Repository: keunwoochoi/dl4mir Branch: master Commit: a7ff9c16d39e Files: 31 Total size: 843.9 KB Directory structure: gitextract_dco3_qnj/ ├── Example 1 - a pitch detection network with Dense layers.ipynb ├── Example 2 - a chord recognition network with Convolutional layers.ipynb ├── Example_3.py ├── Example_4.py ├── Example_5-1.py ├── Example_5-2.py ├── README.md ├── config.json ├── datasets.py ├── figures_in_paper/ │ ├── README.md │ ├── tutorial_paper_autoencoder │ ├── tutorial_paper_flowcharts │ ├── tutorial_paper_layer_conv │ ├── tutorial_paper_layer_dense │ ├── tutorial_paper_layer_recurrent │ ├── tutorial_paper_ml_vs_dl.html │ ├── tutorial_paper_node │ ├── tutorial_paper_seq2seq.html │ ├── tutorial_paper_t-varying_all_layers │ ├── tutorial_paper_t-varying_conv1 │ ├── tutorial_paper_time_inv_conv1d.html │ ├── tutorial_paper_time_inv_conv2d.html │ └── tutorial_paper_time_inv_crnn ├── global_config.py ├── main_preprocess.py ├── models_time_invariant.py ├── models_time_varying.py ├── my_callbacks.py ├── requirements.txt ├── utils_datasets.py └── utils_preprocess.py ================================================ FILE CONTENTS ================================================ ================================================ FILE: Example 1 - a pitch detection network with Dense layers.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Example 1 - a pitch detection network\n", "\n", "In this notebook, we wlll train a single-layer network for a super easy task. The goal is to show\n", "how Dense layer is working.\n", "\n", "We will use a synthesized data here. It consists of a pure sinusoid on 12 differnt pitches. Then we will compute a CQT and feed one of the CQT frames into the network." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import librosa\n", "import keras\n", "from future.utils import implements_iterator # for python 2 compatibility for __next__()\n", "from matplotlib import pyplot as plt\n", "\n", "import warnings\n", "warnings.filterwarnings('ignore')\n", "\n", "plt.rc('figure', titlesize=20) \n", "plt.rc('font', size=20)\n", "plt.rc('xtick', labelsize=12) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Functions to generate data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def sin_wave(secs, freq, sr, gain):\n", " '''\n", " Generates a sine wave of frequency given by freq, with duration of secs.\n", " '''\n", " t = np.arange(sr * secs)\n", " return gain * np.sin(2 * np.pi * freq * t / sr)\n", "\n", "def whitenoise(gain, shape):\n", " '''\n", " Generates white noise of duration given by secs\n", " '''\n", " return gain * np.random.uniform(-1., 1., shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### A class to generate data batches" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class DataGen:\n", " def __init__(self, sr=16000, batch_size=128):\n", " np.random.seed(1209)\n", " self.pitches = [440., 466.2, 493.8, 523.3, 554.4, 587.3,\n", " 622.3, 659.3, 698.5, 740., 784.0, 830.6]\n", "\n", " self.sr = sr\n", " self.n_class = len(self.pitches) # 12 pitches\n", " self.secs = 1.\n", " self.batch_size = batch_size\n", " self.sins = []\n", " self.labels = np.eye(self.n_class)[range(0, self.n_class)] # 1-hot-vectors\n", "\n", " for freq in self.pitches:\n", " cqt = librosa.cqt(sin_wave(self.secs, freq, self.sr, gain=0.5), sr=sr,\n", " fmin=220, n_bins=36, filter_scale=2)[:, 1] # use only one frame!\n", " cqt = librosa.amplitude_to_db(cqt, ref=np.min)\n", " cqt = cqt / np.max(cqt)\n", " self.sins.append(cqt)\n", "\n", " self.cqt_shape = cqt.shape # (36, )\n", "\n", " def __next__(self):\n", " choice = np.random.choice(12, size=self.batch_size, # pick pitches for this batch\n", " replace=True)\n", " noise_gain = 0.1 * np.random.random_sample(1) # a random noise gain \n", " noise = whitenoise(noise_gain, self.cqt_shape) # generate white noise\n", " xs = [noise + self.sins[i] for i in choice] # compose a batch with additive noise\n", " ys = [self.labels[i] for i in choice] # corresponding labels\n", "\n", " return np.array(xs, dtype=np.float32), np.array(ys, dtype=np.float32)\n", "\n", " next = __next__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### A quick look on the data we're generating -- and we'll feed as inputs" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Input: A frame of CQT in a shape of: (36,)\n", "Input batch: CQT frames, (128, 36)\n", "Number of classes (pitches): 12\n", "\n" ] }, { "data": { "image/png": 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p2I7yG8Z9+/zp8+Ex+nQ6RSSJbM368rWC2yvsuTif7MHXWYbrs715PWP5DPeH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\nD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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "datagen = DataGen()\n", "print(\"Input: A frame of CQT in a shape of: {}\".format(datagen.cqt_shape))\n", "x, y = next(datagen)\n", "print(\"Input batch: CQT frames, {}\".format(x.shape))\n", "print(\"Number of classes (pitches): {}\".format(datagen.n_class))\n", "plt.figure(figsize=(20, 6))\n", "\n", "for i in range(2):\n", " x, y = next(datagen)\n", " plt.subplot(2, 2, i+1)\n", " plt.imshow(x.transpose(), cmap=plt.get_cmap('Blues'))\n", " plt.xlabel('data sample index')\n", " plt.ylabel('pitch index')\n", " plt.title('Batch {} (x, input)'.format(i+1))\n", " plt.subplot(2, 2, i+3)\n", " plt.imshow(y.transpose(), cmap=plt.get_cmap('Blues'))\n", " plt.title('Batch {} (y, label)'.format(i+1))\n", "\n", "print('')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Each subplot is a visualisation of each batch. The pitch ranges only in [440 Hz, 830 Hz] (A4 - G#5) but the CQT covers wider range of frequencies (3 octaves from 220 Hz to 880 Hz)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Build a model!" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "val_datagen = DataGen() # this is a generator for validation set" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The model is very simple. No bias, only single dense layer, which will connect a 36-dim input to a 12-dim output." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true, "scrolled": false }, "outputs": [], "source": [ "model = keras.models.Sequential()\n", "model.add(keras.layers.Dense(datagen.n_class, use_bias=False,\n", " input_shape=datagen.cqt_shape)) # A dense layer (36 input nodes --> 12 output nodes)\n", "model.add(keras.layers.Activation('softmax')) # Softmax because it's single-label classification\n", "\n", "model.compile(optimizer=keras.optimizers.SGD(lr=0.01, momentum=0.9, # a pretty standard optimizer\n", " decay=1e-6, nesterov=True),\n", " loss='categorical_crossentropy', # categorical crossentropy makes sense with Softmax\n", " metrics=['accuracy']) # we'll also measure the performance but it's NOT a loss function " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Number of parameters => $432 = 36 \\times 12 $. If there is bias, then it would become $36 \\times 12 + 12$." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "dense_1 (Dense) (None, 12) 432 \n", "_________________________________________________________________\n", "activation_1 (Activation) (None, 12) 0 \n", "=================================================================\n", "Total params: 432\n", "Trainable params: 432\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "model.summary() # Let's see the network." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Train it!\n", "Alright, let's train it!" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/25\n", "200/200 [==============================] - 0s - loss: 2.1811 - acc: 0.5381 - val_loss: 1.8601 - val_acc: 0.9258\n", "Epoch 2/25\n", "200/200 [==============================] - 0s - loss: 1.6234 - acc: 0.9729 - val_loss: 1.4143 - val_acc: 1.0000\n", "Epoch 3/25\n", "200/200 [==============================] - 0s - loss: 1.2387 - acc: 1.0000 - val_loss: 1.0857 - val_acc: 1.0000\n", "Epoch 4/25\n", "200/200 [==============================] - 0s - loss: 0.9700 - acc: 1.0000 - val_loss: 0.8587 - val_acc: 1.0000\n", "Epoch 5/25\n", "200/200 [==============================] - 0s - loss: 0.7810 - acc: 1.0000 - val_loss: 0.7107 - val_acc: 1.0000\n", "Epoch 6/25\n", "200/200 [==============================] - 0s - loss: 0.6424 - acc: 1.0000 - val_loss: 0.5827 - val_acc: 1.0000\n", "Epoch 7/25\n", "200/200 [==============================] - 0s - loss: 0.5405 - acc: 1.0000 - val_loss: 0.5087 - val_acc: 1.0000\n", "Epoch 8/25\n", "200/200 [==============================] - 0s - loss: 0.4618 - acc: 1.0000 - val_loss: 0.4241 - val_acc: 1.0000\n", "Epoch 9/25\n", "200/200 [==============================] - 0s - loss: 0.4014 - acc: 1.0000 - val_loss: 0.3822 - val_acc: 1.0000\n", "Epoch 10/25\n", "200/200 [==============================] - 0s - loss: 0.3546 - acc: 1.0000 - val_loss: 0.3289 - val_acc: 1.0000\n", "Epoch 11/25\n", "200/200 [==============================] - 0s - loss: 0.3149 - acc: 1.0000 - val_loss: 0.3019 - val_acc: 1.0000\n", "Epoch 12/25\n", "200/200 [==============================] - 0s - loss: 0.2843 - acc: 1.0000 - val_loss: 0.2739 - val_acc: 1.0000\n", "Epoch 13/25\n", "200/200 [==============================] - 0s - loss: 0.2566 - acc: 1.0000 - val_loss: 0.2361 - val_acc: 1.0000\n", "Epoch 14/25\n", "200/200 [==============================] - 0s - loss: 0.2353 - acc: 1.0000 - val_loss: 0.2244 - val_acc: 1.0000\n", "Epoch 15/25\n", "200/200 [==============================] - 0s - loss: 0.2161 - acc: 1.0000 - val_loss: 0.2038 - val_acc: 1.0000\n", "Epoch 16/25\n", "200/200 [==============================] - 0s - loss: 0.2011 - acc: 1.0000 - val_loss: 0.1935 - val_acc: 1.0000\n", "Epoch 17/25\n", "200/200 [==============================] - 0s - loss: 0.1860 - acc: 1.0000 - val_loss: 0.1778 - val_acc: 1.0000\n", "Epoch 18/25\n", "200/200 [==============================] - 0s - loss: 0.1737 - acc: 1.0000 - val_loss: 0.1700 - val_acc: 1.0000\n", "Epoch 19/25\n", "200/200 [==============================] - 0s - loss: 0.1629 - acc: 1.0000 - val_loss: 0.1587 - val_acc: 1.0000\n", "Epoch 20/25\n", "200/200 [==============================] - 0s - loss: 0.1535 - acc: 1.0000 - val_loss: 0.1450 - val_acc: 1.0000\n", "Epoch 21/25\n", "200/200 [==============================] - 0s - loss: 0.1451 - acc: 1.0000 - val_loss: 0.1409 - val_acc: 1.0000\n", "Epoch 22/25\n", "200/200 [==============================] - 0s - loss: 0.1369 - acc: 1.0000 - val_loss: 0.1318 - val_acc: 1.0000\n", "Epoch 23/25\n", "200/200 [==============================] - 0s - loss: 0.1307 - acc: 1.0000 - val_loss: 0.1275 - val_acc: 1.0000\n", "Epoch 24/25\n", "200/200 [==============================] - 0s - loss: 0.1238 - acc: 1.0000 - val_loss: 0.1163 - val_acc: 1.0000\n", "Epoch 25/25\n", "200/200 [==============================] - 0s - loss: 0.1177 - acc: 1.0000 - val_loss: 0.1202 - val_acc: 1.0000\n" ] } ], "source": [ "history = model.fit_generator(datagen, steps_per_epoch=200, epochs=25, verbose=1,\n", " validation_data=val_datagen, validation_steps=4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### What happened?" