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Interestingly, Wolf also told me Hugging Face is bucking a trend among AI companies: It's cashflow positive. (The company makes money on consulting projects and by selling tools for enterprise ...
DeepSeek [a] (Chinese: 深度求索; pinyin: Shēndù Qiúsuǒ) is a Chinese artificial intelligence company that develops open-source large language models (LLMs). Based in Hangzhou, Zhejiang, it is owned and funded by Chinese hedge fund High-Flyer, whose co-founder, Liang Wenfeng, established the company in 2023 and serves as its CEO.
On September 23, 2024, to further the International Decade of Indigenous Languages, Hugging Face teamed up with Meta and UNESCO to launch a new online language translator [14] built on Meta's No Language Left Behind open-source AI model, enabling free text translation across 200 languages, including many low-resource languages. [15]
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]
For people living in the US who still want to sign up without the promise of free money, there are 10 locations in four regions: Los Angeles, Miami, New York and San Francisco.
Sam Altman noted on 15 May 2024 that GPT-4o's voice-to-voice capabilities were not yet integrated into ChatGPT, and that the old version was still being used. [9] This new mode, called Advanced Voice Mode, is currently in limited alpha release [10] and is based on the 4o-audio-preview. [11] On 1 October 2024, the Realtime API was introduced. [12]
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.
Deep learning speech synthesis refers to the application of deep learning models to generate natural-sounding human speech from written text (text-to-speech) or spectrum . Deep neural networks are trained using large amounts of recorded speech and, in the case of a text-to-speech system, the associated labels and/or input text.