Make an object-oriented interface for embedding models
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Description
An object interface for extracting embeddings would make it simpler to extract multiple embeddings and would allow for the embedding model to just be loaded once instead of every time the `get_embedding` function is called.
At least to begin with, the interface could just be something like
```
m = EmbeddingModel(input_repr="mel256", content_type="music",
embedding_size=6144, center=True, hop_size=0.1):
emb, ts = m.get_embedding(audio, sr, verbose=1)
m.process_file(filepath, output_dir=None, suffix=None)
```
Basically, we'd just put all of the functions from https://github.com/marl/openl3/blob/master/openl3/core.py into a class.
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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 by reviewing openl3/core.py and the existing get_embedding and process_file entry points. Map those functions to the proposed EmbeddingModel interface, ensuring the embedding model can be loaded once and reused for multiple embeddings; done means the shown audio and file-processing calls work through the class.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 30/100