dns5 / README.md
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metadata
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. 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
    • AudioSet: CC BY 4.0
    • Freesound: CC0 1.0
    • OpenSLR26 and OpenSLR28: Apache 2.0
  • Source: https://github.com/microsoft/DNS-Challenge/
  • Paper: ICASSP 2023 Deep Noise Suppression Challenge

Usage

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

@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},
}