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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NdBSRRkgJdhVy8os0yVFEpDFo1R5atoEdlcdhZ3ZOZvlXBygucVVcKCISOkqw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\nXwOWVLu+Zxljrx9XBu/jxcnynno5n6eLsZ7O5Xn4ParNrtG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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(12, 4))\n", "plt.subplot(1, 2, 1)\n", "plt.plot(history.history['acc'], label='training')\n", "plt.plot(history.history['val_acc'], label='validation', alpha=0.7)\n", "plt.title('Accuracy')\n", "plt.xlabel('epoch')\n", "plt.legend()\n", "plt.subplot(1, 2, 2)\n", "plt.plot(history.history['loss'], label='training')\n", "plt.plot(history.history['val_loss'], label='validation', alpha=0.7)\n", "plt.title('Loss')\n", "plt.xlabel('epoch')\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Validation set loss is similar to training loss, i.e., no overfitting.\n", "\n", "### How about on test set?" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "loss: 0.113415751606, accuracy: 1.0\n" ] } ], "source": [ "loss = model.evaluate_generator(datagen, steps=10)\n", "print(\"loss: {}, accuracy: {}\".format(loss[0], loss[1]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### How does the network work after training?" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(36, 12)\n" ] } ], "source": [ "weights = model.get_weights()[0] # (36, 12)\n", "print(weights.shape)\n", "# weights = weights / np.sum(np.abs(weights), axis=1, keepdims=True)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "data": { "image/png": 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cCXgBMAS8JiLuKXXymoFPRABHAlNJSQEi4lpJvwb2lbRzRFzf5JjjgK3z10eT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rcuFdkTP7ETBY4cbhwGM5u8vwCZuV+fQYQj6/NPf+A7l8sVvOTmlc1s3XLfp9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\nwDd1jdxewBn4Kvs6wB2SvgM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DYgAfBEEQBEEQBEEQBOOAGMAHQRAEQRAEQRAEwTggBvBB\nEARBEARBEARBMA6IAXwQBEEQBEEQBEEQjANiAB8EQRAEQRAEQRAE44AYwAdBEARBEARBEATBOCAG\n8EEQBEEQBEEQBEEwDvj/AR2abmroW+8VAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# weights\n", "plt.figure(figsize=(20, 5))\n", "plt.imshow(weights.transpose(), cmap=plt.get_cmap('Blues'))\n", "plt.colorbar()\n", "plt.title('Visualisation of the trained weights ($W$)')\n", "pitch_names = 'A4 A#4 B4 C5 C#5 D5 D#5 E5 F5 F#5 G5 G#5'.split(' ')\n", "plt.yticks(range(0, 12), ['{} ({})'.format(p, str(i)) for p, i in zip(pitch_names, range(1, 13))])\n", "plt.xticks(range(0, 36), [str(i) for i in range(1, 37)])\n", "plt.xlabel('Frequency bands of CQT')\n", "plt.ylabel('output')\n", "# an example input\n", "plt.figure(figsize=(20, 1))\n", "plt.imshow(x[0:1], cmap=plt.get_cmap('Blues'))\n", "plt.xticks(range(0, 36), [str(i) for i in range(1, 37)])\n", "plt.colorbar()\n", "plt.title('What would be the output if this CQT frame is multiplied to this weights?')\n", "plt.yticks([])\n", "print('')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is visualisation of weights $\\textbf{W}$.\n", "\n", "Each row corresponeds to each output node. For example, the 1st row (A4) is connected to the 1st output node, which will be activated (has a large value) if the network thinks it is A4.\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Estimated pitch: F5\n", "Groundtruth: F5\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def softmax(x):\n", " \"\"\"A softmax function that is not perfect in terms of numerical stability (it might overflow)\"\"\"\n", " return np.exp(x) / np.exp(x).sum(axis=0)\n", "\n", "output_for_x = softmax(np.dot(weights.transpose(), x[0]))\n", "plt.bar(range(len(output_for_x)), output_for_x)\n", "plt.xticks(range(len(output_for_x)), pitch_names)\n", "plt.title('Output node values for x[0]: ')\n", "print('Estimated pitch: {}'.format(pitch_names[np.argmax(output_for_x)]))\n", "print('Groundtruth: {}'.format(pitch_names[np.argmax(y[0])]))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 1 } ================================================ FILE: Example 2 - a chord recognition network with Convolutional layers.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Example 2 - A very simple chord recognition convnet\n", "\n", "We're gonna use synthesize data and use CQT as representation. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import librosa\n", "import keras\n", "import keras.backend as K\n", "from matplotlib import pyplot as plt\n", "from future.utils import implements_iterator # for python 2 compatibility for __next__()\n", "\n", "import warnings\n", "warnings.filterwarnings('ignore')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Utility function to generate data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def sin_wave(secs, freq, sr, gain):\n", " '''\n", " Generates a sine wave of frequency given by freq, with duration of secs.\n", " '''\n", " t = np.arange(sr * secs)\n", " return gain * np.sin(2 * np.pi * freq * t / sr)\n", "\n", "\n", "def whitenoise(gain, shape):\n", " '''\n", " Generates white noise of duration given by secs\n", " '''\n", " return gain * np.random.uniform(-1., 1., shape)\n", "\n", "def chord_wave(secs, f0, sr, gain, major):\n", " \"\"\"major: bool\"\"\"\n", " t = np.arange(sr * secs)\n", " sine_f0 = gain * np.sin(2 * np.pi * f0 * t / sr)\n", " if major:\n", " sine_third = gain * np.sin(2 * np.pi * f0 * 2. ** (4./12.) * t / sr)\n", " else:\n", " sine_third = gain * np.sin(2 * np.pi * f0 * 2. ** (3./12.) * t / sr)\n", " return sine_f0 + sine_third" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def add_channel_axis(cqt):\n", " if K.image_data_format == 'channels_first':\n", " return cqt[np.newaxis, :, :]\n", " else:\n", " return cqt[:, :, np.newaxis]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class DataGen:\n", " def __init__(self, sr=16000, batch_size=128):\n", " np.random.seed(1209)\n", " self.pitches = [440., 466.2, 493.8, 523.3, 554.4, 587.3,\n", " 622.3, 659.3, 698.5, 740., 784.0, 830.6]\n", "\n", " self.sr = sr\n", " self.n_class = 2 # major or minor\n", " self.secs = 1.\n", " self.batch_size = batch_size\n", " self.labels = np.eye(self.n_class)[range(0, self.n_class)] # 1-hot-vectors\n", " \n", " self.major_cqts = []\n", " self.minor_cqts = []\n", " \n", " for freq in self.pitches:\n", " cqt = librosa.cqt(chord_wave(self.secs, freq, self.sr, gain=0.5, major=True), sr=sr,\n", " fmin=220, n_bins=36, filter_scale=2)[:, 2:5] # use three frames!\n", " cqt = librosa.amplitude_to_db(cqt, ref=np.min)\n", " cqt = cqt / np.max(cqt) # cqt in 2d\n", " \n", " self.major_cqts.append(add_channel_axis(cqt))\n", " \n", " cqt = librosa.cqt(chord_wave(self.secs, freq, self.sr, gain=0.5, major=False), sr=sr,\n", " fmin=220, n_bins=36, filter_scale=2)[:, 2:5] # use three frame!\n", " cqt = librosa.amplitude_to_db(cqt, ref=np.min)\n", " cqt = cqt / np.max(cqt)\n", " self.minor_cqts.append(add_channel_axis(cqt))\n", "\n", " self.cqt_shape = add_channel_axis(cqt).shape # (1, 36, 3) or (36, 3, 1)\n", "\n", " def __next__(self): \n", " \"\"\"Yielding half Major, half minor\"\"\"\n", " choice = np.random.choice(12, size=self.batch_size // 2, # pick pitches for this batch\n", " replace=True)\n", " noise_gain = 0.1 * np.random.random_sample(1) # a random noise gain \n", " noise = whitenoise(noise_gain, self.cqt_shape) # generate white noise\n", " xs = [noise + self.major_cqts[i] for i in choice] # compose a batch with additive noise (Major)\n", " xs += [noise + self.minor_cqts[i] for i in choice] # compose a batch with additive noise (minor)\n", " \n", " ys = np.eye(2)[np.hstack((np.zeros(self.batch_size // 2, dtype=np.int), \n", " np.ones(self.batch_size // 2, dtype=np.int)))] # corresponding labels\n", "\n", " return np.array(xs, dtype=np.float32), np.array(ys, dtype=np.float32)\n", " \n", " next = __next__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So, Major is labeled as [1, 0], and minor as [0, 1]." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Experiment" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "datagen = DataGen()\n", "x, y = next(datagen)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### How does input data look like?" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(14, 7))\n", "if K.image_data_format == 'channels_first':\n", " for i in range(4):\n", " plt.subplot(1, 10, i+1)\n", " plt.imshow(x[i, 0], cmap=plt.get_cmap('Blues'))\n", " plt.subplot(1, 10, 6+i)\n", " plt.imshow(x[64 + i, 0], cmap=plt.get_cmap('Blues'))\n", "else:\n", " for i in range(4):\n", " plt.subplot(1, 10, i+1)\n", " plt.imshow(x[i, :, :, 0], cmap=plt.get_cmap('Blues'))\n", " plt.subplot(1, 10, 6+i)\n", " plt.imshow(x[64 + i, :, :, 0], cmap=plt.get_cmap('Blues'))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(Major inputs on left, minor inputs on right.)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Buld a convnet model!" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "val_datagen = DataGen() # this is a generator for validation set" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* This convlayer takes `(36, 3)` with single channel input.\n", "* The kernel size is (5, 3) so that it can cover the spectral patterns of major and minor chords." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# even a simplet network -- one conv layer and that's it!