DBLSTM
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
- University of Toronto
- Organisation type
- Academia
- Country
- Canada
- Published
- 8 December 2013
- Authors
- A Graves, N Jaitly, A Mohamed
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
- 29.9M
- Training data
- 5,040,000 tokens
"The DBLSTM network had five bidirectional hidden levels, with 500 LSTM cells in each of the forward and backward layers, and a size 3385 softmax output layer, giving a total of 29.9M weights."
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Citations
- 1,597
Sources
Where this record came from and when it was last checked.
- Reference
- Hybrid speech recognition with Deep Bidirectional LSTM
- Last updated
- 28 November 2025
What the numbers mean
Background
DBLSTM was published by University of Toronto, in the country recorded as Canada, during December 2013. The publishing organisation is categorised as academia.
It works in the domain of Speech, and is recorded as performing the task of speech recognition (ASR).
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
What went into building it
It was trained on a corpus of about 5,040,000 tokens of text.
Answers
DBLSTM — common questions
DBLSTM— when was it released?
It was published in December 2013. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
DBLSTM— what is it used for?
It works in the domain of Speech, and is recorded as handling the task of speech recognition (ASR). A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
DBLSTM— 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.
DBLSTM— is it open source?
The licensing was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
DBLSTM— how many parameters does it have?
It has a parameter count of 29.9M. "The DBLSTM network had five bidirectional hidden levels, with 500 LSTM cells in each of the forward and backward layers, and a size 3385 softmax output layer, giving a total of 29.9M 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.
DBLSTM— who created it?
It was published by University of Toronto, based in Canada, an organisation categorised as academia.
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.