ContextNet + Noisy Student
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
- 19 January 2020
- Authors
- Daniel S. Park, Yu Zhang, Ye Jia, Wei Han, Chung-Cheng Chiu, Bo Li, Yonghui Wu, Quoc V. Le
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.
- Training data
- tokens
Training compute
The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.
- Training compute
- 8.2 × 10²¹ FLOP
- How it was established
- Hardware
"We train 6 generations of models numbered 0 to 5, where we count the baseline model trained with the supervised set as the zeroth generation. Each generation is trained ... on 32 Google Cloud TPU chips for 10 days." The TPU version is likely v3 given this is a 2020 paper. we get 6 * 10 * 24 * 3600 * 32 * 123 tflops * 0.4 (assumed utilization) = 8.16e21
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- Google TPU v3
- Wall-clock time
- 1,440 hours (60 days)
- Compute cost
- $14,226
roughly 10 days
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
- 264
"We are thus able to improve upon the previous state-of-the-art clean/noisy test WERs achieved on LibriSpeech 100h (4.74%/12.20%) and LibriSpeech (1.9%/4.1%)"
Sources
Where this record came from and when it was last checked.
- Reference
- Improved Noisy Student Training for Automatic Speech Recognition
- Last updated
- 25 May 2026
What the numbers mean
Background
ContextNet + Noisy Student was published by Google, in United States of America, in January 2020. The organisation is categorised as industry.
It works in Speech, and is recorded as doing speech recognition (ASR).
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
How it was trained
The training run consumed about 8.2 × 10²¹ FLOP, on Google TPU v3. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
Its inclusion criterion is sOTA improvement.
Answers
ContextNet + Noisy Student — common questions
Is ContextNet + Noisy Student open source?
No. ContextNet + Noisy Student has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does ContextNet + Noisy Student have?
No parameter count has been published for ContextNet + Noisy Student, which is why no memory or speed figure appears on this page.
Who created ContextNet + Noisy Student?
ContextNet + Noisy Student was published by Google, based in United States of America, categorised as industry.
When was ContextNet + Noisy Student released?
ContextNet + Noisy Student was published in January 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 ContextNet + Noisy Student used for?
ContextNet + Noisy Student works in Speech, and is recorded as handling speech recognition (ASR). These are the areas it was designed around; they describe intent rather than a hard boundary.
How much compute was used to train ContextNet + Noisy Student?
Around 8.2 × 10²¹ FLOP, on Google TPU v3. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
What GPU do I need to run ContextNet + Noisy Student?
None. ContextNet + Noisy Student 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.
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.