├── .gitignore ├── Language Classifier.ipynb ├── README.md ├── data ├── sample_imgs │ ├── 00a4r2sbnef.mp3.jpg │ ├── 00aoqprhbif.mp3.jpg │ ├── 00badrrjmwj.mp3.jpg │ ├── 00baul5uact.mp3.jpg │ ├── 0a01sbrr4tq.mp3.jpg │ ├── 0a0p10uya0h.mp3.jpg │ ├── 0a0sasc4be2.mp3.jpg │ ├── 0a3hcskiu4u.mp3.jpg │ ├── 0a3ltajwksh.mp3.jpg │ ├── 0a3qheozh43.mp3.jpg │ ├── 0a4f3pibqb1.mp3.jpg │ ├── 0a4jwgjox3s.mp3.jpg │ ├── 0a4vxzprwem.mp3.jpg │ ├── 0a4w22penhi.mp3.jpg │ ├── 0a5eixsdsx2.mp3.jpg │ ├── 0a5h2q5r4l5.mp3.jpg │ ├── 0aaatsgitup.mp3.jpg │ ├── 0aardcafriy.mp3.jpg │ ├── 0aaus1iol2h.mp3.jpg │ ├── 0aboxbqzx2n.mp3.jpg │ ├── 0abyp4czxgr.mp3.jpg │ ├── 0acbkn5sl3x.mp3.jpg │ ├── 0acwgcalkob.mp3.jpg │ ├── 0acwmldxgzf.mp3.jpg │ ├── 0adiw4jd2vh.mp3.jpg │ ├── 0ado2ukulj5.mp3.jpg │ ├── 0adw4o0vvko.mp3.jpg │ ├── 0adwmdtxbwk.mp3.jpg │ ├── 0aedosuzh5j.mp3.jpg │ ├── 0aelyqjxzjq.mp3.jpg │ ├── 0aeprbmekrb.mp3.jpg │ ├── 0agau5xpaui.mp3.jpg │ ├── 0ags4n41shp.mp3.jpg │ ├── 0ahnl5arhs2.mp3.jpg │ ├── 0aht55mhvic.mp3.jpg │ ├── 0ahy4d3cq3w.mp3.jpg │ ├── 0ajkmh1elny.mp3.jpg │ ├── 0ajlu0gmvg0.mp3.jpg │ ├── 0ak1jp1z1xe.mp3.jpg │ ├── 0ametwshuyz.mp3.jpg │ ├── 0an5rwx3tzl.mp3.jpg │ ├── 0anskha4rny.mp3.jpg │ ├── 0anxcugagfg.mp3.jpg │ ├── 0ao04vs1uwf.mp3.jpg │ ├── 0apaonvlr0c.mp3.jpg │ ├── 0apcnfdd40e.mp3.jpg │ ├── 0aqoteylzkz.mp3.jpg │ ├── 0aqws40nzu5.mp3.jpg │ ├── 0arta3rjzjw.mp3.jpg │ ├── 0asgtm2tyue.mp3.jpg │ ├── 0asientdpad.mp3.jpg │ ├── 0asqdcv4fpj.mp3.jpg │ ├── 0asqnv2y0ba.mp3.jpg │ ├── 0asrlxt13jq.mp3.jpg │ ├── 0auarszhcxo.mp3.jpg │ ├── 0auavs1zjfs.mp3.jpg │ ├── 0auix0bna5p.mp3.jpg │ ├── 0aumbxzf3ut.mp3.jpg │ ├── 0auuwwxkfyl.mp3.jpg │ ├── 0av2z5ff4dk.mp3.jpg │ ├── 0av5siduzz4.mp3.jpg │ ├── 0avkp1jfwmw.mp3.jpg │ ├── 0avp1jedsmy.mp3.jpg │ ├── 0aw2ahmvu0o.mp3.jpg │ ├── 0aw2jb2byhk.mp3.jpg │ ├── 0awkkpyeeli.mp3.jpg │ ├── 0ax00d44hec.mp3.jpg │ ├── 0axeeqw44ni.mp3.jpg │ ├── 0ayc1jl5ztv.mp3.jpg │ ├── 0ayf2i4a32u.mp3.jpg │ ├── 0b0pkuhjual.mp3.jpg │ ├── 0b0pprwcpuz.mp3.jpg │ ├── 0b0rjtqmmos.mp3.jpg │ ├── 0b0wwgzxa4e.mp3.jpg │ ├── 0b1jkzj2vwa.mp3.jpg │ ├── 0b1qvo1eivi.mp3.jpg │ ├── 0b2k0h4qa4d.mp3.jpg │ ├── 0b2tm3j52vj.mp3.jpg │ ├── 0b3jltgvurj.mp3.jpg │ ├── 0b4bluvhblj.mp3.jpg │ ├── 0b4wo1u4t3z.mp3.jpg │ ├── 0b5fy4402li.mp3.jpg │ ├── 0b5q552oic0.mp3.jpg │ ├── 0b5xbksfgdp.mp3.jpg │ ├── 0baikrinxwh.mp3.jpg │ ├── 0bax2qxgxsr.mp3.jpg │ ├── 0bax5j4qyfo.mp3.jpg │ ├── 0bb12a25bjm.mp3.jpg │ ├── 0bb5znxmiav.mp3.jpg │ ├── 0bbrwb3v0oq.mp3.jpg │ ├── 0bbydhgfoet.mp3.jpg │ ├── 0bcebuzldll.mp3.jpg │ ├── 0bcsixtsndh.mp3.jpg │ ├── 0bcu3he4lt1.mp3.jpg │ ├── 0bd1ft1elr3.mp3.jpg │ ├── 0bdq04j3lbj.mp3.jpg │ ├── 0befwfxsyhf.mp3.jpg │ ├── 0belw4uahoa.mp3.jpg │ ├── 0beybvzisrg.mp3.jpg │ └── 0bfdzjrifsa.mp3.jpg └── train_list.csv └── speech_v9.h5 /.gitignore: -------------------------------------------------------------------------------- 1 | data/y_va.h5 2 | data/y_tr.h5 3 | data/y_te.h5 4 | data/x_va.h5 5 | data/x_tr.h5 6 | data/x_te.h5 7 | .ipynb_checkpoints/Language Classifier-checkpoint.ipynb 8 | -------------------------------------------------------------------------------- /Language Classifier.