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  ## Overview
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- This bandwidth extension model is trained on one specific body conduction sensor data from the [Vibravox dataset](https://huggingface.co/datasets/Cnam-LMSSC/vibravox).
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- The model is designed to to enhance the audio quality of body-conducted captured speech, by denoising and regenerating mid and high frequencies from low frequency content only.
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  ## Disclaimer
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- This model has been trained for **specific non-conventional speech sensors** and is intended to be used with **in-domain data**.
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- Please be advised that using these models outside their intended sensor data may result in suboptimal performance.
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  ## Training procedure
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  enhanced_audio_16kHz = model(cut_audio_16kHz)
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  ```
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- ## Link to BWE models trained on other body conducted sensors :
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-
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- The entry point to all EBEN models for Bandwidth Extension (BWE) is available at [https://huggingface.co/Cnam-LMSSC/vibravox_EBEN_models](https://huggingface.co/Cnam-LMSSC/vibravox_EBEN_models).
 
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  ## Overview
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+ This bandwidth extension model, trained on [Vibravox](https://huggingface.co/datasets/Cnam-LMSSC/vibravox) body conduction sensor data, enhances body-conducted speech audio by denoising and regenerating mid and high frequencies from low-frequency content.
 
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  ## Disclaimer
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+ This model, trained for **a specific non-conventional speech sensor**, is intended to be used with **in-domain data**. Using it with other sensor data may lead to suboptimal performance.
 
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+ ## Link to BWE models trained on other body conducted sensors :
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+
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+ The entry point to all EBEN models for Bandwidth Extension (BWE) is available at [https://huggingface.co/Cnam-LMSSC/vibravox_EBEN_models](https://huggingface.co/Cnam-LMSSC/vibravox_EBEN_models).
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  ## Training procedure
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  enhanced_audio_16kHz = model(cut_audio_16kHz)
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  ```
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