training data for vae
Nobody has claimed this yet.
Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 20/100
- Issue type
- Documentation
- Clarity
- Needs clarification
- Activity status
- Stale
- Tech stack
- python
- Domain
- machine-learning
Research direction
Start by reviewing the technical report referenced in the issue and the audio VAE training documentation or entry points. The issue names no files or tests and does not define a code or documentation change; completion would require an agreed answer or concrete scope for evaluating data quality, quantity, and speech performance.
Written by the indexing model from the issue text.
Description
The technical report mentions that an audio VAE was trained using over 1 million music-related data samples. However, in my testing, I found that this VAE also performs quite well on speech. This makes me curious: in audio training, is audio quality more important than quantity? Is a dataset of 1 million audio samples sufficient?
- Dominant language
- Python
- Stars
- 3.9k
- Forks
- 488
- PR merge metrics
- No merged PRs in 30d
Getting set up
This project ships no dev container, Dockerfile or contributing guide, so setting up is up to you: start from its README, and see our first-contribution guide for the general steps.
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.
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