How to Train New TensorFlow Lite Micro Speech Models

By Adafruit Industries

Courtest of Adafruit

Guide by Lady Ada


Machine learning has come to the 'edge' - small microcontrollers that can run a very miniature version of TensorFlow Lite to do ML computations. The first demos available are for 'micro speech' which is detecting a couple words. The default words are 'yes/no' but the dataset contains many other words! This guide goes through how to train micro speech models on your own

Install Docker

We need to be able to run a specific version/commit of TensorFlow and the dependancy requirements for TF are very extreme. We strongly suggest against trying to compile and run on your native computer OS - that way we don't get weird interactions with your OS, compiler toolchain, Python kit, etc. Also, TF really wants to run on a particular version of Linux and chances are you aren't running it.

Instead, we will be using Docker to containerize and separate the TF build so we have a compact, clean, dependable build. Docker is lighter than VMWare/vagrant, and has a very nice 'hub' backend for saving/restoring your images, all for free!

Signup and log into Docker

Sign up at


You don't need to pay for an account, but be aware the software images we'll be using are public so don't put any private data in em!

Download and Install Desktop Docker


Download Docker software for Windows or Mac, whichever matches your computer

TensorFlow needs a lot of computing resources

Give it as many CPUs and as much RAM as you can spare


You need to give it at least 8 GB of RAM or gcc will fail with a very annoying and somewhat confusing error like this (but on some other file)

Copy Code

ERROR: /root/tensorflow/tensorflow/core/kernels/BUILD:3371:1: C++ compilation of rule '//tensorflow/core/kernels:reduction_ops' failed (Exit 4)
gcc: internal compiler error: Killed (program cc1plus)
Please submit a full bug report,
with preprocessed source if appropriate.
See <file:///usr/share/doc/gcc-7/README.Bugs> for instructions.
Target //tensorflow/examples/speech_commands:train failed to build
Use --verbose_failures to see the command lines of failed build steps.
INFO: Elapsed time: 6058.951s, Critical Path: 3278.24s
INFO: 2606 processes: 2606 local.
FAILED: Build did NOT complete successfully
FAILED: Build did NOT complete successfully

Open a command terminal and try to login, use the same username/password as from the site


OK you're ready to go!

Create/Fork Docker Image

Start with the official TensorFlow Docker image, like github you can pull/commit/push and implictly fork when you do this between sources.

docker pull tensorflow/tensorflow will get you the latest docker image from Google



Log into the Docker image with

docker run -it tensorflow/tensorflow bash


Within the Docker root shell, install some dependencies with

apt-get install -y curl zip git


Install TensorFlow with pip

This technique does not work yet!

Go thru the process to create/fork the official Tensorflow Docker image. After running apt-get install -y curl zip git install the latest tensorflow pip package with:

pip install

to get optimizations enabled on x86 machines, such as AVX2 if your processor supports it, try uninstalling the default tensorflow with pip uninstall tensorflow

and installing the latest build from Intel

pip install



Clone the latest tensorflow repository

git clone

cd tensorflow


Advanced: Build TensorFlow

If you need to compile TensorFlow from scratch, you can do it, but its very slow to get everything compiled. Once its compiled, its really fast to train models!

We have to start this way, until there's more automated here's a guide on how we did it

While this method takes a long time its the only way we were able to build models, hopefully there will be an easy to use pip installer soon!

We need to use version 0.23.1 of bazel (the build tool), so we'll install that specific version like this:

cd ~

curl -O -L

chmod +x




You can verify it with bazel version


For some reason, the image is still using Python 2.7, so grab the future package so we can run python3 code

pip install future

We also need to get the right version of the 'estimator' package (we use it later)

pip uninstall tensorflow_estimator

pip install -I tensorflow_estimator==1.13.0



We need to build a specific commit of TensorFlow, so clone the repo then switch to that commit

git clone

cd tensorflow

git checkout 4a464440b2e8f382f442b6e952d64a56701ab045



Go with the default configuration by running

yes "" | ./configure


Finally start the TensorFlow compile and speech training with

bazel run -c opt --copt=-mavx2 --copt=-mfma tensorflow/examples/speech_commands:train -- --model_architecture=tiny_conv --window_stride=20 --preprocess=micro --wanted_words="yes,no" --silence_percentage=25 --unknown_percentage=25 --quantize=1

This will create a micro model of the large speech data set with only "yes" and "no" words in the model (to keep it small/simple)


This will take many hours especially the first time! Go take a break and do something else (or, you can try using your computer but it will be slow because Docker is sucking up all the computational resources to compile 16,000 files)



After TensorFlow has completed compiling it will take another 2+ hours to run the training. In the end you will get something like this:


Training and freezing models

Start training a new micro speech model with

python tensorflow/examples/speech_commands/ -- --model_architecture=tiny_conv --window_stride=20 --preprocess=micro --wanted_words="yes,no" --silence_percentage=25 --unknown_percentage=25 --quantize=1

or, if using bazel

bazel run -c opt --copt=-mavx2 --copt=-mfma tensorflow/examples/speech_commands:train -- --model_architecture=tiny_conv --window_stride=20 --preprocess=micro --wanted_words="yes,no" --silence_percentage=25 --unknown_percentage=25 --quantize=1


This will run for a few hours


At the end you'll get your final test accuracy and checkpoint file


Checkpoint files are stored in /tmp


In this case we want /tmp/speech_commands_train/conv.ckpt-18000.* (the last place the trainer saved to)



Take the trained weights and turn them into a frozen model on disk.

python tensorflow/examples/speech_commands/ --model_architecture=tiny_conv --window_stride=20 --preprocess=micro --wanted_words="yes,no" --quantize=1 --output_file=/tmp/tiny_conv.pb --start_checkpoint=/tmp/speech_commands_train/conv.ckpt-100

or if using bazel something like:

bazel run tensorflow/examples/speech_commands:freeze -- --model_architecture=tiny_conv --window_stride=20 --preprocess=micro --wanted_words="yes,no" --quantize=1 --output_file=/tmp/tiny_conv.pb --start_checkpoint=/tmp/speech_commands_train/tiny_conv.ckpt-18000




Convert the TensorFlow model into a TF Lite file

bazel run tensorflow/lite/toco:toco -- --input_file=/tmp/tiny_conv.pb --output_file=/tmp/tiny_conv.tflite --input_shapes=1,49,40,1 --input_arrays=Reshape_1 --output_arrays='labels_softmax' --inference_type=QUANTIZED_UINT8 --mean_values=0 --std_values=9.8077


The file can now be found in /tmp/tiny_conf.tflite


Extract & Save

Finally, you can use docker cp to copy the file from your container to your desktop. From the host computer (not the docker contrainer) run docker cp CONTAINERID:/tmp/tiny_conf.tflite.

You should now have access to the file!


Here are some example files

Commit Docker

Now's a good time to 'save' our work. Run docker ps to list all your docker containers


you can 'save' this docker container to your account with

docker commit CONTAINER_ID USERNAME/mytensorflow

where the CONTAINER_ID is the 12 character that is to the left of the image name and USERNAME is your docker login name. So in my case, docker commit c2a0a7f0a7bb ladyada/mytensorflow

It will take a few minutes while Docker runs, eventually you'll get this on the terminal:


Then push it to docker hub with docker push username/containername


Then visit your dockerhub profile to see that you have in fact pushed the docker image