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--- |
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language: "en" |
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thumbnail: |
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tags: |
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- Sound Classification |
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- CNN14 |
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license: "apache-2.0" |
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datasets: |
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- ESC50 |
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--- |
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<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe> |
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<br/><br/> |
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# CNN14 Trained on VGGSound dataset with SimCLR and Fine Tuned on ESC50 |
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This repository provides all the necessary tools to perform audip classification with [CNN14 model](https://arxiv.org/abs/1912.10211) model, implemented with SpeechBrain. For a better experience we encourage you to learn more about |
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[SpeechBrain](https://speechbrain.github.io). The encoder is first trained with SimCLR on the VGGSound dataset, and then fine tuned on ESC50 folds 1,2,3. |
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| Release | Classification Accuracy Valid | Classification Accuracy Test | |
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|:-------------:|:--------------:|:--------------:| |
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| 26-11-22 | 90% | 82% | |
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## Install SpeechBrain |
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First of all, please install SpeechBrain with the following command: |
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``` |
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pip install speechbrain |
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``` |
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Please notice that we encourage you to read our tutorials and learn more about |
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[SpeechBrain](https://speechbrain.github.io). |
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### Perform Classification on your own file |
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```python |
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from speechbrain.inference.classifiers import AudioClassifier |
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model = AudioClassifier.from_hparams(source="speechbrain/cnn14-esc50", savedir='pretrained_models/cnn14-esc50') |
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out_probs, score, index, text_lab = model.classify_file('speechbrain/cnn14-esc50/example_dogbark.wav') |
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print(text_lab) |
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``` |
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### Limitations |
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The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets. |
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#### Referencing SpeechBrain |
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```bibtex |
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@misc{speechbrain, |
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title={{SpeechBrain}: A General-Purpose Speech Toolkit}, |
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author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan and Nauman Dawalatabad and Abdelwahab Heba and Jianyuan Zhong and Ju-Chieh Chou and Sung-Lin Yeh and Szu-Wei Fu and Chien-Feng Liao and Elena Rastorgueva and François Grondin and William Aris and Hwidong Na and Yan Gao and Renato De Mori and Yoshua Bengio}, |
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year={2021}, |
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eprint={2106.04624}, |
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archivePrefix={arXiv}, |
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primaryClass={eess.AS}, |
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note={arXiv:2106.04624} |
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} |
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``` |
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#### Referencing This Pretrained Model |
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The encoder is originally trained for our [paper](https://arxiv.org/pdf/2205.07390.pdf). You can reference our paper if you use this model for your research. |
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```bibtex |
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@inproceedings{wang2022CRL, |
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title={Learning Representations for New Sound Classes With Continual Self-Supervised Learning}, |
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author={Zhepei Wang, Cem Subakan, Xilin Jiang, Junkai Wu, Efthymios Tzinis, Mirco Ravanelli, Paris Smaragdis}, |
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year={2022}, |
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booktitle={Accepted to IEEE Signal Processing Letters} |
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} |
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``` |
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# **About SpeechBrain** |
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- Website: https://speechbrain.github.io/ |
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- Code: https://github.com/speechbrain/speechbrain/ |
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- HuggingFace: https://huggingface.co/speechbrain/ |
