micmon/notebooks/predict.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "This notebook uses a pre-trained Tensorflow model to make live\n",
    "predictions on the audio samples recorded from a microphone."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DEBUG:matplotlib:(private) matplotlib data path: /usr/lib/python3.8/site-packages/matplotlib/mpl-data\n",
      "DEBUG:matplotlib:matplotlib data path: /usr/lib/python3.8/site-packages/matplotlib/mpl-data\n",
      "DEBUG:matplotlib:CONFIGDIR=/home/blacklight/.config/matplotlib\n",
      "DEBUG:matplotlib:matplotlib version 3.3.2\n",
      "DEBUG:matplotlib:interactive is False\n",
      "DEBUG:matplotlib:platform is linux\n",
      "DEBUG:matplotlib:loaded modules: ['sys', 'builtins', '_frozen_importlib', '_imp', '_warnings', '_frozen_importlib_external', '_io', 'marshal', 'posix', '_thread', '_weakref', 'time', 'zipimport', '_codecs', 'codecs', 'encodings.aliases', 'encodings', 'encodings.utf_8', '_signal', '__main__', 'encodings.latin_1', '_abc', 'abc', 'io', '_stat', 'stat', '_collections_abc', 'genericpath', 'posixpath', 'os.path', 'os', '_sitebuiltins', '_locale', '_bootlocale', 'types', 'importlib._bootstrap', 'importlib._bootstrap_external', 'warnings', 'importlib', 'importlib.machinery', 'importlib.abc', '_operator', 'operator', 'keyword', '_heapq', 'heapq', 'itertools', 'reprlib', '_collections', 'collections', '_functools', 'functools', 'contextlib', 'importlib.util', 'google', 'google.assistant', 'google.assistant.embedded', 'googlesamples', 'googlesamples.assistant', 'google.cloud', 'google.logging', 'google.iam', 'mpl_toolkits', 'ruamel', 'sphinxcontrib', 'zope', 'site', '_weakrefset', 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'ipykernel.eventloops', 'ipykernel.ipkernel', 'ipykernel.parentpoller', 'ipykernel.kernelapp', 'netifaces', 'faulthandler', 'ipykernel.codeutil', 'ipykernel.pickleutil', 'ipykernel.serialize', 'ipykernel.datapub', 'IPython.core.completerlib', 'storemagic', 'micmon', 'micmon.audio', 'micmon.audio.directory', 'micmon.audio.segment', 'numpy._globals', 'numpy.__config__', 'numpy.version', 'numpy._distributor_init', 'numpy.core._multiarray_umath', 'numpy.compat._inspect', 'numpy.compat.py3k', 'numpy.compat', 'numpy.core.overrides', 'numpy.core.multiarray', 'numpy.core.umath', 'numpy.core._string_helpers', 'numpy.core._dtype', 'numpy.core._type_aliases', 'numpy.core.numerictypes', 'numpy.core._asarray', 'numpy.core._exceptions', 'numpy.core._methods', 'numpy.core.fromnumeric', 'numpy.core.shape_base', 'numpy.core._ufunc_config', 'numpy.core.arrayprint', 'numpy.core.numeric', 'numpy.core.defchararray', 'numpy.core.records', 'numpy.core.memmap', 'numpy.core.function_base', 'numpy.core.machar', 'numpy.core.getlimits', 'numpy.core.einsumfunc', 'numpy.core._multiarray_tests', 'numpy.core._add_newdocs', 'numpy.core._dtype_ctypes', 'numpy.core._internal', 'numpy._pytesttester', 'numpy.core', 'numpy.lib.mixins', 'numpy.lib.ufunclike', 'numpy.lib.type_check', 'numpy.lib.scimath', 'numpy.lib.twodim_base', 'numpy.linalg.lapack_lite', 'numpy.linalg._umath_linalg', 'numpy.linalg.linalg', 'numpy.linalg', 'numpy.matrixlib.defmatrix', 'numpy.matrixlib', 'numpy.lib.histograms', 'numpy.lib.function_base', 'numpy.lib.stride_tricks', 'numpy.lib.index_tricks', 'numpy.lib.nanfunctions', 'numpy.lib.shape_base', 'numpy.lib.polynomial', 'numpy.lib.utils', 'numpy.lib.arraysetops', 'numpy.lib.format', 'numpy.lib._datasource', 'numpy.lib._iotools', 'numpy.lib.npyio', 'numpy.lib.financial', 'numpy.lib.arrayterator', 