---
license: mit
tags:
  - wav2vec2
  - forced-alignment
  - torchaudio
  - automatic-speech-recognition
---

# wav2vec2 base 960h — torchaudio checkpoint, rehosted unmodified

The checkpoint behind `torchaudio.pipelines.WAV2VEC2_ASR_BASE_960H`, byte-for-byte as PyTorch
publishes it. **Nothing here is modified, retrained, quantized or converted** — this is a rehost, not
a derivative.

| | |
|---|---|
| File | `wav2vec2_fairseq_base_ls960_asr_ls960.pth` |
| Size | 377,664,473 bytes |
| SHA-256 | `488fd4f16de84438ffc945334278c1b9fb9b7159a806c1080b16111a958c945d` |
| Copied from | <https://download.pytorch.org/torchaudio/models/wav2vec2_fairseq_base_ls960_asr_ls960.pth> |
| Model | wav2vec 2.0 BASE, pre-trained and fine-tuned for ASR on 960 h of LibriSpeech |

Rehosted for [Thematic Thing](https://thematicthing.com), an on-device qualitative-analysis
application, which uses it for CTC forced alignment (word-level timestamps) and needs the artefact it
downloads to come from a source it controls, with a written licence chain.

## Usage

`torchaudio` resolves this file by name from its own CDN. To use this copy instead, place it in the
torch hub checkpoint cache under its original filename before building the bundle — `torch.hub`
short-circuits on an existing file and makes no HTTP call:

```python
import torch, torchaudio
dst = Path(torch.hub.get_dir()) / "checkpoints" / "wav2vec2_fairseq_base_ls960_asr_ls960.pth"
# ... download this repo's file to `dst`, verify its SHA-256, then:
model = torchaudio.pipelines.WAV2VEC2_ASR_BASE_960H.get_model()
```

## Licence and attribution

The weights are the work of **Meta Platforms (Facebook, Inc. and its affiliates)**, published with
the *wav2vec 2.0* release in [`facebookresearch/fairseq`](https://github.com/facebookresearch/fairseq)
under the **MIT License**, and redistributed by PyTorch under that same licence. torchaudio states
this in its own source, in the docstring of the bundle that names this exact file
([`torchaudio/pipelines/_wav2vec2/impl.py`](https://github.com/pytorch/audio/blob/v2.8.0/src/torchaudio/pipelines/_wav2vec2/impl.py),
v2.8.0, lines 343–354):

> Originally published by the authors of *wav2vec 2.0* under MIT License and redistributed with the
> same license.
> [[License](https://github.com/facebookresearch/fairseq/blob/ce6c9eeae163ac04b79539c78e74f292f29eaa18/LICENSE),
> [Source](https://github.com/facebookresearch/fairseq/blob/ce6c9eeae163ac04b79539c78e74f292f29eaa18/examples/wav2vec#pre-trained-models)]

That pinned `LICENSE` is the MIT License, **Copyright (c) Facebook, Inc. and its affiliates**, and is
included here verbatim as `LICENSE-fairseq-MIT.txt`. PyTorch is doing exactly what this repository
does — hosting a copy of the fairseq weights under the licence they were released with.

The `.pth` container is torchaudio's own parameter layout for the same weights, so torchaudio's
**BSD 2-Clause** licence (Copyright (c) 2017 Facebook Inc.) is included as
`LICENSE-torchaudio-BSD-2-Clause.txt`.

Please cite the authors of the model:

```bibtex
@article{baevski2020wav2vec,
  title={wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations},
  author={Baevski, Alexei and Zhou, Henry and Mohamed, Abdelrahman and Auli, Michael},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  year={2020}
}
```