Speaker Diarization model
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Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 1.4k
- Forks
- 138
- PR merge metrics
- No merged PRs in 30d
Description
对于原始音频，我们首先使用内部的说话人分离模型进行语音分段和说话人标注。 基于预训练基模，我们的说话人分离模型性能已经优于开源说话人分离模型 pyannote-speaker-diarization-3.1 及其商用版本 pyannoteAI 。
请问 speaker diarization 模型的细节是什么，有公开的代码和文献吗？ @xpqiu
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 names no repository files, tests, or entry points. Start by reviewing the description of the internal speaker diarization model and its comparison with pyannote-speaker-diarization-3.1; done means documenting the model details and identifying any public code or literature available.
Written by the indexing model from the issue text.
Assessment
- Domain
- audio-video-rtc, documentation, machine-learning
- Issue type
- Documentation
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100