wav2vec2-base-960h
The base model pretrained and fine-tuned on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
Params
90 M
Context
—
Downloads 30d
1.8 M
Likes
418
Download history
daily snapshots · 106 days
▲ 11 K in the last 30 days (0.6%)
1.8 M1.6 M
Oct 12Oct 22Nov 1Nov 10
1.8 M1.3 M
Aug 13Sep 12Oct 12Nov 10
1.8 M1.3 M
Jul 28Sep 1Oct 6Nov 10
Can you run it?
Estimated VRAM at 8K context unless noted. Pick your hardware to see the verdict per quantization.
| File | Quant | Size | Est. VRAM | Verdict on RTX 4090 · 24 GB |
|---|---|---|---|---|
| model.safetensors | f32 | 0.4 GB | 0.9 GB | ✅ Runs comfortably |
Estimate: file size × 1.1 + KV cache at 8K + 0.5 GB overhead. Not a benchmark — how we calculate this.
Specifications
- Architecture
- Wav2Vec2ForCTC
- Parameters
- 90 M
- Tensor type
- F32
- Vocabulary
- 32
- Layers / heads
- 12 / 12
- Licence
- apache-2.0
- First seen on the Hub
- 2022-03-02
- Training datasets
- librispeech_asr
- LibriSpeech (clean) (reported)
- 3.4
- LibriSpeech (other) (reported)
- 8.6
- Added to our catalog
- 2026-07-28
Family
Base model and the most-downloaded derivatives in the catalog.
Compare with any automatic-speech-recognition model