Dual-signal Transformation LSTM Network

• This repository provides the pytorch, onnx, ncnn code example

• official repo: https://github.com/breizhn/DTLN

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0. prequisites

• pytorch >= 1.11.0
• librosa

1. pytorch demo

python DTLN_model.py  --model_path ./pretrained/model.pth  \
   --wav_in ./samples/audioset_realrec_airconditioner_2TE3LoA2OUQ.wav \
   --wav_out ./out.wav

(./pretrained/model.pth is converted using cvt_from_keras.py)

realtime (truck by truck, avg 2ms in pytorch with cpu):

python realtime_infer.py  --model_path ./pretrained/model.pth  \
   --wav_in ./samples/audioset_realrec_airconditioner_2TE3LoA2OUQ.wav \
   --wav_out ./out.wav

src wav：./samples/audioset_realrec_airconditioner_2TE3LoA2OUQ.wav

after enhanced: ./samples/enahnced.wav

2. onnx demo

realtime (truck by truck, < 1ms in onnxruntime with cpu):

python realtime_onnx.py --wav_in ./samples/audioset_realrec_airconditioner_2TE3LoA2OUQ.wav \
   --wav_out ./out.wav

3. c++ deploy

see deploy/

Citing

If you are using the DTLN model, please cite:

@inproceedings{Westhausen2020,
  author={Nils L. Westhausen and Bernd T. Meyer},
  title={{Dual-Signal Transformation LSTM Network for Real-Time Noise Suppression}},
  year=2020,
  booktitle={Proc. Interspeech 2020},
  pages={2477--2481},
  doi={10.21437/Interspeech.2020-2631},
  url={http://dx.doi.org/10.21437/Interspeech.2020-2631}
}