Commit against which the comparison was made - https://github.com/Rikorose/DeepFilterNet/commit/ca46bf54afaf8ace3272aaee5931b4317bd6b5f4
Installation:
cd path/to/DeepFilterNet/
pip install maturin poetry poethepoet
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
maturin build --release -m pyDF/Cargo.toml
cd DeepFilterNet
export PYTHONPATH=$PWD
cd ../torchDF
poetry install
poe install-torch-cpu
Here is presented offline and streaming implementation of DeepFilterNet3 on pure torch. Streaming model can be fully exported to ONNX using model_onnx_export.py.
Every script and test have to run inside poetry enviroment.
To run tests:
poetry run python -m pytest -v
We compare this model to existing enhance method (which is partly written on Rust) and tract model (which is purely on Rust). All tests are passing, so model is working.
To enhance audio using streaming implementation:
poetry run python torch_df_streaming_minimal.py --audio-path examples/A1CIM28ZUCA8RX_M_Street_Near_Regular_SP_Mobile_Primary.wav --output-path examples/A1CIM28ZUCA8RX_M_Street_Near_Regular_SP_Mobile_Primary_enhanced.wav
To convert model to onnx and run tests:
poetry run python model_onnx_export.py --test --performance --inference-path examples/A1CIM28ZUCA8RX_M_Street_Near_Regular_SP_Mobile_Primary.wav --ort
TODO:
- Issues about split + simplify
- Thinkging of offline method exportability + compatability with streaming functions
- torch.where(..., ..., 0) export issue
- dynamo.export check
- thinking of torchDF naming
- rfft hacks tests
- torch.nonzero thinking
- rfft nn.module
- more static methods