├── LICENSE ├── README.md ├── example_melgram.py ├── example_stft.py ├── melgram.ipynb ├── melgram.py ├── src └── bensound-cute.mp3 ├── stft.ipynb └── stft.py /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2016 Keunwoo Choi 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Use kapre instead of this 2 | [**kapre**](https://github.com/keunwoochoi/kapre) includes faster STFT/Melspectrogram with multi-channel supports as well as some other stuffs, works with both `tensorflow` and `theano`. For all cases *kapre* is better. 3 | 4 | #### keras_STFT_layer 5 | Do STFT & friends in Keras! For less hassle pre-processing 6 | * [x] STFT 7 | * [x] melgram 8 | 9 | #### why 10 | Because I am planning to compare the performance of some Convnets while changing parameters of STFT and storing all of them doesn't seem to make sense. 11 | 12 | #### how 13 | 14 | **Theano** backend only. `image_dim_ordering()` doesn't matter. 15 | 16 | 17 | ```python 18 | from stft import Spectrogram 19 | import keras 20 | 21 | len_src = 12000 * 8 # 8-second signal is your input 22 | model = keras.Sequential() 23 | specgram = Spectrogram(n_dft=512, n_hop=128, input_shape=(len_src, 1)) 24 | model.add(specgram) 25 | model.add(BatchNormalization(axis=time_axis)) # recommended 26 | model.add(your_awesome_network) 27 | ... 28 | 29 | ``` 30 | 31 | #### More info 32 | * [Jypyter notebook (STFT)](https://github.com/keunwoochoi/keras_STFT_layer/blob/master/stft.ipynb) 33 | * [Jypyter notebook (STFT)](https://github.com/keunwoochoi/keras_STFT_layer/blob/master/melgram.ipynb) 34 | 35 | #### Credits 36 | 37 | I relied on [Librosa codes](http://librosa.github.io). 38 | -------------------------------------------------------------------------------- /example_melgram.py: -------------------------------------------------------------------------------- 1 | import time 2 | import melgram 3 | import numpy as np 4 | import librosa 5 | 6 | 7 | len_src = 12000 * 8 8 | melgram = melgram.Melspectrogram(n_dft=512, 9 | input_shape=(len_src, 1), 10 | trainable=False, 11 | sr=12000) 12 | 13 | src, sr = librosa.load('src/bensound-cute.mp3', sr=12000, duration=8.0) 14 | src = src[:len_src] 15 | src = src[:, np.newaxis] 16 | srcs = np.zeros((16, len_src, 1)) 17 | 18 | for ind in range(srcs.shape[0]): 19 | srcs[ind] = src 20 | 21 | print('Source shape: ', srcs.shape) 22 | start = time.time() 23 | outputs = melgram.predict(srcs) 24 | print("Prediction is done. It took %5.3f seconds." % (time.time()-start)) 25 | print('Computed melgram shape: ', outputs.shape) 26 | np.save('melgram_outputs.npy', outputs) 27 | 28 | -------------------------------------------------------------------------------- /example_stft.py: -------------------------------------------------------------------------------- 1 | '''Example for keras stft layer''' 2 | import time 3 | import stft 4 | import numpy as np 5 | import librosa 6 | 7 | 8 | len_src = 12000*10 9 | 10 | src, sr = librosa.load('src/bensound-cute.mp3', sr=12000, duration=10.0) 11 | src = src[:len_src] 12 | src = src[:, np.newaxis] 13 | src = np.hstack((src, src)) 14 | print(src.shape) 15 | srcs = np.zeros((16, ) + src.shape) 16 | 17 | for ind in range(srcs.shape[0]): 18 | srcs[ind] = src 19 | 20 | specgram = stft.Spectrogram(n_dft=512, input_shape=src.shape, n_hop=256) 21 | print('Source shape: ', srcs.shape) 22 | start = time.time() 23 | outputs = specgram.predict(srcs) 24 | print("Prediction is done. It took %5.3f seconds." % (time.time()-start)) 25 | print('Computed STFT shape: ', outputs.shape) 26 | np.save('stft_outputs.npy', outputs) 27 | -------------------------------------------------------------------------------- /melgram.