W2v-BERT
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- Google Brain,Massachusetts Institute of Technology (MIT)
- Organisation type
- Industry,Academia
- Country
- United States of America
- Published
- 7 August 2021
- Authors
- Yu-An Chung, Yu Zhang, Wei Han, Chung-Cheng Chiu, James Qin, Ruoming Pang, Yonghui Wu
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Speech
- Task
- Speech recognition (ASR)
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Parameters
- 1B
- Training data
- tokens
1B for XXL model
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Closed — provider access only
- Model access
- Unreleased
- Training code
- Unreleased
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- SOTA improvement
- Record confidence
- Confident
- Citations
- 534
"Our experiments show that w2v-BERT achieves competitive results compared to current state-of-the-art pre-trained models on the LibriSpeech benchmarks when using the Libri-Light 60k corpus as the unsupervised data. In particular, when compared to published models such as conformer-based wav2vec 2.0 and HuBERT, our model shows 5% to 10% relative WER reduction on the test-clean and test-other subsets"
Sources
Where this record came from and when it was last checked.
- Reference
- W2v-BERT: Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-Training
- Last updated
- 25 May 2026
What the numbers mean
Background
W2v-BERT was published by Google Brain,Massachusetts Institute of Technology (MIT), in United States of America, in August 2021. industry,Academia is the category the publisher falls under.
It works in Speech, and is recorded as doing speech recognition (ASR).
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Training and provenance
Its inclusion criterion is sOTA improvement.
Answers
W2v-BERT — common questions
Who created W2v-BERT?
W2v-BERT was published by Google Brain,Massachusetts Institute of Technology (MIT), based in United States of America, categorised as industry,Academia.
When was W2v-BERT released?
W2v-BERT was published in August 2021. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
What is W2v-BERT used for?
W2v-BERT works in Speech, and is recorded as handling speech recognition (ASR). Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run W2v-BERT?
None. W2v-BERT is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
Is W2v-BERT open source?
No. W2v-BERT has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does W2v-BERT have?
W2v-BERT has 1B parameters. 1B for XXL model. That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.