Google introduces AudioPaLM, a multimodal language model to generate both text and specch
AudioPaLM, a multimodal language model, which is a combination of large language model PaLM 2 and generative audio model AudioLM. This model is used to generate both text and speech.
The world of artificial intelligence keeps evolving around us since the very first day. While, speculations are being made that AI can render many jobless, AI has proven its worth in the field of education and research.
AudioPaLM
The big tech giant, Google introduces multimodal language model. This model is a combination of large language model PaLM 2 which was introduced in Google I/O 2023 and a generative audio model AudioLM, which was introduced last year.
Google is empowering the AI World with its large language models. The AudioPaLM is an extensive multimodal framework, which is capable of handling and producing both spoken language and written material.
Additionally, Text-based and voice-based language models are combined into a single multimodal architecture, known as AudioPaLM. Moreover, this multimodal can process and generate both text and speech for use in speech recognition and translation applications.
AudioPaLM Capabilities
While, PaLM-2 is a text-based language model that is capable of understanding verbal information of texts, AudioLM is capable of understanding communicative information like speaker and voice identification.
In addition, by integrating these two models, AudioPaLM takes the advantage of producing text and voice in a more explicit way. Further, this capability can be useful for real-world applications such as real-time multilingual communication.
In addition, AudioPaLM can record and replicate separate voices in other languages and transfer voices across languages based on brief spoken instructions.
This multimodal performs voice translation tasks substantially better than the state-of-the-art systems. Further, it can execute zero-shot speech-to-text translation for numerous languages for which the input or target language combinations were not encountered during training.
