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urgent2024-sqa

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metadata
dataset_info:
  features:
    - name: audio
      dtype: audio
    - name: sample_id
      dtype: string
    - name: system_id
      dtype: string
    - name: distill_mos
      dtype: float32
    - name: dnsmos_ovrl
      dtype: float32
    - name: estoi
      dtype: float32
    - name: lps
      dtype: float32
    - name: lsd
      dtype: float32
    - name: mcd
      dtype: float32
    - name: mos
      dtype: float32
    - name: nisqa_mos
      dtype: float32
    - name: pesq
      dtype: float32
    - name: pesqc2
      dtype: float32
    - name: sbert
      dtype: float32
    - name: scoreq
      dtype: float32
    - name: sdr
      dtype: float32
    - name: sigmos_col
      dtype: float32
    - name: sigmos_disc
      dtype: float32
    - name: sigmos_loud
      dtype: float32
    - name: sigmos_noise
      dtype: float32
    - name: sigmos_ovrl
      dtype: float32
    - name: sigmos_reverb
      dtype: float32
    - name: sigmos_sig
      dtype: float32
    - name: spksim
      dtype: float32
    - name: utmos
      dtype: float32
  splits:
    - name: blind_test_mos
      num_bytes: 1389061983.1
      num_examples: 6900
    - name: validation
      num_bytes: 7970138575
      num_examples: 67000
    - name: blind_test
      num_bytes: 17529748955.8
      num_examples: 79400
    - name: nonblind_test
      num_bytes: 31304135944
      num_examples: 171000
  download_size: 57624434841
  dataset_size: 58193085457.9
configs:
  - config_name: default
    data_files:
      - split: blind_test_mos
        path: data/blind_test_mos/*
      - split: validation
        path: data/validation/*
      - split: blind_test
        path: data/blind_test/*
      - split: nonblind_test
        path: data/nonblind_test/*
license: cc-by-nc-sa-4.0
language:
  - en
tags:
  - speech
  - audio
  - speech enhancement
  - speech quality assessment

Dataset Description

This dataset comprises noisy and enhanced speech from the URGENT Speech Enhancement Challenge (NeurIPS 2024), curated for SQA/SE research. Each entry includes audio/IDs with a comprehensive suite of objective and model-predicted quality metrics, and human MOS where available (see Data fields).

The MOS label for each speech sample were collected from 8 distinct human subjects through Amazon Mechanical Turk (MTurk) platform, following the P.808 recommendation. The raw ratings from each subject were averaged to obtain the final MOS score.

Detailed information about the MOS collection process can be found in our summary paper.

All speech samples in this dataset are in English, with a single microphone channel and sampling frequencies ranging from 8 kHz to 48 kHz.

Example Usage

The dataset can be loaded using the datasets library.

from datasets import load_dataset

data = load_dataset("urgent-challenge/urgent2024-sqa")

# Load a single sample
sample = data["blind_test_mos"][100]

print(sample)
# Iterate over all samples
# for idx, sample in enumerate(data["blind_test_mos"]):
#     print(sample)

This will generate the following output:

{
    "audio": {
        "path": "fileid_338.flac",
        "array": array([0.00027466, -0.00161743, 0.00033569, ...,  0., 0., 0.]),
        "sampling_rate": 16000
    },
    "sample_id": "urgent24_blind_test_submission_422_fileid_000338",
    "system_id": "urgent24_blind_test_submission_422",
    "distill_mos": 2.5663137435913086,
    "dnsmos_ovrl": 3.025352716445923,
    "estoi": None,
    "lps": None,
    "lsd": None,
    "mcd": None,
    "mos": 2.5,
    "nisqa_mos": 2.630293607711792,
    "pesq": None,
    "pesqc2": None,
    "sbert": None,
    "scoreq": 2.496799945831299,
    "sdr": None,
    "sigmos_col": 3.276144504547119,
    "sigmos_disc": 2.80405330657959,
    "sigmos_loud": 3.370774984359741,
    "sigmos_noise": 3.403278350830078,
    "sigmos_ovrl": 2.105903148651123,
    "sigmos_reverb": 4.257360935211182,
    "sigmos_sig": 2.3926312923431396,
    "spksim": None,
    "utmos": 2.594034433364868
}

Data fields

Audio

  • audio (dict)

    • path (str | null)
    • array (float32 ndarray): Mono waveform, typically ~[-1, 1].
    • sampling_rate (int): e.g., 16000.

Identifiers

  • sample_id (str): "{system_id}_fileid_{fileid:06d}"
  • system_id (str): Participant/system identifier.