\n", "# If you wanna try it. \n", "\n", "\n", "# model = keras.models.Sequential()\n", "# model.add(keras.layers.convolutional.Conv2D(datagen.n_class, (5, 3), use_bias=False, padding='same',\n", "# input_shape=datagen.cqt_shape)) # A conv2d layer (36 input nodes --> 8 output nodes)\n", "# model.add(keras.layers.pooling.GlobalMaxPooling2D())\n", "# model.add(keras.layers.Activation('softmax')) # Softmax because it's single-label classification\n", "\n", "# model.compile(optimizer=keras.optimizers.SGD(lr=0.01, momentum=0.9, # a pretty standard optimizer\n", "# decay=1e-6, nesterov=True),\n", "# loss='categorical_crossentropy', # categorical crossentropy makes sense with Softmax\n", "# metrics=['accuracy']) # we'll also measure the performance but it's NOT a loss function " ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "np.random.seed(12345)\n", "model = keras.models.Sequential()\n", "model.add(keras.layers.convolutional.Conv2D(4, (5, 3), use_bias=False, padding='valid',\n", " input_shape=datagen.cqt_shape)) # A conv2d layer (36 input nodes --> 8 output nodes)\n", "model.add(keras.layers.pooling.GlobalMaxPooling2D())\n", "model.add(keras.layers.Activation('relu'))\n", "model.add(keras.layers.Dense(datagen.n_class, use_bias=False))\n", "model.add(keras.layers.Activation('softmax'))\n", "\n", "model.compile(optimizer=keras.optimizers.SGD(lr=0.01, momentum=0.9, # a pretty standard optimizer\n", " decay=1e-6, nesterov=True),\n", " loss='categorical_crossentropy', # categorical crossentropy makes sense with Softmax\n", " metrics=['accuracy']) # we'll also measure the performance but it's NOT a loss function " ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "conv2d_1 (Conv2D) (None, 32, 1, 4) 60 \n", "_________________________________________________________________\n", "global_max_pooling2d_1 (Glob (None, 4) 0 \n", "_________________________________________________________________\n", "activation_1 (Activation) (None, 4) 0 \n", "_________________________________________________________________\n", "dense_1 (Dense) (None, 2) 8 \n", "_________________________________________________________________\n", "activation_2 (Activation) (None, 2) 0 \n", "=================================================================\n", "Total params: 68\n", "Trainable params: 68\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "model.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Train it!" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/15\n", "200/200 [==============================] - 1s - loss: 0.6179 - acc: 0.8118 - val_loss: 0.4627 - val_acc: 1.0000\n", "Epoch 2/15\n", "200/200 [==============================] - 0s - loss: 0.2446 - acc: 1.0000 - val_loss: 0.1072 - val_acc: 1.0000\n", "Epoch 3/15\n", "200/200 [==============================] - 1s - loss: 0.0611 - acc: 1.0000 - val_loss: 0.0396 - val_acc: 1.0000\n", "Epoch 4/15\n", "200/200 [==============================] - 0s - loss: 0.0279 - acc: 1.0000 - val_loss: 0.0262 - val_acc: 1.0000\n", "Epoch 5/15\n", "200/200 [==============================] - 0s - loss: 0.0170 - acc: 1.0000 - val_loss: 0.0130 - val_acc: 1.0000\n", "Epoch 6/15\n", "200/200 [==============================] - 0s - loss: 0.0122 - acc: 1.0000 - val_loss: 0.0090 - val_acc: 1.0000\n", "Epoch 7/15\n", "200/200 [==============================] - 1s - loss: 0.0093 - acc: 1.0000 - val_loss: 0.0114 - val_acc: 1.0000\n", "Epoch 8/15\n", "200/200 [==============================] - 1s - loss: 0.0075 - acc: 1.0000 - val_loss: 0.0072 - val_acc: 1.0000\n", "Epoch 9/15\n", "200/200 [==============================] - 0s - loss: 0.0063 - acc: 1.0000 - val_loss: 0.0080 - val_acc: 1.0000\n", "Epoch 10/15\n", "200/200 [==============================] - 0s - loss: 0.0053 - acc: 1.0000 - val_loss: 0.0049 - val_acc: 1.0000\n", "Epoch 11/15\n", "200/200 [==============================] - 0s - loss: 0.0049 - acc: 1.0000 - val_loss: 0.0046 - val_acc: 1.0000\n", "Epoch 12/15\n", "200/200 [==============================] - 0s - loss: 0.0039 - acc: 1.0000 - val_loss: 0.0042 - val_acc: 1.0000\n", "Epoch 13/15\n", "200/200 [==============================] - 1s - loss: 0.0037 - acc: 1.0000 - val_loss: 0.0035 - val_acc: 1.0000\n", "Epoch 14/15\n", "200/200 [==============================] - 1s - loss: 0.0033 - acc: 1.0000 - val_loss: 0.0044 - val_acc: 1.0000\n", "Epoch 15/15\n", "200/200 [==============================] - 1s - loss: 0.0029 - acc: 1.0000 - val_loss: 0.0022 - val_acc: 1.0000\n" ] } ], "source": [ "history = model.fit_generator(datagen, steps_per_epoch=200, epochs=15, verbose=1,\n", " validation_data=val_datagen, validation_steps=4)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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8JwF/Dp4vbxt39w3uXh483w5UAUdZK6nnqCyeiEgfk1UYai2+54OIwyPzBjEo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\nz+Ny4FxJ7ys31YuL4vyYlTzCKum3Kd5e31l2FgB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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(12, 4))\n", "plt.subplot(1, 2, 1)\n", "plt.plot(history.history['acc'], label='training')\n", "plt.plot(history.history['val_acc'], label='validation', alpha=0.7)\n", "plt.title('Accuracy')\n", "plt.xlabel('epoch')\n", "plt.legend()\n", "plt.subplot(1, 2, 2)\n", "plt.plot(history.history['loss'], label='training')\n", "plt.plot(history.history['val_loss'], label='validation', alpha=0.7)\n", "plt.title('Loss')\n", "plt.xlabel('epoch')\n", "plt.legend()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "loss: 0.00349949840456, accuracy: 1.0\n" ] } ], "source": [ "loss = model.evaluate_generator(datagen, steps=10)\n", "print(\"loss: {}, accuracy: {}\".format(loss[0], loss[1]))" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The Convolution2D layer weights shape is: (5, 3, 1, 4)\n" ] } ], "source": [ "weights = model.get_weights()[0]\n", "print(\"The Convolution2D layer weights shape is: {}\".format(weights.shape))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ ", which is (height_kernel, width_kernel, input_channel_number, output_channel_number)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Understanding by visualising" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualising the kernels" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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mqG/fyPZsFQWNz0TEF8aZZYOkBR3j88v3JsReG/Kw6PINoH1kEYAM6s2iSgcJtk+QdIGk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\nl0haExEfbLs9QHJkUZ+QRUBPyKI+IYu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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(20, 4))\n", "titles = ['minor chord positive', 'minor chord negative?', 'wth??', 'minor chord positive']\n", "for i in range(4):\n", " plt.subplot(1, 4, i+1)\n", " plt.imshow(weights[:, :, 0, i], cmap=plt.get_cmap('Blues'))\n", " plt.colorbar()\n", " plt.title(titles[i])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Okay, so out of four kernels, the 1st and 4th are definitely minor chord positive. Not sure for the other two.\n", "\n", "## Visualising the feature maps\n", "\n", "which is `conv(input, kernel)`." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x_major = np.squeeze(x[0])\n", "x_minor = np.squeeze(x[-1])\n", "from scipy import signal\n", "def plot_convolution(x, kernels):\n", " \"\"\"kernels: 3d, (height, width, channel) and four channels.\"\"\"\n", " n_kernels = kernels.shape[2]\n", " plt.figure(figsize=(9, 5))\n", " plt.subplot(1, n_kernels+1, 1)\n", " plt.imshow(x)\n", " plt.title('Input x')\n", " for idx in range(n_kernels):\n", " plt.subplot(1, n_kernels+1, idx+2)\n", " conved = signal.convolve2d(x, kernels[:, :, idx], mode='same')\n", " plt.imshow(conved)\n", " plt.title('max: {:4.2f}'.format(np.max(conved)))" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Here's the feature map activation for a Major chord input\n" ] }, { "data": { "image/png": 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R0HE+LaYwzZ4+k3G8neo0n6JqWjkwFmwfWRKeY/agwtMDo7lO83Ez1YbDG6paRgQeHoLd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\nAABE7v8DRWDSuQ12dQUAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_convolution(x_major, weights[:, :, 0, :])\n", "print(\"Here's the feature map activation for a Major chord input\")" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Here's for a minor chord input\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_convolution(x_minor, weights[:, :, 0, :])\n", "print(\"Here's for a minor chord input\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The 1st and 4th probably are most distinctive kernels. Now Let's move to the dense layer weights.\n", "\n", "## Dense layer weights" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[-0.50099331 2.33464241]\n", " [ 2.9126389 -3.8759408 ]\n", " [-0.6134612 -0.3230224 ]\n", " [-3.05488276 3.24755979]]\n" ] } ], "source": [ "dense_weights = model.get_weights()[1]\n", "print(dense_weights)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Look at the dense layer weights! Each row-->input, each column-->output. \n", "\n", "1. The 1st feature map will have an high activation on minor chords. \n", " - After global maxpooling, this feature map will have large value on minor chord input, and small value on Major chord input. \n", " - The value is multiplied to `-0.496` on the Major output node, and `2.33` on the minor output node. \n", " - Seems alright, huh?\n", "\n", "2. The 2nd feature map will have a large negative activation on minor chord.\n", "\n", "3. Not sure what this kernel is doing. `[-0.6, -0.3]` weights also say this kernel is not too crucial.\n", "\n", "4. The 4th is minor chord positive. `[-3.05, 3.25` supports it again." