Conformer + Wav2vec 2.0 + 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
- Google,Google Research,Google Brain
- Organisation type
- Industry,Industry,Industry
- Country
- United States of America
- Published
- 20 October 2020
- Authors
- Yu Zhang, James Qin, Daniel S. Park, Wei Han, Chung-Cheng Chiu, Ruoming Pang, Quoc V. Le, 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
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
- 7.6 × 10²¹ FLOP
- How it was established
- Hardware
"We train with global batch size 2048 on 256/512 Google TPU V3 cores for 3-4 days for the XL/XXL models respectively... We fine-tune the pre-trained checkpoints (400k steps) with global batch size 1024/512 on 256/512 Google TPU v3 cores for 1-3 days for the XL/XXL models" TPU v3 chips are 123 teraflop/s. 2 chips per core 512 cores * 7 days * 24 * 3600 * 123 tflops * (1 chip/2 cores) * 0.4 (assumed utilization) = 7.6e21
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
- Chips used
- 256
- Wall-clock time
- 168 hours (7 days)
- Power draw
- 234.3 kW
- Compute cost
- $9,450
7 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
- 332
"By doing so, we are able to achieve word-error-rates (WERs) 1.4%/2.6% on the LibriSpeech test/test-other sets against the current state-of-the-art WERs 1.7%/3.3%."
Sources
Where this record came from and when it was last checked.
- Reference
- Pushing the Limits of Semi-Supervised Learning for Automatic Speech Recognition
- Last updated
- 25 May 2026
What the numbers mean
Where it came from
Conformer + Wav2vec 2.0 + Noisy Student was published by Google,Google Research,Google Brain, in the country recorded as United States of America, during October 2020. The publishing organisation is categorised as industry,Industry,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.
Training and provenance
The training run consumed about 7.6 × 10²¹ FLOP, on hardware recorded as Google TPU v3. That figure measures what producing the model cost, and has no bearing on how fast it answers.
The reason it appears in this catalogue at all: sOTA improvement.
Answers
Conformer + Wav2vec 2.0 + Noisy Student — common questions
Conformer + Wav2vec 2.0 + Noisy Student— 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.
Conformer + Wav2vec 2.0 + Noisy Student— how much compute was used to train it?
Training consumed around 7.6 × 10²¹ FLOP, on hardware recorded as 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.
Conformer + Wav2vec 2.0 + Noisy Student— 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.
Conformer + Wav2vec 2.0 + Noisy Student— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
Conformer + Wav2vec 2.0 + Noisy Student— how many parameters does it have?
It has a parameter count of 1B. 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.
Conformer + Wav2vec 2.0 + Noisy Student— who created it?
It was published by Google,Google Research,Google Brain, based in United States of America, an organisation categorised as industry,Industry,Industry.
Conformer + Wav2vec 2.0 + Noisy Student— when was it released?
It was published in October 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.
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.