Kapre
Keras Audio Preprocessors - compute STFT, ISTFT, Melspectrogram, and others on GPU real-time.
Tested on Python 3.6 and 3.7
Why Kapre?
vs. Pre-computation
- You can optimize DSP parameters
- Your model deployment becomes much simpler and consistent.
- Your code and model has less dependencies
vs. Your own implementation
- Quick and easy!
- Consistent with 1D/2D tensorflow batch shapes
- Data format agnostic (
channels_firstandchannels_last) - Less error prone - Kapre layers are tested against Librosa (stft, decibel, etc) - which is (trust me) trickier than you think.
- Kapre layers have some extended APIs from the default
tf.signalsimplementation such as..- A perfectly invertible
STFTandInverseSTFTpair - Mel-spectrogram with more options
- A perfectly invertible
- Reproducibility - Kapre is available on pip with versioning
Workflow with Kapre
- Preprocess your audio dataset. Resample the audio to the right sampling rate and store the audio signals (waveforms).
- In your ML model, add Kapre layer e.g.
kapre.time_frequency.STFT()as the first layer of the model. - The data loader simply loads audio signals and feed them into the model
- In your hyperparameter search, include DSP parameters like
n_fftto boost the performance. - When deploying the final model, all you need to remember is the sampling rate of the signal. No dependency or preprocessing!
Installation
pip install kapre
API Documentation
Please refer to Kapre API Documentation at https://kapre.readthedocs.io
One-shot example
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, BatchNormalization, ReLU, GlobalAveragePooling2D, Dense, Softmax
from kapre import STFT, Magnitude, MagnitudeToDecibel
from kapre.composed import get_melspectrogram_layer, get_log_frequency_spectrogram_layer
# 6 channels (!), maybe 1-sec audio signal, for an example.
input_shape = (44100, 6)
sr = 44100
model = Sequential()
# A STFT layer
model.add(STFT(n_fft=2048, win_length=2018, hop_length=1024,
window_name=None, pad_end=False,
input_data_format='channels_last', output_data_format='channels_last',
input_shape=input_shape))
model.add(Magnitude())
model.add(MagnitudeToDecibel()) # these three layers can be replaced with get_stft_magnitude_layer()
# Alternatively, you may want to use a melspectrogram layer
# melgram_layer = get_melspectrogram_layer()
# or log-frequency layer
# log_stft_layer = get_log_frequency_spectrogram_layer()
# add more layers as you want
model.add(Conv2D(32, (3, 3), strides=(2, 2)))
model.add(BatchNormalization())
model.add(ReLU())
model.add(GlobalAveragePooling2D())
model.add(Dense(10))
model.add(Softmax())
# Compile the model
model.compile('adam', 'categorical_crossentropy') # if single-label classification
# train it with raw audio sample inputs
# for example, you may have functions that load your data as below.
x = load_x() # e.g., x.shape = (10000, 6, 44100)
y = load_y() # e.g., y.shape = (10000, 10) if it's 10-class classification
# then..
model.fit(x, y)
# Done!
- See the Jupyter notebook at the example folder
Citation
Please cite this paper if you use Kapre for your work.
@inproceedings{choi2017kapre,
title={Kapre: On-GPU Audio Preprocessing Layers for a Quick Implementation of Deep Neural Network Models with Keras},
author={Choi, Keunwoo and Joo, Deokjin and Kim, Juho},
booktitle={Machine Learning for Music Discovery Workshop at 34th International Conference on Machine Learning},
year={2017},
organization={ICML}
}
1 Nov 11, 2021
53 Dec 30, 2022
4 Feb 26, 2022
2.3k Jan 03, 2023
2.6k Dec 30, 2022
2 May 12, 2022
310 Jan 01, 2023
188 Dec 14, 2022
8 Nov 25, 2022
1.2k Jan 07, 2023
870 Dec 27, 2022
2 Dec 14, 2021
1.1k Dec 31, 2022
662 Jan 07, 2023
3 Jun 30, 2022
197 Dec 31, 2022
36 Nov 15, 2022
322 Dec 27, 2022
26 Dec 21, 2022
231 Dec 26, 2022