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 1 } ================================================ FILE: Example_3.py ================================================ print("It's not an example.. Check out config.json to setup your dataset folder.") print("Then move to Example 4!") ================================================ FILE: Example_4.py ================================================ print("Now you're gonna download datasets.") print("To download FMA for genre classification,") print("$ python main_preprocess.py fma") print("") print("To download Jamendo for frame-based vocal/nonvocal classification, ") print("$ python main_preprocess.py jamendo") print("") print("and wait till it ends!") ================================================ FILE: Example_5-1.py ================================================ """ Example_5-1.py --------------------------------------------------------- Time-varying classification example using Jamendo dataset --------------------------------------------------------- It assumes Jamendo dataset is downloaded and pre-processed by main_preprocess.py. See Example4.py for more info. """ from __future__ import print_function # (at top of module) from builtins import range import os import pandas as pd import numpy as np import models_time_varying from sklearn.preprocessing import LabelEncoder import utils_preprocess import my_callbacks from global_config import * import pdb N_DATA = {'train': 61, 'valid': 16, 'test': 16} N_HOP = 256 def load_all_data(set_name): srcs = [] ys = [] for i in range(N_DATA[set_name]): srcs.append(np.load(os.path.join(DIR_JAMENDO_NPY, '{}_{:02}_x.npy'.format(set_name, i)))) ys.append(np.load(os.path.join(DIR_JAMENDO_NPY, '{}_{:02}_y.npy'.format(set_name, i)))) return srcs, ys def y_sample_to_frame(y): """ y : (batch_size, N) --> (batch_size, M), M = number of frames """ n_hop = N_HOP nsp_y = len(y) ret = np.array([np.round(np.mean(y[max(0, (i - 1) * n_hop): min(nsp_y, (i + 1) * n_hop)])) \ for i in range(nsp_y // n_hop)], dtype=np.int) return ret def data_gen(set_name, nsp_input, is_shuffle, is_infinite, batch_size): """Data generator for Jamendo Mostly, ['train', N, True, True, K1], ['valid', N, False, False, K2], ['test', None, False, False, 1]. set_name: string, 'train', 'valid', 'test' nsp_input: num_sample of input (length) [sample]. if None, it uses the whole length. is_shuffle: boolean, whether shuffle every batch if True. is_infinite; boolean, whether the generator is infinite or not batch_size: integer, size of batch. len(df_subset) % batch_size should be 0. """ n_data = N_DATA[set_name] assert batch_size <= n_data n_batch = n_data // batch_size if n_data % batch_size != 0: if not is_shuffle: print('WARNING: The generator for {} will ignore the last {} samples'.format(set_name, n_data % batch_size)) srcs, ys = load_all_data(set_name) # ys = [y_sample_to_frame(y) for y in ys] while True: for batch_i in range(n_batch): if is_shuffle: data_idxs = np.random.choice(n_data, batch_size, replace=False) else: data_idxs = range(batch_i * batch_size, (batch_i + 1) * batch_size) src_batch = [] y_batch = [] for data_i in data_idxs: src = srcs[data_i] # (1, N) y = ys[data_i] nsp_src = src.shape[1] if nsp_input is None: src = src[:, : (nsp_src // N_HOP) * N_HOP] y = y[: (nsp_src // N_HOP) * N_HOP] else: offset = np.random.randint(low=0, high=nsp_src - nsp_input) src = src[:, offset: offset + nsp_input] y = y[offset: offset + nsp_input] src_batch.append(src) y_batch.append(y) src_batch = np.array(src_batch, dtype=K.floatx()) y_batch = [y_sample_to_frame(y) for y in y_batch] y_batch = np.array(y_batch, dtype=np.int) # (batch_size, N) y_batch = np.eye(2, dtype=K.floatx())[y_batch] yield src_batch, y_batch if not is_infinite: break def main(model_name, exp_name='fma'): """ DO the work! """ assert model_name in ['crnn', 'lstm', 'lstm_bi'] print("-" * 60) print("Keunwoo: Welcome! Lets do something deep with Jamendo dataset.") print(" I'm assuming you finished pre-processing.") print(" We're gonna use {} model".format(model_name)) print("It's a good practice to use callbacks in Keras.") callbacks = my_callbacks.get_callbacks(name=exp_name) early_stopper, model_saver, weight_saver, csv_logger = callbacks # just to show you print("Preparing data generators for training and validation...") train_batch_size = 40 valid_batch_size = 5 steps_per_epoch = 30 gen_train = data_gen('train', SR * 10, is_shuffle=True, is_infinite=True, batch_size=train_batch_size) gen_valid = data_gen('valid', SR * 10, is_shuffle=False, is_infinite=True, batch_size=valid_batch_size) print("Keunwoo: Getting model...") if model_name == 'crnn': model = models_time_varying.model_convrnn(n_out=2) elif model_name == 'lstm': model = models_time_varying.model_lstm_leglaive_icassp2014(n_out=2, bidirectional=False) elif model_name == 'lstm_bi': model = models_time_varying.model_lstm_leglaive_icassp2014(n_out=2, bidirectional=True) model.compile('adam', 'categorical_crossentropy', metrics=['accuracy']) print("Keunwoo: Starting to train...") model.fit_generator(gen_train, steps_per_epoch, epochs=30, callbacks=callbacks, validation_data=gen_valid, validation_steps=N_DATA['valid'] // valid_batch_size) print("Keunwoo: Training is done. Loading the best weights...") model.load_weights("{}_best_weights.h5".format(exp_name)) gen_test = data_gen('test', None, is_shuffle=False, is_infinite=True, batch_size=1) print(" Evaluating...") scores = model.evaluate_generator(gen_test, N_DATA['test']) print('Keunwoo: Done for {}!'.format(model_name)) print(" test set loss:{}".format(scores[0])) print(" test set accuracy: {}%".format([scores[1]])) if __name__ == '__main__': main('crnn', 'jamendo_crnn') main('lstm', 'jamendo_lstm') main('lstm_bi', 'jamendo_lstm_bi') ================================================ FILE: Example_5-2.py ================================================ """ Example_5-2.py ------------------------------------------------------- Time-invariant classification example using FMA dataset ------------------------------------------------------- It assumes FMA dataset is downloaded and pre-processed by main_preprocess.py. See Example4.py for more info. """ from __future__ import print_function # (at top of module) from builtins import range import os import pandas as pd import numpy as np import models_time_invariant from sklearn.preprocessing import LabelEncoder import utils_preprocess import my_callbacks from global_config import * def data_gen(df_subset, ys, is_shuffle, batch_size=40): """Data generator. df_subset: pandas dataframe, with rows subset ys: numpy arrays, N-by-8 one-hot-encoded labels is_shuffle: shuffle every batch if True. batch_size: integer, size of batch. len(df_subset) % batch_size should be 0. """ n_data = len(df_subset) n_batch = n_data // batch_size if n_data % batch_size != 0: print("= WARNING =") print(" n_data % batch_size != 0 but this code does not assume it") print(" so the residual {} sample(s) will be ignored.".format(n_data % batch_size)) while True: for batch_i in range(n_batch): if is_shuffle: batch_idxs = np.random.choice(n_data, batch_size, replace=False) else: batch_idxs = range(batch_i * batch_size, (batch_i + 1) * batch_size) src_batch = np.array([utils_preprocess.load_npy_fma(df_subset.index[i]) for i in batch_idxs], dtype=K.floatx()) src_batch = src_batch[:, np.newaxis, :] # make (batch, N) to (batch, 1, N) for kapre compatible y_batch = np.array([ys[i] for i in batch_idxs], dtype=K.floatx()) yield src_batch, y_batch def main(model_name, exp_name='fma'): """ DO it! """ assert model_name in ['multi_kernel', 'crnn', 'cnn3x3', 'cnn1d'] print("-" * 60) print("Keunwoo: Welcome! Lets do something deep with FMA dataset.") print(" I'm assuming you finished pre-processing.") print(" We're gonna use {} model".format(model_name)) csv_path = os.path.join(DIR_FMA_CSV, 'tracks.csv') tracks = pd.read_csv(csv_path, index_col=0, header=[0, 1]) small = tracks['set', 'subset'] == 'small' training = (tracks['set', 'split'] == 'training') & small validation = (tracks['set', 'split'] == 'validation') & small test = (tracks['set', 'split'] == 'test') & small print("Keunwoo: We're loading and modifying label values.") enc = LabelEncoder() y_train = enc.fit_transform(tracks[training]['track', 'genre_top']) y_valid = enc.transform(tracks[validation]['track', 'genre_top']) y_test = enc.transform(tracks[test]['track', 'genre_top']) y_train = np.eye(8)[y_train] y_valid = np.eye(8)[y_valid] y_test = np.eye(8)[y_test] print("It's a good practice to use callbacks in Keras.") callbacks = my_callbacks.get_callbacks(name=exp_name) early_stopper, model_saver, weight_saver, csv_logger = callbacks print("Preparing data generators for training and validation...") batch_size = 40 steps_per_epoch = len(y_train) // batch_size gen_train = data_gen(tracks[training], y_train, True, batch_size=batch_size) gen_valid = data_gen(tracks[validation], y_valid, False, batch_size=batch_size) print("Keunwoo: Getting model...") if model_name == 'multi_kernel': model = models_time_invariant.model_multi_kernel_shape(n_out=8) elif model_name == 'crnn': model = models_time_invariant.model_crnn_icassp2017_choi(n_out=8) elif model_name == 'cnn3x3': model = models_time_invariant.model_conv3x3_ismir2016_choi(n_out=8) elif model_name == 'cnn1d': model = models_time_invariant.model_conv1d_icassp2014_sander(n_out=8) model.compile('adam', 'categorical_crossentropy', metrics=['accuracy']) print("Keunwoo: Starting to train...") model.fit_generator(gen_train, steps_per_epoch, epochs=5, callbacks=callbacks, validation_data=gen_valid, validation_steps=len(y_valid) // batch_size) print("Keunwoo: Training is done. Loading the best weights...") model.load_weights("{}_best_weights.h5".format(exp_name)) gen_test = data_gen(tracks[test], y_test, False, batch_size=batch_size) print(" Evaluating...") scores = model.evaluate_generator(gen_test, len(y_test) // batch_size) print('Keunwoo: Done for {}!'.format(model_name)) print(" test set loss:{}".format(scores[0])) print(" test set accuracy: {}%".format([scores[1]])) if __name__ == '__main__': main('multi_kernel', 'fma_multi_kernel') main('crnn', 'fma_crnn') main('cnn3x3', 'fma_cnn3x3') main('cnn1d', 'fma_cnn1d') """ Keunwoo: Welcome! Lets do something deep with FMA dataset. I'm assuming you finished pre-processing. We're gonna use multi_kernel model Keunwoo: We're loading and modifying label values. It's a good practice to use callbacks in Keras. Preparing data generators for training and validation... Keunwoo: Getting model... Keunwoo: Starting to train... Epoch 1/5 160/160 [==============================] - 53s - loss: 1.7421 - acc: 0.3731 - val_loss: 2.0509 - val_acc: 0.2350 Epoch 2/5 160/160 [==============================] - 48s - loss: 1.5444 - acc: 0.4503 - val_loss: 1.8888 - val_acc: 0.2775 Epoch 3/5 160/160 [==============================] - 48s - loss: 1.4819 - acc: 0.4730 - val_loss: 1.6285 - val_acc: 0.4063 Epoch 4/5 160/160 [==============================] - 48s - loss: 1.4061 - acc: 0.5120 - val_loss: 1.6534 - val_acc: 0.3988 Epoch 5/5 160/160 [==============================] - 48s - loss: 1.3584 - acc: 0.5248 - val_loss: 1.5023 - val_acc: 0.4938 Keunwoo: Training is done. Loading the best weights... Evaluating... Keunwoo: Done for multi_kernel! test set loss:1.5535479486 test set accuracy: [0.47750000953674315]% ------------------------------------------------------------ Keunwoo: Welcome! Lets do something deep with FMA dataset. I'm assuming you finished pre-processing. We're gonna use crnn model Keunwoo: We're loading and modifying label values. It's a good practice to use callbacks in Keras. Preparing data generators for training and validation... Keunwoo: Getting model... Keunwoo: Starting to train... Epoch 1/5 160/160 [==============================] - 63s - loss: 1.8359 - acc: 0.2875 - val_loss: 2.2506 - val_acc: 0.1900 Epoch 2/5 160/160 [==============================] - 61s - loss: 1.6170 - acc: 0.4120 - val_loss: 2.3764 - val_acc: 0.2500 Epoch 3/5 160/160 [==============================] - 60s - loss: 1.5181 - acc: 0.4548 - val_loss: 1.8484 - val_acc: 0.3513 Epoch 4/5 160/160 [==============================] - 60s - loss: 1.4217 - acc: 0.4989 - val_loss: 1.5957 - val_acc: 0.4375 Epoch 5/5 160/160 [==============================] - 60s - loss: 1.3699 - acc: 0.5247 - val_loss: 1.5935 - val_acc: 0.4250 Keunwoo: Training is done. Loading the best weights... Evaluating... Keunwoo: Done for crnn! test set loss:1.67180262804 test set accuracy: [0.39250000640749932]% ------------------------------------------------------------ Keunwoo: Welcome! Lets do something deep with FMA dataset. I'm assuming you finished pre-processing. We're gonna use cnn3x3 model Keunwoo: We're loading and modifying label values. It's a good practice to use callbacks in Keras. Preparing data generators for training and validation... Keunwoo: Getting model... Keunwoo: Starting to train... Epoch 1/5 160/160 [==============================] - 30s - loss: 1.7715 - acc: 0.3659 - val_loss: 2.0864 - val_acc: 0.2013 Epoch 2/5 160/160 [==============================] - 29s - loss: 1.5308 - acc: 0.4581 - val_loss: 1.7897 - val_acc: 0.3275 Epoch 3/5 160/160 [==============================] - 29s - loss: 1.4375 - acc: 0.5031 - val_loss: 1.6883 - val_acc: 0.3975 Epoch 4/5 160/160 [==============================] - 29s - loss: 1.3595 - acc: 0.5261 - val_loss: 2.0865 - val_acc: 0.3238 Epoch 5/5 160/160 [==============================] - 29s - loss: 1.3319 - acc: 0.5344 - val_loss: 2.0630 - val_acc: 0.3263 Keunwoo: Training is done. Loading the best weights... Evaluating... Keunwoo: Done for cnn3x3! test set loss:1.86332789063 test set accuracy: [0.3225000061094761]% ------------------------------------------------------------ Keunwoo: Welcome! Lets do something deep with FMA dataset. I'm assuming you finished pre-processing. We're gonna use cnn1d model Keunwoo: We're loading and modifying label values. It's a good practice to use callbacks in Keras. Preparing data generators for training and validation... Keunwoo: Getting model... Keunwoo: Starting to train... Epoch 1/5 160/160 [==============================] - 31s - loss: 1.7900 - acc: 0.3372 - val_loss: 2.5954 - val_acc: 0.1300 Epoch 2/5 160/160 [==============================] - 29s - loss: 1.6272 - acc: 0.4186 - val_loss: 1.9440 - val_acc: 0.2625 Epoch 3/5 160/160 [==============================] - 28s - loss: 1.5502 - acc: 0.4498 - val_loss: 3.3067 - val_acc: 0.1913 Epoch 4/5 160/160 [==============================] - 28s - loss: 1.5200 - acc: 0.4548 - val_loss: 2.0652 - val_acc: 0.2288 Epoch 5/5 160/160 [==============================] - 28s - loss: 1.4641 - acc: 0.4764 - val_loss: 3.2042 - val_acc: 0.2100 Keunwoo: Training is done. Loading the best weights... Evaluating... Keunwoo: Done for cnn1d! test set loss:1.92252177596 test set accuracy: [0.23250000523403286]% """ ================================================ FILE: README.md ================================================ # dl4mir: A Tutorial on Deep Learning for MIR by [Keunwoo Choi](https://keunwoochoi.wordpress.com) (first.last@qmul.ac.uk) This is a repo for my tutorial paper; [A Tutorial on Deep Learning for Music Information Retrieval](https://arxiv.org/abs/1709.04396). ## Tutorials 1. [Example 1: Pitch detector with a dense layer](https://github.com/keunwoochoi/dl4mir/blob/master/Example%201%20-%20a%20pitch%20detection%20network%20with%20Dense%20layers.ipynb) 2. [Example 2: Chord recogniser with a convnet](https://github.com/keunwoochoi/dl4mir/blob/master/Example%202%20-%20a%20chord%20recognition%20network%20with%20Convolutional%20layers.ipynb) 3. [Example 3: Setup `config.json`](https://github.com/keunwoochoi/dl4mir/blob/master/Example_3.py) 4. [Example 4: download and preprocess](https://github.com/keunwoochoi/dl4mir/blob/master/Example_4.py) 5. Real examples with real datasets! * [Example 5-1: Time-varying classification example using Jamendo dataset](https://github.com/keunwoochoi/dl4mir/blob/master/Example_5-1.py) * [Example 5-2: Time-invariant classification example using FMA dataset](https://github.com/keunwoochoi/dl4mir/blob/master/Example_5-2.py) ## Prerequisites ``` $ pip install -r requirements.txt $ git clone https://github.com/keunwoochoi/kapre.git $ cd kapre $ python setup.py install ``` to install * Librosa, Keras, Numpy, Matplotlib, Future * kapre ## Notes * `Datasets` is removed from `Kapre` and the codes are directly imported into here. ### Datasets Dataset management * [GTZan](http://marsyasweb.appspot.com/download/data_sets/): (30s, 10 genres, 1,000 mp3) * [MagnaTagATune](http://mirg.city.ac.uk/codeapps/the-magnatagatune-dataset): (29s, 188 tags, 25,880 mp3) for tagging and triplet similarity * [MusicNet](https://homes.cs.washington.edu/~thickstn/musicnet.html): (full length 330 classicals music, note-wise annotations) * [FMA](https://github.com/mdeff/fma): small/medium/large/full collections, up to 100+K songs from free music archieve, for genre classification. With genre hierarchy, pre-computed features, splits, etc. * [Jamendo](http://www.mathieuramona.com/wp/data/jamendo/): 61/16/24 songs for vocal activity detection ## Some links * Repo * [DL_MIR_TUTORIAL](https://github.com/tuwien-musicir/DL_MIR_Tutorial): Another DL for MIR tutorial repo by Thomas Lidy * [Awesome deep learning music](https://github.com/ybayle/awesome-deep-learning-music): A long list of deep learning x music papers + etc. * Slides * [Deep Neural Networks in MIR](https://www.audiolabs-erlangen.de/resources/MIR/2017-GI-Tutorial-Musik/2017_MuellerWeissBalke_GI_DeepLearningMIR.pdf): A tutorial focusing on feature learning, beat/rhythm analysis, structure analysis. Also a nice literature overview including publications by year, conference, task, network types, input representations, frame work, etc. By Meinard Muller et al. * [DL in music informatics](http://steinhardt.nyu.edu/marl/research/deep_learning_in_music_informatics): ISMIR 2014 tutorial. * Documents, books * [Deep learning book](http://www.deeplearningbook.org): The first deep learning textbook. by Ian Goodfellow and Yoshua Bengio and Aaron Courville. * Online * [cs213n](http://cs231n.stanford.edu): Perhaps the best lecture on convnets. From Stanford university. * [librosa tutorial](https://librosa.github.io/librosa/tutorial.html): If you're interested in learning a bit more of MIR and its implementations. * [MIRDL: state-of-the-art](http://jordipons.me/wiki/index.php/MIRDL): A wikipedia on MIR and DL by Jordi Pons. * [MIR datasets](https://www.audiocontentanalysis.org/data-sets/): An awesome list of MIR datasets * [/r/musicir](https://www.reddit.com/r/musicir/): A subreddit for MIR ## Cite? ``` @article{choi2017tutorial, title={A Tutorial on Deep Learning for Music Information Retrieval}, author={Choi, Keunwoo and Fazekas, Gy{\"o}rgy and Cho, Kyunghyun and Sandler, Mark}, journal={arXiv:1709.04396}, year={2017} } ``` Or visit [the paper page on Google scholar](https://scholar.google.co.kr/citations?view_op=view_citation&hl=en&user=ZrqdSu4AAAAJ&sortby=pubdate&citation_for_view=ZrqdSu4AAAAJ:W5xh706n7nkC) for potential updates. ================================================ FILE: config.json ================================================ { "dir_fma_mp3" : "dataset_download/fma/fma_small", "dir_fma_csv": "dataset_download/fma/fma_metadata", "dir_fma_npy" : "dataset_ready/fma", "dir_jamendo_download" : "dataset_download/jamendo", "dir_jamendo_npy" : "dataset_ready/jamendo" } ================================================ FILE: datasets.py ================================================ # -*- coding: utf-8 -*- """ Datasets ======== """ from __future__ import absolute_import import utils_datasets import librosa import numpy as np import os def load_jamendo(save_path='datasets', sr=16000, mono=True, duration=None, offset=0.0): """Download jamendo http://www.mathieuramona.com/wp/data/jamendo/ It creates `save_path/jamendo` and the sub-directories, `jamendo_lab`, `train`, `valid`, `test`. It does not remove the downloaded `.tar.gz` files in `save_path/jamendo`. As it takes quite a while for decoding the audio files, it would be better to store the returned value as npy/hdf/whatever and use it. Parameters ---------- save_path: str, absolute/relative path to store the dataset sr: int > 0 sampling rate of audio sources. It is also used to compute label arrays mono: bool Whether downmix the audio signals to mono or not. duration: float [second] Duration of audio files offset: float [second] Offset to load Returns ------- srcs: list, length of 3. | each element is a list of train/valid/test sources | e.g. ``srcs[0][0].shape = (1, 3959745)`` when ``sr=16000`` and ``mono=True`` ys: list, length of 3. | each element is a list of train/valid/test groundtruths | e.g., ``ys[0][0].shape = (3959745, )`` """ set_names = ['train', 'valid', 'test'] for set in set_names: datadir = utils_datasets.get_file('jam_{}_audio.tar.gz'.format(set), 'http://www.mathieuramona.com/uploads/Main/jam_{}_audio.tar.gz'.format(set), save_path, untar=True, cache_subdir='jamendo', tar_folder_name=set) datadir = utils_datasets.get_file('jam_annote.tar.gz', 'http://www.mathieuramona.com/uploads/Main/jam_annote.tar.gz', save_path, untar=True, cache_subdir='jamendo', tar_folder_name='jamendo_lab') # load file names in train/valid/test folder x_filenames = [] y_filenames = [] for set in set_names: fnames = [f.lstrip('._') for f in os.listdir(os.path.join(save_path, 'jamendo', set)) \ if f.split('.')[-1] in ('ogg', 'mp3')] x_filenames.append(fnames) y_filenames.append([f.split('.')[0] + '.lab' for f in fnames]) srcs = [] ys = [] for set, x_fnames, y_fnames in zip(set_names, x_filenames, y_filenames): srcs_set = [] ys_set = [] for idx, (x_fname, y_fname) in enumerate(zip(x_fnames, y_fnames)): # process srcs print('Loading {}/{}: {}...'