TEMPO: Temporally-grounded Multi-task Post-training for Large Audio-Language Models
The episode discusses 'TEMPO,' a model for large audio-language models that improves temporal understanding. Hosts detail how TEMPO uses atomic timestamp tokens and a time-aware projector to achieve significant gains in multi-task performance, particularly in ASR. The framework treats time as an explicit, integrated component of audio understanding.