Last updated on December 29, 2025 by Editorial Team
Author(s): Gautam Boyina
Originally published on Towards AI.
voice ai compute problem
Most large audio language models process speech at a 12.5 Hz or 25 Hz frame rate – 12.5 to 25 audio features per second. Higher frame rates capture more detail but require more calculations. For voice interactions in real-time, this creates a problem: you need fast responses (low latency), but processing high-frame-rate audio on the GPU is expensive.
In this article, Alibaba’s Fun-Audio-Chat presents a new approach to voice AI that combines a low-resolution backbone with high-resolution refinements to reduce computational costs while maintaining voice quality. The design of the model aims to not only reduce processing demands by operating at a low base frame rate, but also to use advanced training techniques to ensure that text understanding capabilities are preserved while integrating audio processing. This innovative dual-resolution method results in significant compute savings, improved response times, and enhanced NLP capabilities, making it suitable for real-time applications such as voice assistants and customer service, despite heavy infrastructure requirements.
Read the entire blog for free on Medium.
Published via Towards AI
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Comment: The content represents the views of the contributing authors and not those of AI.
