DL scaling speech
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
- Baidu
- Organisation type
- Industry
- Country
- China
- Published
- 1 December 2017
- Authors
- Joel Hestness, Sharan Narang, Newsha Ardalani, G. Diamos, Heewoo Jun, Hassan Kianinejad, Md. Mostofa Ali Patwary, Yang Yang, Yanqi Zhou
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
- 193M
- Training data
- 2,149,200,000 tokens
8M utterances, 11940 hours, trained for up to 2048h Estimated number of words: 11940*120*60=85968000 Training size: 2048/11940=0.172 Training words: 85968000*0.172=14786496
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
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
- Training cost
- Record confidence
- Likely
Sources
Where this record came from and when it was last checked.
- Reference
- Deep Learning Scaling is Predictable, Empirically
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
DL scaling speech was published by Baidu, in the country recorded as China, during December 2017. It comes out of an organisation categorised as 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
Training consumed a corpus of around 2,149,200,000 tokens of text.
The reason it appears in this catalogue at all: training cost.
Answers
DL scaling speech — common questions
DL scaling speech— 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.
DL scaling speech— 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.
DL scaling speech— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
DL scaling speech— how many parameters does it have?
It has a parameter count of 193M. 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.
DL scaling speech— who created it?
It was published by Baidu, based in China, an organisation categorised as industry.
DL scaling speech— when was it released?
It was published in December 2017. 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.