README.md

---
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},
}
```