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, 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:
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
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,
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, Source]
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:
@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}
}