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": { 6 | "deletable": true, 7 | "editable": true 8 | }, 9 | "source": [ 10 | "# Spoken Language Classifier\n", 11 | "\n", 12 | "This notebook trains a convolutional neural network to classfify audio files of voice recordings into the languages that were spoken. The dataset I used contained 65.000 files across 176 languages. I found it on TopCoder (https://goo.gl/G5XBJl). I liked the idea behind this problem, because it's very hard for humans to do. It's intersting to see that CNNs perform well on problems where intuition doesn't get you anywhere." 13 | ] 14 | }, 15 | { 16 | "cell_type": "markdown", 17 | "metadata": { 18 | "deletable": true, 19 | "editable": true 20 | }, 21 | "source": [ 22 | "## 1 Imports, Variables and Functions" 23 | ] 24 | }, 25 | { 26 | "cell_type": "code", 27 | "execution_count": 1, 28 | "metadata": { 29 | "collapsed": false, 30 | "deletable": true, 31 | "editable": true 32 | }, 33 | "outputs": [ 34 | { 35 | "name": "stderr", 36 | "output_type": "stream", 37 | "text": [ 38 | "Using TensorFlow backend.\n" 39 | ] 40 | } 41 | ], 42 | "source": [ 43 | "import numpy as np\n", 44 | "import matplotlib.pyplot as plt\n", 45 | "import pandas as pd\n", 46 | "import scipy as sp\n", 47 | "import os\n", 48 | "import librosa as lr\n", 49 | "import shutil\n", 50 | "import dask.array as da\n", 51 | "import h5py\n", 52 | "import glob\n", 53 | "\n", 54 | "from keras.models import Model, load_model\n", 55 | "from keras.layers import Conv2D, MaxPooling2D, Dense, Flatten\n", 56 | "from keras.layers import Dropout, Input, BatchNormalization\n", 57 | "from keras.optimizers import Nadam\n", 58 | "from keras.preprocessing.image import ImageDataGenerator\n", 59 | "from keras.utils import np_utils" 60 | ] 61 | }, 62 | { 63 | "cell_type": "code", 64 | "execution_count": 2, 65 | "metadata": { 66 | "collapsed": true, 67 | "deletable": true, 68 | "editable": true 69 | }, 70 | "outputs": [], 71 | "source": [ 72 | "in_dim = (192,192,1)\n", 73 | "out_dim = 176\n", 74 | "batch_size = 32\n", 75 | "mp3_path = 'data/mp3/'\n", 76 | "tr_path = 'data/train/'\n", 77 | "va_path = 'data/valid/'\n", 78 | "te_path = 'data/test/'\n", 79 | "data_size = 66176\n", 80 | "tr_size = 52800\n", 81 | "va_size = 4576\n", 82 | "te_size = 8800" 83 | ] 84 | }, 85 | { 86 | "cell_type": "markdown", 87 | "metadata": { 88 | "deletable": true, 89 | "editable": true 90 | }, 91 | "source": [ 92 | "This will convert a single mp3 file to a spectrogram and return the image. The mel-spectrogram is used to get more information in the lower frequencies similar to human hearing. The intensities and the frequencies are then scaled logarithmically. This function will also cut away 5% of the beginning and end of the file. This is to get rid of silence and ensure the same dimensions of each file it outputs. The conversion takes roughly 1sec per minute of audio." 