\n",
      "shape:  (16000,)\n",
      "data:  [  -5  -95 -156 ...  116  102   82]\n",
      "\n",
      "\n",
      "src: DALI\n",
      "shape:  (16000,)\n",
      "data:  [  -5  -95 -156 ...  116  102   82]\n",
      "\n",
      "Are the arrays equal? YES\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import simpleaudio as sa\n",
    "\n",
    "wav = sa.WaveObject.from_wave_file(\"../data/audio/wav/three.wav\")\n",
    "three_audio = np.frombuffer(wav.audio_data, dtype=np.int16)\n",
    "\n",
    "print(\"src: simpleaudio\")\n",
    "print(\"shape: \", three_audio.shape)\n",
    "print(\"data: \", three_audio)\n",
    "print(\"\\n\")\n",
    "print(\"src: DALI\")\n",
    "print(\"shape: \", audio_data.shape)\n",
    "print(\"data: \", audio_data)\n",
    "print(\"\\nAre the arrays equal?\", \"YES\" if np.all(audio_data == three_audio) else \"NO\")\n",
    "\n",
    "fig, ax = plt.subplots(1, 2)\n",
    "ax[0].plot(three_audio)\n",
    "ax[1].plot(audio_data)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}