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Distil-Whisper distil-medium.en
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Distil-Whisper distil-medium.en is an open-weights speech-to-text model from Hugging Face (Distil-Whisper), released October 2023. Its published size is 0.39B parameters. Licence: mit.
Catalogue identity notes
Exact checkpoint distil-whisper/distil-medium.en; distinct from catalogue distil-large-v3-5. Params: HF safetensors total. released: HF repo creation month.
Score within the Open ASR English short-form WER (March 2026) comparison set only.
$ how it compares
The speech-to-text models ranked either side of Distil-Whisper distil-medium.en, out of 176 with a score, then more from Hugging Face (Distil-Whisper).
| # | model | score | where | ||
|---|---|---|---|---|---|
| 136 |  ESPnet Conformer + S4 decoder (no LM) | 45±4 | local | ||
| 137 |  Omnilingual ASR CTC 300M | 45±2 | local | ||
| 138 | Distil-Whisper distil-medium.en | 44±3 | local | ||
| 139 |  Whisper small.en | 44±3 | local | ||
| 140 | ESPnet Branchformer 18-layer (70 epochs, no LM) | 44±4 | local | ||
| more from Hugging Face (Distil-Whisper) | |||||
| 60 | Distil-Whisper distil-large-v3.5 | 58±1 | local | ||
| 77 | Distil-Whisper distil-large-v3 | 56±2 | local | ||
| 122 | Distil-Whisper distil-large-v2 | 48±2 | local | ||
see all speech-to-text models · find the ones that fit your machine
$ what it's good at
long-form transcription▃▁▄▁·▁▂█▄▁▁▁ #27 of 27 * · 39% of its score · 1 test
Trails STT En Conformer-CTC Small, STT En Conformer-Transducer Small here, though they rank lower overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| Open ASR English long-form WER | 19.0% ✎ | ▃▁▅▁·▃▅█▆▁▂▂ beats 0/30 | 39% |
other tests2 not in a task · 57% of its score
| test | result | vs the others in its run | counts |
|---|---|---|---|
| Open ASR English short-form WER (March 2026) | 8.8% ✎ | ▁▁▁▃··▁▁▂██▆ beats 24/86 | 39% |
| Distil-Whisper · 4-set out-of-distribution short-form mean WER lab | 11.1% ✎ | █·█··█·█···█ beats 1/4 | 18% |
$ where to run it
Other ways to run it
- Run it as published · needs ~1.44 GB · from huggingface.co/distil-whisper (official)
uv run --with transformers --with torch python -c "from transformers import pipeline; print(pipeline('automatic-speech-recognition', model='distil-whisper/distil-medium.en')('audio.wav', return_timestamps=True)['text'])"
Official weights and documentation: huggingface.co/distil-whisper ↗
$ how the score was worked out · Open ASR Leaderboard 78%, huggingface/distil-whisper RE… 18%
Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its counts column above; here it's added up by who ran the tests. How it works
| source | results | share of the score |
|---|---|---|
 Open ASR Leaderboard | 2 | 78% |
 huggingface/distil-whisper README (Distil-Whisper headline WER table) · lab-reported | 1 | 18% |
| prior · pull toward the middle | 4% | |
| weighted average | = 44.1 |
$ improve this page
Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.
data updated · sources and licences · json