.format(idx, len(x_fnames), x_fname)) src, _ = librosa.load(os.path.join(save_path, 'jamendo', set, x_fname), sr=sr, mono=mono, offset=offset, duration=duration) if mono: src = src[np.newaxis, :] # to make it (1, N) instead of (N,) len_src = src.shape[1] srcs_set.append(src) # process ys y = np.zeros((len_src,), dtype=np.bool) with open(os.path.join(save_path, 'jamendo', 'jamendo_lab', y_fname)) as f_label: for line in f_label: start, end, label = line.rstrip('\n').split(' ') if label == 'sing': start, end = int(np.round(float(start) * sr)), int(np.round(float(end) * sr)) y[start:end] = True ys_set.append(y) srcs.append(srcs_set) ys.append(ys_set) # return return srcs, ys def load_fma(save_path='datasets', size='small'): """Download fma:free music archive (https://github.com/mdeff/fma) It would be better to download directly from the link for large/full.. Parameters ---------- save_path: str, absolute/relative path to store the dataset size: str, 'small', 'medium', 'large', 'huge' | small: 8,000 tracks of 30s, 8 balanced genres (GTZAN-like) (7.2 GiB) | medium: 25,000 tracks of 30s, 16 unbalanced genres (22 GiB) | large: 106,574 tracks of 30s, 161 unbalanced genres (93 GiB) | full: 106,574 untrimmed tracks, 161 unbalanced genres (879 GiB) """ assert size in ('small', 'medium', 'large', 'full') if size == 'small': zip_filename = 'fma_small.zip' zip_path = utils_datasets.get_file(zip_filename, 'https://os.unil.cloud.switch.ch/fma/fma_small.zip', save_path, untar=False, cache_subdir='fma', md5_hash='4edb51c99a19d31fe01a7d44d5cac19b') elif size == 'medium': zip_filename = 'fma_medium.zip' zip_path = utils_datasets.get_file(zip_filename, 'https://os.unil.cloud.switch.ch/fma/fma_medium.zip', save_path, untar=False, cache_subdir='fma') elif size == 'large': zip_filename = 'fma_large.zip' zip_path = utils_datasets.get_file(zip_filename, 'https://os.unil.cloud.switch.ch/fma/fma_large.zip', save_path, untar=False, cache_subdir='fma') elif size == 'full': zip_filename = 'fma_full.zip' zip_path = utils_datasets.get_file(zip_filename, 'https://os.unil.cloud.switch.ch/fma/fma_full.zip', save_path, untar=False, cache_subdir='fma') print("unzipping audio files...") os.system('unzip {} -d {}'.format(os.path.join(zip_path, zip_filename), zip_path)) metadata_zip_filename = 'fma_metadata.zip' metadata_zip_path = utils_datasets.get_file(metadata_zip_filename, 'https://os.unil.cloud.switch.ch/fma/fma_metadata.zip', save_path, untar=False, cache_subdir='fma', md5_hash='d3ebfd86e283345ee2366a5492495935') print("unzipping metadata files...") os.system('unzip {} -d {}'.format(os.path.join(metadata_zip_path, metadata_zip_filename), metadata_zip_path)) def load_musicnet(save_path='datasets', format='hdf'): """Download musicnet (https://homes.cs.washington.edu/~thickstn/start.html) Parameters ---------- save_path: str, absolute/relative path to store the dataset format: str, Data format to download. Either 'hdf' or 'npz' """ assert format in ('hdf', 'npz') if format == 'hdf': utils_datasets.get_file('musicnet.h5', 'https://homes.cs.washington.edu/~thickstn/media/musicnet.h5', save_path, untar=False, cache_subdir='musicnet', md5_hash='05103753391a8019029b29b790f7e1f7') else: utils_datasets.get_file('musicnet.npz', 'https://homes.cs.washington.edu/~thickstn/media/musicnet.npz', save_path, untar=False, cache_subdir='musicnet', md5_hash='9303e5338adefd3715c51997553fb45f') utils_datasets.get_file('musicnet_metadata.csv', 'https://homes.cs.washington.edu/~thickstn/media/musicnet_metadata.csv', save_path, untar=False, cache_subdir='musicnet', md5_hash=None) def load_magnatagatune(save_path='datasets'): """Download magnatagatune dataset, concate the zip files, unzip it, to `save_path`. Parameters ---------- save_path: absolute or relative path to store the dataset """ # 1GB for each zip_path = utils_datasets.get_file('mp3.zip.001', 'http://mi.soi.city.ac.uk/datasets/magnatagatune/mp3.zip.001', save_path, untar=False, cache_subdir='magnatagatune', md5_hash='179c91c8c2a6e9b3da3d4e69d306fd3b') utils_datasets.get_file('mp3.zip.002', 'http://mi.soi.city.ac.uk/datasets/magnatagatune/mp3.zip.002', save_path, untar=False, cache_subdir='magnatagatune', md5_hash='acf8265ff2e35c6ff22210e46457a824') utils_datasets.get_file('mp3.zip.003', 'http://mi.soi.city.ac.uk/datasets/magnatagatune/mp3.zip.003', save_path, untar=False, cache_subdir='magnatagatune', md5_hash='582dc649cabb8cd991f09e14b99349a5') print("appending zip files...") os.system('cat {}/mp3.zip.* > {}/mp3s.zip'.format(zip_path, zip_path)) print("unzipping...") os.system('unzip {} -d {}/mp3s'.format(os.path.join(zip_path, 'mp3s.zip'), zip_path)) # labels utils_datasets.get_file('clip_info_final.csv', 'http://mi.soi.city.ac.uk/datasets/magnatagatune/clip_info_final.csv', save_path, untar=False, cache_subdir='magnatagatune', md5_hash='03ef3cb8ddcfe53fdcdb8e0cda005be2') utils_datasets.get_file('annotations_final.csv', 'http://mi.soi.city.ac.uk/datasets/magnatagatune/annotations_final.csv', save_path, untar=False, cache_subdir='magnatagatune', md5_hash='f04fa01752a8cc64f6e1ca142a0fef1d') utils_datasets.get_file('comparisons_final.csv', 'http://mi.soi.city.ac.uk/datasets/magnatagatune/comparisons_final.csv', save_path, untar=False, cache_subdir='magnatagatune') # echonest feature (377.4 MB) utils_datasets.get_file('mp3_echonest_xml.zip', 'http://mi.soi.city.ac.uk/datasets/magnatagatune/mp3_echonest_xml.zip', save_path, untar=False, cache_subdir='magnatagatune', md5_hash='09be4ac8c682a8c182279276fadb37f9') def load_gtzan_speechmusic(save_path='datasets'): """ Download gtzan speech/music dataset, untar it, and create a helper csv file Arguments --------- save_path: str, Absolute or relative path to store the dataset """ datadir = utils_datasets.get_file('gtzan_speechmusic.tar.gz', 'http://opihi.cs.uvic.ca/sound/music_speech.tar.gz', save_path, untar=True, cache_subdir='gtzan_speechmusic', md5_hash='b063639094c169062940becacd3108a0') labels = ['music', 'speech'] rows = utils_datasets.get_rows_from_folders(folder_dataset='music_speech', folders=labels, dataroot=datadir) columns = ['id', 'filepath', 'label'] csv_path = os.path.join(datadir, 'dataset_summary_kapre.csv') utils_datasets.write_to_csv(rows=rows, column_names=columns, csv_fname=csv_path) def load_gtzan_genre(save_path='datasets'): """Load gtzan muusic dataset from http://opihi.cs.uvic.ca/sound/genres.tar.gz It downloads gtzan tarball on save_path/gtzan_music.tar.gz . After untarring, we got files as below: ``` for genre_name in ['blues', ..., 'rock']: for idx in xrange(100): "genres/{}/{}.{:05d}.au".format(genre_name, genre_name, idx) ``` Then it creates a helper csv file. Parameters ---------- save_path: str, Absolute or relative path to store the dataset """ datadir = utils_datasets.get_file('gtzan_genre.tar.gz', 'http://opihi.cs.uvic.ca/sound/genres.tar.gz', save_path, untar=True, cache_subdir='gtzan_genre', md5_hash='fe37942310e589be16b04b6d631790de') labels = ['blues', 'classical', 'country', 'disco', 'hiphop', 'jazz', 'metal', 'pop', 'reggae', 'rock'] rows = utils_datasets.get_rows_from_folders(folder_dataset='genres', folders=labels, dataroot=datadir) columns = ['id', 'filepath', 'label'] csv_path = os.path.join(datadir, 'dataset_summary_kapre.csv') utils_datasets.write_to_csv(rows=rows, column_names=columns, csv_fname=csv_path) ================================================ FILE: figures_in_paper/README.md ================================================ # Figures in the paper Drawing network structures is tedious, right? I used draw.io to draw these figures. You can just go to [the website](https://www.draw.io) and open the html files in this folder, maybe edit as you want. ================================================ FILE: figures_in_paper/tutorial_paper_autoencoder ================================================ 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================================================ FILE: figures_in_paper/tutorial_paper_layer_conv ================================================ 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================================================ FILE: figures_in_paper/tutorial_paper_ml_vs_dl.html ================================================ Draw.io Diagram
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================================================ FILE: figures_in_paper/tutorial_paper_t-varying_all_layers ================================================ 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 ================================================ FILE: figures_in_paper/tutorial_paper_t-varying_conv1 ================================================ 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 ================================================ FILE: figures_in_paper/tutorial_paper_time_inv_conv1d.html ================================================ Draw.io Diagram
================================================ FILE: figures_in_paper/tutorial_paper_time_inv_conv2d.html ================================================ Draw.io Diagram
================================================ FILE: figures_in_paper/tutorial_paper_time_inv_crnn ================================================ 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 ================================================ FILE: global_config.py ================================================ import json from keras import backend as K if K.image_data_format() == 'channels_first': print('Channel-first, i.e., (None, n_ch, n_freq, n_time)') channel_axis = 1 freq_axis = 2 time_axis = 3 else: print('Channel-last, i.e., (None, n_freq, n_time, n_ch)') channel_axis = 3 freq_axis = 1 time_axis = 2 # Constants SR = 16000 LEN_SRC = 10. NSP_SRC = int(SR * LEN_SRC) INPUT_SHAPE = (1, NSP_SRC) # Paths with open('config.json') as json_data: config = json.load(json_data) DIR_FMA_MP3 = config['dir_fma_mp3'] DIR_FMA_CSV = config['dir_fma_csv'] DIR_FMA_NPY = config['dir_fma_npy'] DIR_JAMENDO_DOWNLOAD = config['dir_jamendo_download'] DIR_JAMENDO_NPY = config['dir_jamendo_npy'] ================================================ FILE: main_preprocess.py ================================================ """ 16 May 2017, Keunwoo Choi (keunwoo.choi@qmul.ac.uk) """ from __future__ import print_function # (at top of module) import os import sys import pandas as pd import librosa import kapre import numpy as np import multiprocessing from global_config import * import utils_preprocess import datasets import pdb def load_decode_save_fma(track_id): """load, decode, and save tracks of FMA. Load/Save paths are set by `config.json`. track_id : integer. e.g. 2 """ tid_str = '{:06d}'.format(track_id) audio_path = os.path.join(DIR_FMA_MP3, tid_str[:3], tid_str + '.mp3') src, _ = librosa.load(audio_path, sr=SR, duration=LEN_SRC) src = src[:NSP_SRC] src = src.astype(np.float16) try: os.mkdir(os.path.join(DIR_FMA_NPY, tid_str[:3])) except: pass np.save(os.path.join(DIR_FMA_NPY, tid_str[:3], tid_str + '.npy'), src) print('Done: {}'.format(track_id)) def prep_fma_small(): """ Decode fma dataset and store them in numpy arrays. Small FMA, 16-bit Float, SR=16000, and 10s, => 2.5GB """ utils_preprocess.make_dir_recursively(DIR_FMA_NPY) # pre-process tracks = pd.read_csv(os.path.join(DIR_FMA_CSV, 'tracks.csv'), index_col=0, header=[0, 1]) small = tracks['set', 'subset'] == 'small' indices = tracks.loc[small].index # decoding p = multiprocessing.Pool() p.map(load_decode_save_fma, indices) def prep_jamendo(): """decode jamendo and .. """ utils_preprocess.make_dir_recursively(DIR_JAMENDO_NPY) utils_preprocess.make_dir_recursively(DIR_JAMENDO_DOWNLOAD) # pre-process duration = None # TEST srcs_sets, ys_sets = datasets.load_jamendo(save_path=DIR_JAMENDO_DOWNLOAD, duration=duration) sets = ['train', 'valid', 'test'] # decoding audio and label, and save them. for set_name, srcs, ys in zip(sets, srcs_sets, ys_sets): for i, (src, y) in enumerate(zip(srcs, ys)): fn_x = '{}_{:02}_x.npy'.format(set_name, i) fn_y = '{}_{:02}_y.npy'.format(set_name, i) np.save(os.path.join(DIR_JAMENDO_NPY, fn_x), np.array(src, dtype=np.float16)) np.save(os.path.join(DIR_JAMENDO_NPY, fn_y), np.array(y, dtype=np.float16)) def main(dataset_name): assert dataset_name in ['fma', 'jamendo'] if dataset_name == 'fma': prep_fma_small() elif dataset_name == 'jamendo': prep_jamendo() def print_usage(): print('This script decode audio of some predefined datasets and save in numpy files.') print('$ python main_preprocess.py $dataset_name$') print('Example:') print('$ python main_preprocess.py fma') print('$ python main_preprocess.py jamendo') print('') print('Ps. Make sure you downloaded the dataset and set the dirs/paths in config.json') if __name__ == '__main__': if len(sys.argv) < 2: print_usage() main(sys.argv[1]) ================================================ FILE: models_time_invariant.py ================================================ """ This module contains several time-invariant models. I'm assuming a raw-audio input, which is converted to melspectrogram using Kapre. """ from __future__ import print_function from keras.models import Sequential, Model from keras.layers import Activation, Dense, Flatten, Input, Reshape, Dropout, Permute from keras.layers.convolutional import Conv2D from keras.layers.normalization import BatchNormalization from keras.layers.recurrent import GRU from keras.layers.pooling import MaxPooling2D, GlobalAveragePooling2D from keras.layers.merge import Concatenate from keras import backend as K from kapre.time_frequency import Melspectrogram from global_config import * def model_multi_kernel_shape(n_out, input_shape=INPUT_SHAPE, out_activation='softmax'): """ Symbolic summary: > c2' - p2 - c2 - p2 - c2 - p2 - c2 - p3 - d1 where c2' -> multiple kernel shapes Parameters ---------- n_out: integer, number of output nodes input_shape: tuple, an input shape, which doesn't include batch-axis. out_activation: activation function on the output """ audio_input = Input(shape=input_shape) x = Melspectrogram(sr=SR, n_mels=64, power_melgram=2.0, return_decibel_melgram=True)(audio_input) x = BatchNormalization(axis=channel_axis)(x) x1 = Conv2D(7, (20, 3), padding='same')(x) x2 = Conv2D(7, (3, 3), padding='same')(x) x3 = Conv2D(7, (3, 20), padding='same')(x) x = Concatenate(axis=channel_axis)([x1, x2, x3]) x = BatchNormalization(axis=channel_axis)(x) x = Activation('relu')(x) x = MaxPooling2D((2, 2), padding='same')(x) x = Conv2D(21, (3, 3), padding='same')(x) x = BatchNormalization(axis=channel_axis)(x) x = Activation('relu')(x) x = MaxPooling2D((2, 2), padding='same')(x) x = Conv2D(21, (3, 3), padding='same')(x) x = BatchNormalization(axis=channel_axis)(x) x = Activation('relu')(x) x = MaxPooling2D((2, 2), padding='same')(x) x = Conv2D(21, (3, 3), padding='same')(x) x = BatchNormalization(axis=channel_axis)(x) x = Activation('relu')(x) x = MaxPooling2D((4, 4), padding='same')(x) x = GlobalAveragePooling2D()(x) out = Dense(n_out, activation=out_activation)(x) model = Model(audio_input, out) return model def model_crnn_icassp2017_choi(n_out, input_shape=INPUT_SHAPE, out_activation='softmax'): """A simplified model of Convolutional Recurrent Neural Networks for Music Classification, K Choi, G Fazekas, M Sandler, K Choi, ICASSP, 2017, New Orleans, USA Symbolic summary: > c2 - p2 - c2 - p2 - c2 - p2 - c2 - p2 - r1 - r2 - d1 Parameters ---------- n_out: integer, number of output nodes input_shape: tuple, an input shape, which doesn't include batch-axis. out_activation: activation function on the output """ audio_input = Input(shape=input_shape) x = Melspectrogram(sr=SR, n_mels=64, power_melgram=2.0, return_decibel_melgram=True)(audio_input) x = BatchNormalization(axis=channel_axis)(x) x = Conv2D(21, (3, 3), padding='same')(x) x = BatchNormalization(axis=channel_axis)(x) x = Activation('relu')(x) x = MaxPooling2D((2, 2), padding='same')(x) x = Conv2D(21, (3, 3), padding='same')(x) x = BatchNormalization(axis=channel_axis)(x) x = Activation('relu')(x) x = MaxPooling2D((2, 2), padding='same')(x) x = Conv2D(21, (3, 3), padding='same')(x) x = BatchNormalization(axis=channel_axis)(x) x = Activation('relu')(x) x = MaxPooling2D((2, 2), padding='same')(x) x = Conv2D(21, (3, 3), padding='same')(x) x = BatchNormalization(axis=channel_axis)(x) x = Activation('relu')(x) x = MaxPooling2D((4, 4), padding='same')(x) if K.image_dim_ordering() == 'channels_first': x = Permute((3, 1, 2))(x) x = Reshape((-1, 21))(x) # GRU block 1, 2, output x = GRU(41, return_sequences=True, name='gru1')(x) x = GRU(41, return_sequences=False, name='gru2')(x) x = Dropout(0.3)(x) out = Dense(n_out, activation=out_activation)(x) model = Model(audio_input, out) return model def model_conv3x3_ismir2016_choi(n_out, input_shape=INPUT_SHAPE, out_activation='softmax'): """ A simplified model of Automatic Tagging Using Deep Convolutional Neural Networks, K Choi, G Fazekas, M Sandler, ISMIR, 2016, New York, USA Symbolic summary: > c2 - p2 - c2 - p2 - c2 - p2 - c2 - p2 - c2 - p3 - d1 Modifications: * n_mels (96 -> 32) * n_channels (many -> [16, 24, 32, 40, 48]) * remove dropout * maxpooling (irregular to fit the size -> all (2, 2)) * add GlobalAveragePooling2D """ model = Sequential() model.add(Melspectrogram(sr=SR, n_mels=64, power_melgram=2.0, return_decibel_melgram=True, input_shape=input_shape)) model.add(BatchNormalization(axis=channel_axis)) model.add(Conv2D(10, (3, 3), padding='same')) model.add(BatchNormalization(axis=channel_axis)) model.add(Activation('relu')) model.add(MaxPooling2D((2, 2), padding='same')) model.add(Conv2D(15, (3, 3), padding='same')) model.add(BatchNormalization(axis=channel_axis)) model.add(Activation('relu')) model.add(MaxPooling2D((2, 2), padding='same')) model.add(Conv2D(15, (3, 3), padding='same')) model.add(BatchNormalization(axis=channel_axis)) model.add(Activation('relu')) model.add(MaxPooling2D((2, 2), padding='same')) model.add(Conv2D(20, (3, 3), padding='same')) model.add(BatchNormalization(axis=channel_axis)) model.add(Activation('relu')) model.add(MaxPooling2D((2, 2), padding='same')) model.add(Conv2D(20, (3, 3), padding='same')) model.add(BatchNormalization(axis=channel_axis)) model.add(Activation('relu')) model.add(MaxPooling2D((2, 2), padding='same')) model.add(GlobalAveragePooling2D()) model.add(Dense(n_out, activation=out_activation)) return model def model_conv1d_icassp2014_sander(n_out, input_shape=INPUT_SHAPE, out_activation='softmax'): """A simplified model of End-to-end learning for music audio, Sander Dieleman and Benjamin Schrauwen, ICASSP, 2014 Symbolic summary: > c1 - p1 - c1 - p1 - c1 - p1 - p3 - d1 Modifications: * Add BatchNormalization * n_mels (128 -> 32) * n_layers (2 -> 3) * add GlobalAveragePooling2D Parameters ---------- n_out: integer, number of output nodes input_shape: tuple, an input shape, which doesn't include batch-axis. out_activation: activation function on the output """ model = Sequential() model.add(Melspectrogram(sr=SR, n_mels=64, power_melgram=2.0, return_decibel_melgram=True, input_shape=input_shape)) model.add(Conv2D(30, (32, 4), padding='valid')) # (None, 16, 1, N) model.add(BatchNormalization(axis=channel_axis)) model.add(Activation('relu')) model.add(MaxPooling2D((1, 4), padding='same')) model.add(Conv2D(30, (1, 4), padding='same')) model.add(BatchNormalization(axis=channel_axis)) model.add(Activation('relu')) model.add(MaxPooling2D((1, 4), padding='same')) model.add(Conv2D(30, (1, 4), padding='same')) model.add(BatchNormalization(axis=channel_axis)) model.add(Activation('relu')) model.add(MaxPooling2D((1, 4), padding='same')) model.add(GlobalAveragePooling2D()) model.add(Dense(n_out, activation=out_activation)) return model if __name__ == "__main__": model = model_multi_kernel_shape(8) model.summary() ================================================ FILE: models_time_varying.py ================================================ """ This module contains several time-varying models. I'm assuming a raw-audio input, which is converted to melspectrogram using Kapre. """ from __future__ import print_function from keras.models import Sequential, Model from keras.layers import Activation, Dense, Input, Reshape, Permute, Lambda from keras.layers.convolutional import Conv2D from keras.layers.normalization import BatchNormalization from keras.layers.recurrent import LSTM from keras.layers.wrappers import Bidirectional, TimeDistributed from keras.layers.pooling import MaxPooling2D, GlobalAveragePooling2D from keras.layers.merge import Concatenate from keras import backend as K from kapre.time_frequency import Melspectrogram from global_config import * def model_convrnn(n_out, input_shape=(1, None), out_activation='softmax'): """No reference, just ConvRNN. Symbolic summary: > c2 - c2 - c2 - c2 - r2 - r2 - d1 Parameters ---------- n_out: integer, number of output nodes input_shape: tuple, an input shape, which doesn't include batch-axis. (1, None) means (mono channel, variable length). out_activation: activation function on the output """ assert input_shape[0] == 1, 'Mono input please!' model = Sequential() n_mels = 40 model.add(Melspectrogram(sr=SR, n_mels=n_mels, power_melgram=2.0, return_decibel_melgram=True, input_shape=input_shape)) model.add(Conv2D(32, (3, 3), padding='same')) model.add(BatchNormalization(axis=channel_axis)) model.add(Activation('relu')) model.add(Conv2D(32, (3, 