Speech Recognition Using TensorFlow

This tutorial will show you how to runs a simple speech recognition model built by the audio training tutorial. Listens for a small set of words, and display them in the UI when they are recognized.

It’s important to know that real speech and audio recognition systems are much more complex, but like MNIST for images, it should give you a basic understanding of the techniques involved. Once you’ve completed this tutorial, you’ll have a application that tries to classify a one second audio clip as either silence, an unknown word, “yes”, “no”, “up”, “down”, “left”, “right”, “on”, “off”, “stop”, or “go”.

TensorFow speech recognition model


You can train your model on the desktop or on the laptop or on the server and then you can use that pre-trained model on our mobile device.So there’s no training that would happen on the device the training would happen on our bigger machine either a server or our laptop.You can download a pretrained model from tensorflow.org

2. Adding Dependencies

The TensorFlow Inference Interface is available as a JCenter package and can be included quite simply in your android project with a couple of lines in the project’s build.gradle file:

Add the following dependency in app’s build.gradle

This will tell Gradle to use the latest version of the TensorFlow AAR that has been released to https://bintray.com/google/tensorflow/tensorflow-android. You may replace the + with an explicit version label if you wish to use a specific release of TensorFlow in your app.

3.Add Pre-trained Model to Project

You need the pre-trained model and label file.You can download the model from here.Unzip this zip file, You will get conv_actions_labels.txt(label for objects) and conv_actions_frozen.pb(pre-trained model).

Put conv_actions_labels.txt and conv_actions_frozen.pb into android/assets directory.

4.Microphone Permission

To request microphone, you should be requesting RECORD_AUDIO permission in your manifest file as below:

Since Android 6.0 Marshmallow, the application will not be granted any permission at installation time. Instead, the application has to ask the user for a permission one-by-one at runtime.

5.Recording Audio

The AudioRecord class manages the audio resources for Java applications to record audio from the audio input hardware of the platform. This is achieved by “pulling” (reading) the data from the AudioRecord object. The application is responsible for polling the AudioRecord object in time using read(short[], int, int).

6.Run TensorFlow Model

TensorFlowInferenceInterface class that provides a smaller API surface suitable for inference and summarizing the performance of model execution.

7.Recognize Commands

RecognizeCommands class is fed the output of running the TensorFlow model over time, it averages the signals and returns information about a label when it has enough evidence to think that a recognized word has been found. The implementation is fairly small, just keeping track of the last few predictions and averaging them.

The demo app updates its UI of results automatically based on the labels text file you copy into assets alongside your frozen graph, which means you can easily try out different models without needing to make any code changes. You will need to updaye LABEL_FILENAME and MODEL_FILENAME to point to the files you’ve added if you change the paths though.


You can easily replace it with a model you’ve trained yourself. If you do this, you’ll need to make sure that the constants in the main MainActivity Java source file like SAMPLE_RATE and SAMPLE_DURATION match any changes you’ve made to the defaults while training. You’ll also see that there’s a Java version of the RecognizeCommands module that’s very similar to the C++ version in this tutorial. If you’ve tweaked parameters for that, you can also update them in MainActivity to get the same results as in your server testing.


Download this project from GitHub


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Train your Object Detection model locally with TensorFlow

In this post, we’re going to train machine learning models capable of localizing and identifying multiple objects in an image. You’ll need to install TensorFlow and you’ll need to understand how to use the command line.

Tensorflow Object Detection API

The TensorFlow Object Detection API is an open source framework built on top of TensorFlow that makes it easy to construct, train and deploy object detection models.

This post walks through the steps required to train an object detection model locally.

1.Cloning an Object Detection API repository

or you can download directly ZIP file.


Tensorflow Object Detection API depends on the following libraries.

  • Protobuf 2.6
  • Protobuf 2.6
  • Pillow 1.0
  • Lxml

The Tensorflow Object Detection API uses Protobufs to configure model and training parameters. Before the framework can be used, the Protobuf libraries must be compiled. This should be done by running the following command from the tensorflow/models directory:

  • Jupyter notebook

  • Matplotlib

Add Libraries to PYTHONPATH

When running locally, the tensorflow/models/ and slim directories should be appended to PYTHONPATH. This can be done by running the following from tensorflow/models/:

Note: This command needs to run from every new terminal you start. If you wish to avoid running this manually, you can add it as a new line to the end of your ~/.bashrc file.