Metrics

Metric Range Opt. Reference Type
PESQ [1, 4.5] ↑ Signal
PESQc2 [1, 4.5] ↑ Signal
DNSMOS [1, 5] ↑ No-reference
SIGMOS_OVRL [1, 5] ↑ No-reference
SIGMOS_DISC [1, 5] ↑ No-reference
SIGMOS_NOISE [1, 5] ↑ No-reference
SIGMOS_REVERB [1, 5] ↑ No-reference
SIGMOS_COL [1, 5] ↑ No-reference
SIGMOS_LOUD [1, 5] ↑ No-reference
SIGMOS_SIG [1, 5] ↑ No-reference
LSD [0, ∞) ↓ Signal
SDR (−∞, ∞) ↑ Signal
UTMOS [1, 5] ↑ No-reference
Distill_MOS [1, 5] ↑ No-reference
NISQA_MOS [1, 5] ↑ No-reference
SCOREQ [1, 5] ↑ No-reference
ESTOI [0, 1] ↑ Signal
SpeechBERTScore (sbert) [0, 1] ↑ Signal
PhonemeSimilarity (lps) [0, 1] ↑ Signal
SpeakerSimilarity (spksim) [−1, 1] ↑ Signal
MCD [0, ∞) ↓ Signal
MOS (human) [1, 5] ↑ No-reference

More information and analysis

For more information about the dataset, please refer to our speech quality assessment paper and analysis paper.

Acknowledgment and license information

The 2024 URGENT Challenge data were created based on the following source speech, noise, and room impulse response (RIR) data, which are publicly available with varying licenses:

Source speech

Source License
LibriVox in DNS5 Challenge CC BY 4.0
LibriTTS CC BY 4.0
CommonVoice 11.0 (English portion) CC0
VCTK ODC-BY
SLR83 CC BY-SA 4.0
CHiME-6 CC BY-SA 4.0
VOiCES CC BY 4.0
EasyCom CC BY-NC 4.0
Audios extracted from Youtube videos CC BY 4.0
Expand to see a full list of Youtube audio data used in this competition

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Source noise

Source License
Audioset+FreeSound noise in DNS5 Challenge CC BY 4.0
WHAM! 48kHz noise CC BY-NC 4.0
DEMAND CC BY-SA 3.0
TUT Urban Acoustic Scenes 2018 Mobile, Evaluation dataset Non-Commercial
FSD50K mixed: CC0, CC BY, CC BY-NC, CC Sampling+
SoundingEarth CC BY 4.0
additional noise audios from Freesound CC0

Source RIRs

Source License
Simulated RIRs in DNS5 Challenge CC BY 4.0
SLR28 Apache 2.0
MYRiAD CC BY-NC-SA 4.0
BRAS CC BY-SA 4.0
BUT_ReverbDB Apache 2.0
voiceHome CC BY-NC-SA 4.0
additional RIRs from Freesound CC0

Citation:

Please cite the following papers if you use this dataset in your research. The first reference creates the dataset annotation, the second describes the URGENT Challenge, and the third details the MOS annotation protocol:

@article{UniVersaExt-Wang2025,
  title={Improving Speech Enhancement with Multi-Metric Supervision from Learned Quality Assessment},
  author={Wang, Wei and Zhang, Wangyou and Li, Chenda and Shi, Jiatong and Watanabe, Shinji and Qian, Yanmin},
  journal={arXiv preprint arXiv:2506.12260},
  year={2025}
}

@inproceedings{URGENT-Zhang2024,
  title={{URGENT} Challenge: Universality, Robustness, and Generalizability For Speech Enhancement},
  author={Zhang, Wangyou and Scheibler, Robin and Saijo, Kohei and Cornell, Samuele and Li, Chenda and Ni, Zhaoheng and Pirklbauer, Jan and Sach, Marvin and Watanabe, Shinji and Fingscheidt, Tim and Qian, Yanmin},
  booktitle={Proc. Interspeech},
  pages={4868--4872},
  year={2024},
}

@article{P808-Sach2025,
  title={P.808 Multilingual Speech Enhancement Testing: Approach and Results of {URGENT} 2025 Challenge},
  author={Sach, Marvin and Fu, Yihui and Saijo, Kohei and Zhang, Wangyou and Cornell, Samuele and Scheibler, Robin and Li, Chenda and Kumar, Anurag and Wang, Wei and Qian, Yanmin and Watanabe, Shinji and Fingscheidt, Tim},
  journal={arXiv preprint arXiv:2507.11306},
  year={2025}
}