Hugging Face's logo

Datasets:
urgent-challenge
/
urgent2024_mos

Modalities:
Audio
Text
Formats:
parquet
Languages:
English
Size:
1K - 10K
Tags:
speech
audio
speech enhancement
speech quality assessment
Libraries:
Datasets
pandas
License:
Dataset card Data Studio Files Community
1
urgent2024_mos / README.md
urgent-challenge's picture
urgent-challenge
Update README.md
5e187d2 verified
preview code
|
raw
history blame contribute delete
30 kB
metadata
license: cc-by-nc-sa-4.0
language:
  - en
tags:
  - speech
  - audio
  - speech enhancement
  - speech quality assessment
size_categories:
  - 100M<n<1B

Dataset Description

This dataset contains noisy and enhanced speech samples with human-labeled mean opinion scores (MOS), which was constructed in the 2024 URGENT Speech Enhancement Challenge (https://urgent-challenge.github.io/urgent2024/), an official NeurIPS 2024 Competition. The dataset is intended to facilitate research on speech quality assessment (SQA) and speech enhancement (SE) systems.

The dataset consists of the noisy and 22 enhanced versions of a subset (300 samples) of the blind test dataset in the 2024 URGENT Speech Enhancement Challenge. In total, it 6900 speech samples with MOS labels (~13.8 hours).

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 (to be released).

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_mos")

# Load a single sample
sample = data["test"][100]
print(sample)

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

This will generate the following output:

{
  'id': 'P422-fileid_338',
  'audio': {'path': None, 'array': array([ 0.00027466, -0.00161743,  0.00033569, ...,  0.        , 0.        ,  0.        ]), 'sampling_rate': 16000},
  'sampling_rate': 16000,
  'mos': 2.5,
  'raw_ratings': [5, 4, 1, 2, 3, 1, 2, 2],
}

Data fields

  • id: Unique identifier for the sample.
  • audio: Dictionary containing the audio data.
    • path: Path to the audio file (always None in this dataset).
    • array: Numpy array of the audio signal.
    • sampling_rate: Sampling rate of the audio signal in Hz.
  • sampling_rate: Sampling rate of the audio signal in Hz.
  • mos: Mean opinion score (MOS) for the audio sample, ranging from 1 (bad) to 5 (excellent).
  • raw_ratings: List of raw ratings from human subjects, each ranging from 1 (bad) to 5 (excellent).

The id field follows the following unified format: "{participant_id}-{fileid}", where participant_id is the ID of the participant who generated the audio sample, and fileid is the ID of the audio file (shared among different participants).

More information and analysis

For more information about the dataset, including the data collection process and analysis of the results, please refer to our analysis paper (to be released).

