Datasets:
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license: apache-2.0
task_categories:
- text-to-speech
- automatic-speech-recognition
configs:
- config_name: small
default: true
data_files:
- split: train
path:
- default/small/small-*.parquet
- config_name: medium
data_files:
- split: train
path:
- default/medium/medium-*.parquet
- config_name: large
data_files:
- split: train
path:
- default/large/shard-*/large-*.parquet
- config_name: dev
data_files:
- split: train
path:
- default/dev/dev-*.parquet
- config_name: test_clean
data_files:
- split: train
path:
- default/test_clean/test_clean-*.parquet
- config_name: test_clean_large
data_files:
- split: train
path:
- default/test_clean_large/test_clean_large-*.parquet
- config_name: test_other
data_files:
- split: train
path:
- default/test_other/test_other-*.parquet
- config_name: test_other_large
data_files:
- split: train
path:
- default/test_other_large/test_other_large-*.parquet
language:
- en
pretty_name: Libriheavy
size_categories:
- 10M<n<100M
---
# Libriheavy
Libriheavy: a 50,000 hours ASR corpus with punctuation casing and context. Libriheavy is a labeled version of Librilight.
This uploaded version replaces the default Libri-Light audio files with the highest quality available versions
from librivox. In most cases, this consists an upgrade of the source audio from a 64kbps mp3 to a 128kbps mp3.
Audio files are then re-encoded using the Opus 68kbps codec to retain quality and reduce size.
- Homepage: https://github.com/k2-fsa/libriheavy
- License: apache-2.0
## Configs
Each dataset config exposes a single split named `train`.
- `small` (`train`): 509 hours of speech. 417 speakers averaging 1.22 hours per speaker.
- `medium` (`train`): 5042 hours of speech. 1531 speakers averaging 3.29 hours per speaker.
- `large` (`train`): 50794 hours of speech. 6736 speakers averaging 7.54 hours per speaker.
- `dev` (`train`): 22.3 hours of speech. 141 speakers averaging 0.16 hours per speaker.
- `test_clean` (`train`): 10.5 hours of speech. 70 speakers averaging 0.15 hours per speaker.
- `test_other` (`train`): 11.5 hours of speech. 72 speakers averaging 0.16 hours per speaker.
- `test_clean_large` (`train`): 107.5 hours of speech. 72 speakers averaging 1.49 hours per speaker.
- `test_other_large` (`train`): 100.3 hours of speech. 73 speakers averaging 1.37 hours per speaker.
## Usage
### Load a Single Config
```python
from datasets import load_dataset
small = load_dataset("mythicinfinity/libriheavy", "small", split="train")
```
Targeting a specific config only downloads files declared for that config, which is a good way to control disk usage.
### Load the Full Dataset (All Configs)
```python
from datasets import concatenate_datasets, load_dataset
ALL_CONFIGS = [
"small",
"medium",
"large",
"dev",
"test_clean",
"test_clean_large",
"test_other",
"test_other_large",
]
def load_libriheavy_all_train(configs: list[str] | None = None):
cfgs = configs or ALL_CONFIGS
parts = [load_dataset("mythicinfinity/libriheavy", cfg, split="train") for cfg in cfgs]
return concatenate_datasets(parts)
full = load_libriheavy_all_train()
```
## Citation
```bibtex
@misc{kang2023libriheavy,
title={Libriheavy: a 50,000 hours ASR corpus with punctuation casing and context},
author={Wei Kang and Xiaoyu Yang and Zengwei Yao and Fangjun Kuang and Yifan Yang and Liyong Guo and Long Lin and Daniel Povey},
year={2023},
eprint={2309.08105},
archivePrefix={arXiv},
primaryClass={eess.AS}
}
```
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