| 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 | |
| --- | |