EQ correction on the reference audio is crucial for achieving high-quality results.
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- Python
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Description
The model tends to exaggerate certain harsh high frequencies. Applying pre-EQ to the reference audio can make a significant difference, especially with certain voice types. Adding a pre-EQ tool and support for short preview samples would be a major improvement.
The approach should include applying pre-EQ to reduce some of the high frequencies and possibly applying post-EQ for fine-tuning and compensation.
De-essing is whats needed, model tends to exaggerate voices sibilance.
Analyze the frequency of the voice's ess sound by sampling several instances and calculating the range of ess frequencies. Male voices sibilance range in 3–6 kHz, while female voice's typically range in 6–8 kHz. A multiband compressor or equalizer should do the trick.
Contributor guide
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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
The issue mentions only sample.zip and no repository files, tests, or entry points. Start by inspecting the supplied audio and locating the reference-audio processing path; done would require an agreed scope for pre/post-EQ, de-essing, and short previews, plus validation of the resulting audio quality.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- audio-video-rtc
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100