vmc2026-track1-dev

Development subset of the VMC 2026 Track 1 data.

The data is organized into two configs corresponding to two subjective evaluation paradigms: absolute rating (acr) and pairwise comparison (ccr). The sample_id values are namespaced strings such as vmc2026-track1-dev-acr_489 and vmc2026-track1-dev-ccr_7233.

acr -- Absolute Category Rating

1,008 samples. Each row pairs a sample_id with one speech audio file. Predict a Mean Opinion Score (MOS) on a 1--5 scale for each sample.

Column	Type	Description
sample_id	string	Namespaced identifier such as vmc2026-track1-dev-acr_489
audio	Audio	Speech audio (FLAC, mono);
sample_rate	int	Sample rate in Hz
duration	float	Duration in seconds

ccr -- Comparative Category Rating

2,520 samples. Each row pairs a sample_id with two speech audio files from different systems processing the same source utterance. Predict a CMOS (Comparative Mean Opinion Score) on a -3 to +3 scale, where positive means audio_a is better than audio_b.

Column	Type	Description
sample_id	string	Namespaced identifier such as vmc2026-track1-dev-ccr_7233
audio_a	Audio	Speech audio from system A (FLAC, mono);
audio_b	Audio	Speech audio from system B (FLAC, mono);
sample_rate	int	Sample rate in Hz
duration	float	Duration in seconds

Submission Format

Submit one space-delimited, predictions.csv file with one prediction per line:

sample_id,pred_score
vmc2026-track1-test-acr_4588,3.42
vmc2026-track1-test-ccr_3061,-0.15

The file should contain exactly 1,008 ACR rows and 2,520 CCR rows -- one prediction per sample_id in this dataset. ACR scores must lie in [1, 5]; CCR scores in [-3, +3].

Loading

Check https://github.com/pytorch/torchcodec to install the right version. Then load and iterate normally:

from datasets import load_dataset

acr = load_dataset("urgent-challenge/vmc2026-track1-dev", "acr", split="dev")  # 1,008 rows
ccr = load_dataset("urgent-challenge/vmc2026-track1-dev", "ccr", split="dev")  # 2,520 rows

Each row's audio (or audio_a / audio_b) is a torchcodec AudioDecoder -- not a dict. Call get_all_samples() to materialise the waveform as a torch.Tensor of shape [num_channels, num_samples]:

>>> acr[0]
{'sample_id': 'vmc2026-track1-dev-acr_489',
 'audio': <datasets.features._torchcodec.AudioDecoder object at 0x...>,
 'sample_rate': 32000,
 'duration': 8.424}

>>> samples = acr[0]["audio"].get_all_samples()
>>> samples
AudioSamples:
  data (shape): torch.Size([1, 269568])
  pts_seconds: 0.0
  duration_seconds: 8.424
  sample_rate: 32000

>>> waveform = samples.data        # torch.float32, shape [1, 269568]
>>> sr = samples.sample_rate       # 32000

>>> ccr[0]
{'sample_id': 'vmc2026-track1-dev-ccr_7233',
 'audio_a': <datasets.features._torchcodec.AudioDecoder object at 0x...>,
 'audio_b': <datasets.features._torchcodec.AudioDecoder object at 0x...>,
 'sample_rate': 16000,
 'duration': 7.41}

>>> ccr[0]["audio_a"].get_all_samples()
AudioSamples:
  data (shape): torch.Size([1, 118560])
  pts_seconds: 0.0
  duration_seconds: 7.41
  sample_rate: 16000

Note

All audio is mono FLAC. Sample rates vary across samples (16 / 22.05 / 24 / 32 / 44.1 kHz); see the sample_rate column on the row, or samples.sample_rate after decoding.

The audios are from the subjective listening test data from the ICASSP 2026 URGENT Challenge.

@inproceedings{urgent2026,
  title={{ICASSP 2026 URGENT Speech Enhancement Challenge}},
  author={Li, Chenda and Wang, Wei and Sach, Marvin and Zhang, Wangyou and Saijo, Kohei and Cornell, Samuele and Fu, Yihui and Ni, Zhaoheng and Fingscheidt, Tim and Watanabe, Shinji and Qian, Yanmin},
  booktitle={Proc. ICASSP 2026},
  year={2026}
}

License

CC-BY-4.0