3), padding='same')) model.add(BatchNormalization(axis=channel_axis)) model.add(Activation('relu')) model.add(Conv2D(16, (3, 3), padding='same')) model.add(BatchNormalization(axis=channel_axis)) model.add(Activation('relu')) model.add(Conv2D(1, (1, 1), padding='same')) model.add(BatchNormalization(axis=channel_axis)) model.add(Activation('relu')) if K.image_data_format() == 'channels_first': # (ch, freq, time) model.add(Permute((3, 2, 1))) # (time, freq, ch) else: # (freq, time, ch) model.add(Permute((2, 1, 3))) # (time, ch, freq) # model.add(Reshape((-1, n_mels * n_ch))) # (time, ch * freq) # Reshape for LSTM model.add(Lambda(lambda x: K.squeeze(x, axis=3), output_shape=squeeze_output_shape)) model.add(LSTM(25, return_sequences=True)) model.add(LSTM(25, return_sequences=True)) model.add(TimeDistributed(Dense(n_out, activation=out_activation))) return model def model_lstm_leglaive_icassp2014(n_out, input_shape=(1, None), out_activation='softmax', bidirectional=True): """Singing voice detection with deep recurrent neural networks Simon Leglaive, Romain Hennequin, Roland Badeau, ICASSP 2015 Symbolic summary: > bi_r1 - bi_r1 - bi_r1 - > r1 - r1 - r1 - d1 Parameters ---------- n_out: integer, number of output nodes input_shape: tuple, an input shape, which doesn't include batch-axis. out_activation: activation function on the output bidirectional: boolean, to specify whether rnn is bidirectional or not. """ assert input_shape[0] == 1, 'Mono input please!' model = Sequential() model.add(Melspectrogram(sr=SR, n_mels=40, power_melgram=2.0, return_decibel_melgram=True, input_shape=input_shape)) model.add(BatchNormalization(axis=channel_axis)) if K.image_data_format() == 'channels_first': model.add(Permute((3, 2, 1))) # ch, freq, time -> time, freq, ch else: model.add(Permute((2, 1, 3))) # freq, time, ch -> time, freq, ch # Reshape for LSTM model.add(Lambda(lambda x: K.squeeze(x, axis=3), output_shape=squeeze_output_shape)) if bidirectional: # Use Bidirectional LSTM model.add(Bidirectional(LSTM(30, return_sequences=True))) model.add(Bidirectional(LSTM(20, return_sequences=True))) model.add(Bidirectional(LSTM(40, return_sequences=True))) else: # Use normal LSTM model.add(LSTM(30 * 2, return_sequences=True)) model.add(LSTM(20 * 2, return_sequences=True)) model.add(LSTM(40 * 2, return_sequences=True)) model.add(TimeDistributed(Dense(n_out, activation=out_activation))) return model def squeeze_output_shape(input_shape): return input_shape[:3] if __name__ == '__main__': model = model_lstm_leglaive_icassp2014(2) model.summary() model = model_lstm_leglaive_icassp2014(2, bidirectional=False) model.summary() model = model_convrnn(2, input_shape=INPUT_SHAPE) model.summary() model = model_convrnn(2, input_shape=(1, None)) model.summary() ================================================ FILE: my_callbacks.py ================================================ import keras def get_callbacks(name): early_stopper = keras.callbacks.EarlyStopping(patience=5) model_saver = keras.callbacks.ModelCheckpoint("{}_best_model.h5".format(name), save_best_only=True) weight_saver = keras.callbacks.ModelCheckpoint("{}_best_weights.h5".format(name), save_best_only=True, save_weights_only=True) csv_logger = keras.callbacks.CSVLogger("{}.log".format(name)) return [early_stopper, model_saver, weight_saver, csv_logger] ================================================ FILE: requirements.txt ================================================ keras==2.13.1 librosa==0.5 numpy future matplotlib ================================================ FILE: utils_datasets.py ================================================ """data downloader. Keunwoo Choi, 14 Mar 2017. Copied from keras.utils.data_utils and modify path/api a little. 99.999% credit to Keras. """ from __future__ import absolute_import from __future__ import print_function import functools import tarfile import os import sys import shutil # import hashlib from six.moves.urllib.request import urlopen from six.moves.urllib.error import URLError from six.moves.urllib.error import HTTPError from keras.utils.generic_utils import Progbar from keras.utils.data_utils import validate_file import pandas as pd allowed_exts = set(['mp3', 'wav', 'au', 'm4a']) if sys.version_info[0] == 2: def urlretrieve(url, filename, reporthook=None, data=None): """Replacement for `urlretrive` for Python 2. Under Python 2, `urlretrieve` relies on `FancyURLopener` from legacy `urllib` module, known to have issues with proxy management. # Arguments url: url to retrieve. filename: where to store the retrieved data locally. reporthook: a hook function that will be called once on establishment of the network connection and once after each block read thereafter. The hook will be passed three arguments; a count of blocks transferred so far, a block size in bytes, and the total size of the file. data: `data` argument passed to `urlopen`. """ def chunk_read(response, chunk_size=8192, reporthook=None): total_size = response.info().get('Content-Length').strip() total_size = int(total_size) count = 0 while 1: chunk = response.read(chunk_size) count += 1 if not chunk: reporthook(count, total_size, total_size) break if reporthook: reporthook(count, chunk_size, total_size) yield chunk response = urlopen(url, data) with open(filename, 'wb') as fd: for chunk in chunk_read(response, reporthook=reporthook): fd.write(chunk) else: from six.moves.urllib.request import urlretrieve def get_file(fname, origin, save_path, untar=False, md5_hash=None, cache_subdir='datasets', tar_folder_name=None): """Downloads a file from a URL if it not already in the cache. Passing the MD5 hash will verify the file after download as well as if it is already present in the cache. Usually it downloads the file to save_path/cache_dubdir/fname Arguments --------- fname: name of the file origin: original URL of the file save_path: path to create cache_subdir. untar: boolean, whether the file should be decompressed md5_hash: MD5 hash of the file for verification cache_subdir: directory being used as the cache tar_folder_name: string, if inside of abc.tar.gz is not abc but def, pass def here. Returns ------- Path to the downloaded file """ datadir_base = save_path if not os.access(datadir_base, os.W_OK): datadir_base = os.path.expanduser(os.path.join('~', '.kapre')) print('Given path {} is not accessible. Trying to use~/.kapre instead..') if not os.access(datadir_base, os.W_OK): print('~/.kapre is not accessible, using /tmp/kapre instead.') datadir_base = os.path.join('/tmp', '.kapre') datadir = os.path.join(datadir_base, cache_subdir) if not os.path.exists(datadir): os.makedirs(datadir) if untar: assert fname.endswith('.tar.gz'), fname fpath = os.path.join(datadir, fname) if tar_folder_name: untar_fpath = os.path.join(datadir, tar_folder_name) else: untar_fpath = fpath.rstrip('.tar.gz') else: fpath = os.path.join(datadir, fname) download = False if os.path.exists(fpath): # File found; verify integrity if a hash was provided. if md5_hash is not None: if not validate_file(fpath, md5_hash): print('A local file was found, just checked md5 hash, but it might be ' 'incomplete or outdated') download = True else: download = True if download: print('Downloading data from', origin) progbar = None def dl_progress(count, block_size, total_size, progbar=None): if progbar is None: progbar = Progbar(total_size) else: progbar.update(count * block_size) error_msg = 'URL fetch failure on {}: {} -- {}' try: try: urlretrieve(origin, fpath, functools.partial(dl_progress, progbar=progbar)) except URLError as e: raise Exception(error_msg.format(origin, e.errno, e.reason)) except HTTPError as e: raise Exception(error_msg.format(origin, e.code, e.msg)) except (Exception, KeyboardInterrupt) as e: if os.path.exists(fpath): os.remove(fpath) raise progbar = None if untar: if not os.path.exists(untar_fpath): print('Untaring file...') tfile = tarfile.open(fpath, 'r:gz') try: tfile.extractall(path=datadir) except (Exception, KeyboardInterrupt) as e: if os.path.exists(untar_fpath): if os.path.isfile(untar_fpath): os.remove(untar_fpath) else: shutil.rmtree(untar_fpath) raise tfile.close() # return untar_fpath return datadir def write_to_csv(rows, column_names, csv_fname): '''write a csv file using given rows, columns, and filename Arguments --------- rows: list of rows (= which are lists.) column_names: names for columns csv_fname: string, csv file name Returns ------- None ''' df = pd.DataFrame(rows, columns=column_names) df.to_csv(csv_fname) def get_rows_from_folders(folder_dataset, folders, dataroot=''): '''Utility function to create a csv file for a dataset. For gtzan music, gtzan speech, ballroom extended, jamendo, where each class in different folders. Arguments --------- folder_dataset: folder name in the dataset folders: name of subfolders. Usually same as class names. dataroot: base path of the dataset. Default is '' in case if you just wanna specify the whole path in folder_dataset. Returns ------- list of lists, [file_id, file_path, file_label] where file_id: just file names file_path: file paths of each file (without data_root for more universal usage) file_label: integer, 0 to N-1, according to the order in `folders`. Example ------- folders = ['blues', 'classical', 'country', 'disco', 'hiphop', 'jazz', 'metal', 'pop', 'reggae', 'rock'] rows = get_rows_from_folders('gtzan_music', folders, '/usr/keunwoo/datasets') ''' rows = [] for label_idx, folder in enumerate(folders): # assumes different labels per folders. files = os.listdir(os.path.join(dataroot, folder_dataset, folder)) files = [f for f in files if f.split('.')[-1].lower() in allowed_exts] for fname in files: file_path = os.path.join(folder_dataset, folder, fname) file_id = os.path.splitext(fname)[0] file_label = label_idx rows.append([file_id, file_path, file_label]) return rows ================================================ FILE: utils_preprocess.py ================================================ import numpy as np import os from global_config import * def load_npy_fma(track_id): tid_str = '{:06d}'.format(track_id) src = np.load(os.path.join(DIR_FMA_NPY, tid_str[:3], tid_str + '.npy')) if len(src) <= NSP_SRC: src = np.concatenate((np.zeros(NSP_SRC - len(src)), src)) return src def make_dir_recursively(path): # recursively create folder to save dirs = path.split('/') npy_path_sub = '' for dr in dirs: npy_path_sub = os.path.join(npy_path_sub, dr) try: os.mkdir(npy_path_sub) except: pass