- Both Windows and Linux are supported. A GPU is recommended for training and for inference speed, but is not mandatory.
- Python 3.7 is recommended. Python 3.5 or greater should work, but you'll probably have to tweak the dependencies' versions. I recommend setting up a virtual environment using
venv, but this is optional. When using Python 3.11, you should customize some files, especially ones in a library-librosa. - Install ffmpeg. This is necessary for reading audio files.
- Install PyTorch. Pick the latest stable version, your operating system, your package manager (pip by default) and finally pick any of the proposed CUDA versions if you have a GPU, otherwise pick CPU. Run the given command.
- Install the remaining requirements with
pip install -r requirements.txt
Pretrained models are now downloaded automatically. If this doesn't work for you, you can manually download them here.
Before you download any dataset, you can begin by testing your configuration with:
python demo_cli.py
If all tests pass, you're good to go.
For playing with the toolbox alone, I only recommend downloading LibriSpeech/train-clean-100. Extract the contents as <datasets_root>/LibriSpeech/train-clean-100 where <datasets_root> is a directory of your choosing. Other datasets are supported in the toolbox. You're free not to download any dataset, but then you will need your own data as audio files or you will have to record it with the toolbox.
You can then try the toolbox:
python demo_toolbox.py -d <datasets_root>
or
python demo_toolbox.py
depending on whether you downloaded any datasets.