Model Detail
audio-flamingo-next-hf
—audio-flamingo-next-hf is a multimodal model with 4.1B parameters released by NVIDIA. The model is registered under the audio-text-to-text pipeline tag on Hugging Face, distributed under a other license.
audio-flamingo-next-hf ships with 4.1B parameters. Total weight footprint is approximately 8.3 GB, which is the relevant figure when planning local-inference VRAM. Distribution is governed by the other license — review the exact terms before commercial deployment.
audio-flamingo-next-hf is best fit for mixed text-and-image reasoning tasks such as document understanding. Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.
ER-EDF: A Psychology-Grounded Emotion Regulation Framework for Speech Empathetic Dialogue Generation in Large Audio-Language Models
arXiv:2609.15089v2 Announce Type: replace Abstract: In human conversation, empathy involves not only recognizing a partner's emotion but also regulating it, listeners identify the emotional state, select a suitable regulation strategy, and implement it in their reply. Current large audio-language mo
Uncovering Audio-Text Alignment in Large Audio-Language Models through Cross-Modal Concept Neurons
arXiv:2610.28189v1 Announce Type: cross Abstract: While the language model backbone is pretrained on text alone, large audio-language models (LALMs) can, to a great extent, recognize acoustic attributes, such as emotion, speaker gender, and spoken language. However, it is unclear how the audio repre
Multimodal Ambivalence and Hesitancy Recognition with Bidirectional Audio--Text Cross-Attention
arXiv:2610.26868v1 Announce Type: new Abstract: Ambivalence and hesitancy (A/H) are subtle behavioural states that can be expressed through both what a person says and how it is spoken. This paper presents an audio--text framework for video-level A/H recognition. Full-video transcripts and speech wa
Abstract Compression of Conversational Audio Context for LLM-based ASR
arXiv:2603.26246v2 Announce Type: replace-cross Abstract: Standard LLM-based speech recognition systems process utterances in isolation, limiting their ability to leverage conversational context. Here we study whether multimodal context from prior turns improves LLM-based ASR and how to represent th
WoW-Bench: Evaluating Fine-Grained Acoustic Perception in Audio-Language Models via Marine Mammal Vocalizations
arXiv:2508.20976v2 Announce Type: replace-cross Abstract: Large audio-language models (LALMs) extend language understanding into the auditory domain, yet their ability to perform low-level listening, such as pitch and duration detection, remains underexplored. However, low-level listening is critica
MiCLA: Mixture-to-Constituent Latent Alignment for Polyphonic Audio Representation Learning
arXiv:2610.27491v1 Announce Type: cross Abstract: Real-world audio typically contains concurrent sound events, yet standard self-supervised methods treat each clip as an isolated training unit without explicitly modeling mixture--constituent relationships. We propose Mixture-to-Constituent Latent Al
