arXiv AI
Stride-k Subsampling: Train-Free Audio Token Reduction for Whisper
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Aug 21
Towards Audio Token Compression in Large Audio Language Models
arXiv:2511. 20973v2 Announce Type: replace-cross Abstract: Large Audio Language Models (LALMs) deliver strong performance across speech and audio tasks, but their audio encoders generate high-rate token sequences (e.
arXiv Machine Learning
Jul 23
Cumsum-Composable Phase Transport for Low-Cost Streaming Keyword Spotting
arXiv:2607. 20086v1 Announce Type: cross Abstract: State-space sequence models are attractive for streaming speech because they maintain compact recurrent state, but scan-style training kernels can have unfavorable constants for short audio tasks.
arXiv AI
Aug 11
VoxZip: Semantic-Anchored Temporal KV Cache Compression for Long-Context Audio Inference
arXiv:2608. 08569v1 Announce Type: new Abstract: Recent advancements in Speech Large Language Models have demonstrated remarkable capabilities in understanding complex audio tasks.
arXiv Machine Learning
3d ago
WhisTLE: Deeply Supervised, Text-Only Domain Adaptation for Small Pretrained Speech Recognition Transformers
arXiv:2509.10452v3 Announce Type: replace-cross Abstract: Pretrained automatic speech recognition (ASR) models such as Whisper perform well but still need domain adaptation to handle unseen parlance....
arXiv AI
Jun 2
MURMUR: An Efficient Inference System for Long-Form ASR
arXiv:2606. 01483v1 Announce Type: cross Abstract: Long-form automatic speech recognition (ASR) requires both high accuracy and low latency, but existing systems force a trade-off between the two.
Hugging Face Trending Papers
Jun 3
CleanCodec: Efficient and Robust Speech Tokenization via Perceptually Guided Encoding
Neural audio codecs are a key component of speech processing pipelines, compressing audio into discrete tokens for downstream modeling. However, existing codecs struggle to balance reconstruction quality with token efficiency, often encoding perceptually irrelevant information such as background noise and recording artifacts at the expense of linguistically and acoustically meaningful content.