[DOC] Tutorial could include a classification example
Nobody has claimed this yet.
- Dominant language
- Jupyter Notebook
- Stars
- 604
- Forks
- 66
- PR merge metrics
- No merged PRs in 30d
Description
One of the common questions that I get is "how should i build an audio classifier?"
My canned response is "openl3 + sklearn RandomForest". The tutorial in the docs is great for the first half, but if it was expanded to show how to combine it with a classifier on some example dataset (eg urbansed or something), it would be much easier for novices to pick up and run with.
Contributor guide
No contributing guide indexed for this repository
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with the existing tutorial in the docs and review how it produces embeddings before considering the proposed urbansed example. Add a runnable classification example using scikit-learn's RandomForest, and ensure the tutorial takes a novice from embeddings through classifier results.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- jupyter-notebook, scikit-learn
- Domain
- documentation, machine-learning
- Issue type
- Documentation
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 45/100