AI models
Conformer + Wav2vec 2.0 + Noisy Student
Training compute
7.6×10²¹ FLOP
Parameters
1B
Published
Oct 20, 2020
Conformer + Wav2vec 2.0 + Noisy Student is an AI model developed by Google, Google Research and Google Brain (United States), first published in October 2020. It works in the speech domain, on tasks such as speech recognition (asr).
Training it took an estimated 7.6×10²¹ FLOP of compute (estimation method: hardware). The model has 1,000,000,000 parameters. Training ran on 256 Google TPU v3 for about 168 hours. The compute alone is estimated at $9K in 2023 dollars.
Access: Unreleased. Its weights are not openly released. The reference paper has 332 citations. Epoch AI rates the confidence of this record as confident.
Full record
- Organization
- Google, Google Research, Google Brain
- Country of organization
- United States
- Domain
- Speech
- Task
- Speech recognition (ASR)
- Training compute
- 7.6×10²¹ FLOP
- Compute estimation method
- Hardware
- Parameters
- 1,000,000,000
- Training hardware
- Google TPU v3
- Chips used
- 256
- Training time
- 168 h
- Training power draw
- 234.3 kW
- Training cost (2023 USD)
- $9K
- Model accessibility
- Unreleased
- Open weights
- No
- Citations
- 332
- Epoch confidence
- Confident
More from Google,Google Research,Google Brain
SourceEpoch AI, 'AI Models'. Published online at epoch.ai. Retrieved 2026-07-29 from https://epoch.ai/data/ai-models. Licensed under CC BY 4.0.
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