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  2. Hugging Face - Wikipedia

    en.wikipedia.org/wiki/Hugging_Face

    The Hugging Face Hub is a platform (centralized web service) for hosting: [20] Git-based code repositories, including discussions and pull requests for projects. models, also with Git-based version control; datasets, mainly in text, images, and audio;

  3. Suno AI - Wikipedia

    en.wikipedia.org/wiki/Suno_AI

    In April 2023, Suno released their open-source text-to-speech and audio model called "Bark" on GitHub and Hugging Face, under the MIT License. [4] [5] On March 21, 2024, Suno released its v3 version for all users. [6] The new version allows users to create a limited number of 4-minute songs using a free account. [7]

  4. BLOOM (language model) - Wikipedia

    en.wikipedia.org/wiki/BLOOM_(language_model)

    BigScience Large Open-science Open-access Multilingual Language Model (BLOOM) [1] [2] is a 176-billion-parameter transformer-based autoregressive large language model (LLM). The model, as well as the code base and the data used to train it, are distributed under free licences. [3]

  5. Hugging Face cofounder Thomas Wolf says open-source AI’s ...

    www.aol.com/finance/hugging-face-cofounder...

    Hugging Face, of course, is the world’s leading repository for open-source AI models—the GitHub of AI, if you will. ... compared to tens of billions or even hundreds of billions—that perform ...

  6. Google Cloud partners with Hugging Face to attract AI ... - AOL

    www.aol.com/news/google-cloud-partners-hugging...

    The cloud computing arm of Alphabet Inc said on Thursday it had formed a partnership with startup Hugging Face to ease artificial intelligence (AI) software development in the company's Google Cloud.

  7. IBM Watsonx - Wikipedia

    en.wikipedia.org/wiki/IBM_Watsonx

    Watsonx.ai is a platform that allows AI developers to leverage a wide range of LLMs under IBM's own Granite series and others such as Facebook's LLaMA-2, free and open-source model Mistral and many others present in Hugging Face community for a diverse set of AI development tasks.

  8. Audio deepfake - Wikipedia

    en.wikipedia.org/wiki/Audio_deepfake

    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.

  9. Gemini (language model) - Wikipedia

    en.wikipedia.org/wiki/Gemini_(language_model)

    This iteration boasts improved speed and performance over its predecessor, Gemini 1.5 Flash. Key features include a Multimodal Live API for real-time audio and video interactions, enhanced spatial understanding, native image and controllable text-to-speech generation (with watermarking), and integrated tool use, including Google Search. [42]

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