accraze / audio-soup Goto Github PK

All url links on feature cards should open in a new tab, instead of navigating away from the current page.

Users should have the ability to compare features across different audio samples.

This would most likely be a separate view, like :/compare?sample_id1=333&sample_id2=444&feature_type=spectral

There should be more documentation (and maybe testing) of the load-dataset command.
This would include docstrings/comments/README updates.

We should think about expanding the annotations section in the Sample Review grid view.

For now the text property is a free-text field, although it is a JSON field within the DB.

Semantic text representation could be added as a way to help users create more robust annotations as mentioned by Ishibashi et. al in https://dl.acm.org/doi/pdf/10.1145/3377325.3377483.

We should make a Github workflow that auto-deploys the demo image to Heroku.

Something like this: https://github.com/marketplace/actions/deploy-to-heroku#deploy-with-docker

There's an issue with how the MFCC delta's graph gets generated, especially with how the higher-order labels get covered by the above graph. See screenshot below:

The “Deltas” features should eventually be expanded into something like a “Feature Manipulation” or “Feature Augmentation” filter, which would allow users to perform different types of transformations or filters across all the files in a dataset. A sandbox would be helpful for users to experiment with adding different manipulation types prior to applying the changes across the dataset.

When using the load-dataset CLI command, additional non-audio files are being added (like .DS_Store).
The script should check to make sure it is only adding audio files to the dataset and skip non-audio files.

There is a ton of duplicated code related to feature extraction and image generation.
It is all currently in src/public/views.py and should be moved into it's own class or module somewhere else.

Due to performance concerns, the backend WSGI server should be migrated to ASGI.

There are a couple of decent framework options:

• FastAPI - https://fastapi.tiangolo.com/
• Quart - https://pgjones.gitlab.io/quart/ (this is supposed to be a drop-in Flask replacement)

We would probably want to use uvicorn or hypercorn as well.