README.md

metadata

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 features:

• 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 correct transient dropouts in speech. It is set to 1 by default, but switches to 0 for up to ~0.1 seconds (3 frames) following a transition from speech to silence.

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 zero blips in the speech feature during natural short pauses. Since some VAD models may react more slowly, the confidence feature offers a way to optionally ignore these blips when evaluating performance.

Example usage of dataset

The 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 probabilities from model under test (block size 512).
speech_probs = model_under_test(audio)

# Add test code here such as AUC metrics.
# In practice you would run this across the entire test split.
roc_auc = roc_auc_score(speech, speech_probs)

The confidence values can also be used to filter the data. Removing low-confidence frames excludes about 6.8% of the dataset and helps improve precision when evaluating VAD performance.

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.

License Information

This dataset retains the same license as the source dataset.

CC BY 4.0

Citation Information

Derived from this 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}
}