Tacotron
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
- Organisation type
- Industry
- Country
- United States of America
- Published
- 6 April 2017
- Authors
- Yuxuan Wang, RJ Skerry-Ryan, Daisy Stanton, Yonghui Wu, Ron J. Weiss, Navdeep Jaitly, Zongheng Yang, Ying Xiao, Zhifeng Chen, Samy Bengio, Quoc Le, Yannis Agiomyrgiannakis, Rob Clark, Rif A. Saurous
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Speech
- Task
- Text-to-speech (TTS), Speech synthesis
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.
- Training data
- tokens
"We train Tacotron on an internal North American English dataset, which contains about 24.6 hours of speech data spoken by a professional female speaker. The phrases are text normalized, e.g. “16” is converted to “sixteen”" "We train using a batch size of 32, where all sequences are padded to a max length." "frame length: 50 ms; frame shift: 12.5 ms" "We use 24 kHz sampling rate for all experiments" 2M steps
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.
- Record confidence
- Unknown
Sources
Where this record came from and when it was last checked.
- Reference
- Tacotron: Towards End-to-End Speech Synthesis
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
Tacotron was published by Google, in United States of America, in April 2017. The organisation is categorised as industry.
It works in Speech, and is recorded as doing text-to-speech (TTS), Speech synthesis.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Answers
Tacotron — common questions
When was Tacotron released?
Tacotron was published in April 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.
What is Tacotron used for?
Tacotron works in Speech, and is recorded as handling text-to-speech (TTS), Speech synthesis. These are the areas it was designed around; they describe intent rather than a hard boundary.
What GPU do I need to run Tacotron?
None. Tacotron 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 Tacotron open source?
No. Tacotron has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Tacotron have?
No parameter count has been published for Tacotron, which is why no memory or speed figure appears on this page.
Who created Tacotron?
Tacotron was published by Google, based in United States of America, categorised as industry.
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.