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