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Melgram layer test code" 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": 16, 13 | "metadata": { 14 | "collapsed": false 15 | }, 16 | "outputs": [ 17 | { 18 | "name": "stdout", 19 | "output_type": "stream", 20 | "text": [ 21 | "1.1.1\n", 22 | "theano tf\n" 23 | ] 24 | } 25 | ], 26 | "source": [ 27 | "import matplotlib\n", 28 | "%matplotlib inline\n", 29 | "import matplotlib.pyplot as plt\n", 30 | "plt.style.use('ggplot')\n", 31 | "import numpy as np\n", 32 | "import melgram\n", 33 | "import librosa\n", 34 | "import keras\n", 35 | "print keras.__version__\n", 36 | "print keras.backend._BACKEND, keras.backend.image_dim_ordering()\n" 37 | ] 38 | }, 39 | { 40 | "cell_type": "markdown", 41 | "metadata": {}, 42 | "source": [ 43 | "## Load audio signal" 44 | ] 45 | }, 46 | { 47 | "cell_type": "code", 48 | "execution_count": 17, 49 | "metadata": { 50 | "collapsed": false 51 | }, 52 | "outputs": [ 53 | { 54 | "name": "stderr", 55 | "output_type": "stream", 56 | "text": [ 57 | "/Users/gnu/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:6: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n" 58 | ] 59 | } 60 | ], 61 | "source": [ 62 | "SR = 12000 # sampling rate\n", 63 | "duration = 8.0\n", 64 | "len_src = int(SR * duration)\n", 65 | "\n", 66 | "src, sr = librosa.load('src/bensound-cute.mp3', sr=12000, duration=8.0) # whole signal \n", 67 | "src = src[:duration*SR]\n", 68 | "\n", 69 | "n_fft = 512\n", 70 | "n_dft = n_fft\n", 71 | "n_hop = 256\n", 72 | "n_mels = 96" 73 | ] 74 | }, 75 | { 76 | "cell_type": "markdown", 77 | "metadata": {}, 78 | "source": [ 79 | "## Compute melgram using librosa" 80 | ] 81 | }, 82 | { 83 | "cell_type": "code", 84 | "execution_count": 18, 85 | "metadata": { 86 | "collapsed": false 87 | }, 88 | "outputs": [ 89 | { 90 | "name": "stdout", 91 | "output_type": "stream", 92 | "text": [ 93 | "(n_mel, n_time) = (96, 376)\n", 94 | "Librosa Melgram dynamic range is [-80.00, 0.00]\n" 95 | ] 96 | } 97 | ], 98 | "source": [ 99 | "D = librosa.logamplitude(librosa.feature.melspectrogram(src, \n", 100 | " n_fft=n_fft, \n", 101 | " hop_length=n_hop,\n", 102 | " n_mels=n_mels,\n", 103 | " fmax=6000,\n", 104 | " sr=SR), \n", 105 | " ref_power=np.max)\n", 106 | "print '(n_mel, n_time) = ', D.shape\n", 107 | "print 'Librosa Melgram dynamic range is [%4.2f, %4.2f]' % (np.min(D), np.max(D))" 108 | ] 109 | }, 110 | { 111 | "cell_type": "markdown", 112 | "metadata": {}, 113 | "source": [ 114 | "## Compute melgram using keras-melspectrogram" 115 | ] 116 | }, 117 | { 118 | "cell_type": "code", 119 | "execution_count": 7, 120 | "metadata": { 121 | "collapsed": false, 122 | "scrolled": false 123 | }, 124 | "outputs": [ 125 | { 126 | "name": "stdout", 127 | "output_type": "stream", 128 | "text": [ 129 | "Input shape for keras-melgram (1, 96000, 1)\n", 130 | "Output shape: (n_data_sample, n_mel, n_time, n_channel) (1, 96, 375, 1)\n", 131 | " if it was \"th\", (n_data_sample, n_channel, n_mel, n_time)\n", 132 | "Keras-melgram dynanic range is [-80.00, 0.00]\n" 133 | ] 134 | } 135 | ], 136 | "source": [ 