Testing the Installation

You can test that you have correctly installed the Tensorflow Object Detection API by running the following command:

Above command generate following output.

Install Object Detection API

3.Preparing Inputs

Tensorflow Object Detection API reads data using the TFRecord file format. Two sample scripts (create_pascal_tf_record.py and create_pet_tf_record.py) are provided to convert \dataset to TFRecords.

Directory Structure for Training input data

  • To prepare the input file for the sample scripts you need to consider two things. Firstly, you need an RGB image which is encoded as jpg or png and secondly, you need a list of bounding boxes (xmin, ymin, xmax, ymax) for the image and the class of the object in the bounding box.
  • I scraped 200 pet from Google Images.Here is a subset of the pet image data set that I collected in images folder:


Afterward, labeled them manually with LabelImg. LabelImg is a graphical image annotation tool that is written in Python. It’s super easy to use and the annotations are saved as XML files.Save image annotations xml in /annotations/xmls folder.

Image Annotation

Create trainval.txt in annotations folder which content name of the images without extension.Use the following command to generate trainval.txt.

Label Maps

Each dataset is required to have a label map associated with it. This label map defines a mapping from string class names to integer class Ids.Label maps should always start from id 1.Create label.pbtxt file with the following label map:

Generating the Pet TFRecord files.

Run the following commands.

You should end up with two TFRecord files named    pet_train.record and pet_val.record in the tensorflow/modelsdirectory.

4.Training the model

After creating the required input file for the API, Now you can train your model.For training, you need the following command:

An object detection training pipeline also provide sample config files on the repo. For my training, I used ssd_mobilenet_v1_pets.config basis. I needed to adjust the num_classes to one and also set the path (PATH_TO_BE_CONFIGURED) for the model checkpoint, the train, and test data files as well as the label map. In terms of other configurations like the learning rate, batch size and many more, I used their default settings.

Running the Evaluation Job

Evaluation is run as a separate job. The eval job will periodically poll the train directory for new checkpoints and evaluate them on a test dataset. The job can be run using the following command:

where ${PATH_TO_YOUR_PIPELINE_CONFIG} points to the pipeline config, ${PATH_TO_TRAIN_DIR} points to the directory in which training checkpoints were saved (same as the training job) and ${PATH_TO_EVAL_DIR} points to the directory in which evaluation events will be saved. As with the training job, the eval job run until terminated by default.

Running Tensorboard

Progress for training and eval jobs can be inspected using Tensorboard. If using the recommended directory structure, Tensorboard can be run using the following command:

where ${PATH_TO_MODEL_DIRECTORY} points to the directory that contains the train and eval directories. Please note it may take Tensorboard a couple minutes to populate with data.

5.Exporting the Tensorflow Graph

After your model has been trained, you should export it to a Tensorflow graph proto. First, you need to identify a candidate checkpoint to export. The checkpoint will typically consist of three files in pet folder:

  1.  model.ckpt-${CHECKPOINT_NUMBER}.data-00000-of-00001
  2. model.ckpt-${CHECKPOINT_NUMBER}.index
  3. model.ckpt-${CHECKPOINT_NUMBER}.meta

Run the following command to export Tensorflow graph.Change the check point number.

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TensorFlow is an open source software library for Machine Learning.It was originally developed by researchers and engineers working on the Google Brain team within Google’s Machine Intelligence Research organization for the purposes of conducting machine learning and deep neural networks research.

TensorFlow is programming paradigm and some of its main features are that numeric computation is expressed as a computational graph backbone.TensorFlow program is going to be a graph where the graph nodes are the group to be operations, shorthand as an operation in your code.They have any number of inputs and a single output.The edges between our nodes are going to be tensors that flow between them.

