https://github.com/arm-software/ml-kws-for-mcu

Keyword spotting on Arm Cortex-M Microcontrollers
https://github.com/arm-software/ml-kws-for-mcu

arm cmsis-nn deep-neural-networks machine-learning microcontrollers python

Last synced: 3 days ago
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Keyword spotting on Arm Cortex-M Microcontrollers

• Host: GitHub
• URL: https://github.com/arm-software/ml-kws-for-mcu
• Owner: ARM-software
• License: apache-2.0
• Created: 2017-12-13T12:55:15.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2019-04-10T15:17:43.000Z (over 5 years ago)
• Last Synced: 2024-09-30T13:41:54.326Z (3 days ago)
• Topics: arm, cmsis-nn, deep-neural-networks, machine-learning, microcontrollers, python
• Language: C
• Homepage:
• Size: 19 MB
• Stars: 1,125
• Watchers: 86
• Forks: 414
• Open Issues: 53
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Keyword spotting for Microcontrollers 

This repository consists of the tensorflow models and training scripts used 

in the paper: 

[Hello Edge: Keyword spotting on Microcontrollers](https://arxiv.org/pdf/1711.07128.pdf). 

The scripts are adapted from [Tensorflow examples](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/speech_commands) 

and some are repeated here for the sake of making these scripts self-contained.

To train a DNN with 3 fully-connected layers with 128 neurons in each layer, run:

```

python train.py --model_architecture dnn --model_size_info 128 128 128 

```

The command line argument *--model_size_info* is used to pass the neural network layer

dimensions such as number of layers, convolution filter size/stride as a list to models.py, 

which builds the tensorflow graph based on the provided model architecture 

and layer dimensions. 

For more info on *model_size_info* for each network architecture see 

[models.py](models.py).

The training commands with all the hyperparameters to reproduce the models shown in the 

[paper](https://arxiv.org/pdf/1711.07128.pdf) are given [here](train_commands.txt).

To run inference on the trained model from a checkpoint on train/val/test set, run:

```

python test.py --model_architecture dnn --model_size_info 128 128 128 --checkpoint 

```

To freeze the trained model checkpoint into a .pb file, run:

```

python freeze.py --model_architecture dnn --model_size_info 128 128 128 --checkpoint 

 --output_file dnn.pb

```

## Pretrained models

Trained models (.pb files) for different neural network architectures such as DNN,

CNN, Basic LSTM, LSTM, GRU, CRNN and DS-CNN shown in 

this [arXiv paper](https://arxiv.org/pdf/1711.07128.pdf) are added in 

[Pretrained_models](Pretrained_models). Accuracy of the models on validation set, 

their memory requirements and operations per inference are also summarized in the 

following table.



To run an audio file through the trained model (e.g. a DNN) and get top prediction, 

run:

```

python label_wav.py --wav  --graph Pretrained_models/DNN/DNN_S.pb 

--labels Pretrained_models/labels.txt --how_many_labels 1

```

## Quantization Guide and Deployment on Microcontrollers

A quick guide on quantizing the KWS neural network models is [here](Deployment/Quant_guide.md). 

The example code for running a DNN model on a Cortex-M development board is also provided [here](Deployment).