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festcat_trimmed_denoised

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martillopartbsc commited on Dec 19, 2023

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@@ -52,9 +52,8 @@ We processed the data of the Catalan Festcat with the following recipe:
   - [py-webrtcvad](https://pypi.org/project/webrtcvad/) -> Python interface to the Voice Activity Detector (VAD) developed by Google for the WebRTC.
 - **Resampling:** From 48000 Hz to 22050 Hz, which is the most common sampling rate for training TTS models
   - Resampler from [CoquiTTS](https://github.com/coqui-ai/TTS/tree/dev) framework
-- **De-noising:** Although base quality of the audios is high, we could remove some background noise and small artifcats thanks to the CleanUNet denoiser developed by NVIDIA.
-  - [CleanUNet](https://github.com/NVIDIA/CleanUNet)
-  - [paper](https://arxiv.org/abs/2202.07790)
 We kept the same number of wave files, also the original anonymized file names and transcriptions.

   - [py-webrtcvad](https://pypi.org/project/webrtcvad/) -> Python interface to the Voice Activity Detector (VAD) developed by Google for the WebRTC.
 - **Resampling:** From 48000 Hz to 22050 Hz, which is the most common sampling rate for training TTS models
   - Resampler from [CoquiTTS](https://github.com/coqui-ai/TTS/tree/dev) framework
+- **Denoising:** Although base quality of the audios is high, we could remove some background noise and small artifcats thanks to the CleanUNet denoiser developed by NVIDIA.
+  - [CleanUNet](https://github.com/NVIDIA/CleanUNet) - [arXiv](https://arxiv.org/abs/2202.07790)
 We kept the same number of wave files, also the original anonymized file names and transcriptions.