Andrei Lopatenko 🇺🇦’s Post

GPT-4o (“o” for “omni”) is a step towards much more natural human-computer interaction—it accepts as input any combination of text, audio, and image and generates any combination of text, audio, and image outputs,

GPT-4o (“o” for “omni”) is a step towards much more natural human-computer interaction—it accepts as input any combination of text, audio, and image and generates any combination of text, audio, and image outputs.

OpenAI launches “GPT-4o”, that can reason across audio, vision, and text in real time. It  is a step towards much more natural human-computer interaction—it accepts as input any combination of text, audio, and image and generates any combination of text, audio, and image outputs.

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. https://lnkd.in/dUt7PdE8

Harnessing AI for the Spoken Word: The Whisper API Revolution A silent revolution is underway in the realm of computational linguistics, and OpenAI's Whisper API stands at the forefront. Harnessing the power of machine learning, Whisper transcends language and dialect barriers with its state-of-the-art speech recognition capabilities. This model is not merely a tool but a polyglot companion, adept at understanding and transcribing multilingual dialogues, making it a valuable asset in facilitating seamless communication in international forums and aiding the hearing-impaired. Whisper's architecture is a marvel of machine learning – a Transformer sequence-to-sequence framework that interprets the nuances of speech as a symphony of tokens. This design enables Whisper to perform a concerto of tasks: transcription, translation, and language identification, all within the confines of a single, unified model. The Whisper API is accessible to developers and researchers, with support for Python and PyTorch. Its open-source innovation allows for detailed documentation and installation guides for a seamless user experience. It stands as a testament to the power of artificial intelligence in bridging human communication gaps. The future of human-computer interaction is bright, and Whisper API is a harbinger of a future where language barriers are dissolved, and the spoken word is universally understood. It's a future that is not whispered but proclaimed loudly for all to hear. Now GA on Azure AI. Try it out here - https://lnkd.in/gTkTefMc

GPT-4o (“o” for “omni”) is a step towards much more natural human-computer interaction—it accepts as input any combination of text, audio, and image and generates any combination of text, audio, and image outputs. https://lnkd.in/eDanfi49

New article published on LLMs Research, a Medium publication! This article provides the research directions and categorization of research papers improving the performance of large language models (LLMs). #LLMs #largelanguagemodel #LLM https://lnkd.in/gtXj8ACE

Apple has introduced Denoising Language Model (DLM), a scaled error correction model trained with extensive synthetic data, surpassing previous methods and achieving SOTA automatic speech recognition (ASR) performance. The process involves using text-to-speech (TTS) systems to create audio that is fed into an ASR system, generating noisy hypotheses. These hypotheses are paired with the original texts to train the DLM. Read the complete article to learn more Link in the comments below

NVIDIA Introduces Hymba 1.5B: A Hybrid Small Language Model Outperforming Llama 3.2 and SmolLM v2 Large language models

Experience a game-changer in Automatic Speech Recognition (ASR). AssemblyAI's Universal-1 model is not your average ASR. Transcribes multi-language audio files? Check. Delivers precise timestamp estimation for editing and analytics? Absolutely. Processes lengthy audios efficiently? Indeed, it's 5x quicker than Whisper Large-v3 on similar hardware. It's built with Convolutional Layers (CNNs), Positional Encoding, and 24 Conformer Layers. The design, showcasing 600 million parameters, leverages chunk-wise attention for audio duration variation robustness and speed. While other models falter, Universal-1 thrives. It excels at Word Error Rate (WER) against commercial and open-source competitors. It's not just about the speed; it's also the accuracy that it brings to the table. #assemblyai #asr #voicemodels #aiinaudio https://lnkd.in/d2d6gMZu