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}
}
52 Dec 29, 2022
1 Jul 25, 2022
7 Oct 19, 2022
17 Dec 25, 2022
17 Nov 17, 2022
11 Nov 12, 2022
228 Nov 19, 2022
2 May 27, 2022
106 Nov 28, 2022
5.6k Jan 06, 2023
2.3k Jan 03, 2023
1 Dec 30, 2021
12 Nov 12, 2022
11 Nov 06, 2022
2 Feb 04, 2022
3 Oct 20, 2021
3k Dec 31, 2022
1 Jan 24, 2022
327 Dec 19, 2022
10 Oct 24, 2022