File size: 2,848 Bytes
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license: cc-by-4.0
dataset_info:
features:
- name: audio
dtype: audio
- name: name
dtype: string
configs:
- config_name: default
data_files:
- split: speech_english
path: data/speech-english-*.parquet
- split: speech_french
path: data/speech-french-*.parquet
- split: speech_german
path: data/speech-german-*.parquet
- split: speech_italian
path: data/speech-italian-*.parquet
- split: speech_russian
path: data/speech-russian-*.parquet
- split: speech_spanish
path: data/speech-spanish-*.parquet
- split: noise
path: data/noise-*.parquet
- split: rir_slr26
path: data/rir-slr26-*.parquet
- split: rir_slr28
path: data/rir-slr28-*.parquet
---
# DNS5 Challenge data
This is a mirror of the [DNS5 Challenge data](https://github.com/microsoft/DNS-Challenge/).
The original files were converted from WAV to Opus to reduce the size and accelerate streaming.
⚠️ Only the LibriVox, AudioSet, Freesound, OpenSLR26, and OpenSLR28 data is included. The VCTK, VocalSet, CREMA-D, VoxCeleb2, and DEMAND data is excluded. ⚠️
- **Sampling rate**: 48 kHz
- **Channels**: 1
- **Format**: Opus
- **Splits**:
- **speech_english**: 245 hours, 186743 files
- **speech_french**: 95 hours, 60454 files
- **speech_german**: 137 hours, 119175 files
- **speech_italian**: 70 hours, 59525 files
- **speech_russian**: 25 hours, 16566 files
- **speech_spanish**: 86 hours, 78603 files
- **noise**: 177 hours, 63810 files
- **rir_slr26**: 17 hours, 60000 files
- **rir_slr28**: 0.1 hours, 248 files
- **License:**
- **LibriVox**: [Public domain](https://librivox.org/pages/public-domain/)
- **AudioSet**: CC BY 4.0
- **Freesound**: CC0 1.0
- **OpenSLR26 and OpenSLR28**: Apache 2.0
- **Source:** [https://github.com/microsoft/DNS-Challenge/](https://github.com/microsoft/DNS-Challenge/)
- **Paper:** [ICASSP 2023 Deep Noise Suppression Challenge](https://arxiv.org/abs/2303.11510)
## Usage
```python
import io
import soundfile as sf
from datasets import Features, Value, load_dataset
for item in load_dataset(
"philgzl/dns5",
split="speech_english",
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
@article{dubey2024icassp,
title = {{ICASSP} {2023} {Deep} {Noise} {Suppression} {Challenge}},
author = {Dubey, Harishchandra and Aazami, Ashkan and Gopal, Vishak and Naderi, Babak and Braun, Sebastian and Cutler, Ross and Gamper, Hannes and Golestaneh, Mehrsa and Aichner, Robert},
journal = {IEEE Open J. Signal Process.},
volume = {5},
pages = {725--737},
year = {2024},
}
```
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