openai/whisper
Robust Speech Recognition via Large-Scale Weak Supervision
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Where it stands
Rank, growth pace and release cadence relative to everything GStars tracks.
No releases in the last 90 days; the most recent was 495 days ago, with 84 contributors on GitHub.
Peaked at #62 in the weekly ranking the week of Nov 9, its best in the past year.
Use Cases & Benefits
- Provides a multitask Transformer model for speech recognition, translation, and language identification from audio.
- Combines multiple speech processing tasks into a single model, simplifying pipelines and supporting many languages.
- Use for transcribing English or multilingual audio files with configurable model sizes for speed or accuracy.
- Use for translating non-English speech into English using multilingual models with built-in translation capabilities.
- Use for detecting spoken language in audio to enable language-specific processing or routing in applications.
Should you depend on it?
License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.
License
MIT
Permissive: commercial use, modification and redistribution are allowed with attribution.
Community files
- README: present
Watch-outs
No watch-outs from GStars' checks.
General guidance from repository metadata, not legal advice. How we score
From the README
Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse audio and is also a multitasking model that can perform multilingual speech recognition, speech translation, and language identification.
A Transformer sequence-to-sequence model is trained on various speech processing tasks, including multilingual speech recognition, speech translation, spoken language identification, and voice activity detection. These tasks are jointly represented as a sequence of tokens to be predicted by the decoder, allowing a single model to replace many stages of a traditional speech-processing pipeline. The multitask training format uses a set of special tokens that serve as task specifiers or classification targets.
Daily snapshots
Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots
+4,706 stars over 83 days across 8 snapshot days; best day +47 on Sep 7.
Forks
13.6K+544
Open issues
179+44
Compared with peers in Backend
Fastest-growing repositories GStars tracks in this cohort for the week of Nov 8, by 7-day star growth
| Repository | Stars | +7d | Health | Last release |
|---|---|---|---|---|
| anthropics/skills | 192.3K | +5.2K | 75 | — |
| zed-industries/zed | 94.7K | +2.3K | 75 | 6 days ago |
| google/skills | 23.8K | +2.0K | 75 | — |
| NationalSecurityAgency/ghidra | 99.4K | +1.8K | 75 | 19 days ago |
| DuarteSantos8/openGym | 20.8K | +1.7K | 75 | 7 days ago |
| openai/whisper | 111.9K | — | 35 | 495 days ago |
| Category median | 94.7K | +2.0K | 75 | — |
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