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license: cc-by-4.0
dataset_info:
features:
- name: audio
dtype: audio
- name: name
dtype: string
---
# LibriSpeech: An ASR corpus based on public domain audio books
This is a mirror of the [LibriSpeech ASR corpus](https://www.openslr.org/12).
The original files were converted from FLAC to Opus to reduce the size and accelerate streaming.
The transcripts are not included. This mirror is thus best suited for audio-to-audio tasks.
- **Sampling rate**: 16 kHz
- **Channels**: 1
- **Format**: Opus
- **Splits**:
- **Train**: 460 hours, 132553 utterances, `train-clean-100` and `train-clean-360` sets.
- **Validation**: 7 hours, 2703 utterances, `dev-clean` set.
- **Test**: 7 hours, 2620 utterances, `test-clean` set.
- **License**: CC BY 4.0
- **Source**: [https://www.openslr.org/12](https://www.openslr.org/12)
- **Paper**: [Librispeech: An ASR corpus based on public domain audio books](https://ieeexplore.ieee.org/document/7178964)
## Usage
```python
import io
import soundfile as sf
from datasets import Features, Value, load_dataset
for item in load_dataset(
"philgzl/libri",
split="train",
streaming=True,
features=Features({"audio": Value("binary"), "name": Value("string")}),
):
print(item["name"])
buffer = io.BytesIO(item["audio"])
x, fs = sf.read(buffer)
# do stuff...
```
## Citation
```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 = {Proc. ICASSP},
pages = {5206--5210},
year = {2015},
}
```
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