

The noise_sequence argument for [UnivNetModel.forward] should be standard Gaussian noise (such as from torch.randn) of shape ([batch_size], noise_length, model.config.model_in_channels), where noise_length should match the length dimension (dimension 1) of the input_features argument. If not supplied, it will be randomly generated; a torch.Generator can be supplied to the generator argument so that the forward pass can be reproduced. (Note that [UnivNetFeatureExtractor] will return generated noise by default, so it shouldn't be necessary to generate noise_sequence manually.)
Padding added by [UnivNetFeatureExtractor] can be removed from the [UnivNetModel] output through the [UnivNetFeatureExtractor.batch_decode] method, as shown in the usage example below.
Padding the end of each waveform with silence can reduce artifacts at the end of the generated audio sample. This can be done by supplying pad_end = True to [UnivNetFeatureExtractor.__call__]. See this issue for more details.