Duplicate from kahrendt/microwakeword

Co-authored-by: Kevin Ahrendt <kahrendt@users.noreply.huggingface.co>

.gitattributes

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+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
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README.md

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+This dataset contains spectrogram features in an mmap ninja format intended to use for microWakeWord training. The features are generated using TensorFlow's microfrontend with the following settings:
+```
+            sample_rate=16000,
+            window_size=30,
+            window_step=20,
+            num_channels=40,
+            upper_band_limit=7500,
+            lower_band_limit=125,
+            enable_pcan=True,
+            min_signal_remaining=0.05,
+            out_scale=1,
+            out_type=tf.uint16,
+```
+
+These features are not scaled or converted to float. To do so, multiply by a factor of 0.0390625 after casting to a float. The current version (June 8th, 2024) of microWakeWord doesn't automatically do this, but it will be implented.
+
+The ``dinner_party_background`` file contains features from the CHiME6 training set to use while training, the CHiME6 dev and evaluation sets for validating ambient background, and all DipCo audios for testing ambient background.
+
+The ``no_speech_background`` file contains features from the FMA-medium, FSD50K, and WHAM datasets for training. Any source audio clips less than 6 seconds long were repeated until at least that length. All spectrograms were split over 5 second non-overlapping intervals. The first 25 features were discarded.
+
+The ``speech_background`` file contains features from the LibriSpeech training other and VOiCES datasets for training. Any source audio clips less than 6 seconds long were repeated until at least that length. All spectrograms were split over 5 second non-overlapping intervals. The first 25 features were discarded.
+
+---
+license: cc-by-nc-4.0
+---

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