metadata
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
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:
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.
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:
@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}
}