Refernce text
In computational theory, a system of data-manipulation rules is said to be "Turing complete" or "computationally universal" if it can be used to simulate any Turing machine.
This concept is named after the British mathematician Alan Turing, who is one of the founding figures in computer science. In the 1930s, he proposed a theoretical machine, now known as the Turing machine, which manipulates symbols on a strip of tape according to a table of rules. Despite its simplicity, the Turing machine can be adapted to simulate the logic of any computer algorithm, and is used as a reference point in theoretical computations.
To say that a programming language or a cellular automaton or any other computational system is Turing complete means it can solve any problem that a Turing machine can, given enough time and enough storage space.
Turing completeness is a fundamental concept in computer science because it's a way of talking about the general power or capability of a particular programming language or computational system.
However, it's worth noting that Turing completeness doesn't say anything about efficiency, ease of programming, security, or other practical considerations. Many real-world programming tasks could be done with a Turing-complete language, but would be impractical to implement for reasons such as those.
Using model: Tacotron2 Generator Model: hifigan_generator Discriminator Model: hifigan_discriminator
Output Duration: 92s
| CPU | Processing Time | Realtime factor |
|---|---|---|
| 3.1 GHz Dual-Core Intel Core i7 | 62.39 | 0.67 |
Default model on OrangePi 4 TLS