ContextNet
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
- 7 May 2020
- Authors
- Wei Han, Zhengdong Zhang, Yu Zhang, Jiahui Yu, Chung-Cheng Chiu, James Qin, Anmol Gulati, 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
- 112.7M
- Training data
- 349,200,000 tokens
Table 5
970 hours of speech in the LibreSpeech experiments. There is mention of a "large scale experiment" which trained on audio from YouTube videos. Tracing the references, it appears to be similar to the dataset used in https://arxiv.org/abs/1610.09975, which has 125,000 hours of transcribed audio for training. However, they mention in footnote 2 that the training and evaluation set have changed from previous experiments. 125k hours at 13,680 words per hour = 1.71B words
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
- Likely
- Citations
- 302
"We demonstrate that on the widely used Librispeech benchmark, ContextNet achieves a word error rate (WER) of 2.1%/4.6% without external language model (LM), 1.9%/4.1% with LM and 2.9%/7.0% with only 10M parameters on the clean/noisy LibriSpeech test sets. This compares to the best previously published model of 2.0%/4.6% with LM and 3.9%/11.3% with 20M parameters"
Sources
Where this record came from and when it was last checked.
- Reference
- ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context
- Last updated
- 25 May 2026
What the numbers mean
What this model is
ContextNet was published by Google, in the country recorded as United States of America, during May 2020. The publishing organisation is categorised as industry.
It works in the domain of Speech, and is recorded as performing the task of speech recognition (ASR).
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
What went into building it
The training set ran to roughly 349,200,000 tokens of text.
It is tracked in the underlying dataset for one reason in particular: sOTA improvement.
Answers
ContextNet — common questions
ContextNet— how many parameters does it have?
It has a parameter count of 112.7M. Table 5. 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.
ContextNet— who created it?
It was published by Google, based in United States of America, an organisation categorised as industry.
ContextNet— when was it released?
It 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.
ContextNet— what is it used for?
It works in the domain of Speech, and is recorded as handling the task of speech recognition (ASR). These are the areas it was designed around; they describe intent rather than a hard boundary.
ContextNet— what GPU do I need to run it?
None. This 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.
ContextNet— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
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.