Datasetpublic

zhifeixie/Voices-in-the-Wild-Bench

Voices-in-the-Wild-Bench Voices-in-the-Wild-Bench is a bilingual benchmark for evaluating robust automatic speech recognition under real-world acoustic conditions. It contains 5,000 speech examples covering Chinese and English, synthetic and real-recorded speech, and eight acoustic scenarios. Dataset Composition Group Count Synthetic speech 3,500 Real-recorded speech 1,500 Chinese 2,500 English 2,500 Total 5,000 Splits Splits… See the full description on the dataset page: https://huggingface.co/datasets/zhifeixie/Voices-in-the-Wild-Bench.

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Dataset Card

Voices-in-the-Wild-Bench

Voices-in-the-Wild-Bench is a bilingual benchmark for evaluating robust automatic speech recognition under real-world acoustic conditions. It contains 5,000 speech examples covering Chinese and English, synthetic and real-recorded speech, and eight acoustic scenarios.

Dataset Composition

Group	Count
Synthetic speech	3,500
Real-recorded speech	1,500
Chinese	2,500
English	2,500
Total	5,000

Splits

Splits are organized by recording source and acoustic category. Prefixes denote source type:

—real_: real-recorded speech
—syn_: synthetic speech

Available categories are noise, far_field, obstructed, distortion, recording, echo, dropout, and mixed.

Example split names:

text

real_noise
syn_noise
real_mixed
syn_mixed

Fields

—audio: embedded audio feature.
—file_name: release-relative audio file name.
—audio_path: release-relative audio path.
—question: transcription instruction.
—answer: reference transcription.
—text: same as answer, provided for ASR tooling compatibility.
—subset: original subset label.
—prediction: empty placeholder for model outputs.
—name: sample name.
—index: sample index.

Usage

python

from datasets import load_dataset

dataset = load_dataset(
    "zhifeixie/Voices-in-the-Wild-Bench",
    split="syn_noise",
)

sample = dataset[0]
audio = sample["audio"]
reference = sample["answer"]

Evaluation

We report CER for Chinese samples and WER for English samples. See the project repository and leaderboard for evaluation scripts, model wrappers, and submission instructions:

—Repository: https://github.com/xzf-thu/Voices-in-the-Wild-Bench
—Leaderboard: https://xzf-thu.github.io/Voices-in-the-Wild-Bench/

Citation

If you use this benchmark, please cite the associated paper. Citation metadata will be updated after release.