Deduct OpenAI GPT-4o's Neural Network Architecture

GPT-4o, ... new flagship model that can reason across audio, vision, and text in real time.”
GPT-4o (“o” for “omni”) is a step towards much more natural human-computer interaction-it accepts as input any combination of text, audio, image, and video and generates any combination of text, audio, and image outputs. It can respond to audio inputs in as little as 232 milliseconds, with an average of 320 milliseconds, which is similar to human response time(opens in a new window) in a conversation. It matches GPT-4 Turbo performance on text in English and code, with significant improvement on text in non-English languages, while also being much faster and 50% cheaper in the API. GPT-4o is especially better at vision and audio understanding compared to existing models.
#google #gpt4o #gpt-4o #openai #whisper #multimodal #audio #voicemode #voiceengine #voice #speech #projectastra #astra
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[@reynoldsVincent]

Working my way through this, it looks like you did a lot of insightful work, Sub'd--thanks!

[@haibinwu1568]

What do you think is the most likely tokenizer for audio in GPT-4O? What do you think are the possible solutions? Could it be BASE TTS style tokens or Codec-style tokens?

[@olewave]

What do you think is the most likely tokenizer for audio in GPT-4O? What do you think are the possible solutions?
> I think you meant 'encoder' other than 'tokenizer'. I mentioned that in the video, please watch the video.
Could it be BASE TTS style tokens or Codec-style tokens?
> This is very specific, I am not OpenAI employees. I only infer their architecture, not their implementation.

[@augmentos]

Great video ser thanks subd

[@augmentos]

Would love you to expand on how they ‘optimize heavily’ to run very fast