WORK IN PROGRESS

REAL-TIME DENOISING AND DEREVERBERATION WITH TINY RECURRENT U-NET

Unofficial implementation of REAL-TIME DENOISING AND DEREVERBERATION WTIH TINY RECURRENT U-NET in PyTorch. Tiny Recurrent U-Net (TRU-Net) is a lightweight online inference model that matches the performance of current (23 Jun 2021) state-of-the-art models. The size of the quantized version of TRU-Net is 362 kilobytes (~300k parameters), which is small enough to be deployed on edge devices. In addition, the small-sized model with a new masking method called phase-aware β-sigmoid mask enables simultaneous denoising and dereverberation.

Colab notebook:

Requirements

Create and activate a virtual environment and install dependencies.

pip install -r requirements.txt

Dataset

• The code uses Microsoft DNS 2020 dataset. The dataset, pre-processing codes, and instruction to generate training data can be found in this link. Assume the dataset is stored under ./dns. Prior to generating clean-noisy data pairs, to comply with the paper's configurations, alter the following parameters in their noisyspeech_synthesizer.cfg file:

total_hours: 300, 
snr_lower: -5, 
snr_upper: 25, 
total_snrlevels: 30

Generate training data:

python noisyspeech_synthesizer_singleprocess.py

Now we assume that the structure of the dataset folder is:

Training set: 
.../dns/dataset/clean/fileid_{0..49999}.wav
.../dns/dataset/noisy/fileid_{0..49999}.wav
.../dns/dataset/noise/fileid_{0..49999}.wav

Training

The tiny.json file complies with the paper's configurations and hyperparameters. Should you wish to initiate a training with a different set of hyperparameters, create .json file in the configs directory or simply modify the paramteres in the pre-existing file. We recommend leaving the network hyperparameters untouched if faithfull replication of the model size is intended. To start training run:

python3 distributed.py -c config/tiny.json

The model recieves data with shape of (Time-step, 4, Frequency) where dimension 1 encomapasses a channel-wise concatenation of log-magnitude spectrogram, PCEN spectrogram, and real/imaginary part of demodulated phase respectively. To compensate memory over-load, our code utilises the aforementiond data information to reconstruct time-domain audio in order to calculate Multi-Resolution STFT Loss instead loading audio file pairs on the GPU.

Denoising

TODO

Evaluation

TODO

Export as onnx

To export model in onnx format, run the script below, specifying paths as described:

python onnx.py -c 'PATH_TO_JSON_CONFIG' -i 'PATH_TO_TRAINED_MODEL_CKPTs' -o 'ONNX_EXPORT_PATH'