Conformer
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
- Organisation type
- Industry
- Country
- United States of America
- Published
- 16 May 2020
- Authors
- Anmol Gulati, James Qin, Chung-Cheng Chiu, Niki Parmar, Yu Zhang, Jiahui Yu, Wei Han, Shibo Wang, Zhengdong Zhang, Yonghui Wu, Ruoming Pang
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
- 118.8M
- Training data
- tokens
118.8M for Conformer(L)
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
- Highly cited,SOTA improvement
- Record confidence
- Confident
- Citations
- 4,054
"Conformer significantly outperforms the previous Transformer and CNN based models achieving state-of-the-art accuracies. On the widely used LibriSpeech benchmark, our model achieves WER of 2.1%/4.3% without using a language model and 1.9%/3.9% with an external language model on test/testother"
Sources
Where this record came from and when it was last checked.
- Reference
- Conformer: Convolution-augmented Transformer for Speech Recognition
- Last updated
- 25 May 2026
What the numbers mean
What this model is
Conformer was published by Google, in United States of America, in May 2020. It comes out of industry.
It works in Speech, and is recorded as doing speech recognition (ASR).
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
What went into building it
Its inclusion criterion is highly cited,SOTA improvement.
Answers
Conformer — common questions
Who created Conformer?
Conformer was published by Google, based in United States of America, categorised as industry.
When was Conformer released?
Conformer was published in May 2020. 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 Conformer used for?
Conformer 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 Conformer?
None. Conformer 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 Conformer open source?
No. Conformer has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Conformer have?
Conformer has 118.8M parameters. 118.8M for Conformer(L). 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.