README.md

---
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}
}
```