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!

• See the Jupyter notebook at the example folder

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}
}

Author: Keunwoochoi
Source Code: https://github.com/keunwoochoi/kapre
License: MIT License

#python #audio #tensorflow #keras 

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Keras Tutorial - Ultimate Guide to Deep Learning - DataFlair

Welcome to DataFlair Keras Tutorial. This tutorial will introduce you to everything you need to know to get started with Keras. You will discover the characteristics, features, and various other properties of Keras. This article also explains the different neural network layers and the pre-trained models available in Keras. You will get the idea of how Keras makes it easier to try and experiment with new architectures in neural networks. And how Keras empowers new ideas and its implementation in a faster, efficient way.

Introduction to Keras

Keras is an open-source deep learning framework developed in python. Developers favor Keras because it is user-friendly, modular, and extensible. Keras allows developers for fast experimentation with neural networks.

Keras is a high-level API and uses Tensorflow, Theano, or CNTK as its backend. It provides a very clean and easy way to create deep learning models.

Characteristics of Keras

Keras has the following characteristics:

• It is simple to use and consistent. Since we describe models in python, it is easy to code, compact, and easy to debug.
• Keras is based on minimal substructure, it tries to minimize the user actions for common use cases.
• Keras allows us to use multiple backends, provides GPU support on CUDA, and allows us to train models on multiple GPUs.
• It offers a consistent API that provides necessary feedback when an error occurs.
• Using Keras, you can customize the functionalities of your code up to a great extent. Even small customization makes a big change because these functionalities are deeply integrated with the low-level backend.

Benefits of using Keras

The following major benefits of using Keras over other deep learning frameworks are:

• The simple API structure of Keras is designed for both new developers and experts.
• The Keras interface is very user friendly and is pretty optimized for general use cases.
• In Keras, you can write custom blocks to extend it.
• Keras is the second most popular deep learning framework after TensorFlow.
• Tensorflow also provides Keras implementation using its tf.keras module. You can access all the functionalities of Keras in TensorFlow using tf.keras.

Keras Installation

Before installing TensorFlow, you should have one of its backends. We prefer you to install Tensorflow. Install Tensorflow and Keras using pip python package installer.

Starting with Keras

The basic data structure of Keras is model, it defines how to organize layers. A simple type of model is the Sequential model, a sequential way of adding layers. For more flexible architecture, Keras provides a Functional API. Functional API allows you to take multiple inputs and produce outputs.

Keras Sequential model

Keras Functional API

It allows you to define more complex models.

#keras tutorials #introduction to keras #keras models #keras tutorial #layers in keras #why learn keras

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!

• See the Jupyter notebook at the example folder

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}
}

Author: Keunwoochoi
Source Code: https://github.com/keunwoochoi/kapre
License: MIT License

#python #audio #tensorflow #keras 

Keras Tutorial For Beginners | What is Keras | Keras Sequential Model | Keras Training

In this video on Keras, you will understand what is Keras and why do we need it, how to compose different models in Keras like the Sequential model and functional model, and later on how to define the inputs, how to connect layers over, and finally hands-on demo.
Why Keras is important

Keras is an Open Source Neural Network library written in Python that runs on top of Theano or Tensorflow. It is designed to be modular, fast, and easy to use. Keras is very quick to make a network model. If you want to make a simple network model with a few lines, Keras can help you with that.

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Link: https://www.youtube.com/watch?v=nS1J-2uoKto

#keras tutorial for beginners #what is keras #keras sequential model #keras training

Why Learn Keras - Reasons Why Choose Keras - DataFlair

This article is the spotlight on the need for python deep learning library, Keras. Keras offers a uniform face for various deep learning frameworks including Tensorflow, Theano, and MXNet. Let us see why you should choose and learn keras now.

Why Learn Keras?

Keras makes deep learning accessible and local on your computer.It also acts as a frontend for other big cloud providers. It is the most voted recommendation for beginners who want to start their journey in machine learning. It provides a minimal approach to run neural networks. This allows students to learn complex features from input data sequentially.

Features of Keras

Let us see some of the features of keras that make you learn Keras.

1. Simple API

Keras is the most easy to use the library for machine learning for beginners. Being simple helps it to bring machine learning from imaginations to reality. It provides an infrastructure that can be learned in very less time. Using Keras, you will be able to stack layers like experts.

2. Pythonic Nature

Python is the most popular library for machine learning and Data Science. The compatibility with python allows Keras to have many useful features. Writing less code, easy to debug, easy to deploy, extensibility is due to the support of Keras with python 2.7 and python 3.6.

3. Strong Backend Support

Keras being a high-level API provides support for multiple popular and powerful backend frameworks. Tensorflow, theano, CNTK are very dominant for backend computations and Keras supports all of them.

4. Base for Innovations

The importance of Keras leads to many other innovative tools to explore deep learning. These tools are built on top of Keras making Keras as the base. The following tools are:

• Deepjazz: This is deep learning-driven jazz built using Keras and theano, available on github.
• Eclipse Picasso: It is a visualization tool that works with Keras checkpoints.
• Auto Keras: It is built upon Keras and used for machine learning model automation.

Reasons to Learn and Use Keras

• Keras allows us to switch between the backends as per the requirement of our applications. It acts as a wrapper that gives us the privilege to use either TensorFlow, theano, or any other framework.
• Keras is very easy and enjoyable to use. It uses great guiding principles like extensibility, python nativeness, and modularity.
• The ability of Keras to create the state of the art implementations of common deep neural networks. These are fast and it is easy to get them running using Keras.
• Being Keras user, you will be more faster and productive, you will have the ability to try more ideas.
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• We can deploy Keras deep learning models on multiple platforms. For example, We can deploy in the browser using tensorflow.js, on the server using either TensorFlow serving or using Node.js runtime. On mobile devices i.e in android or IOS, we can deploy using TensorFlow Lite.
• Keras has a large ecosystem of products to support your deep learning development. Some of the popular products are Tensorflow Cloud, Keras Tuner, Tensorflow Lite,Tensorflow.js, and Tensorflow Model Optimizatio

#keras tutorials #importance of keras #keras features #learn keras #deep learning

Keras Modules - Types and Examples

Keras modules provide various predefined classes and functions for deep learning algorithms. In this Keras tutorial, we will learn various modules in Keras. We will study the features and few of the applications of these modules.

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• Backend
• Utils
• Image Processing
• Sequence Processing
• Text Processing
• Callback

#keras tutorials #keras modules #modules in keras #keras