Introducing new audio and vision documentation in 🤗 Datasets

A Complete Guide to Audio Datasets

AudioWorldSim: Realistic Binaural Audio Datasets For World Models

AudioWorldSim is an open‑source platform that generates realistic binaural audio datasets for training and evaluating audio‑based machine learning models, especially world models. It extends Meta’s SoundSpaces 2.0 by automating random agent navigation and correcting continuous sound composition. The project is publicly available on GitHub to support reproducibility in research.

Audio-FLAN: An Instruction-Following Dataset for Unified Audio Understanding and Generation of Speech, Music, and Sound

arXiv:2502. 16584v2 Announce Type: replace-cross Abstract: Recent advancements in audio tokenization have significantly enhanced the integration of audio capabilities into large language models (LLMs).

VieSpeaker: A Large-Scale Vietnamese Speaker Recognition Dataset Beyond Visual Dependency

Speaker recognition has advanced rapidly with large-scale training datasets, yet Vietnamese remains under-resourced, with existing corpora limited in scale and acoustic diversity. Most large-scale datasets rely on facial cues to link speech with speaker identities, restricting data collection to recordings where speakers appear on camera.

TriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific Scenarios

arXiv:2607. 06179v1 Announce Type: cross Abstract: There are some datasets of varying scales for audio classification (AC) applied to different tasks.

VGGSounder: Audio-Visual Evaluations for Foundation Models

arXiv:2508. 08237v4 Announce Type: replace-cross Abstract: The emergence of audio-visual foundation models underscores the importance of reliably assessing their multi-modal understanding.