Datasets:
metadata
license: cc-by-4.0
mutilinguality:
- monolingual
task_categories:
- audio-text-to-text
size_categories:
- 1K<n<10K
source_datasets:
- original
pretty_name: BLAB (Brutally Long Audio Bench)
tags:
- speech
- audio
- speech-llm
- audio-lm
- long-audio
- spoken-language-understanding
viewer: true
configs:
- config_name: word_localization
features:
- name: video_url
dtype: string
- name: audio
dtype: string
- name: question
dtype: string
- name: answer_type
dtype: string
- name: groundtruth
dtype: LargeList
inner_dtype:
- name: word
dtype: string
- name: start
dtype: float32
- name: end
dtype: float32
- config_name: advertisement_localization
features:
- name: video_url
dtype: string
- name: audio
dtype: string
- name: question
dtype: string
- name: answer_type
dtype: string
- name: groundtruth
dtype: Struct
fields:
- name: ads_segment
dtype: LargeList
inner_dtype:
- name: text
dtype: string
- name: start
dtype: float32
- name: end
dtype: float32
- name: word_timestamp
dtype: LargeList
inner_dtype:
- name: word
dtype: string
- name: start
dtype: float32
- name: end
dtype: float32
- config_name: named_entity_localization
features:
- name: video_url
dtype: string
- name: audio
dtype: string
- name: question
dtype: string
- name: answer_type
dtype: string
- name: groundtruth
dtype: Struct
fields:
- name: entities
dtype: LargeList
inner_dtype:
- name: entity_type
dtype: string
- name: entity
dtype: string
- name: start
dtype: float32
- name: end
dtype: float32
- name: word_timestamp
dtype: LargeList
inner_dtype:
- name: word
dtype: string
- name: start
dtype: float32
- name: end
dtype: float32
- config_name: speaker_number_estimation
features:
- name: video_url
dtype: string
- name: audio
dtype: string
- name: question
dtype: string
- name: groundtruth
dtype: Sequence
inner_dtype:
dtype: int32
- config_name: entire_duration
features:
- name: video_url
dtype: string
- name: audio
dtype: string
- name: question
dtype: string
- name: groundtruth
dtype: float32
- config_name: event_duration
features:
- name: video_url
dtype: string
- name: audio
dtype: string
- name: question
dtype: string
- name: answer_type
dtype: string
- name: groundtruth
dtype: float32
- config_name: emotion_ranking
features:
- name: video_url
dtype: string
- name: audio
dtype: string
- name: type
dtype: string
- name: question
dtype: string
- name: correct_option
dtype: string
- name: option_A
dtype: string
- name: option_B
dtype: string
- name: option_C
dtype: string
- name: option_D
dtype: string
- name: option_E
dtype: string
- name: correct_answer
dtype: string
- config_name: emotion_reasoning
features:
- name: video_url
dtype: string
- name: audio
dtype: string
- name: type
dtype: string
- name: question
dtype: string
- name: correct_option
dtype: string
- name: option_A
dtype: string
- name: option_B
dtype: string
- name: option_C
dtype: string
- name: option_D
dtype: string
- name: correct_answer
dtype: string
BLAB: Brutally Long Audio Bench
Dataset Summary
Brutally Long Audio Bench (BLAB) is a challenging long-form audio benchmark that evaluates audio LMs on localization, duration estimation, emotion, and counting tasks using audio segments averaging 51 minutes in length. BLAB consists of 833+ hours of diverse, full-length Youtube audio clips, each paired with human-annotated, text-based natural language questions and answers. Our audio data were collected from permissively licensed sources and underwent a human-assisted filtering process to ensure task compliance.
NB: This data should only be used for evaluation purposes and not for model training.
Tasks Covered in BLAB
Localization
- Word Localization: Locate the exact start and end times of specific words within the audio.
- Named Entity Localization: Detect and locate the exact start and end times of named entities (e.g., people, organizations, locations).
- Advertisement Localization: Locate and transcribe advertisement segments within a podcast.
Counting
- Speaker Number Estimation: Determine the number of unique speakers present in the full audio segment.
Duration
- Event Duration: Calculate the duration of specific acoustic events (e.g., laughter in a comedy special, question-and-answer segments in a panel session, or a particular speaker’s total speaking time in a meeting) within an audio sample,.
- Entire Duration: Estimate the total duration of an audio file, expressed in seconds.
Emotion
- Emotion Reasoning: Reason over emotional expressions conveyed in the audio.
- Emotion Ranking: Rank different emotional expressions of speech and non-verbal sound present in the audio.
Dataset Structure
To load a specific task from BLAB, you'll need to specify its configuration name. Keep in mind that BLAB provides URLs to the YouTube audio files, not the actual audio files themselves. You'll need to download the audio from these URLs separately.
from datasets import load_dataset
# Load the Word Localization task
word_localization_data = load_dataset("oreva/blab_long_audio", "word_localization")
# Load the Named Entity Localization task
named_entity_localization_data = load_dataset("oreva/blab_long_audio", "named_entity_localization")
# You can load any other task similarly:
# speaker_number_estimation_data = load_dataset("oreva/blab_long_audio", "speaker_number_estimation")
# entire_duration_data = load_dataset("oreva/blab_long_audio", "entire_duration")
# event_duration_data = load_dataset("oreva/blab_long_audio", "event_duration")
# emotion_reasoning_data = load_dataset("oreva/blab_long_audio", "emotion_reasoning")
# emotion_ranking_data = load_dataset("oreva/blab_long_audio", "emotion_ranking")
Citation
@misc{ahia2025blabbrutallylongaudio,
title={BLAB: Brutally Long Audio Bench},
author={Orevaoghene Ahia and Martijn Bartelds and Kabir Ahuja and Hila Gonen and Valentin Hofmann and Siddhant Arora and Shuyue Stella Li and Vishal Puttagunta and Mofetoluwa Adeyemi and Charishma Buchireddy and Ben Walls and Noah Bennett and Shinji Watanabe and Noah A. Smith and Yulia Tsvetkov and Sachin Kumar},
year={2025},
eprint={2505.03054},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2505.03054},
}