Model family
Whisper Models
Whisper models are used for ASR, transcription, subtitles, podcast processing, meeting notes, and multilingual audio.
Best for
Audio
Use this family hub to compare Whisper variants for audio workflows, then open the detail page for deeper deployment notes.
Transcription
Use this family hub to compare Whisper variants for transcription workflows, then open the detail page for deeper deployment notes.
Speech recognition
Use this family hub to compare Whisper variants for speech recognition workflows, then open the detail page for deeper deployment notes.
Local
Use this family hub to compare Whisper variants for local workflows, then open the detail page for deeper deployment notes.
Variants
Whisper models grouped by workflow
Audio
Whisper Large V3
OpenAI · Whisper
Best for: Builders adding local transcription, podcast processing, meeting notes, or audio translation.
Local: Commonly used for local transcription workflows; GPU improves batch throughput.
Whisper Large V3 Turbo
OpenAI · Whisper
Best for: Fast transcription and multilingual audio workflows
Local: Use the exact checkpoint and quantization that matches your hardware and latency target.
Whisper Large V2
OpenAI · Whisper
Best for: Teams running accuracy-first transcription, subtitles, meeting notes, or archive workflows on multilingual audio.
Local: Use the exact checkpoint and quantization that matches your hardware and latency target.
Whisper Medium
OpenAI · Whisper
Best for: Builders balancing local transcription quality and runtime cost for meetings, media, and batch audio processing.
Local: Use the exact checkpoint and quantization that matches your hardware and latency target.
Whisper Small
OpenAI · Whisper
Best for: Local transcription setups that need a lighter model for captions, notes, and general speech-to-text tasks.
Local: Use the exact checkpoint and quantization that matches your hardware and latency target.
Whisper Base
OpenAI · Whisper
Best for: Developers who want a practical starting point for local captioning, voice notes, and CPU-leaning ASR experiments.
Local: Use the exact checkpoint and quantization that matches your hardware and latency target.
Whisper Tiny
OpenAI · Whisper
Best for: Fast prototypes, edge-style experiments, and low-memory ASR tests where accuracy tradeoffs are acceptable.
Local: Use the exact checkpoint and quantization that matches your hardware and latency target.
Distil-Whisper Large V3
Hugging Face / community · Whisper
Best for: Builders who want strong multilingual transcription quality with a lighter checkpoint for production-style speech pipelines.
Local: Use the exact checkpoint and quantization that matches your hardware and latency target.
Faster-Whisper Large V3
SYSTRAN / community · Whisper
Best for: Teams optimizing Whisper-style batch or local transcription pipelines where runtime efficiency and deployment control matter.
Local: Use the exact checkpoint and quantization that matches your hardware and latency target.
Compare
All Whisper models in the directory
| Model | Type | Best for | Local runner notes | License | Detail |
|---|---|---|---|---|---|
| Whisper Large V3 | Audio | Builders adding local transcription, podcast processing, meeting notes, or audio translation. | Commonly used for local transcription workflows; GPU improves batch throughput. | MIT | Open |
| Whisper Large V3 Turbo | Audio | Fast transcription and multilingual audio workflows | Use the exact checkpoint and quantization that matches your hardware and latency target. | Check exact model card | Open |
| Whisper Large V2 | Audio | Teams running accuracy-first transcription, subtitles, meeting notes, or archive workflows on multilingual audio. | Use the exact checkpoint and quantization that matches your hardware and latency target. | Apache 2.0 | Open |
| Whisper Medium | Audio | Builders balancing local transcription quality and runtime cost for meetings, media, and batch audio processing. | Use the exact checkpoint and quantization that matches your hardware and latency target. | Apache 2.0 | Open |
| Whisper Small | Audio | Local transcription setups that need a lighter model for captions, notes, and general speech-to-text tasks. | Use the exact checkpoint and quantization that matches your hardware and latency target. | Apache 2.0 | Open |
| Whisper Base | Audio | Developers who want a practical starting point for local captioning, voice notes, and CPU-leaning ASR experiments. | Use the exact checkpoint and quantization that matches your hardware and latency target. | Apache 2.0 | Open |
| Whisper Tiny | Audio | Fast prototypes, edge-style experiments, and low-memory ASR tests where accuracy tradeoffs are acceptable. | Use the exact checkpoint and quantization that matches your hardware and latency target. | Apache 2.0 | Open |
| Distil-Whisper Large V3 | Audio | Builders who want strong multilingual transcription quality with a lighter checkpoint for production-style speech pipelines. | Use the exact checkpoint and quantization that matches your hardware and latency target. | MIT | Open |
| Faster-Whisper Large V3 | Audio | Teams optimizing Whisper-style batch or local transcription pipelines where runtime efficiency and deployment control matter. | Use the exact checkpoint and quantization that matches your hardware and latency target. | Check exact model card | Open |