README.md

---
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: 48517243701.0
    num_examples: 560
  - name: dev_2020
    num_bytes: 1365959644.0
    num_examples: 14
  - name: dev_2022
    num_bytes: 1104577536.0
    num_examples: 11
  - name: test_2020
    num_bytes: 1581799930.0
    num_examples: 15
  - name: test_2022
    num_bytes: 3155188963.0
    num_examples: 32
  download_size: 52181447847
  dataset_size: 55724769774.0
- 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](https://www.mllp.upv.es/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:

```python
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:

```bibtex
@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](https://www.mllp.upv.es/lhcp-asr).

LEGAL DISCLAIMER
---------------
Speech and text data were provided by the [European Organization 
for Nuclear Research (CERN)](https://home.cern/) under PO OV9177345.
The following disclaimers are those available in the [CERN Document Server (CDS)](https://cds.cern.ch/) repository on May 30th, 2025:

#### CERN Document Server - Terms and Conditions
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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/