137 | "src_input = src[np.newaxis, :, np.newaxis]\n", 138 | "print 'Input shape for keras-melgram', src_input.shape\n", 139 | "\n", 140 | "melgram_model = melgram.Melspectrogram(n_dft=n_fft, \n", 141 | " input_shape=(len_src, 1), \n", 142 | " trainable=False,\n", 143 | " n_hop=n_hop, \n", 144 | " sr=SR,\n", 145 | " n_mels=n_mels,\n", 146 | " fmax=6000)\n", 147 | "output = melgram_model.predict(src_input, batch_size=2)\n", 148 | "if keras.backend.image_dim_ordering() == 'th':\n", 149 | " print 'Output shape: (n_data_sample, n_mel, n_time, n_channel)', output.shape\n", 150 | " print ' if it was \"tf\", (n_data_sample, n_mel, n_time, n_channel)',\n", 151 | "elif keras.backend.image_dim_ordering() == 'tf':\n", 152 | " print 'Output shape: (n_data_sample, n_mel, n_time, n_channel)', output.shape\n", 153 | " print ' if it was \"th\", (n_data_sample, n_channel, n_mel, n_time)\\n', \n", 154 | "\n", 155 | "print 'Keras-melgram dynanic range is [%4.2f, %4.2f]' % (np.min(output), np.max(output))" 156 | ] 157 | }, 158 | { 159 | "cell_type": "markdown", 160 | "metadata": {}, 161 | "source": [ 162 | "## Plot them to compare" 163 | ] 164 | }, 165 | { 166 | "cell_type": "code", 167 | "execution_count": 13, 168 | "metadata": { 169 | "collapsed": false, 170 | "scrolled": false 171 | }, 172 | "outputs": [ 173 | { 174 | "name": "stdout", 175 | "output_type": "stream", 176 | "text": [ 177 | "(96, 376)\n", 178 | "(1, 96, 375, 1)\n" 179 | ] 180 | }, 181 | { 182 | "data": { 183 | "image/png": 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cuG1v+xWm9ZrDekpyJKcoWbRo0aRTpEciEW655ZaDrn/ttddOuvwrX/nKYZft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/uy/w8mrGqNTiPhyOOx+PpwB2oL0Loj3bkdifak4EmHXKUEqZlMBKW0HRiNxbOTQOab2SXx69On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EpgtZggrn0YC6+5ScExsNOc6qNZnDZlmAkvO3aaJWViTmeXAgnL3lg07BsOVznRCsAKMwi+na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\nnXPQ7lD1jhBSgiUJHl7ltb//Mjf2h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184 | "text/plain": [ 185 | "" 186 | ] 187 | }, 188 | "metadata": {}, 189 | "output_type": "display_data" 190 | } 191 | ], 192 | "source": [ 193 | "plt.figure(figsize=(12, 6))\n", 194 | "plt.subplot(1,2,1)\n", 195 | "if keras.backend.image_dim_ordering() == 'th':\n", 196 | " librosa.display.specshow(output[0, 0, :, :], y_axis='linear', sr=SR, fmax=3000, hop_length=n_hop)\n", 197 | "else:\n", 198 | " librosa.display.specshow(output[0, :, :, 0], y_axis='linear', sr=SR, fmax=3000, hop_length=n_hop)\n", 199 | "plt.colorbar(format='%+2.0f dB')\n", 200 | "plt.title('keras melgram layer')\n", 201 | "plt.subplot(1,2,2)\n", 202 | "librosa.display.specshow(D, y_axis='linear', sr=SR, fmax=3000, hop_length=n_hop)\n", 203 | "plt.colorbar(format='%+2.0f dB')\n", 204 | "plt.title('librosa melgram')\n", 205 | "\n", 206 | "print D.shape\n", 207 | "print output.shape" 208 | ] 209 | }, 210 | { 211 | "cell_type": "markdown", 212 | "metadata": {}, 213 | "source": [ 214 | "## Compare the average (over time) energy patterns" 215 | ] 216 | }, 217 | { 218 | "cell_type": "code", 219 | "execution_count": 15, 220 | "metadata": { 221 | "collapsed": false 222 | }, 223 | "outputs": [ 224 | { 225 | "data": { 226 | "text/plain": [ 227 | "" 228 | ] 229 | }, 230 | "execution_count": 15, 231 | "metadata": {}, 232 | "output_type": "execute_result" 233 | }, 234 | { 235 | "data": { 236 | "image/png": 