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

This guide was first published on Jul 17, 2019. It was last updated on Jul 17, 2019.

This page (Training and freezing models) was last updated on May 17, 2021.

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