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

2ziV6vH-5wo, 4SzRZvEvyr8, 7XcyE1Rrp8g, 5ZqqXHz_xyo, 617jBfdO8MQ, AOvV9m7feKo, JDAYv4tv7yk, JQkewbO-qf4, rj27kDNtl2c, uB_ykR_yjnY, 4G1__QdEp0U, AbixsuKqfLQ, G3zK1aB_zU4, LWC3dS8EEDM, LZnNzQse2OA, QCltA8fDdQw, RaQ96aK4f58, WFAf2kEEBaI, c6UOJfoenss, kBJ2yTaq-UU, klvdARnvpjg, kmQBICeWhAQ, mWF5GXvfP-4, q_Tjw0SnRu8, ud0Aqc3lqLU, wr1hs6geXyk, omhUqE0SANk, d5NEhm7ZOyc, xw39PKAXkdE, qQrY8ZjSCnQ, GLeoET3gZWg, i_r3gCZrdSc, omhUqE0SANk, qQrY8ZjSCnQ, COls47WPPNk, qQrY8ZjSCnQ, xw39PKAXkdE, In35ZJEGtu8, bLVLhLMoV70, pkc-AJTfNDY, nIWnC9RMW08, GLeoET3gZWg, qQrY8ZjSCnQ, FtN8XzP8ZWg, 9up7LiqU9u4, i_r3gCZrdSc, omhUqE0SANk, nIWnC9RMW08, zWDJojt_KTU, AAckOJEBODk, VE0v1gsOMlw, kDDEQUfMT_8, d5NEhm7ZOyc, DUAdarwNCRs, qQrY8ZjSCnQ, 9jiyee7zCXo, 9up7LiqU9u4, pauj3PKE6L4, GLeoET3gZWg, 5YrhJMiyaPM, qQrY8ZjSCnQ, 5YrhJMiyaPM, POXAC1WpDBU, FtN8XzP8ZWg, qQrY8ZjSCnQ, DUAdarwNCRs, pauj3PKE6L4, qQrY8ZjSCnQ, 4geYwpsHQd8, 2r2BRN2tAFc, pauj3PKE6L4, xw39PKAXkdE, ZtUv67HHw6g, POXAC1WpDBU, Ab6ryHD_ahQ, zWDJojt_KTU, nIWnC9RMW08, d5NEhm7ZOyc, COls47WPPNk, 9up7LiqU9u4, omhUqE0SANk, 9R4O3bzONQQ, l6L2xaYRm7U, nIWnC9RMW08, qQrY8ZjSCnQ, zWDJojt_KTU, COls47WPPNk, GLeoET3gZWg, nIWnC9RMW08, mL6-d5mTQuM, qQrY8ZjSCnQ, bLVLhLMoV70, qQrY8ZjSCnQ, kDDEQUfMT_8, zY4K3sEL5lU, 9jiyee7zCXo, qQrY8ZjSCnQ, sG548miBvh0, 9up7LiqU9u4, qQrY8ZjSCnQ, GLeoET3gZWg, 2t18v4HnQxY, qQrY8ZjSCnQ, zWDJojt_KTU, Ab6ryHD_ahQ, qQrY8ZjSCnQ, COls47WPPNk, FtN8XzP8ZWg, In35ZJEGtu8, l6L2xaYRm7U, qQrY8ZjSCnQ, 9zy1SdoIpkA, nIWnC9RMW08, l6rHUHZx5Vc, xw39PKAXkdE, qQrY8ZjSCnQ, omhUqE0SANk, R5gE6vK8c7Y, 9jiyee7zCXo, pauj3PKE6L4, 9R4O3bzONQQ, omhUqE0SANk, zY4K3sEL5lU, omhUqE0SANk, 6vcXN7ko3Fk, COls47WPPNk, FmtbYlybAlo, zWDJojt_KTU, 9R4O3bzONQQ, 9jiyee7zCXo, i_r3gCZrdSc, qQrY8ZjSCnQ, omhUqE0SANk, qQrY8ZjSCnQ, 541Dq1D1_JY, 9R4O3bzONQQ, 9up7LiqU9u4, l6rHUHZx5Vc, qQrY8ZjSCnQ, sk53YzngvEs, ZtUv67HHw6g, qQrY8ZjSCnQ, l6rHUHZx5Vc, bLVLhLMoV70, COls47WPPNk, 9jiyee7zCXo, omhUqE0SANk, qQrY8ZjSCnQ, POXAC1WpDBU, qQrY8ZjSCnQ, d5NEhm7ZOyc, DUAdarwNCRs, 5YrhJMiyaPM, l6L2xaYRm7U, zWDJojt_KTU, DUAdarwNCRs, pauj3PKE6L4, sk53YzngvEs, omhUqE0SANk, Ab6ryHD_ahQ, OB9GAgiEVqk, xw39PKAXkdE, WyugIciIeq8, s4HerxySu4c, pauj3PKE6L4, 9R4O3bzONQQ, qQrY8ZjSCnQ, omhUqE0SANk, DUAdarwNCRs, 9jiyee7zCXo, qQrY8ZjSCnQ, l6L2xaYRm7U, WyugIciIeq8, GLeoET3gZWg, In35ZJEGtu8, WyugIciIeq8, pkc-AJTfNDY, jzcrUdexbNE, POXAC1WpDBU, i_r3gCZrdSc, POXAC1WpDBU, FtN8XzP8ZWg, Ab6ryHD_ahQ, qQrY8ZjSCnQ, 6vcXN7ko3Fk, VE0v1gsOMlw, GLeoET3gZWg, s4HerxySu4c, omhUqE0SANk, 9R4O3bzONQQ, 2MZR4cT7GbY, nIWnC9RMW08, d5NEhm7ZOyc, xw39PKAXkdE, Yvy_WkwkufU, d5NEhm7ZOyc, s4HerxySu4c, 