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nn3L06FFSU1MxGAw4Ojo+MtZ/PjD8c5ySkvLQ63wWSCKdB2lpoSQkrEBR9KjV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\nwv369aN79+7s3LnzrPoKDQ3lyiuvxGw2M3LkSDp27MiOHTs8nhsVFcW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237 | "text/plain": [ 238 | "" 239 | ] 240 | }, 241 | "metadata": {}, 242 | "output_type": "display_data" 243 | } 244 | ], 245 | "source": [ 246 | "plt.figure(figsize=(12, 3))\n", 247 | "if keras.backend.image_dim_ordering() == 'th':\n", 248 | " plt.plot(np.mean(output[0, 0, :, :], axis=1), 'y', linewidth=2, label='keras melgram layer')\n", 249 | "else:\n", 250 | " plt.plot(np.mean(output[0, :, :, 0], axis=1), 'y', linewidth=2, label='keras melgram layer')\n", 251 | "plt.plot(np.mean(D, axis=1), 'g--', linewidth=2, label='librosa melgram')\n", 252 | "plt.xlabel('mel_bin')\n", 253 | "plt.title('Average energy')\n", 254 | "plt.legend()" 255 | ] 256 | }, 257 | { 258 | "cell_type": "code", 259 | "execution_count": null, 260 | "metadata": { 261 | "collapsed": true 262 | }, 263 | "outputs": [], 264 | "source": [] 265 | } 266 | ], 267 | "metadata": { 268 | "kernelspec": { 269 | "display_name": "Python 2", 270 | "language": "python", 271 | "name": "python2" 272 | }, 273 | "language_info": { 274 | "codemirror_mode": { 275 | "name": "ipython", 276 | "version": 2 277 | }, 278 | "file_extension": ".py", 279 | "mimetype": "text/x-python", 280 | "name": "python", 281 | "nbconvert_exporter": "python", 282 | "pygments_lexer": "ipython2", 283 | "version": "2.7.11" 284 | } 285 | }, 286 | "nbformat": 4, 287 | "nbformat_minor": 0 288 | } 289 | -------------------------------------------------------------------------------- /melgram.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | from __future__ import absolute_import 3 | from keras.layers.convolutional import Convolution2D 4 | from keras.models import Sequential 5 | from keras.layers import Input, Lambda, merge, Reshape, Permute 6 | from keras.models import Model 7 | from keras import backend as K 8 | import numpy as np 9 | 10 | from stft import Spectrogram, get_spectrogram_model 11 | from stft import Logam_layer 12 | 13 | 14 | def _mel_frequencies(n_mels=128, fmin=0.0, fmax=11025.0): 15 | """Compute the center frequencies of mel bands. 16 | `htk` is removed. 17 | copied from Librosa 18 | """ 19 | def _mel_to_hz(mels): 20 | """Convert mel bin numbers to frequencies 21 | copied from Librosa 22 | """ 23 | mels = np.atleast_1d(mels) 24 | 25 | # Fill in the linear scale 26 | f_min = 0.0 27 | f_sp = 200.0 / 3 28 | freqs = f_min + f_sp * mels 29 | 30 | # And now the nonlfinear scale 31 | min_log_hz = 1000.0 # beginning of log region 32 | min_log_mel = (min_log_hz - f_min) / f_sp # same (Mels) 33 | logstep = np.log(6.4) / 27.0 # step size for log region 34 | log_t = (mels >= min_log_mel) 35 | 36 | freqs[log_t] = min_log_hz \ 37 | * np.exp(logstep * (mels[log_t] - min_log_mel)) 38 | 39 | return freqs 40 | 41 | def _hz_to_mel(frequencies): 42 | """Convert Hz to Mels 43 | copied from Librosa 44 | """ 45 | frequencies = np.atleast_1d(frequencies) 46 | 47 | # Fill in the linear part 48 | f_min = 0.0 49 | f_sp = 200.0 / 3 50 | 51 | mels = (frequencies - f_min) / f_sp 52 | 53 | # Fill in the log-scale part 54 | min_log_hz = 1000.0 # beginning of log region 55 | min_log_mel = (min_log_hz - f_min) / f_sp # same (Mels) 56 | logstep = np.log(6.4) / 27.0 # step size for log region 57 | 58 | log_t = (frequencies >= min_log_hz) 59 | mels[log_t] = min_log_mel \ 60 | + np.log(frequencies[log_t] / min_log_hz) / logstep 61 | 62 | return mels 63 | 64 | ''' mel_frequencies body starts ''' 65 | # 'Center freqs' of mel bands - uniformly spaced between limits 66 | min_mel = _hz_to_mel(fmin) 67 | max_mel = _hz_to_mel(fmax) 68 | 69 | mels = np.linspace(min_mel, max_mel, n_mels) 70 | 71 | return _mel_to_hz(mels) 72 | 73 | 74 | def _dft_frequencies(sr=22050, n_dft=2048): 75 | '''Alternative implementation of `np.fft.fftfreqs` (said Librosa) 76 | copied from Librosa 77 | 78 | ''' 79 | return np.linspace(0, 80 | float(sr) / 2, 81 | int(1 + n_dft//2), 82 | endpoint=True) 83 | 84 | 85 | def _mel(sr, n_dft, n_mels=128, fmin=0.0, fmax=None): 86 | ''' create a filterbank matrix to combine stft bins into mel-frequency bins 87 | use Slaney 88 | copied from Librosa, librosa.filters.mel 89 | 90 | n_mels: numbre of mel bands 91 | fmin : lowest frequency [Hz] 92 | fmax : highest frequency [Hz] 93 | If `None`, use `sr / 2.0` 94 | ''' 95 | if fmax is None: 96 | fmax = float(sr) / 2 97 | 98 | # init 99 | n_mels = int(n_mels) 100 | weights = np.zeros((n_mels, int(1 + n_dft // 2))) 101 | 102 | # center freqs of each FFT bin 103 | dftfreqs = _dft_frequencies(sr=sr, n_dft=n_dft) 104 | 105 | # centre freqs of mel bands 106 | freqs = _mel_frequencies(n_mels + 2, 107 | fmin=fmin, 108 | fmax=fmax) 109 | # Slaney-style mel is scaled to be approx constant energy per channel 110 | enorm = 2.0 / (freqs[2:n_mels+2] - freqs[:n_mels]) 111 | 112 | for i in range(n_mels): 113 | # lower and upper slopes qfor all bins 114 | lower = (dftfreqs - freqs[i]) / (freqs[i + 1] - freqs[i]) 115 | upper = (freqs[i + 2] - dftfreqs) / (freqs[i + 2] - freqs[i + 1]) 116 | 117 | # .. then intersect them with each other and zero 118 | weights[i] = np.maximum(0, np.minimum(lower, upper)) * enorm[i] 119 | 120 | return weights 121 | 122 | 123 | def Melspectrogram(n_dft, input_shape, trainable, n_hop=None, 124 | border_mode='same', logamplitude=True, sr=22050, 125 | n_mels=128, fmin=0.0, fmax=None, name='melgram'): 126 | '''Return a Mel-spectrogram keras layer 127 | 128 | Parameters 129 | ---------- 130 | n_dft : int > 0 and power of 2 [scalar] 131 | number of dft components. 132 | 133 | input_shape : tuple (length=2), 134 | Input shape of raw audio input. 135 | It should (num_audio_samples, 1), e.g. (441000, 1) 136 | 137 | trainable : boolean 138 | If it is `True`, the STFT kernels (=weights of two 1d conv layer) 139 | AND hz->mel filter banks are set as `trainable`, 140 | therefore they are updated. 141 | 142 | n_hop : int > 0 [scalar] 143 | number of audio samples between successive frames. 144 | 145 | border_mode : 'valid' or 'same'. 146 | if 'valid' the edges of input signal are ignored. 147 | 148 | logamplitude : boolean 149 | whether logamplitude to stft or not 150 | 151 | sr : int > 0 [scalar] 152 | sampling rate (used to compute mel-frequency filterbanks) 153 | 154 | n_mels : int > 0 [scalar] 155 | number of mel-bins 156 | 157 | fmin : float > 0 [scalar] 158 | minimum frequency of mel-filterbanks 159 | 160 | fmax : float > fmin [scalar] 161 | maximum frequency of mel-filterbanks 162 | 163 | name : string 164 | name of the model 165 | 166 | Returns 167 | ------- 168 | A Keras model that compute mel-spectrogram. 