Upload README.md with huggingface_hub

README.md

@@ -1,38 +1,109 @@
 ---
-dataset_info:
-  features:
-  - name: file
-    dtype: string
-  - name: audio
-    dtype:
-      audio:
-        sampling_rate: 16000
-  - name: text
-    dtype: string
-  - name: speaker_id
-    dtype: int64
-  - name: chapter_id
-    dtype: int64
-  - name: id
-    dtype: string
-  - name: speech
-    sequence: int64
-  - name: confidence
-    sequence: int64
-  splits:
-  - name: test.clean
-    num_bytes: 633124402.5
-    num_examples: 2620
-  - name: test.other
-    num_bytes: 625951259.625
-    num_examples: 2939
-  download_size: 1212479246
-  dataset_size: 1259075662.125
-configs:
-- config_name: default
-  data_files:
-  - split: test.clean
-    path: data/test.clean-*
-  - split: test.other
-    path: data/test.other-*
+language:
+- en
+pretty_name: librispeech_asr_test_vad
+tags:
+- speech
+license: cc-by-4.0
+task_categories:
+- text-classification
 ---
+
+# librispeech_asr_test_vad
+
+A dataset for testing voice activity detection.
+
+This dataset uses test splits [`test.clean`, `test.other`] extracted
+from the
+[`librispeech_asr` dataset](https://huggingface.co/datasets/openslr/librispeech_asr).
+
+There are two additional features.
+
+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.
+
+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.
+
+The effective chunk size is 512 audio samples for each `speech` feature.
+
+# License Information
+
+This dataset retains the same license as the source dataset.
+
+[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)
+
+# Example usage of dataset
+
+```console
+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 method under test (block size 512).
+speech_probs = method_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)
+
+# Data for plotting
+time_step = 512 / 16000
+audio_x_ticks = np.linspace(0.0, len(audio) / 16000, len(audio))
+speech_x_ticks = np.linspace(0.0, len(speech) * time_step, len(speech))
+
+# Data for inspecting masked audio with plotting or playback.
+speech_mask = np.repeat(speech, 512)
+masked_audio = audio[:len(speech_mask)] * speech_mask
+```
+
+The confidence values can be used prior to computing metric
+```console
+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[confidence],
+    speech_probs_array[confidence],
+)
+```
+
+Example plots.
+
+<img src="assets/test_other_item_02.png" alt="Example from test.other"/>
+
+The following example demonstrates short zero blips in the `speech` feature for
+valid short pauses in the talker's speech.  However a VAD method under test may
+have slower reaction time.  The `confidence` feature provides an optional means
+for reducing the impact of these short zero blips when computing metrics for a
+method under test.
+
+<img src="assets/test_clean_item_02.png" alt="Example from test.other"/>
+
+# VAD testing
+
+The VAD method shall supply a voice activity prediction for audio chunks of
+512 samples at rate 16000 Hz.
+
+Example AUC plots computed for `test.clean` split and Silero-VAD model.
+
+<img src="assets/roc_test_clean.png" alt="Example from test.clean with Silero-VAD"/>
+
+# 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}
+}
+```