BIODENOISING: Animal vocalization denoising

Here we provide the inference repository. The training code is at the following github repo

Check the biodenoising web page for demos and more info.

The proposed model is based on the Demucs architecture, originally proposed for music source-separation and real-time speech enhancement.

We publish the pre-print on arXiv.

Colab

If you want to play with the pretrained model inside colab for instance, start from this Colab Example for Biodenoising.

Installation

First, install Python >= 3.8 (recommended with miniconda).

Through pip (you just want to use pre-trained model out of the box)

Just run

pip install biodenoising_inference

Development

Clone this repository and install the dependencies. We recommend using a fresh virtualenv or Conda environment.

git clone https://github.com/earthspecies/biodenoising-inference-internal
cd biodenoising-inference-internal
pip install -r requirements.txt  

Live Denoising

If you want to use biodenoising live, you will need a specific loopback audio interface.

Mac OS X

On Mac OS X, this is provided by [Soundflower][soundflower]. First install Soundflower, and then you can just run

python -m biodenoising.denoiser.live

In your favorite video conference call application, just select "Soundflower (2ch)" as input to enjoy your denoised speech.

Watch our live demo presentation in the following link: [Demo][demo].

Linux (tested on Ubuntu 20.04)

You can use the pacmd command and the pavucontrol tool:

• run the following commands:

pacmd load-module module-null-sink sink_name=denoiser
pacmd update-sink-proplist denoiser device.description=denoiser

This will add a Monitor of Null Output to the list of microphones to use. Select it as input in your software.

• Launch the pavucontrol tool. In the Playback tab, after launching python -m biodenoising.denoiser.live --out INDEX_OR_NAME_OF_LOOPBACK_IFACE and the software you want to denoise for (here an in-browser call), you should see both applications. For denoiser interface as Playback destination which will output the processed audio stream on the sink we previously created.

Other platforms

At the moment, we do not provide official support for other OSes. However, if you have a a soundcard that supports loopback (for instance Steinberg products), you can try to make it work. You can list the available audio interfaces with python -m sounddevice. Then once you have spotted your loopback interface, just run

python -m biodenoising.denoiser.live --out INDEX_OR_NAME_OF_LOOPBACK_IFACE

By default, biodenoising will use the default audio input. You can change that with the --in flag.

Note that on Windows you will need to replace python by python.exe.

Troubleshooting bad quality in separation

This is from the original denoiser implementation:

denoiser can introduce distortions for very high level of noises. Audio can become crunchy if your computer is not fast enough to process audio in real time. In that case, you will see an error message in your terminal warning you that denoiser is not processing audio fast enough. You can try exiting all non required applications.

denoiser was tested on a Mac Book Pro with an 2GHz quadcore Intel i5 with DDR4 memory. You might experience issues with DDR3 memory. In that case you can trade overall latency for speed by processing multiple frames at once. To do so, run

python -m biodenoising.denoiser.live -f 2

You can increase to -f 3 or more if needed, but each increase will add 16ms of extra latency.

Denoising received speech

You can also denoise received speech, but you won't be able to both denoise your own speech and the received speech (unless you have a really beefy computer and enough loopback audio interfaces). This can be achieved by selecting the loopback interface as the audio output of your VC software and then running

python -m biodenoising.denoiser.live --in "Soundflower (2ch)" --out "NAME OF OUT IFACE"

The way experiments are automatically named, as explained hereafter.

Usage

Generating the denoised files can be done by:

python -m biodenoising.denoiser.denoise --input=<path to the dir with the noisy files> --output=<path to store enhanced files>

Notice, you can either provide noisy_dir or noisy_json for the test data. Note that the path given to --model_path should be obtained from one of the best.th file, not checkpoint.th. It is also possible to use pre-trained model, using --dns48. For more details regarding possible arguments, please see:

usage: biodenoising.denoiser.denoise [-h] [-m MODEL_PATH | --dns48 ]
                        [--device DEVICE] [--dry DRY]
                        [--num_workers NUM_WORKERS] [--streaming]
                        [--output OUT_DIR] [--batch_size BATCH_SIZE] [-v]
                        [--input NOISY_DIR]

Speech enhancement using biodenoising - Generate enhanced files

optional arguments:
  -h, --help            show this help message and exit
  -m MODEL_PATH, --model_path MODEL_PATH
                        Path to local trained model.
  --dns48               Use pre-trained real time H=48 model trained on biodenoising-datasets.
  --device DEVICE
  --dry DRY             dry/wet knob coefficient. 0 is only input signal, 1
                        only denoised.
  --num_workers NUM_WORKERS
  --streaming           true streaming evaluation for biodenoising
  --output OUT_DIR     directory putting enhanced wav files
  --batch_size BATCH_SIZE
                        batch size
  -v, --verbose         more loggging
  --input NOISY_DIR
                        directory including noisy wav files

Online Evaluation

This is from the original denoiser implementation:

Our online implementation is based on pure python code with some optimization of the streaming convolutions and transposed convolutions. We benchmark this implementation on a quad-core Intel i5 CPU at 2 GHz. The Real-Time Factor (RTF) of the proposed models are:

Model	Threads	RTF
H=48	1	0.8
H=48	4	0.6

In order to compute the RTF on your own CPU launch the following command:

python -m biodenoising.denoiser.demucs --hidden=48 --num_threads=1

The output should be something like this:

total lag: 41.3ms, stride: 16.0ms, time per frame: 12.2ms, delta: 0.21%, RTF: 0.8

Feel free to explore different settings, i.e. bigger models and more CPU-cores.

Citation

If you use the code in your research, then please cite it as:

@misc{miron2024biodenoisinganimalvocalizationdenoising,
      title={Biodenoising: animal vocalization denoising without access to clean data}, 
      author={Marius Miron and Sara Keen and Jen-Yu Liu and Benjamin Hoffman and Masato Hagiwara and Olivier Pietquin and Felix Effenberger and Maddie Cusimano},
      year={2024},
      eprint={2410.03427},
      archivePrefix={arXiv},
      primaryClass={cs.SD},
      url={https://arxiv.org/abs/2410.03427}, 
}

License

This model is released under the CC-BY-NC 4.0. license as found in the LICENSE file.