README.md

---
dataset_info:
  features:
  - name: audio
    dtype: audio
  - name: text
    dtype: string
configs:
- config_name: default
  data_files:
  - split: train-clean-100
    path: train-clean-100/metadata.jsonl
  - split: train-clean-360
    path: train-clean-360/metadata.jsonl
  - split: train-other-500
    path: train-other-500/metadata.jsonl
  - split: dev-clean
    path: dev-clean/metadata.jsonl
  - split: dev-other
    path: dev-other/metadata.jsonl
  - split: test-clean
    path: test-clean/metadata.jsonl
  - split: test-other
    path: test-other/metadata.jsonl
---

# Amicus_LibriSpeech

Self-contained Amicus speech dataset packaged for Hugging Face `audiofolder`.


## Splits

- train-clean-100: 28539 samples, 100.5905 hours
- train-clean-360: 104014 samples, 363.6054 hours
- train-other-500: 148686 samples, 496.8568 hours
- dev-clean: 2694 samples, 5.3076 hours
- dev-other: 2857 samples, 5.0578 hours
- test-clean: 2611 samples, 5.3232 hours
- test-other: 2932 samples, 5.2775 hours

## Load with Hugging Face Datasets

```python
from datasets import load_dataset

ds = load_dataset("audiofolder", data_dir=".")
print(ds)
print(ds["train-clean-100"][0]["audio"])
```

After uploading this folder to a dataset repository, replace `data_dir="."` with
the dataset repo id if automatic builder detection works for your repository:

```python
from datasets import load_dataset

ds = load_dataset("YOUR_NAMESPACE/Amicus_LibriSpeech")
```

## Use with Amicus

Download the whole dataset repository, including Git LFS files, then launch
Amicus training from the downloaded dataset root so relative `audio_path` values
resolve correctly.

```bash
huggingface-cli download YOUR_NAMESPACE/Amicus_LibriSpeech \
  --repo-type dataset \
  --local-dir data/Amicus_LibriSpeech

cd data/Amicus_LibriSpeech
python /path/to/Amicus/training/stage1/1_semantic_alignment.py \
  --train_data train-clean-100.jsonl
```

## Source

This dataset is based on LibriSpeech, available from OpenSLR:
https://www.openslr.org/12

## Citation

If you use this dataset, please cite the original LibriSpeech paper:

```bibtex
@inproceedings{panayotov2015librispeech,
  title={Librispeech: an ASR corpus based on public domain audio books},
  author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},
  booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on},
  pages={5206--5210},
  year={2015},
  organization={IEEE}
}
```