Output.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Speech Recognition Of Digits (Testing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import librosa\n",
    "import numpy as np\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def wav2mfcc(file_path, max_len=8):\n",
    "    wave, sr = librosa.load(file_path, mono=True, sr=16000)\n",
    "    amplitude = []\n",
    "    for i in wave:\n",
    "        if abs(i)>0.009:\n",
    "            amplitude.append(i)\n",
    "    mfcc = librosa.feature.mfcc(np.asarray(amplitude))\n",
    "\n",
    "    # If maximum length exceeds mfcc lengths then pad the remaining ones\n",
    "    if (max_len > mfcc.shape[1]):\n",
    "        pad_width = max_len - mfcc.shape[1]\n",
    "        mfcc = np.pad(mfcc, pad_width=((0, 0), (0, pad_width)), mode='constant')\n",
    "\n",
    "    # Else cutoff the remaining parts\n",
    "    else:\n",
    "        mfcc = mfcc[:, :max_len]\n",
    "    \n",
    "    return mfcc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow import keras\n",
    "model = keras.models.load_model('SRD')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Choose File and Get Output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5]\n",
      "The Execution Time: 218ms\n"
     ]
    }
   ],
   "source": [
    "t1 = datetime.datetime.now()\n",
    "two = wav2mfcc('test/five.wav')\n",
    "print(model.predict_classes(np.array([np.asarray(two).reshape(20,8,1),])))\n",
    "t2 = datetime.datetime.now()\n",
    "print(\"The Execution Time: \"+ str(round((t2-t1).microseconds/(1000)))+\"ms\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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