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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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# librispeech_asr_test_vad |
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A dataset for testing voice activity detection (VAD). |
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This dataset uses test splits [`test.clean`, `test.other`] extracted |
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from the |
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[`librispeech_asr` dataset](https://huggingface.co/datasets/openslr/librispeech_asr). |
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There are two additional features. |
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1. Binary classification of speech activity, called `speech`. These binary values [0, 1] were computed from speech audio samples using a dynamic threshold method with background noise estimation and smoothing. |
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2. Binary classification of confidence, called `confidence`. These binary values [0, 1] are computed as follows. The default confidence is 1. After a `speech` transition from 1 to 0 the confidence is set to 0 up to a maximum of three 0s in `speech` (approximately 0.1 second). This can be used to correct for temporary blips in the `speech` feature and unknown decay in the method under test. |
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This test dataset has little background noise thus enables mixing with noise |
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samples to assess voice activity detection 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 following example demonstrates short zero blips in the `speech` feature for |
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valid short pauses in the talker's speech. However a VAD model under test may |
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have slower reaction time. The `confidence` feature provides an optional means |
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for reducing the impact of these short zero blips when computing metrics for a |
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method under test. |
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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 model under test must support processing a chunk size of 512 audio samples |
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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 probabilities from model under test (block size 512). |
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speech_probs = model_under_test(audio) |
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# Add test code here such as AUC metrics. |
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# In practice you would run this across the entire test split. |
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roc_auc = roc_auc_score(speech, speech_probs) |
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``` |
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The confidence values can be used to slice the data. This removes 6.8% of the |
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entire dataset `speech` features and removing these low confidence values |
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increases precision. |
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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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# Silero-VAD model testing |
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Example AUC plots computed for Silero-VAD model 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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<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 dataset retains the same license as the source dataset. |
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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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Derived from this 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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