Add model card and metadata
Browse filesThis PR adds a model card for WavCube, which includes:
- Metadata for the `audio-to-audio` pipeline, `transformers` library, and `mit` license.
- Links to the research paper "[WavCube: Unifying Speech Representation for Understanding and Generation via Semantic-Acoustic Joint Modeling](https://huggingface.co/papers/2605.06407)" and the official GitHub repository.
- Sample usage instructions for extracting speech representations and reconstructing waveforms based on the GitHub README.
README.md
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---
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license: mit
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library_name: transformers
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pipeline_tag: audio-to-audio
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---
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# WavCube
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WavCube is a 128-dim, 50Hz continuous representation that unifies speech understanding, reconstruction, and generation within a single space. It is presented in the paper [WavCube: Unifying Speech Representation for Understanding and Generation via Semantic-Acoustic Joint Modeling](https://huggingface.co/papers/2605.06407).
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- **Code:** [GitHub Repository](https://github.com/yanghaha0908/WavCube)
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- **Paper:** [arXiv:2605.06407](https://arxiv.org/abs/2605.06407)
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## Usage
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Before using the model, ensure you have installed the requirements as described in the [official repository](https://github.com/yanghaha0908/WavCube).
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### Extract Representation from Speech
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You can get continuous representations from raw wav using the following command:
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```bash
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python wav_to_feature.py \
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--audio 19_198_000000_000002.wav \
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--config configs/WavCube-stage2.yaml \
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--ckpt WavCube/checkpoints/vocos_checkpoint_epoch=177_step=195000_val_loss=3.3080.ckpt \
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--output 19_198_000000_000002.pt
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```
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### Reconstruct Speech from Representation
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You can reconstruct waveform from representations using the following command:
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```bash
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python feature_to_wav.py \
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--feature 19_198_000000_000002.pt \
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--config configs/WavCube-stage2.yaml \
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--ckpt WavCube/checkpoints/vocos_checkpoint_epoch=177_step=195000_val_loss=3.3080.ckpt
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```
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## Citation
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```bibtex
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@misc{yang2025wavcube,
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title={WavCube: Unifying Speech Representation for Understanding and Generation via Semantic-Acoustic Joint Modeling},
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author={Haohan Yang and others},
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year={2025},
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eprint={2605.06407},
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archivePrefix={arXiv},
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primaryClass={cs.SD},
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url={https://arxiv.org/abs/2605.06407},
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}
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```
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