169 | The output shape follows general 2d-representations, 170 | i.e., (None, n_ch, height, width) for `theano` or etc. 171 | ''' 172 | if input_shape is None: 173 | raise RuntimeError('specify input shape') 174 | 175 | Melgram = Sequential() 176 | # Prepare STFT. 177 | stft_model = get_spectrogram_model(n_dft, 178 | n_hop=n_hop, 179 | border_mode=border_mode, 180 | input_shape=input_shape, 181 | logamplitude=False) 182 | # output: 2d shape, either (None, 1, freq, time) or.. 183 | stft_model.trainable = trainable 184 | Melgram.add(stft_model) 185 | 186 | # build a Mel filter 187 | mel_basis = _mel(sr, n_dft, n_mels, fmin, fmax) # (128, 1025) (mel_bin, n_freq) 188 | mel_basis = np.fliplr(mel_basis) # to make it from low-f to high-freq 189 | n_freq = mel_basis.shape[1] 190 | 191 | if K.image_dim_ordering() == 'th': 192 | mel_basis = mel_basis[:, np.newaxis, :, np.newaxis] 193 | # print('th', mel_basis.shape) 194 | else: 195 | mel_basis = np.transpose(mel_basis, (1, 0)) 196 | mel_basis = mel_basis[:, np.newaxis, np.newaxis, :] 197 | # print('tf', mel_basis.shape) 198 | 199 | stft2mel = Convolution2D(n_mels, n_freq, 1, border_mode='valid', bias=False, 200 | name='stft2mel', weights=[mel_basis]) 201 | stft2mel.trainable = trainable 202 | 203 | Melgram.add(stft2mel) # output: (None, 128, 1, 375) if theano. 204 | if logamplitude: 205 | Melgram.add(Logam_layer()) 206 | # i.e. 128ch == 128 mel-bin, for 375 time-step, therefore, 207 | if K.image_dim_ordering() == 'th': 208 | Melgram.add(Permute((2, 1, 3), name='ch_freq_time')) 209 | else: 210 | Melgram.add(Permute((3, 2, 1), name='ch_freq_time')) 211 | # output dot product of them 212 | return Melgram 213 | 214 | -------------------------------------------------------------------------------- /src/bensound-cute.mp3: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/keunwoochoi/keras_STFT_layer/7d99459651d78c182578b8aec679061f6ce3780d/src/bensound-cute.mp3 -------------------------------------------------------------------------------- /stft.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | from __future__ import absolute_import 3 | import numpy as np 4 | import scipy.signal 5 | from keras.models import Sequential 6 | from keras.layers.convolutional import Convolution1D 7 | from keras.layers import Input, Lambda, merge, Permute, Reshape 8 | from keras.models import Model 9 | from keras import backend as K 10 | 11 | 12 | def _get_stft_kernels(n_dft, keras_ver='new'): 13 | '''Return dft kernels for real/imagnary parts assuming 14 | the input signal is real. 15 | An asymmetric hann window is used (scipy.signal.hann). 16 | 17 | Parameters 18 | ---------- 19 | n_dft : int > 0 and power of 2 [scalar] 20 | Number of dft components. 21 | 22 | keras_ver : string, 'new' or 'old' 23 | It determines the reshaping strategy. 24 | 25 | Returns 26 | ------- 27 | dft_real_kernels : np.ndarray [shape=(nb_filter, 1, 1, n_win)] 28 | dft_imag_kernels : np.ndarray [shape=(nb_filter, 1, 1, n_win)] 29 | 30 | * nb_filter = n_dft/2 + 1 31 | * n_win = n_dft 32 | 33 | ''' 34 | assert n_dft > 1 and ((n_dft & (n_dft - 1)) == 0), \ 35 | ('n_dft should be > 1 and power of 2, but n_dft == %d' % n_dft) 36 | 37 | nb_filter = n_dft / 2 + 1 38 | 39 | # prepare DFT filters 40 | timesteps = range(n_dft) 41 | w_ks = [(2 * np.pi * k) / float(n_dft) for k in xrange(n_dft)] 42 | dft_real_kernels = np.array([[np.cos(w_k * n) for n in timesteps] 