| ---
|
| 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_16.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},
|
| }
|
| ```
|
|
|
