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--- |
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language: en |
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license: bsd-3-clause |
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library_name: pytorch-lightning |
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tags: |
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- pytorch-lightning |
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- audio-to-audio |
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datasets: vctk |
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model_name: nu-wave-x2 |
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--- |
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# nu-wave-x2 |
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## Model description |
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NU-Wave: A Diffusion Probabilistic Model for Neural Audio Upsampling |
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- [GitHub Repo](https://github.com/mindslab-ai/nuwave) |
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- [Paper](https://arxiv.org/pdf/2104.02321.pdf) |
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This model was trained by contributor [Frederico S. Oliveira](https://huggingface.co/freds0), who graciously [provided the checkpoint](https://github.com/mindslab-ai/nuwave/issues/18) in the original author's GitHub repo. |
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This model was trained using source code written by Junhyeok Lee and Seungu Han under the BSD 3.0 License. All credit goes to them for this work. |
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This model takes in audio at 24kHz and upsamples it to 48kHz. |
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## Intended uses & limitations |
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#### How to use |
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You can try out this model here: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/gist/nateraw/bd78af284ef78a960e18a75cb13deab1/nu-wave-x2.ipynb) |
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#### Limitations and bias |
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Provide examples of latent issues and potential remediations. |
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## Training data |
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Describe the data you used to train the model. |
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If you initialized it with pre-trained weights, add a link to the pre-trained model card or repository with description of the pre-training data. |
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## Training procedure |
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Preprocessing, hardware used, hyperparameters... |
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## Eval results |
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You can check out the authors' results at [their project page](https://mindslab-ai.github.io/nuwave/). The project page contains many samples of upsampled audio from the authors' models. |
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### BibTeX entry and citation info |
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```bibtex |
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@inproceedings{lee21nuwave, |
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author={Junhyeok Lee and Seungu Han}, |
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title={{NU-Wave: A Diffusion Probabilistic Model for Neural Audio Upsampling}}, |
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year=2021, |
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booktitle={Proc. Interspeech 2021}, |
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pages={1634--1638}, |
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doi={10.21437/Interspeech.2021-36} |
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} |
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``` |