ml-explore / ml-explore/mlx-examples
Using fine tuned whisper models in MLX must support beam search
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- Python
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Description
The ability to specify the beam search size is very important for whisper-based models to achieve good accuracy, but it seems that MLX does not support it.
Please expand this capability of MLX.
Contributor guide
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 locating the Whisper fine-tuning or inference entry point in the MLX examples and trace how decoding is configured. Done means fine-tuned Whisper models can accept and use a configurable beam-search size, with focused coverage if an existing test path is found.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 35/100