93 | ] 94 | }, 95 | { 96 | "cell_type": "code", 97 | "execution_count": 3, 98 | "metadata": { 99 | "collapsed": true, 100 | "deletable": true, 101 | "editable": true 102 | }, 103 | "outputs": [], 104 | "source": [ 105 | "def mp3_to_img(path, height=192, width=192):\n", 106 | " signal, sr = lr.load(path, res_type='kaiser_fast')\n", 107 | " hl = signal.shape[0]//(width*1.1) #this will cut away 5% from start and end\n", 108 | " spec = lr.feature.melspectrogram(signal, n_mels=height, hop_length=int(hl))\n", 109 | " img = lr.logamplitude(spec)**2\n", 110 | " start = (img.shape[1] - width) // 2\n", 111 | " return img[:, start:start+width]" 112 | ] 113 | }, 114 | { 115 | "cell_type": "markdown", 116 | "metadata": { 117 | "deletable": true, 118 | "editable": true 119 | }, 120 | "source": [ 121 | "Batch convert all mp3-files to spectrogram jpgs. process_audio_with_classes() will use the labels to sort all jpgs in coresponding subfolders. This is useful for the flow_from_directory function in Keras." 122 | ] 123 | }, 124 | { 125 | "cell_type": "code", 126 | "execution_count": 4, 127 | "metadata": { 128 | "collapsed": true, 129 | "deletable": true, 130 | "editable": true 131 | }, 132 | "outputs": [], 133 | "source": [ 134 | "def process_audio(in_folder, out_folder):\n", 135 | " os.makedirs(out_folder, exist_ok=True)\n", 136 | " files = glob.glob(in_folder+'*.mp3')\n", 137 | " start = len(in_folder)\n", 138 | " for file in files:\n", 139 | " img = mp3_to_img(file)\n", 140 | " sp.misc.imsave(out_folder + file[start:] + '.jpg', img)\n", 141 | " \n", 142 | "def process_audio_with_classes(in_folder, out_folder, labels):\n", 143 | " os.makedirs(out_folder, exist_ok=True)\n", 144 | " for i in range(len(labels['Sample Filename'])):\n", 145 | " file = labels['Sample Filename'][i]\n", 146 | " lang = labels['Language'][i]\n", 147 | " os.makedirs(out_folder + lang, exist_ok=True)\n", 148 | " img = mp3_to_img(in_folder+file)\n", 149 | " sp.misc.imsave(out_folder + lang + '/' + file + '.jpg', img)" 150 | ] 151 | }, 152 | { 153 | "cell_type": "markdown", 154 | "metadata": { 155 | "deletable": true, 156 | "editable": true 157 | }, 158 | "source": [ 159 | "Convert a directory of images to a HDF5 file storing the images in an array. The shape of the array will be (img_num, height, width[, channels])." 160 | ] 161 | }, 162 | { 163 | "cell_type": "code", 164 | "execution_count": 5, 165 | "metadata": { 166 | "collapsed": false, 167 | "deletable": true, 168 | "editable": true, 169 | "scrolled": true 170 | }, 171 | "outputs": [], 172 | "source": [ 173 | "def jpgs_to_h5(source, target, name):\n", 174 | " da.image.imread(source + '*.jpg').to_hdf5(target, name)" 175 | ] 176 | }, 177 | { 178 | "cell_type": "markdown", 179 | "metadata": { 180 | "deletable": true, 181 | "editable": true 182 | }, 183 | "source": [ 184 | "## 2 Preparing the data" 185 | ] 186 | }, 187 | { 188 | "cell_type": "markdown", 189 | "metadata": { 190 | "deletable": true, 191 | "editable": true 192 | }, 193 | "source": [ 194 | "The raw data consists of 66176 44,1kHz stereo mp3 file with a length of 10 seconds each. The dataset is perfectly balanced with 376 files per language." 