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Its speed and accuracy have led many to note that its generated voices sound near-indistinguishable from "real life", provided that sufficient computational specifications and resources (e.g., a powerful GPU and ample RAM) are available when running it locally and that a high-quality voice model is used. [2] [3] [4]
Open-source artificial intelligence is an AI system that is freely available to use, study, modify, and share. [1] These attributes extend to each of the system's components, including datasets, code, and model parameters, promoting a collaborative and transparent approach to AI development. [1]
Terms proposed included "AI garbage", "AI pollution", and "AI-generated dross". [5] Early uses of the term "slop" as a descriptor for low-grade AI material apparently came in reaction to the release of AI image generators in 2022. [7] Its early use has been noted among 4chan, Hacker News and YouTube commentators as a form of in-group slang. [7]
Model collapse in generative models is reduced when data accumulates. Some researchers and commentators on model collapse warn that the phenomenon could fundamentally threaten future generative AI development: As AI-generated data is shared on the Internet, it will inevitably end up in future training datasets, which are often crawled from the Internet.
The incident was later documented in the AI Incident Database (AIID), cataloging it as an example of "an AI-synthetic audio sold as an NFT on Voiceverse's platform [that] was acknowledged by the company for having been created by 15.ai, a free web app specializing in text-to-speech and AI-voice generation, and reused without proper attribution."
AI Dungeon is a text adventure game that uses artificial intelligence to generate random storylines in response to player-submitted stimuli. [1] [2] [3] [4]In the game, players are prompted to choose a setting for their adventure (e.g. fantasy, mystery, apocalyptic, cyberpunk, zombies), [5] [6] followed by other options relevant to the setting (such as character class for fantasy settings).
It is necessary to collect clean and well-structured raw audio with the transcripted text of the original speech audio sentence. Second, the text-to-speech model must be trained using these data to build a synthetic audio generation model. Specifically, the transcribed text with the target speaker's voice is the input of the generation model.
Reactive solutions retrain the model in reaction to a triggering mechanism, such as a change-detection test, [9] [10] to explicitly detect concept drift as a change in the statistics of the data-generating process. When concept drift is detected, the current model is no longer up-to-date and must be replaced by a new one to restore prediction ...