Comments (2)
Hi,
In the DNS challenge test dataset, there are many speeches with low sound volume. If we directly listen to the results enhanced by neural networks, the volume of different speeches is not equal and uncontrolled, i.e., it could be higher or lower than the original input. We may need to constantly change the volume of speech to adapt to different volume levels during the hearing test.
For this reason, in FullSubNet, we normalize the speech volume https://github.com/haoxiangsnr/FullSubNet/blob/main/audio_zen/inferencer/base_inferencer.py#L177. In this way, the resulting speeches will have comparable sound volume.
from fullsubnet.
Hi,
This just sidesteps the volume issue, how can I use the FullSubNet model to reduce the noise without changing the volume.
from fullsubnet.
Related Issues (20)
- error
- Training and Validation cRM Mismatch HOT 2
- 仓库里模型比比赛时提交的结果好很多, 这个是有什么不同吗
- Unable to fine-tune pre-trained model (fullsubnet_best_model_58epochs.tar) HOT 1
- Normalization HOT 2
- [Question] How to do streaming reasoning (Real-Time), is there any documentation?
- [Question] The real-time speech enhance is poor, Need help! HOT 3
- Training dataset bug HOT 1
- New
- 😍😍
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- Fast FullSubNet m=infinity HOT 1
- Low volume vocal segments are suppressed HOT 2
- How to use the cpu training model? HOT 2
- Improved FullSubNet Usage HOT 2
- Will there be fast fullsubnet pre-trained model released? HOT 1
- Real-time streaming Fast FullSubNet (LSTMCell) HOT 3
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