The best way of thinking about what tensors are in practice is as n-dimensional arrays.The advantage of using flow graphs as the backbone of your deep learning framework is that it allows you to build complex models in terms of small and simple operations.This is going to make your gradient calculation extremely simple when we get to that.You’re going to be very grateful for the automatic different when you’re coding large models in your project and in the feature.

Another way of thinking about a TensorFlow graph is that each operation is a function that can be evaluated at that point.

Neural Network computational graph


Neural network with one hidden layer what it’s computational graph in TensorFlow might look like.



So we have some hidden layer that we are trying to compute, as the ReLU activation of some parameter matrix W times some input x plus a bias term.

ReLu is an activation function standing for rectified linear unit.We are applying some nonlinear function over our linear input that is what gives the neural networks their expressive function.The ReLU takes the max of your input and zero.

We have variables b and W.We have a placeholder with the x. Nodes for each of the operations in our graph.

Variables are going to be stateful nodes which output their current value.In our case, Variable b and W is retained their current value over multiple executions.It’s easy to restore saved values to variables.

Variable has a number of other useful features.they can be saved to your disk during and after training. That allows people from different companies and group to save, store, and send over their model parameter to other people.They also make gradient update by default.It will apply over all of the variables and your graph.

The variable is the things that you wanna tune to minimize the loss.It is really important to remember that variable in the graph like b and W are still operations.

All of your node in the graph are operations.When you evaluate the operation that is these variables in our run time you will get the value of those variables.

Placeholders(x) is nodes whose value is fed in at execution time. Placeholder values that we’re going to add into our computation during training.So this going to be our input.

So for placeholders, we don’t give any initial values.we just assign a data type, and we assign a shape of tensor so the graph still knows what to computer even though it doesn’t have any stored values yet.

The third type of node is mathematical operations. This going to be your matrix multiplication, your addition, and your ReLU. All of these are nodes in your TensorFlow graphs.It’s very important that we’re actually calling on TensorFlow mathematical operations as opposed to Numpy operations.

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Android TensorFlow Machine Learning

TensorFlow is an open source software library for machine learning, developed by Google and currently used in many of their projects.

In this article, we will create an Android app that can recognize five types of fruits.

Machine Learning

There have been so many buzzwords such as AI, Machine Learning, Neural Network, or Deep Learning.What’s the difference?

AI or Artificial Intelligence–you can say that is a science for making smart things like building an autonomous driving car or having a computer drawing a beautiful picture or composing music.One way to realize that vision of AI is in Machine Learning.Machine learning is a technology where you can a computer train itself, rather than having human programmers instructing every step, to process the data by itself.

One of the many different algorithms in MI is a neural network.Since around 2012, Google has been seeing a big breakthrough in the world of the neural network, especially for image recognition, voice recognition, or natural language processing and many other applications.

Neural Network

You can think of it just like a function in mathematics or a function in the programming language.So you can put any kind of data as an input and do some matrix operation or calculations inside neural networks. You would get an output vector which has the many labels or speculative values.

Neural Network

For example, if you have a bunch of images, you can train the neural network to classify which one is the image of a cat or the image of a dog, this is just one example of the use cases of neural networks.You can apply the technology to solve any kind of business problems you have.
There are so many possible use cases for the combination between ML and mobile applications, starting from image recognition, OCR, speech-to-text, and text-to-speech, translation.You can apply machine learning to mobile-specification applications such as motion detection or GPS location tracking.

Why do you want to run machine learning model inside your mobile applications?

By using the machine learnings, you can reduce the significant amount of traffic, and you can get much faster responses from your cloud services.Because you can extract the meaning from the raw data.For example, if you are using machine learning for image recognition, the easiest way to implement that is to send all the raw image data taken by the camera to the server. But instead, you can have the machine learning model running inside your mobile application so that your mobile application can recognize what kind of object is in each image.So that you can just send the label, such as a flower or human face, to the server.That can reduce the traffic to 1/10 or 1/100 It’s a significant amount of saving of traffic.