43 | for w_k in w_ks]) 44 | dft_imag_kernels = np.array([[np.sin(w_k * n) for n in timesteps] 45 | for w_k in w_ks]) 46 | 47 | # windowing DFT filters 48 | dft_window = scipy.signal.hann(n_dft, sym=False) 49 | dft_window = dft_window.reshape((1, -1)) 50 | dft_real_kernels = np.multiply(dft_real_kernels, dft_window) 51 | dft_imag_kernels = np.multiply(dft_imag_kernels, dft_window) 52 | 53 | if keras_ver == 'old': # 1.0.6: reshape filter e.g. (5, 8) -> (5, 1, 8, 1) 54 | dft_real_kernels = dft_real_kernels[:nb_filter] 55 | dft_imag_kernels = dft_imag_kernels[:nb_filter] 56 | dft_real_kernels = dft_real_kernels[:, np.newaxis, :, np.newaxis] 57 | dft_imag_kernels = dft_imag_kernels[:, np.newaxis, :, np.newaxis] 58 | else: 59 | dft_real_kernels = dft_real_kernels[:nb_filter].transpose() 60 | dft_imag_kernels = dft_imag_kernels[:nb_filter].transpose() 61 | dft_real_kernels = dft_real_kernels[:, np.newaxis, np.newaxis, :] 62 | dft_imag_kernels = dft_imag_kernels[:, np.newaxis, np.newaxis, :] 63 | 64 | return dft_real_kernels, dft_imag_kernels 65 | 66 | 67 | def Logam_layer(name='log_amplitude'): 68 | '''Return a keras layer for log-amplitude. 69 | The computation is simplified from librosa.logamplitude by 70 | not having parameters such as ref_power, amin, tob_db. 71 | 72 | Parameters 73 | ---------- 74 | name : string 75 | Name of the logamplitude layer 76 | 77 | Returns 78 | ------- 79 | a Keras layer : Keras's Lambda layer for log-amplitude-ing. 80 | ''' 81 | def logam(x): 82 | log_spec = 10 * K.log(K.maximum(x, 1e-10))/K.log(10) 83 | log_spec = log_spec - K.max(log_spec) # [-?, 0] 84 | log_spec = K.maximum(log_spec, -80.0) # [-80, 0] 85 | return log_spec 86 | 87 | def logam_shape(shapes): 88 | '''shapes: shape of input(s) of the layer''' 89 | # print('output shape of logam:', shapes) 90 | return shapes 91 | 92 | return Lambda(lambda x: logam(x), name=name, 93 | output_shape=logam_shape) 94 | 95 | 96 | def get_spectrogram_model(n_dft, input_shape, trainable=False, 97 | n_hop=None, border_mode='same', 98 | logamplitude=True): 99 | '''Returns two tensors, x as input, stft_magnitude as result. 100 | x(input) and STFT_magnitude(tensor) (#freq, #time shape) 101 | 102 | It assumes mono input. 103 | 104 | These tensors can be use to build a Keras model 105 | using Functional API, 106 | `e.g., model = keras.models.Model(x, STFT_magnitude)` 107 | to build a model that does STFT. 108 | 109 | It uses two `Convolution1D` to compute real/imaginary parts of 110 | STFT and sum(real**2, imag**2). 111 | 112 | Parameters 113 | ---------- 114 | n_dft : int > 0 and power of 2 [scalar] 115 | number of dft components. 116 | 117 | input_shape : tuple (length=2), 118 | Input shape of raw audio input. 119 | It should (num_audio_samples, 1), e.g. (441000, 1) 120 | 121 | trainable : boolean 122 | If it is `True`, the STFT kernels (=weights of two 1d conv layer) 123 | is set as `trainable`, therefore they are initiated with STFT 124 | kernels but then updated. 125 | 126 | n_hop : int > 0 [scalar] 127 | number of samples between successive frames. 128 | 129 | border_mode : 'valid' or 'same'. 130 | if 'valid' the edges of input signal are ignored. 