RNN-WER
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
- DeepMind,University of Toronto
- Organisation type
- Industry,Academia
- Country
- United Kingdom of Great Britain and Northern Ireland, Canada
- Published
- 22 June 2014
- Authors
- Alex Graves, Navdeep Jaitly
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Speech
- Task
- Speech recognition (ASR)
- Approach
- Supervised
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
- 26.5M
- Training data
- tokens
"The network had five levels of bidirectional LSTM hidden layers, with 500 cells in each layer, giving a total of ∼ 26.5M weights."
dataset is 81 hours At 228 wpm (https://docs.google.com/document/d/1G3vvQkn4x_W71MKg0GmHVtzfd9m0y3_Ofcoew0v902Q/edit) that's 81*228*60 = 1,108,080 another source says WSJ contains 37k sentences, so this would be ~30 words per sentence which seems high but roughly right: https://www.arxiv-vanity.com/papers/1903.00216/
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
- Likely
- Citations
- 2,805
"Finally, by combining the new model with a baseline, we have achieved state-of-the-art accuracy on the Wall Street Journal corpus for speaker independent recognition."
Sources
Where this record came from and when it was last checked.
- Reference
- Towards End-To-End Speech Recognition with Recurrent Neural Networks
- Last updated
- 28 November 2025
What the numbers mean
About this model
RNN-WER was published by DeepMind,University of Toronto, in United Kingdom of Great Britain and Northern Ireland, in June 2014. industry,Academia is the category the publisher falls under.
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.
Training and provenance
The reason it appears in this catalogue at all is highly cited,SOTA improvement.
Answers
RNN-WER — common questions
What is RNN-WER used for?
RNN-WER 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.
What GPU do I need to run RNN-WER?
None. RNN-WER 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 RNN-WER open source?
The licensing for RNN-WER was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
How many parameters does RNN-WER have?
RNN-WER has 26.5M parameters. "The network had five levels of bidirectional LSTM hidden layers, with 500 cells in each layer, giving a total of ∼ 26.5M weights.". 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.
Who created RNN-WER?
RNN-WER was published by DeepMind,University of Toronto, based in United Kingdom of Great Britain and Northern Ireland, categorised as industry,Academia.
When was RNN-WER released?
RNN-WER was published in June 2014. 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.