Build an application that is powered by machine learning

The starting point could be the TensorFlow, which is the open-source library for machine intelligence from Google.TensorFlow is the latest framework for building machine learning or AI-based service developed in Google.Google open source it in November 2015. TensorFlow is the most popular framework for building neural networks or deep learning in the world.One benefit you could get with TensorFlow is easy of development.So It’s really easy to get started.You can just write a few lines of Python code.

TensorFlow is very valuable for people like me because I don’t have any sophisticated mathematical background.So when you started reading the textbook on neural networks, you found many mathematical equations on the textbook, like differentiation backpropagation and gradient descent.You really didn’t want to implement everything by yourself.Instead, now you can just download TensorFlow, Where you can write a single line of Python code, like GradientDescentOptimizer.That single line of code can encapsulate all these obfuscated algorithms such as gradient descent, backpropagation, or any other latest algorithm implemented by the Google Engineers.So you yourself don’t have to have the skill set to implement the neural network technologies from scratch.The main benefits of the TensorFlow is the portability and scalability.

Implement TensorFlow in Android

Android just added a JSON integration, which makes step a lot, a lot easier.Just add one line to the build.gradle, and the Gradle take care or the rest of steps.Under the library archive, holding TensorFlow shared object is downloaded from JCenter, linked against the application automatically.

Android release inference library to integrate TensorFlow for Java Application.

Add your model to the project

We need the pre-trained model and label file.In the previous tutorial, we train model.which does the object detection on a given image.You can download the model from here.Unzip this zip file, we will get retrained_labels.txt(label for objects) and rounded_graph.pb (pre-trained model).

Put retrained_labels.txt and rounded_graph.pb into android/assets directory.


At first, create TensorFlow inference interface, opening the model file from the asset in the APK.Then, Set up the input feed using Feed API.On mobile, the input feed tends to be retrieved from various sensors like a camera, accelerometer, Then run the interface, finally, you can fetch the results using fetch method over there.You would notice that those calls are all blocking calls.So you’d want to run them in a worker thread, rather than the main thread because API would take a long time.This one is Java API.you can use regular C++ API as well.

Download this project from GitHub


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Train Image classifier with TensorFlow

In this post, we’re going to train an Image Classifier for the Android Machine Learning demo app. You’ll need to install TensorFlow and you’ll need to understand how to use the command line.

What is Machine Learning? and Why important?

You can think of Machine Learning as a subfield of AI.Machine Learning is the study of algorithms that learn from examples and experience instead of relying on hard-coded rules.So That’s the state-of-the-art.
Can you write code to tell the difference between an apple and an orange? Takes file as input, does some analysis, and outputs the type of fruit.

We’re going to train a classifier.For now, you can think of a classifier as a function.It takes some data as input and assigns a label to It as output.

The technique to write the classifier automatically is called supervised learning.It begins with the example of the problems you want to solve.

To use supervised learning, we’ll follow a few standard steps.

1.Collect training data

We’re going to write a function to classify a piece of fruit Image. For starters, it will take an image of the fruit as input and predict whether it’s an apple or oranges as output.The more training data you have, the better a classifier you can create (at least 30 images of each, more is better).

Image classifier folder

We will create a ~/tf_files/fruits folder and place each set of jpeg images in subdirectories (such as ~/tf_files/fruits/apple, ~/tf_files/fruits/orange etc)

A quick way to download multiple images at once is to search something on Google Images, and use a Chrome extension for batch download.

2.Train an Image Classifier with TensorFLow for Poets

You want to build a classifier that can tell the difference between a picture of an Apple and Orange.TensorFlow for Poets This is a great way to get started learning about and working with image classification.
To train our classifier we’ll basically just need to run a couple of scripts.To train an image classifier with TensorFlow for Poets, we’ll only need to provide one thing–training data.

The classifier we’ll be using is called a neural network.At a high level, that’s just another type of classifier, like the nearest neighbor one wrote last lime.The different a neural network can learn more complex functions.

Training Inception

Step1: The retrain.py script is part of the TensorFlow repo.You need to download it manually, to the current directory(~/tf_files):

download retrain.py
Now, we have a trainer, we have data(Image), so let’s train! We will train the Inception v3 network.

