https://github.com/dharness/sqwak-app

Audio classifying service
https://github.com/dharness/sqwak-app

audio-classification machine-learning

Last synced: about 1 year ago
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Audio classifying service

• Host: GitHub
• URL: https://github.com/dharness/sqwak-app
• Owner: dharness
• Created: 2017-01-12T01:09:57.000Z (over 9 years ago)
• Default Branch: master
• Last Pushed: 2023-07-31T19:30:06.000Z (almost 3 years ago)
• Last Synced: 2025-06-14T04:02:55.312Z (about 1 year ago)
• Topics: audio-classification, machine-learning
• Language: JavaScript
• Size: 114 MB
• Stars: 5
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README/screenshot_app_view.png

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README

          
# Sqwak



  


    Sqwak allows users to train and deploy custom audio-models from a web browser. No coding needed, just upload your audio files and deploy!

  



## [Demo Video](https://vimeo.com/850298237)

[![Watch the video](README/video-poster.png)](https://vimeo.com/850298237)

### Description

Sqwak lets you create an audio classifier without writing any code!

To train your classifier, create some bins (such as 'clanking' 'quacking' and 'rock music') and upload your music into the appropriate bin.

After that, click the train button and voila, your classifier is now available at an api endpoint.

You can now POST to that endpoint with completely new sounds your classifier has never seen before, and it'll attempt to label it.




### Screenshots

  

    

      

      
Video Poster

    

    

      

      
App View

    

  

  

    

      

      
Upload Files

    

    

      

      
Train Model

    

  

  

    

      

      
Predict

    

    

      

      
Publish

    

  


### How it Works

1. Create a class you want to detect (example: a dog barking)

2. Upload audio files of that sound

3. Click "Train" and Sqwak handles all the audio parsing and training for you.

4. Publish your model to an API endpoint with 1 click.

5. Access your personal endpoint from your product to classify new audio files!

### Local Development

```

yarn install

yarn start

```