getting_started.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import fnmatch\n",
    "import os\n",
    "import read_mp3\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# http://stackoverflow.com/questions/2186525/use-a-glob-to-find-files-recursively-in-python\n",
    "# Here's the Python 3.4 way...\n",
    "matches = []\n",
    "for root, dirnames, filenames in os.walk('music'):\n",
    "    for filename in fnmatch.filter(filenames, '*.mp3'):\n",
    "        matches.append(os.path.join(root, filename))\n",
    "# tunes_list = glob.glob('music/*.mp3')\n",
    "\n",
    "\n",
    "audio, sample_rate = read_mp3.ffmpeg_load_audio(matches[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.        ,  0.        ,  0.        , ..., -0.45796075,\n",
       "        -0.47401348, -0.48908964],\n",
       "       [ 0.        ,  0.        ,  0.        , ..., -0.31727043,\n",
       "        -0.28098392, -0.26096377]], dtype=float32)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "audio[:,:20000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 8673408)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "audio.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rory/anaconda3/envs/tunes/lib/python3.4/site-packages/ipykernel/__main__.py:4: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb9b10222e8>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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WX+9VcAwZAmy9tXWLwSheD6gYjHu9YMcd483v5euvvYcAHzAA2HXX+rh69rRaO7VvX1/V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\nImpiTAJERE2MSYCIqIkxCRARNbH/B/Yd3qA95XcQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb9b10b5fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "audio, sample_rate = read_mp3.ffmpeg_load_audio(matches[0])\n",
    "\n",
    "y = audio[0,1e4:1e5]\n",
    "x = np.arange(len(y))/sample_rate\n",
    "\n",
    "plt.plot(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rory/anaconda3/envs/tunes/lib/python3.4/site-packages/numpy/core/numeric.py:482: ComplexWarning: Casting complex values to real discards the imaginary part\n",
      "  return array(a, dtype, copy=False, order=order)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb9b07afdd8>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2K4/ar5K64oriE/Xs2c3fZmteviGxxEFiVWrWN2a/xK1qg+aGRLO+duCBxde4m9mucZDY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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb9b106bef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_fft = np.fft.fft(y)\n",
    "x_freqs = np.fft.fftfreq(len(y), 1/sample_rate)\n",
    "plt.plot(x_freqs, y_fft)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Try out the new ffmpeg op in TensorFlow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.contrib import ffmpeg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('test_file.mp3', 'rb') as f:\n",
    "    audio_content = f.read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sess = tf.Session()\n",
    "with sess.as_default():\n",
    "    x = ffmpeg.decode_audio(audio_content, file_format='mp3', \n",
    "                            samples_per_second=44000, \n",
    "                            channel_count=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'DecodeAudio_3:0' shape=(?, 2) dtype=float32>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "with sess.as_default():\n",
    "    y = x.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11973357, 2)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'audio' is not defined",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-16-a388483c3e64>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maudio\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'audio' is not defined"
     ],
     "output_type": "error"
    }
   ],
   "source": [
    ""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    ""
   ]
  }
 ],
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