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--- |
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language: |
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- en |
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pretty_name: librispeech_asr_test_vad |
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tags: |
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- speech |
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license: cc-by-4.0 |
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task_categories: |
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- text-classification |
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--- |
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Voice Activity Detection (VAD) Test Dataset |
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This dataset is based on the `test.clean` and `test.other` splits from the |
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[librispeech_asr](https://huggingface.co/datasets/openslr/librispeech_asr) |
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corpus. It includes two binary labels: |
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- **speech**: Indicates presence of speech ([0, 1]), computed using a dynamic threshold method with background noise estimation and smoothing. |
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- **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 are `confidence` 0. The remaining 93% are `confidence` 1 enabling VAD testing. |
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The dataset has minimal background noise, making it suitable for mixing with |
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external noise samples to test VAD robustness. |
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## Example data |
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A plot for an example showing audio samples and the `speech` feature. |
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<img src="assets/test_other_item_02.png" alt="Example from test.other"/> |
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The example below shows brief dropouts in the `speech` feature during natural |
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short pauses of quiet in the talker's speech. Since some VAD models may react |
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more slowly, the `confidence` feature offers a way to optionally ignore these |
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transient droputs when evaluating performance. |
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<img src="assets/test_clean_item_02.png" alt="Example from test.other"/> |
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# Example usage of dataset |
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The VAD model under test must support processing a chunk size of 512 audio |
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samples at 16000 Hz generating a prediction for each `speech` feature. |
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```console |
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import datasets |
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import numpy as np |
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from sklearn.metrics import roc_auc_score |
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dataset = datasets.load_dataset("guynich/librispeech_asr_test_vad") |
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audio = dataset["test.clean"][0]["audio"]["array"] |
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speech = dataset["test.clean"][0]["speech"] |
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# Compute voice activity probabilities |
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speech_probs = vad_model(audio) |
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# Add test code here |
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roc_auc = roc_auc_score(speech, speech_probs) |
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``` |
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In practice you would run the AUC computation across the entire test split. |
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## Ignore transient dropouts |
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The `confidence` values can be used to filter the data. Removing zero confidence |
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values excludes 6.8% of the dataset and causes numerical increase in |
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computed precision. This compensates for slower moving voice activity decisions |
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as encountered in real-world applications. |
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```console |
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confidence = dataset["test.clean"][0]["confidence"] |
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speech_array = np.array(speech) |
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speech_probs_array = np.array(speech_probs) |
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roc_auc_confidence = roc_auc_score( |
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speech_array[np.array(confidence) == 1], |
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speech_probs_array[np.array(confidence) == 1], |
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) |
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``` |
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# Model evaluation example |
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Example AUC plots computed for |
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[Silero VAD](https://github.com/snakers4/silero-vad?tab=readme-ov-file) |
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model with `test.clean` split. |
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<img src="assets/roc_test_clean.png" alt="Example from test.clean with Silero-VAD"/> |
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Precision values are increased when data is sliced by `confidence` values. |
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These low-confidence `speech` labels are flagged rather than removed, allowing |
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users to either exclude them (as shown here) or handle them with other methods. |
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<img src="assets/roc_test_clean_exclude_low_confidence.png" alt="Example from test.clean with Silero-VAD"/> |
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# License Information |
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This derivative dataset retains the same license as the source dataset |
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[librispeech_asr](https://huggingface.co/datasets/openslr/librispeech_asr). |
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[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) |
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# Citation Information |
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Labels contributed by Guy Nicholson were added to the following dataset. |
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``` |
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@inproceedings{panayotov2015librispeech, |
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title={Librispeech: an ASR corpus based on public domain audio books}, |
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author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev}, |
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booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on}, |
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pages={5206--5210}, |
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year={2015}, |
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organization={IEEE} |
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} |
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``` |
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