131 | 132 | logamplitude : boolean 133 | whether logamplitude to stft or not 134 | 135 | 136 | this is then used in Keras - Functional model API 137 | STFT_real and STFT_imag is set as non_trainable 138 | 139 | Returns 140 | ------- 141 | x : input tensor 142 | 143 | STFT_magnitude : STFT magnitude, either in shape: 144 | (None, 1, n_freq, n_frame) or (None, n_freq, n_frame, 1) 145 | ''' 146 | 147 | assert trainable in (True, False) 148 | 149 | if n_hop is None: 150 | n_hop = n_dft / 2 151 | 152 | n_channel = input_shape[1] 153 | # get DFT kernels 154 | dft_real_kernels, dft_imag_kernels = _get_stft_kernels(n_dft) 155 | nb_filter = n_dft / 2 + 1 156 | 157 | # layers - one for the real, one for the imaginary 158 | x = Input(shape=input_shape, name='audio_input', dtype='float32') 159 | 160 | STFT_real = Convolution1D(nb_filter, n_dft, 161 | subsample_length=n_hop, 162 | border_mode=border_mode, 163 | weights=[dft_real_kernels], 164 | bias=False, 165 | name='dft_real', 166 | input_shape=input_shape)(x) 167 | 168 | STFT_imag = Convolution1D(nb_filter, n_dft, 169 | subsample_length=n_hop, 170 | border_mode=border_mode, 171 | weights=[dft_imag_kernels], 172 | bias=False, 173 | name='dft_imag', 174 | input_shape=input_shape)(x) 175 | 176 | STFT_real.trainable = trainable 177 | STFT_imag.trainable = trainable 178 | 179 | STFT_real = Lambda(lambda x: x ** 2, name='real_pow')(STFT_real) 180 | STFT_imag = Lambda(lambda x: x ** 2, name='imag_pow')(STFT_imag) 181 | 182 | STFT_magnitude = merge([STFT_real, STFT_imag], mode='sum', name='sum') 183 | 184 | if logamplitude: 185 | STFT_magnitude = Logam_layer()(STFT_magnitude) 186 | 187 | STFT_magnitude = Permute((2, 1))(STFT_magnitude) # (sample, freq, time) 188 | model_conv1d = Model(input=x, output=STFT_magnitude, name='stft_conv1d') 189 | model_stft = Sequential(name='stft_model') 190 | model_stft.add(model_conv1d) 191 | 192 | 193 | if K.image_dim_ordering() == 'th': 194 | model_stft.add(Reshape((1, ) + model_conv1d.output_shape[1:])) 195 | else: 196 | model_stft.add(Reshape(model_conv1d.output_shape[1:] + (1, ))) 197 | 198 | return model_stft 199 | 200 | 201 | def Spectrogram(n_dft, input_shape, trainable=False, n_hop=None, 202 | border_mode='same', logamplitude=True): 203 | '''A keras model for Spectrogram using STFT 204 | 205 | Parameters 206 | ---------- 207 | n_dft : int > 0 and power of 2 [scalar] 208 | number of dft components. 209 | 210 | input_shape : tuple (length=2), 211 | Input shape of raw audio input. 212 | It should (num_audio_samples, n_ch), e.g. (441000, 1), (16000, 2) 213 | 214 | trainable : boolean 215 | If it is `True`, the STFT kernels (=weights of two 1d conv layer) 216 | is set as `trainable`, therefore they are initiated with STFT 217 | kernels but then updated. 218 | 219 | n_hop : int > 0 [scalar] 220 | number of audio samples between successive frames. 221 | 222 | border_mode : 'valid' or 'same'. 223 | if 'valid' the edges of input signal are ignored. 224 | 225 | logamplitude : boolean 226 | whether logamplitude to stft or not 227 | 228 | Returns 229 | ------- 230 | A keras model that has output shape of 231 | (None, n_ch, n_freq, n_frame) (if `img_dim_ordering() == 'th'`) or 232 | (None, n_freq, n_frame, n_ch) (if `img_dim_ordering() == 'tf'`). 233 | 234 | ''' 235 | model = get_spectrogram_model(n_dft, input_shape=input_shape, 236 | trainable=trainable, 237 | n_hop=n_hop, 238 | border_mode=border_mode, 239 | logamplitude=logamplitude) 240 | 241 | model.trainable = trainable 242 | return model 243 | --------------------------------------------------------------------------------