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---
license: apache-2.0
dataset_info:
features:
- name: hypothesis
sequence: string
- name: transcription
dtype: string
- name: input1
dtype: string
- name: hypothesis_concatenated
dtype: string
- name: source
dtype: string
- name: id
dtype: string
- name: dummy_str
dtype: string
- name: dummy_list
sequence: 'null'
- name: prompt
dtype: string
splits:
- name: train
num_bytes: 394598912.0809314
num_examples: 240924
- name: test
num_bytes: 20857940.265010692
num_examples: 15783
download_size: 112593018
dataset_size: 415456852.34594214
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# Dataset Name: Pilot dataset for Multi-domain ASR corrections
## Description
This dataset is a pilot version of a larger dataset for automatic speech recognition (ASR) corrections across multiple domains.
It contains paired hypotheses and corrected transcriptions for various ASR tasks consolidated from [PeacefulData/HyPoradise-v0](https://huggingface.co/datasets/PeacefulData/HyPoradise-v0)
## Structure
### Data Split
The dataset is divided into training and test splits:
- Training Data: 281,082 entries
- Approximately 6,255,198 tokens for transcriptions
- Approximately 31,211,083 tokens for concatenated hypotheses
- Test Data: 16,108 entries
- Approximately 327,750 tokens for transcriptions
- Approximately 1,629,093 tokens for concatenated hypotheses
### Columns
- `hypothesis`: N-best hypothesis from beam search.
- `transcription`: Corrected asr transcription.
- `hypothesis_concatenated`: An alternative version of the text output.
- `source`: The source of the text entry, indicating the origin dataset.
- `prompt`: Instructional prompt for correction task
- `score`: An acoustic model score (not all entries have this).
### Source Datasets
The dataset combines entries from various sources:
- **Training Sources**:
- `train_td3`: 50,000 entries
- `train_other_500`: 50,000 entries
- `train_cv`: 47,293 entries
- `train_lrs2`: 42,940 entries
- `train_wsj_score`: 37,514 entries ## disable for challenge
- `train_swbd`: 36,539 entries
- `train_chime4`: 9,600 entries
- `train_coraal`: 3,232 entries
- **Test Sources**:
- `test_ls_other`: 2,939 entries
- `test_ls_clean`: 2,620 entries
- `test_lrs2`: 2,259 entries
- `test_swbd`: 2,000 entries
- `test_cv`: 2,000 entries
- `test_chime4`: 1,320 entries
- `test_td3`: 1,155 entries
- `test_coraal`: 170 entries
- **Diff from NeurIPS 23** we remove follow n-best for SLT challenge
- `train_wsj_score`: 37,514 entries
- `train_atis`: 3,964 entries
- `test_wsj_score`: 836 entries
- `test_atis`: 809 entries
## Access
The dataset can be accessed and downloaded through the HuggingFace Datasets library. Use the following command to load the dataset:
```python
from datasets import load_dataset
dataset = load_dataset("PeacefulData/HyPoradise-pilot")
```
## Acknowledgments
This dataset is consolidated from the PeacefulData/HyPoradise-v0 dataset. Thanks to the original creators for making this data available.
### References
```bib
@inproceedings{yang2023generative,
title={Generative speech recognition error correction with large language models and task-activating prompting},
author={Yang, Chao-Han Huck and Gu, Yile and Liu, Yi-Chieh and Ghosh, Shalini and Bulyko, Ivan and Stolcke, Andreas},
booktitle={2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)},
pages={1--8},
year={2023},
organization={IEEE}
}
```
```bib
@inproceedings{chen2023hyporadise,
title={HyPoradise: An Open Baseline for Generative Speech Recognition with Large Language Models},
author={CHEN, CHEN and Hu, Yuchen and Yang, Chao-Han Huck and Siniscalchi, Sabato Marco and Chen, Pin-Yu and Chng, Ensiong},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2023}
}
```
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