language:
- en
pretty_name: librispeech_asr_test_vad
tags:
- speech
license: cc-by-4.0
task_categories:
- text-classification
Voice Activity Detection (VAD) Test Dataset
This dataset is based on the test.clean
and test.other
splits from the
librispeech_asr
corpus. It includes two binary labels:
speech: Indicates presence of speech ([0, 1]), computed using a dynamic threshold method with background noise estimation and smoothing.
confidence: A post-processing flag to optionally correct transient dropouts in speech. It is set to 1 by default, but switches to 0 for up to ~0.1 seconds (3 chunks of audio) following a transition from speech to silence. Approximately 7% of the
speech
labels in this dataset areconfidence
0. The remaining 93% areconfidence
1 enabling VAD testing.
The dataset has minimal background noise, making it suitable for mixing with external noise samples to test VAD robustness.
Example data
A plot for an example showing audio samples and the speech
feature.

The example below shows brief dropouts in the speech
feature during natural
short pauses of quiet in the talker's speech. Since some VAD models may react
more slowly, the confidence
feature offers a way to optionally ignore these
transient droputs when evaluating performance.

Example usage of dataset
The VAD model under test must support processing a chunk size of 512 audio
samples at 16000 Hz generating a prediction for each speech
feature.
import datasets
import numpy as np
from sklearn.metrics import roc_auc_score
dataset = datasets.load_dataset("guynich/librispeech_asr_test_vad")
audio = dataset["test.clean"][0]["audio"]["array"]
speech = dataset["test.clean"][0]["speech"]
# Compute voice activity probabilities
speech_probs = vad_model(audio)
# Add test code here
roc_auc = roc_auc_score(speech, speech_probs)
In practice you would run the AUC computation across the entire test split.
Ignore transient dropouts
The confidence
values can be used to filter the data. Removing zero confidence
values excludes 6.8% of the dataset and causes numerical increase in
computed precision. This compensates for slower moving voice activity decisions
as encountered in real-world applications.
confidence = dataset["test.clean"][0]["confidence"]
speech_array = np.array(speech)
speech_probs_array = np.array(speech_probs)
roc_auc_confidence = roc_auc_score(
speech_array[np.array(confidence) == 1],
speech_probs_array[np.array(confidence) == 1],
)
Model evaluation example
Example AUC plots computed for
Silero VAD
model with test.clean
split.

Precision values are increased when data is sliced by confidence
values.
These low-confidence speech
labels are flagged rather than removed, allowing
users to either exclude them (as shown here) or handle them with other methods.

License Information
This derivative dataset retains the same license as the source dataset librispeech_asr.
Citation Information
Labels contributed by Guy Nicholson were added to the following dataset.
@inproceedings{panayotov2015librispeech,
title={Librispeech: an ASR corpus based on public domain audio books},
author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},
booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on},
pages={5206--5210},
year={2015},
organization={IEEE}
}