195 | ] 196 | }, 197 | { 198 | "cell_type": "markdown", 199 | "metadata": { 200 | "deletable": true, 201 | "editable": true 202 | }, 203 | "source": [ 204 | "We can visualize this file by converting it to a log-mel-spectrogram.\n", 205 | "\n", 206 | "- the y-axis shows the frequency\n", 207 | "- the x-axis shows the time\n", 208 | "- the color shows the intensity of a frequency at a given time" 209 | ] 210 | }, 211 | { 212 | "cell_type": "markdown", 213 | "metadata": { 214 | "deletable": true, 215 | "editable": true 216 | }, 217 | "source": [ 218 | "The spectrograms gave me headaches at first. Although it's easy to read a spectrogram, it's hard to \"intuitively\" judge the content of an audio file. My first try of converting them looked totally fine, but I wasn't able to train my network at all. I used a regular spectrogram which I then converted to log scale frequencies. The trouble was that by squeezing the higher frequencies I was pulling apart the lower frequencies. That's the general idea of a log scale and all, but I didn't take into account that the resolution in the lower frequencies would suffer badly. To my luck I wasn't the first person with this problem, so in 1980 a couple of guys came up with the mel-spectrogram which gives more resolution to lower frequencies. After that I also log scaled the intensities of my data points which seemed to help as well.\n", 219 | "\n", 220 | "I started out with a resolution of 224x448 pixels but this took forever on my computer. I applied some asymetrical resizing and noticed that my assumption of reserving more space for the time axis was wrong. Square images seemed to perform best. So, I went ahead and converted everything to 192x192, which didn't hurt the performance all that much.\n", 221 | "\n", 222 | "The \"sanity check\" of the data turned out to be difficult with this dataset. Apparently you can have 176 different languages without including english, german or french. But all the dutch samples sounded like what dutch people sound like, so I figured it couldn't be that wrong." 223 | ] 224 | }, 225 | { 226 | "cell_type": "markdown", 227 | "metadata": { 228 | "deletable": true, 229 | "editable": true 230 | }, 231 | "source": [ 232 | "Convert the mp3 files to jpgs" 233 | ] 234 | }, 235 | { 236 | "cell_type": "code", 237 | "execution_count": null, 238 | "metadata": { 239 | "collapsed": false, 240 | "deletable": true, 241 | "editable": true, 242 | "scrolled": true 243 | }, 244 | "outputs": [], 245 | "source": [ 246 | "process_audio('data/mp3/', 'data/jpg/')" 247 | ] 248 | }, 249 | { 250 | "cell_type": "markdown", 251 | "metadata": { 252 | "deletable": true, 253 | "editable": true 254 | }, 255 | "source": [ 256 | "Covert the folder of images to a compressed container file" 257 | ] 258 | }, 259 | { 260 | "cell_type": "code", 261 | "execution_count": null, 262 | "metadata": { 263 | "collapsed": false, 264 | "deletable": true, 265 | "editable": true, 266 | "scrolled": true 267 | }, 268 | "outputs": [], 269 | "source": [ 270 | "jpgs_to_h5('data/jpg/', 'data/data.h5', 'data')" 271 | ] 272 | }, 273 | { 274 | "cell_type": "markdown", 275 | "metadata": { 276 | "deletable": true, 277 | "editable": true 278 | }, 279 | "source": [ 280 | "Shuffle the data and split it into train, valid and test" 281 | ] 282 | }, 283 | { 284 | "cell_type": "code", 285 | "execution_count": null, 286 | "metadata": { 287 | "collapsed": false, 288 | "deletable": true, 289 | "editable": true 290 | }, 291 | "outputs": [], 292 | "source": [ 293 | "y = pd.read_csv('data/train_list.csv')['Language']\n", 294 | "y = pd.get_dummies(y)\n", 295 | "y = y.reindex_axis(sorted(y.columns), axis=1)\n", 296 | "y = y.values\n", 297 | "y = da.from_array(y, chunks=1000)\n", 298 | "y" 299 | ] 300 | }, 301 | { 302 | "cell_type": "code", 303 | "execution_count": null, 304 | "metadata": { 305 | "collapsed": false, 306 | "deletable": true, 307 | "editable": true, 308 | "scrolled": true 309 | }, 310 | "outputs": [], 311 | "source": [ 312 | "x = h5py.File('data/data.h5')['data']\n", 313 | "x = da.from_array(x, chunks=1000)\n", 314 | "x" 315 | ] 316 | }, 317 | { 318 | "cell_type": "code", 319 | "execution_count": null, 320 | "metadata": { 321 | "collapsed": false, 322 | "deletable": true, 323 | "editable": true, 324 | "scrolled": false 325 | }, 326 | "outputs": [], 327 | "source": [ 328 | "shfl = np.random.permutation(data_size)\n", 329 | "\n", 330 | "tr_idx = shfl[:tr_size]\n", 331 | "va_idx = shfl[tr_size:tr_size+va_size]\n", 332 | "te_idx = shfl[tr_size+va_size:]\n", 333 | "\n", 334 | "x[tr_idx].to_hdf5('data/x_tr.h5', 'x_tr')\n", 335 | "y[tr_idx].to_hdf5('data/y_tr.h5', 'y_tr')\n", 336 | "x[va_idx].to_hdf5('data/x_va.h5', 'x_va')\n", 337 | "y[va_idx].to_hdf5('data/y_va.h5', 'y_va')\n", 338 | "x[te_idx].to_hdf5('data/x_te.h5', 'x_te')\n", 339 | "y[te_idx].to_hdf5('data/y_te.h5', 'y_te')" 340 | ] 341 | }, 342 | { 343 | "cell_type": "markdown", 344 | "metadata": { 345 | "deletable": true, 346 | "editable": true 347 | }, 348 | "source": [ 349 | "## 3 Load and process the data" 350 | ] 351 | }, 352 | { 353 | "cell_type": "markdown", 354 | "metadata": { 355 | "deletable": true, 356 | "editable": true 357 | }, 358 | "source": [ 359 | "Read the data we've prepared and check its dimensions" 360 | ] 361 | }, 362 | { 363 | "cell_type": "code", 364 | "execution_count": 6, 365 | "metadata": { 366 | "collapsed": false, 367 | "deletable": true, 368 | "editable": true 369 | }, 370 | "outputs": [ 371 | { 372 | "name": "stdout", 373 | "output_type": "stream", 374 | "text": [ 375 | "(52800, 192, 192, 1) (52800, 176)\n", 376 | "(4576, 192, 192, 1) (4576, 176)\n", 377 | "(8800, 192, 192, 1) (8800, 176)\n" 378 | ] 379 | } 380 | ], 381 | "source": [ 382 | "x_tr = da.from_array(h5py.File('data/x_tr.h5')['x_tr'], chunks=1000)\n", 383 | "y_tr = da.from_array(h5py.File('data/y_tr.h5')['y_tr'], chunks=1000)\n", 384 | "print(x_tr.shape, y_tr.shape)\n", 385 | "\n", 386 | "x_va = da.from_array(h5py.File('data/x_va.h5')['x_va'], chunks=1000)\n", 387 | "y_va = da.from_array(h5py.File('data/y_va.h5')['y_va'], chunks=1000)\n", 388 | "print(x_va.shape, y_va.shape)\n", 389 | "\n", 390 | "x_te = da.from_array(h5py.File('data/x_te.h5')['x_te'], chunks=1000)\n", 391 | "y_te = da.from_array(h5py.File('data/y_te.h5')['y_te'], chunks=1000)\n", 392 | "print(x_te.shape, y_te.shape)" 393 | ] 394 | }, 395 | { 396 | "cell_type": "code", 397 | "execution_count": 7, 398 | "metadata": { 399 | "collapsed": true 400 | }, 401 | "outputs": [], 402 | "source": [ 403 | "x_tr /= 255.