Step2: Before starting the training, active  TensorFlow.

active tensorflow environment

Step3: Start your image retraining with one big command.

Train Image classifier

These commands will make TensorFlow download the inception model and retrain it to detect images from ~/tf_files/fruits.

Train Image classifier accuracy

This operation can take several minutes depending on how many images you have and how many training steps you specified.

The script will generate two files: the model in a protobuf file (retrained_graph.pb) and a label list of all the objects it can recognize (retrained_labels.txt).

retrained grape files

Clone the Git repository for test model

The following command will clone the Git repository containing the files required for the test model.

Copy tf file

The repo contains two directories: android/, and scripts/

1.android/: Directory contains nearly all the files necessary to build a simple Android app that classifies images.

2.scripts/: Directory contains the python scripts. These include scripts to prepare, test and evaluate the model.

Now copy the tf_files directory from the first part, into /tensorflow-for-poets-2 working directory.

Test the Model

The scripts/ directory contains a simple command line script, label_image.py, to test the network.

test trained model

Optimize model for Android

TensorFlow installation includes a tool, optimize_for_inference, that removes all nodes that aren’t needed for a given set of input and output nodes.

Optimize for inference

It creates a new file at tf_files/optimized_graph.pb.

Make the model compressible

The retrained model is still 84MB in size at this point. That large download size may be a limiting factor for any app that includes it.

Neural network operation requires a bunch of matrix characterizations, which means tons of multiply and add operations.current mobile devices are capable of doing some of them with specialized hardware.


Quantization is one of the techniques to reduce both memory footprint and computer load.Usually, TensorFlow takes a single precision floating value for input and math, and also output as well.As you know, the single precision floating point takes 32 bits each.W can reduce the precision to 16 bits,8 bit, or even less while keeping a good result, just because our learning process involves some noise by nature.Adding some extra noise wouldn’t matter much.So quantized weight is the optimization for storage size, which reduces the precision of the constant node in the graph file.

Quantize Image

Quantized calculations.

With the quantized calculations, we can reduce computing precision by using the quantized value directory.This one is good for first memory bandwidth, which is a limiting factor in mobile devices.Also, hardware can handle these precision values faster than single precision floating values.

Now use the quantize_graph script to apply changes:

quantize graph

It does this without any changes to the structure of the network, it simply quantizes the constants in place.It creates a new file at tf_files/rounded_graph.pb.


Every mobile app distribution system compresses the package before distribution. So test how much the graph can be compressed:


Quantize compare

You should see a significant improvement. I get 73% optimize model.


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Install TensorFlow

Installing TensorFlow for CPU on Ubuntu using virtualenv.

This blog explains how to install TensorFlow on Ubuntu using Virtualenv. Virtualenv is a virtual Python environment isolated from other Python development, incapable of interfering with or being affected by other Python programs on the same machine. To start working with TensorFlow, you simply need to “activate” the virtual environment. All in all, virtualenv provides a safe and reliable mechanism for installing and running TensorFlow.

Take the following steps to install TensorFlow with virtualenv.


Step 1: Install pip and virtualenv by issuing following commands:

TensorFlow Step 1

Step 2: Create a virtualenv environment by issuing the following commands:

The preceding source command should change your prompt to the following:

Tensorflow step 2

Step 3:

Activate the virtualenv environment by issuing one of the following commands:

The preceding source command should change your prompt to the following:

TensorFlow step 3


Step 4:

Issue one of the following commands to install TensorFlow in the active virtualenv environment:

TensorFlow step 4


No module named tensorflow

Activate TensorFlow

Note that you must activate the virtualenv environment each time you use TensorFlow. If the virtualenv environment is not currently active, invoke one of the following commands:

When the virtualenv environment is active, you may run TensorFlow programs from this shell. Your prompt will become the following to indicate that your tensorflow environment is active:

active tensorflow environment


Deactivate TensorFlow

you may deactivate the TensorFlow environment by invoking the deactivate function as follows:

Run TensorFlow program

Enter the following short program inside the python interactive shell:

The preceding source code should change your prompt to the following.

TensorFlow Hello World



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