Datasets:

mllp
/

LHCP-ASR

license: cc-by-nc-nd-4.0
task_categories:
  - automatic-speech-recognition
language:
  - en
tags:
  - particle-physics
  - speech-corpus
  - domain-adaptation
  - longform
  - segments
  - manual-transcription
  - cern
size_categories:
  - 100K<n<1M
dataset_info:
  - config_name: longform
    features:
      - name: audio
        dtype: audio
      - name: transcription
        dtype: string
    splits:
      - name: train
        num_bytes: 48517243701
        num_examples: 560
      - name: dev_2020
        num_bytes: 1365959644
        num_examples: 14
      - name: dev_2022
        num_bytes: 1104577536
        num_examples: 11
      - name: test_2020
        num_bytes: 1581799930
        num_examples: 15
      - name: test_2022
        num_bytes: 3155188963
        num_examples: 32
    download_size: 52181447847
    dataset_size: 55724769774
  - config_name: segments
    features:
      - name: audio
        dtype: audio
      - name: transcription
        dtype: string
    splits:
      - name: train
        num_bytes: 40009686180.696
        num_examples: 174376
      - name: dev_2020
        num_bytes: 1318912292.128
        num_examples: 3896
      - name: dev_2022
        num_bytes: 754694121.774
        num_examples: 3622
      - name: test_2020
        num_bytes: 1672421485.652
        num_examples: 4017
      - name: test_2022
        num_bytes: 2277429244.504
        num_examples: 9738
    download_size: 46780473298
    dataset_size: 46033143324.754
configs:
  - config_name: longform
    data_files:
      - split: train
        path: longform/train-*
      - split: dev_2020
        path: longform/dev_2020-*
      - split: dev_2022
        path: longform/dev_2022-*
      - split: test_2020
        path: longform/test_2020-*
      - split: test_2022
        path: longform/test_2022-*
  - config_name: segments
    data_files:
      - split: train
        path: segments/train-*
      - split: dev_2020
        path: segments/dev_2020-*
      - split: dev_2022
        path: segments/dev_2022-*
      - split: test_2020
        path: segments/test_2020-*
      - split: test_2022
        path: segments/test_2022-*

LHCP-ASR

This dataset is another version of the LHCP-ASR corpus, an English speech dataset for narrow-domain ASR benchmarking in high-energy physics. Unlike the original distribution, which includes video, slides and text data, this version focuses entirely on audio-text pairs

DESCRIPTION

The speech data are 30 hours of LHCP plenary conference talks (2020, 2022) with manual (human) verbatim transcriptions and 205 hours of LHCP conference talks (2020-2022) with automatic verbatim transcriptions (pseudo-labels) for training/adaptation. This version has been released in two formats: segments of the talks (less than 30s each) and longform, which provides the full talk.

Data structure

Each sample in the dataset contains:

audio
transcription

USAGE

You can load this dataset directly using the datasets library:

from datasets import load_dataset

# Segmented version
segmented_dataset = load_dataset("mllp/LHCP-ASR", "segments")

# Longform version
longform_dataset = load_dataset("mllp/LHCP-ASR", "longform")

Both configurations (longform and segments) include the following splits:

train
dev_2020
dev_2022
test_2020
test_2022

CITATION

If you use this dataset, please cite the original work:

@inproceedings{santamariajorda25_interspeech,
  title     = {{LHCP-ASR: An English Speech Corpus of High-Energy Particle Physics Talks for Narrow-Domain ASR Benchmarking}},
  author    = {Jaume Santamaría-Jordà and Pablo Segovia-Martínez and Gonçal V. {Garcés Díaz-Munío} and Joan Albert Silvestre-Cerdà and Adrià Giménez and Rubén {Gaspar Aparicio} and René {Fernández Sánchez} and Jorge Civera and Albert Sanchis and Alfons Juan},
  year      = {2025},
  booktitle = {{Interspeech 2025}},
  pages     = {4033--4037},
  doi       = {10.21437/Interspeech.2025-2630},
  issn      = {2958-1796},
}

For more details on the original dataset, visit www.mllp.upv.es/lhcp-asr.

LEGAL DISCLAIMER

Speech and text data were provided by the European Organization for Nuclear Research (CERN) under PO OV9177345. The following disclaimers are those available in the CERN Document Server (CDS) repository on May 30th, 2025:

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[1] CDS Content Policy

LICENSE

This dataset is licenced under CC-BY-NC-ND 4.0. To view a copy of this licence, visit https://creativecommons.org/licenses/by-nc-nd/4.0/