\n", 404 | "x_va /= 255.\n", 405 | "x_te /= 255." 406 | ] 407 | }, 408 | { 409 | "cell_type": "markdown", 410 | "metadata": { 411 | "deletable": true, 412 | "editable": true 413 | }, 414 | "source": [ 415 | "Let's check a sample just to be sure" 416 | ] 417 | }, 418 | { 419 | "cell_type": "code", 420 | "execution_count": 8, 421 | "metadata": { 422 | "collapsed": false, 423 | "deletable": true, 424 | "editable": true 425 | }, 426 | "outputs": [ 427 | { 428 | "data": { 429 | "image/png": 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/6OpHAPiTz/4SC6rMsleZAnSTe0MRSmnj4m+jHVDnFTjLuMCy4gJbmISDSeIM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cw0br7XLrjVL6ca+8lpU2y36e7PRmqOf+wSU8pdtV3/rIz5FefYB0aQWk7HcXxpUME1aVtm8z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\nws9GDJsLdGkuWS3VdlVDKQmfwVr0r1k1cun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25WZYl6hQ8CRwZ/bYaT7e/SLbkkXyno6K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\nTkSuQmCC\n", 430 | "text/plain": [ 431 | "" 432 | ] 433 | }, 434 | "metadata": {}, 435 | "output_type": "display_data" 436 | } 437 | ], 438 | "source": [ 439 | "test_img = x_tr[0, :, :, 0]\n", 440 | "plt.imshow(test_img)\n", 441 | "plt.show()" 442 | ] 443 | }, 444 | { 445 | "cell_type": "markdown", 446 | "metadata": { 447 | "deletable": true, 448 | "editable": true 449 | }, 450 | "source": [ 451 | "## 4 Model" 452 | ] 453 | }, 454 | { 455 | "cell_type": "markdown", 456 | "metadata": { 457 | "deletable": true, 458 | "editable": true 459 | }, 460 | "source": [ 461 | "I tried roughly 30 different models with focus on newer architectures like residual networks, networks in networks, squeezing and expanding convolutions, but in the end a 5x-Conv-MaxPool worked best. I really wanted to replace the last Dense layers with AveragePooling. They give a little more insight to what's happening in comparison to the \"black box\"-model that results from dense layers. However it didn't work out as well. I'm guessing this is because spectrograms show a different abstraction of information in comparison to a regular photo showing one object.\n", 462 | "\n", 463 | "In my tests the use of Elu replaced the need for Batch Normalization, which usually improves any model. I didn't include them for performance reasons." 