SSLZip 256

--- license: cc-by-4.0 datasets: - openslr/librispeech_asr language: - en pipeline_tag: audio-to-audio --- # SSLZip ## Usage ```py import onnxruntime as ort from transformers import HubertModel import torch # Load the upstream HuBERT model. upstream = HubertModel.from_pretrained("facebook/hubert-base-ls960") upstream.eval() # Load the autoencoder model. postprocessor = ort.InferenceSession("sslzip_256.onnx") node_name = postprocessor.get_inputs()[0].name # Prepare an input waveform (assuming 16kHz audio). x = torch.randn(1, 16000) # Extract the latent representation for downstream tasks. with torch.inference_mode(): h = upstream(x, output_hidden_states=True).hidden_states[-1] z = postprocessor.run(None, {node_name: h.cpu().numpy()})[0] # Use z as you like. print(z.shape) ``` ## License The pretrained model was developed using the LibriSpeech corpus and is distributed under the same license (CC BY 4.0). Please include credit to Nagoya Institue of Technology and Techno-Speech, Inc. when using this model. ## Citation ```bibtex @InProceedings{yoshimura2025sslzip, author = {Takenori Yoshimura and Shinji Takaki and Kazuhiro Nakamura and Keiichiro Oura and Takato Fujimoto and Kei Hashimoto and Yoshihiko Nankaku and Keiichi Tokuda}, title = {{SSLZip}: Simple autoencoding for enhancing self-supervised speech representations in speech generation}, booktitle = {13th ISCA Speech Synthesis Workshop (SSW 2025)}, pages = {xxx--xxx}, year = {2025}, } ```

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