whatmodel.sh / stt / wav2vec 2.0 Base 960h
wav2vec 2.0 Base 960h
↗ share
+ contribute
wav2vec 2.0 Base 960h is an open-weights speech-to-text model from Meta, released June 2020. Its published size is 0.094B parameters. Licence: apache-2.0.
Catalogue identity notes
Exact checkpoint facebook/wav2vec2-base-960h (CTC fine-tuned on LibriSpeech 960h). Params: HF safetensors total; released: arXiv 2006.11477v1 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 wav2vec 2.0 Base 960h, out of 176 with a score, then more from Meta.
| # | model | score | where | ||
|---|---|---|---|---|---|
| 172 | wav2vec 2.0 Large 960h | 7±3 | local | ||
| 173 | data2vec Audio Base 960h | 4±4 | local | ||
| 174 | wav2vec 2.0 Base 960h | 2±3 | local | ||
| 175 | MMS-1B-FL102 | -9±5 | local | ||
| 176 |  MedASR | -14±4 | local | ||
| more from Meta | |||||
| 1 | Muse Voice Transcribe 1.0 | 71±3 | api $0.0030/audio min | ||
| 58 | Omnilingual ASR LLM 7B | 58±2 | local | ||
| 59 | Omnilingual ASR LLM 7B v2 | 58±3 | local | ||
see all speech-to-text models · find the ones that fit your machine
$ what it's good at
other tests4 not in a task · 95% of its score
| test | result | vs the others in its run | counts |
|---|---|---|---|
| Open ASR English short-form WER (March 2026) | 29.4% ✎ | ▁▃▁▃··▁▁▂██▆ beats 1/86 | 41% |
| LibriSpeech test-other WER lab | 8.6% ✎ | ▃·▂·▂······█ beats 0/6 | 36% |
| LibriSpeech test-clean WER +1 | 3.7% ✎ | ▂··▂·▃▂█▃▃▃▃ beats 10/16 | 15% |
| TED-LIUM test WER (short-form) | 18.8% ✎ | ▂····▃▂▂█▃▃▂ beats 1/13 | 3.0% |
$ where to run it
Other ways to run it
- Run it as published · needs ~0.95 GB · from huggingface.co/facebook
uv run --with transformers --with torch python -c "from transformers import pipeline; print(pipeline('automatic-speech-recognition', model='facebook/wav2vec2-base-960h', chunk_length_s=30)('audio.wav')['text'])"
Weights and documentation: huggingface.co/facebook ↗
$ how the score was worked out · Meta (facebook org) Hugging F… 49%, Open ASR Leaderboard 41%, Speech Robust Bench (arXiv:24… 5%
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 |
|---|---|---|
 Meta (facebook org) Hugging Face ASR model cards: published LibriSpeech WER for its own checkpoints · lab-reported | 2 | 49% |
| Open ASR Leaderboard | 1 | 41% |
 Speech Robust Bench (arXiv:2403.07937v3) · clean-speech WER of 25+ open ASR models | 2 | 5% |
| prior · pull toward the middle | 5% | |
| weighted average | = 1.8 |
$ 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