play_commands.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pathlib\n",
    "import inspect\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from IPython.display import display, Audio, Image\n",
    "\n",
    "from scipy.io import wavfile\n",
    "\n",
    "from utils import preprocessing_utils\n",
    "\n",
    "plt.rcParams['font.size'] = '12'\n",
    "# %matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_path = '../data/full_speech_commands'\n",
    "pathlib.Path(data_path).exists()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TESTING FILES:\n",
      "       label                   file  class\n",
      "0      right  bb05582b_nohash_3.wav     22\n",
      "1      right  97f4c236_nohash_2.wav     22\n",
      "2      right  f2e59fea_nohash_3.wav     22\n",
      "3      right  fdb5155e_nohash_2.wav     22\n",
      "4      right  dc75148d_nohash_0.wav     22\n",
      "...      ...                    ...    ...\n",
      "11000   four  189cbabe_nohash_2.wav     10\n",
      "11001   four  8fe67225_nohash_3.wav     10\n",
      "11002   four  c9e251d2_nohash_1.wav     10\n",
      "11003   four  0c40e715_nohash_0.wav     10\n",
      "11004   four  8769c34c_nohash_3.wav     10\n",
      "\n",
      "[11005 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "commands = ['backward', 'bed', 'bird', 'cat', 'dog', 'down', 'eight', 'five', 'follow', 'forward', 'four', 'go', 'happy', 'house', 'learn', 'left', 'marvin', 'nine', 'no', 'off', 'on', 'one', 'right', 'seven', 'sheila', 'six', 'stop', 'three', 'tree', 'two', 'up', 'visual', 'wow', 'yes', 'zero']\n",
    "label_to_class = {commands[i]:i for i in range(len(commands))}\n",
    "class_to_label = {i:commands[i] for i in range(len(commands))}\n",
    "\n",
    "\n",
    "reference_df_test = pd.read_csv(data_path+'/testing_list.txt', sep='/', header=None, names=['label', 'file'])\n",
    "reference_df_test['class']  = reference_df_test['label'].map(label_to_class)\n",
    "\n",
    "print('TESTING FILES:')\n",
    "print(reference_df_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "def load_data(file_name, file_label, data_path_=data_path):\n",
      "    if isinstance(file_name, bytes):\n",
      "        file_name = file_name.decode()\n",
      "    if isinstance(file_label, bytes):\n",
      "        file_label = file_label.decode()\n",
      "    if isinstance(data_path_, bytes):\n",
      "        data_path_ = data_path_.decode()\n",
      "\n",
      "    if not isinstance(file_label, str):\n",
      "        file_label = class_to_label[file_label]\n",
      "\n",
      "    file_path = data_path_ + '/' + file_label + '/' + file_name\n",
      "    _, data = wavfile.read(file_path)\n",
      "\n",
      "    return data.squeeze()\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(inspect.getsource(preprocessing_utils.load_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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\" type=\"audio/wav\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "command_to_play = 'sheila'\n",
    "\n",
    "df_obj = reference_df_test[reference_df_test.label == command_to_play].sample(1)\n",
    "signal = preprocessing_utils.load_data(df_obj.file.values[0], df_obj.label.values[0], data_path_=data_path)\n",
    "signal = preprocessing_utils.padding_trimming(signal)\n",
    "\n",
    "Audio(signal, rate=16000)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.9.7"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "dabd4eacb9e3ddd54eefcebcf933935be170cfdef29c95630fe9376e024beb9c"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}