464 | ] 465 | }, 466 | { 467 | "cell_type": "code", 468 | "execution_count": 9, 469 | "metadata": { 470 | "collapsed": false, 471 | "deletable": true, 472 | "editable": true, 473 | "scrolled": true 474 | }, 475 | "outputs": [ 476 | { 477 | "name": "stdout", 478 | "output_type": "stream", 479 | "text": [ 480 | "_________________________________________________________________\n", 481 | "Layer (type) Output Shape Param # \n", 482 | "=================================================================\n", 483 | "input_1 (InputLayer) (None, 192, 192, 1) 0 \n", 484 | "_________________________________________________________________\n", 485 | "conv2d_1 (Conv2D) (None, 192, 192, 16) 160 \n", 486 | "_________________________________________________________________\n", 487 | "max_pooling2d_1 (MaxPooling2 (None, 96, 96, 16) 0 \n", 488 | "_________________________________________________________________\n", 489 | "conv2d_2 (Conv2D) (None, 96, 96, 32) 4640 \n", 490 | "_________________________________________________________________\n", 491 | "max_pooling2d_2 (MaxPooling2 (None, 48, 48, 32) 0 \n", 492 | "_________________________________________________________________\n", 493 | "conv2d_3 (Conv2D) (None, 48, 48, 64) 18496 \n", 494 | "_________________________________________________________________\n", 495 | "max_pooling2d_3 (MaxPooling2 (None, 24, 24, 64) 0 \n", 496 | "_________________________________________________________________\n", 497 | "conv2d_4 (Conv2D) (None, 24, 24, 128) 73856 \n", 498 | "_________________________________________________________________\n", 499 | "max_pooling2d_4 (MaxPooling2 (None, 12, 12, 128) 0 \n", 500 | "_________________________________________________________________\n", 501 | "conv2d_5 (Conv2D) (None, 12, 12, 256) 295168 \n", 502 | "_________________________________________________________________\n", 503 | "max_pooling2d_5 (MaxPooling2 (None, 6, 6, 256) 0 \n", 504 | "_________________________________________________________________\n", 505 | "flatten_1 (Flatten) (None, 9216) 0 \n", 506 | "_________________________________________________________________\n", 507 | "dense_1 (Dense) (None, 512) 4719104 \n", 508 | "_________________________________________________________________\n", 509 | "dropout_1 (Dropout) (None, 512) 0 \n", 510 | "_________________________________________________________________\n", 511 | "dense_2 (Dense) (None, 176) 90288 \n", 512 | "=================================================================\n", 513 | "Total params: 5,201,712.0\n", 514 | "Trainable params: 5,201,712.0\n", 515 | "Non-trainable params: 0.0\n", 516 | "_________________________________________________________________\n" 517 | ] 518 | } 519 | ], 520 | "source": [ 521 | "i = Input(shape=in_dim)\n", 522 | "m = Conv2D(16, (3, 3), activation='elu', padding='same')(i)\n", 523 | "m = MaxPooling2D()(m)\n", 524 | "m = Conv2D(32, (3, 3), activation='elu', padding='same')(m)\n", 525 | "m = MaxPooling2D()(m)\n", 526 | "m = Conv2D(64, (3, 3), activation='elu', padding='same')(m)\n", 527 | "m = MaxPooling2D()(m)\n", 528 | "m = Conv2D(128, (3, 3), activation='elu', padding='same')(m)\n", 529 | "m = MaxPooling2D()(m)\n", 530 | "m = Conv2D(256, (3, 3), activation='elu', padding='same')(m)\n", 531 | "m = MaxPooling2D()(m)\n", 532 | "m = Flatten()(m)\n", 533 | "m = Dense(512, activation='elu')(m)\n", 534 | "m = Dropout(0.5)(m)\n", 535 | "o = Dense(out_dim, activation='softmax')(m)\n", 536 | "\n", 537 | "model = Model(inputs=i, outputs=o)\n", 538 | "model.summary()" 539 | ] 540 | }, 541 | { 542 | "cell_type": "code", 543 | "execution_count": null, 544 | "metadata": { 545 | "collapsed": false, 546 | "deletable": true, 547 | "editable": true, 548 | "scrolled": false 549 | }, 550 | "outputs": [], 551 | "source": [ 552 | "model.compile(loss='categorical_crossentropy', optimizer=Nadam(lr=1e-3), metrics=['accuracy'])\n", 553 | "model.fit(x_tr, y_tr, epochs=2, verbose=1, validation_data=(x_va, y_va))\n", 554 | "model.compile(loss='categorical_crossentropy', optimizer=Nadam(lr=1e-4), metrics=['accuracy'])\n", 555 | "model.fit(x_tr, y_tr, epochs=3, verbose=1, validation_data=(x_va, y_va))" 556 | ] 557 | }, 558 | { 559 | "cell_type": "code", 560 | "execution_count": 10, 561 | "metadata": { 562 | "collapsed": false, 563 | "deletable": true, 564 | "editable": true 565 | }, 566 | "outputs": [], 567 | "source": [ 568 | "model = load_model('speech_v9.h5')" 569 | ] 570 | }, 571 | { 572 | "cell_type": "code", 573 | "execution_count": 11, 574 | "metadata": { 575 | "collapsed": false, 576 | "deletable": true, 577 | "editable": true 578 | }, 579 | "outputs": [ 580 | { 581 | "name": "stdout", 582 | "output_type": "stream", 583 | "text": [ 584 | "8800/8800 [==============================] - 182s 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585 | ] 586 | }, 587 | { 588 | "data": { 589 | "text/plain": [ 590 | "[0.050450170478698882, 0.98795454545454542]" 591 | ] 592 | }, 593 | "execution_count": 11, 594 | "metadata": {}, 595 | "output_type": "execute_result" 596 | } 597 | ], 598 | "source": [ 599 | "model.evaluate(x_te, y_te)" 600 | ] 601 | }, 602 | { 603 | "cell_type": "code", 604 | "execution_count": null, 605 | "metadata": { 606 | "collapsed": true, 607 | "deletable": true, 608 | "editable": true 609 | }, 610 | "outputs": [], 611 | "source": [] 612 | } 613 | ], 614 | "metadata": { 615 | "kernelspec": { 616 | "display_name": "Python 3", 617 | "language": "python", 618 | "name": "python3" 619 | }, 620 | "language_info": { 621 | "codemirror_mode": { 622 | "name": "ipython", 623 | "version": 3 624 | }, 625 | "file_extension": ".py", 626 | "mimetype": "text/x-python", 627 | "name": "python", 628 | "nbconvert_exporter": "python", 629 | "pygments_lexer": "ipython3", 630 | "version": "3.6.0" 631 | } 632 | }, 633 | "nbformat": 4, 634 | "nbformat_minor": 2 635 | } 636 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Spoken Language Recognition 2 | 3 | This notebook trains a convolutional neural network to classify audio files of voice recordings into the languages that were spoken. The dataset I used contained 66.000 files across 176 languages. I found it on TopCoder (https://goo.gl/G5XBJl). I liked the idea behind this problem, because it's very hard for humans to do. It's intersting to see that CNNs perform well on problems where intuition doesn't get you anywhere. 4 | 5 | I included a saved version of my pretrained model, which evaluates to an accuracy of **98,79%**. 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