9up7LiqU9u4, l6rHUHZx5Vc, WyugIciIeq8, 9zy1SdoIpkA, l6L2xaYRm7U, qQrY8ZjSCnQ, xw39PKAXkdE, i_r3gCZrdSc, jzcrUdexbNE, DUAdarwNCRs, d5NEhm7ZOyc, 5YrhJMiyaPM, qQrY8ZjSCnQ, nIWnC9RMW08, Yvy_WkwkufU, 5YrhJMiyaPM, sk53YzngvEs, qQrY8ZjSCnQ, l6L2xaYRm7U, 2t18v4HnQxY, qQrY8ZjSCnQ, l6L2xaYRm7U, DUAdarwNCRs, ZtUv67HHw6g, i_r3gCZrdSc, s4HerxySu4c, COls47WPPNk, zWDJojt_KTU, l6L2xaYRm7U, ZtUv67HHw6g, omhUqE0SANk, nIWnC9RMW08, 9up7LiqU9u4, 5YrhJMiyaPM, qQrY8ZjSCnQ, 9up7LiqU9u4, pauj3PKE6L4, VE0v1gsOMlw, GLeoET3gZWg, VE0v1gsOMlw, Ab6ryHD_ahQ, WyugIciIeq8, qQrY8ZjSCnQ, jzcrUdexbNE, l6L2xaYRm7U, POXAC1WpDBU, WyugIciIeq8, FtN8XzP8ZWg, qQrY8ZjSCnQ, GLeoET3gZWg, In35ZJEGtu8, 6vcXN7ko3Fk, GLeoET3gZWg, pauj3PKE6L4, nIWnC9RMW08, FtN8XzP8ZWg, WyugIciIeq8, DUAdarwNCRs, xw39PKAXkdE, l6L2xaYRm7U, SPxCCLU0cTs, Ab6ryHD_ahQ, xw39PKAXkdE, pauj3PKE6L4, sG548miBvh0, i_r3gCZrdSc, SSNfXY_xwxc, POXAC1WpDBU, WyugIciIeq8, qQrY8ZjSCnQ, s4HerxySu4c, 5YrhJMiyaPM, qQrY8ZjSCnQ, bLVLhLMoV70, 9jiyee7zCXo, qQrY8ZjSCnQ, Yvy_WkwkufU, POXAC1WpDBU, 2t18v4HnQxY, qQrY8ZjSCnQ, zWDJojt_KTU, Ab6ryHD_ahQ, l6L2xaYRm7U, POXAC1WpDBU, l6rHUHZx5Vc, COls47WPPNk, VE0v1gsOMlw, pkc-AJTfNDY, VE0v1gsOMlw, UFADTgwc4ew, COls47WPPNk, s4HerxySu4c, l6L2xaYRm7U, 6vcXN7ko3Fk, l6L2xaYRm7U, omhUqE0SANk, s4HerxySu4c, FmtbYlybAlo, xw39PKAXkdE, GLeoET3gZWg, qQrY8ZjSCnQ, l6L2xaYRm7U, d5NEhm7ZOyc, 9jiyee7zCXo, d5NEhm7ZOyc, qQrY8ZjSCnQ, VE0v1gsOMlw, i_r3gCZrdSc, 9R4O3bzONQQ, d5NEhm7ZOyc, 9R4O3bzONQQ, 9up7LiqU9u4, COls47WPPNk, 9jiyee7zCXo, bLVLhLMoV70, GLeoET3gZWg, zWDJojt_KTU, UFADTgwc4ew, 9up7LiqU9u4, 9R4O3bzONQQ, VE0v1gsOMlw, POXAC1WpDBU, ZtUv67HHw6g, WyugIciIeq8, d5NEhm7ZOyc, qQrY8ZjSCnQ, 9R4O3bzONQQ, s4HerxySu4c, i_r3gCZrdSc, 4geYwpsHQd8, Ab6ryHD_ahQ, omhUqE0SANk, qQrY8ZjSCnQ, l6rHUHZx5Vc, pauj3PKE6L4, omhUqE0SANk, COls47WPPNk, qQrY8ZjSCnQ, 9R4O3bzONQQ, SPxCCLU0cTs, l6rHUHZx5Vc, POXAC1WpDBU, i_r3gCZrdSc, qQrY8ZjSCnQ, SPxCCLU0cTs, l6rHUHZx5Vc, qQrY8ZjSCnQ, sk53YzngvEs, l6rHUHZx5Vc, POXAC1WpDBU, 9jiyee7zCXo, Ab6ryHD_ahQ, WyugIciIeq8, COls47WPPNk, WyugIciIeq8, GLeoET3gZWg, qQrY8ZjSCnQ, VE0v1gsOMlw, 6vcXN7ko3Fk, bLVLhLMoV70, VE0v1gsOMlw, xw39PKAXkdE, UFADTgwc4ew, qQrY8ZjSCnQ, GLeoET3gZWg, l6rHUHZx5Vc, omhUqE0SANk, xw39PKAXkdE, COls47WPPNk, 2t18v4HnQxY, l6L2xaYRm7U, SSNfXY_xwxc, GLeoET3gZWg, WyugIciIeq8, FmtbYlybAlo, qQrY8ZjSCnQ, l6L2xaYRm7U, Ab6ryHD_ahQ, pkc-AJTfNDY, xw39PKAXkdE, mL6-d5mTQuM, l6L2xaYRm7U

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 this paper if you use this dataset in your research:

@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},
}