metadata
dataset_info:
features:
- name: audio
dtype: audio
- name: text
dtype: string
configs:
- config_name: default
data_files:
- split: train
path: train/metadata.jsonl
- split: validation
path: validation/metadata.jsonl
- split: test
path: test/metadata.jsonl
Amicus_AISHELL3
Self-contained Amicus speech dataset packaged for Hugging Face audiofolder.
Splits
- train: 59693 samples, 59.4776 hours
- validation: 3569 samples, 3.6945 hours
- test: 24751 samples, 22.4399 hours
Load with Hugging Face Datasets
from datasets import load_dataset
ds = load_dataset("audiofolder", data_dir=".")
print(ds)
print(ds["train"][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_AISHELL3")
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_AISHELL3 \
--repo-type dataset \
--local-dir data/Amicus_AISHELL3
cd data/Amicus_AISHELL3
python /path/to/Amicus/training/stage1/1_semantic_alignment.py \
--train_data train.jsonl
Source
This dataset is based on AISHELL3, available from OpenSLR: https://www.openslr.org/93/
Citation
If you use this dataset, please cite the original AISHELL-3 paper:
@inproceedings{AISHELL-3_2020,
title={AISHELL-3: A Multi-speaker Mandarin TTS Corpus and the Baselines},
author={Yao Shi, Hui Bu, Xin Xu, Shaoji Zhang, Ming Li},
year={2015},
url={https://arxiv.org/abs/2010.11567}
}