Deep Complex U-Net
Unofficial PyTorch Implementation of Phase-Aware Speech Enhancement with Deep Complex U-Net, (H. Choi et al., 2018)
Note
This is NOT author's implementation.
Architecture
(TO BE)
Requirements
torch==1.1
soundfile==0.9.0
easydict==1.9
git+https://github.com/keunwoochoi/torchaudio-contrib@61fc6a804c941dec3cf8a06478704d19fc5e415a
git+https://github.com/sweetcocoa/PinkBlack@e45a65623c1b511181f7ea697ca841a7b2900f17
torchcontrib==0.0.2
git+https://github.com/vBaiCai/python-pesq
# gputil # if you need to execute multiple training process
Train
- Download Datasets:
-
Separate each train / test wavs
-
Downsample wavs
# prerequisite : ffmpeg.
# sudo apt-get install ffmpeg (Ubuntu)
bash downsample.sh # all wavs below $PWD will be converted to .flac, 16k samplerate- Train
python train_dcunet.py --batch_size 12 \
--train_signal /path/to/train/clean/speech/ \
--train_noise /path/to/train/noisy/speech/ \
--test_signal /path/to/test/clean/speech/ \
--test_noise /path/to/test/noisy/speech/ \
--ckpt /path/to/save/checkpoint.pth \
--num_step 50000 \
--validation_interval 500 \
--complex
# You can check other arguments from the source code. ( Sorry for the lack description. ) Test
python estimate_directory.py --input_dir /path/to/noisy/speech/ \
--output_dir /path/to/estimate/dir/ \
--ckpt /path/to/saved/checkpoint.pthResults
| PESQ(cRMCn/cRMRn) | Paper | Mine* |
|---|---|---|
| DCUNet - 10 | 2.72/2.51 | 3.03/3.07 |
| DCUNet - 20 | 3.24/2.74 | 3.12/3.11 |
- cRMCn : Complex-valued input/Output
- cRMRn : Real-valued input/Output
Comparing the two(Paper's, Mine) values above is inappropriate for the following reasons:
-
* I did not use matlab code that the author used to calculate pesq, but instead used pypesq.
-
* The Architecture of model is slightly different from the original paper. (Such as kernel size of convolution filters)
-
MelSpec
Notes
- Log amplitute estimate has slightly worse performance than non-log amplitude
- Complex-valued network does not make the metric better..
Sample Wavs
| Mixture | Estimated Speech | GT(Clean Speech) |
|---|---|---|
| mixture1.wav | Estimated1.wav | GroundTruth1.wav |
| mixture2.wav | Estimated2.wav | GroundTruth2.wav |
Contact
- Jongho Choi([email protected] / Seoul National Univ., ESTsoft )