ProductPromotion
Logo

Python.py

made by https://0x3d.site

Things
we have.

GitHub - keunwoochoi/kapre: kapre: Keras Audio Preprocessors
kapre: Keras Audio Preprocessors. Contribute to keunwoochoi/kapre development by creating an account on GitHub.
Visit Site

GitHub - keunwoochoi/kapre: kapre: Keras Audio Preprocessors

GitHub - keunwoochoi/kapre: kapre: Keras Audio Preprocessors

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_first and channels_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.signals implementation such as..
    • A perfectly invertible STFT and InverseSTFT pair
    • Mel-spectrogram with more options
  • Reproducibility - Kapre is available on pip with versioning

Workflow with Kapre

  1. Preprocess your audio dataset. Resample the audio to the right sampling rate and store the audio signals (waveforms).
  2. In your ML model, add Kapre layer e.g. kapre.time_frequency.STFT() as the first layer of the model.
  3. The data loader simply loads audio signals and feed them into the model
  4. In your hyperparameter search, include DSP parameters like n_fft to boost the performance.
  5. 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!

Tflite compatbility

The STFT layer is not tflite compatible (due to tf.signal.stft). To create a tflite compatible model, first train using the normal kapre layers then create a new model replacing STFT and Magnitude with STFTTflite, MagnitudeTflite. Tflite compatible layers are restricted to a batch size of 1 which prevents use of them during training.

# assumes you have run the one-shot example above.
from kapre import STFTTflite, MagnitudeTflite
model_tflite = Sequential()

model_tflite.add(STFTTflite(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_tflite.add(MagnitudeTflite())
model_tflite.add(MagnitudeToDecibel())  
model_tflite.add(Conv2D(32, (3, 3), strides=(2, 2)))
model_tflite.add(BatchNormalization())
model_tflite.add(ReLU())
model_tflite.add(GlobalAveragePooling2D())
model_tflite.add(Dense(10))
model_tflite.add(Softmax())

# load the trained weights into the tflite compatible model.
model_tflite.set_weights(model.get_weights())

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}
}

Resources
which are currently available to browse on.

mail [email protected] to add your project or resources here 🔥.

Queries
or most google FAQ's about Python.

mail [email protected] to add more queries here 🔍.

More Sites
to check out once you're finished browsing here.

0x3d
https://www.0x3d.site/
0x3d is designed for aggregating information.
NodeJS
https://nodejs.0x3d.site/
NodeJS Online Directory
Cross Platform
https://cross-platform.0x3d.site/
Cross Platform Online Directory
Open Source
https://open-source.0x3d.site/
Open Source Online Directory
Analytics
https://analytics.0x3d.site/
Analytics Online Directory
JavaScript
https://javascript.0x3d.site/
JavaScript Online Directory
GoLang
https://golang.0x3d.site/
GoLang Online Directory
Python
https://python.0x3d.site/
Python Online Directory
Swift
https://swift.0x3d.site/
Swift Online Directory
Rust
https://rust.0x3d.site/
Rust Online Directory
Scala
https://scala.0x3d.site/
Scala Online Directory
Ruby
https://ruby.0x3d.site/
Ruby Online Directory
Clojure
https://clojure.0x3d.site/
Clojure Online Directory
Elixir
https://elixir.0x3d.site/
Elixir Online Directory
Elm
https://elm.0x3d.site/
Elm Online Directory
Lua
https://lua.0x3d.site/
Lua Online Directory
C Programming
https://c-programming.0x3d.site/
C Programming Online Directory
C++ Programming
https://cpp-programming.0x3d.site/
C++ Programming Online Directory
R Programming
https://r-programming.0x3d.site/
R Programming Online Directory
Perl
https://perl.0x3d.site/
Perl Online Directory
Java
https://java.0x3d.site/
Java Online Directory
Kotlin
https://kotlin.0x3d.site/
Kotlin Online Directory
PHP
https://php.0x3d.site/
PHP Online Directory
React JS
https://react.0x3d.site/
React JS Online Directory
Angular
https://angular.0x3d.site/
Angular JS Online Directory