Integration of Audio Evaluation in LMMs-Eval
In the journey towards multimodal intelligence, the development of LMMs has progressed remarkably, transitioning from handling static images to processing complex video inputs. This evolution is crucial, enabling models to understand and interpret dynamic scenes with temporal dependencies, motion dynamics, and contextual continuity. The importance of video evaluation is also increasing across various applications. However, there has been a noticeable absence of comprehensive benchmarks to evaluate the diverse array of video tasks. The introduction of lmms-eval/v0.2.0 is both necessary and significant as it addresses this critical gap in the evaluation of video-based LMMs.
Nov 27, 2024
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6
min read
Humans perceive the world through both sight and sound, integrating visual cues with auditory signals such as speech, environmental sounds, and emotional tones.
This dual sensory input enhances decision-making and overall understanding. Similarly, for multimodal models to achieve human-like comprehension, it is essential to make them process both visual and auditory data together.
While many models have made progress in integrating audio understanding, there is still no reproducible and efficient evaluation toolkit to fairly assess their capabilities.
To address this, we introduce an upgrade to the lmms-eval framework, focusing on audio understanding. Building on the success of lmms-eval/v0.2.0, the new lmms-eval/v0.3.0 includes dedicated modules and designs for audio tasks, ensuring consistent evaluation across audio and visual modalities.
This upgrade includes multiple benchmarks for audio understanding and instruction following, enabling standardized and reproducible comparisons of various audio models.
For more information, please refer to our Github documentation.
https://github.com/EvolvingLMMs-Lab/lmms-eval/blob/main/docs/lmms-eval-0.3.md