'numpy.lib.arraypad', 'numpy.lib._version', 'numpy.lib', 'numpy.fft._pocketfft_internal', 'numpy.fft._pocketfft', 'numpy.fft.helper', 'numpy.fft', 'numpy.polynomial.polyutils', 'numpy.polynomial._polybase', 'numpy.polynomial.polynomial', 'numpy.polynomial.chebyshev', 'numpy.polynomial.legendre', 'numpy.polynomial.hermite', 'numpy.polynomial.hermite_e', 'numpy.polynomial.laguerre', 'numpy.polynomial', '_cython_0_29_21', 'numpy.random._common', 'secrets', 'numpy.random.bit_generator', 'numpy.random._bounded_integers', 'numpy.random._mt19937', 'numpy.random.mtrand', 'numpy.random._philox', 'numpy.random._pcg64', 'numpy.random._sfc64', 'numpy.random._generator', 'numpy.random._pickle', 'numpy.random', 'numpy.ctypeslib', 'numpy.ma.core', 'numpy.ma.extras', 'numpy.ma', 'numpy', 'matplotlib', 'gzip', 'matplotlib.cbook.deprecation', 'matplotlib.cbook', 'matplotlib._animation_data', 'matplotlib.animation', 'pyparsing', 'matplotlib.fontconfig_pattern', 'matplotlib.docstring', 'matplotlib._color_data', 'matplotlib.colors', 'cycler', 'matplotlib.rcsetup', 'matplotlib._version', 'matplotlib.ft2font', 'kiwisolver']\n",
      "DEBUG:matplotlib:CACHEDIR=/home/blacklight/.cache/matplotlib\n",
      "DEBUG:matplotlib.font_manager:Using fontManager instance from /home/blacklight/.cache/matplotlib/fontlist-v330.json\n",
      "DEBUG:matplotlib.pyplot:Loaded backend module://ipykernel.pylab.backend_inline version unknown.\n",
      "DEBUG:matplotlib.pyplot:Loaded backend module://ipykernel.pylab.backend_inline version unknown.\n",
      "DEBUG:tensorflow:Falling back to TensorFlow client; we recommended you install the Cloud TPU client directly with pip install cloud-tpu-client.\n",
      "WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation.\n",
      "WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation.\n",
      "WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation.\n",
      "WARNING:root:Limited tf.summary API due to missing TensorBoard installation.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "from micmon.audio import AudioDevice, AudioPlayer\n",
    "from micmon.model import Model\n",
    "\n",
    "model_dir = os.path.expanduser(os.path.join('~', 'models', 'baby-monitor'))\n",
    "audio_system = 'alsa'\n",
    "audio_device = 'plughw:3,0'\n",
    "label_names = ['negative', 'positive']"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "Load the model"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "model = Model.load(model_dir)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Play some audio from the input device"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "with AudioDevice(audio_system, device=audio_device) as source, AudioPlayer() as player:\n",
    "    for sample in source:\n",
    "        player.play(sample)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Record frames from an audio source and make predictions using the model"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "negative\n",
      "negative\n",
      "negative\n",
      "negative\n",
      "negative\n",
      "negative\n",
      "negative\n",
      "negative\n",
      "negative\n",
      "negative\n"
     ]
    }
   ],
   "source": [
    "with AudioDevice(audio_system, device=audio_device) as source:\n",
    "    for sample in source:\n",
    "        source.pause()\n",
    "        prediction = model.predict(sample)\n",
    "        print(prediction)\n",
    "        source.resume()\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
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