README.md

---
license: cc-by-nc-4.0
dataset_info:
  features:
  - name: audio
    dtype: audio
  - name: name
    dtype: string
---

# WHAM!48kHz noise dataset

This is a mirror of the [WHAM!48kHz noise dataset](http://wham.whisper.ai/).
The original files were segmented and converted from WAV to Opus to reduce the size and accelerate streaming.

- **Sampling rate**: 48 kHz
- **Channels**: 2
- **Format**: Opus
- **Splits**:
  - **Train**: 59 hours, 21216 segments, files 000 to 188
  - **Validation**: 12 hours, 4444 segments, files 189 to 225
  - **Test**: 7 hours, 2613 segments, files 226 to 249
- **License:** CC BY-NC 4.0
- **Source:** [http://wham.whisper.ai/](http://wham.whisper.ai/)
- **Paper:** [WHAM!: Extending Speech Separation to Noisy Environments](https://arxiv.org/abs/1907.01160)

## Usage

```python
import io

import soundfile as sf
from datasets import Features, Value, load_dataset

for item in load_dataset(
    "philgzl/wham",
    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{wichern2019wham,
  title = {{WHAM!}: {Extending} speech separation to noisy environments},
  author = {Wichern, Gordon and Antognini, Joe and Flynn, Michael and Zhu, Licheng Richard and McQuinn, Emmett and Crow, Dwight and Manilow, Ethan and Roux, Jonathan Le},
  booktitle = {Proc. Interspeech},
  pages = {1368--1372},
  year = {2019},
}
```