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Web app for keyword spotting using TensorflowJS
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Honkling : In-Browser Keyword Spotting System

Honkling is a novel web application with an in-browser keyword spotting system implemented with TensorFlow.js.

Honkling can efficiently identify simple commands (e.g., "stop" and "go") in-browser without a network connection. It demonstrates cross-platform speech recognition capabilities for interactive intelligent agents with its pure JavaScript implementation. For more details, please consult our writeup:

Honkling implements a residual convolutional neural network [1] and utilizes Speech Commands Dataset for training.

Click here to have the keyword spotting system in your hand!

Honkling-node & Honkling-assistant

Node.js implementation of Honkling is also available under Honking-node folder.

Honkling-assistant is a customizable voice-enabled virtual assistants implemented using Honkling-node and Electron.

Details about Honkling-node and Honkling-assistant can be found in:

  • Jaejun Lee, Raphael Tang, and Jimmy Lin. 2019. Universal voice-enabled user interfaces using JavaScript. In Proceedings of the 24th International Conference on Intelligent User Interfaces: Companion (IUI '19). ACM, New York, NY, USA, 81-82. DOI:

Pre-trained Weights

Pre-trained weights are available at Honkling-models.

Please run the following command to obtain pre-trained weights:

git submodule update --init --recursive

Customizing Honkling

Please refer honkling branch of honk to customize keyword set or train a new model.

Once you obtain weight file in json format using honk, move the file into weights/ directory and append weights[<wight_id>] = to link it to weights object.

Depending on change, config.js has to be updated and a model object can be instantiated as let model = new SpeechResModel(<wight_id>, commands);

Performance Evaluation

It is possible to evaluate the in-browser neural network inference performance of your device on the Evaluate Performance page of Honkling.

Evaluation is conducted on a subset of the validation and test sets used in training. Once the evaluation is complete, it will generate reports on input processing time (MFCC) and inference time.

As part of our research, we explored the network slimming [2] technique to analyze trade-offs between accuracy and inference latency. With honkling, it is possible to evaluate the performance on a pruned model as well!

The following is the evaluation result on Macbook Pro (2017) with Firefox:

Model Amount Pruned (%) Accuracy (%) Innput Processing (ms) Inference (ms)
RES8-NARROW - 90.78 21 10
RES8-NARROW-40 40 88.99 21 9
RES8-NARROW-80 80 84.90 22 9
RES8 - 93.96 23 24
RES8-40 40 93.99 23 17
RES8-80 80 91.66 22 11
  • Note that WebGL is disabled on Chrome and enabled on Firefox by default
  • Honkling uses RES8-NARROW
  • Details on model architecture can be found in the paper


[1]. Deep Residual Learning for Small-Footprint Keyword Spotting, Raphael Tang, Jimmy Lin, ICASSP 2018

[2]. Learning Efficient Convolutional Networks through Network Slimming, Zhuang Liu, Jianguo Li, Zhiqiang Shen, Gao Huang, Shoumeng Yan, Changshui Zhang, ICCV 2017

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