Comments (2)
Hi!
I agree that the code is a little (too) complex. It was part of a bigger project and had to support complex use-cases there. The hardest part in this code is support for variable-length sequences, which is not required for simple source separation.
To create the JSON file for WSJ0-2mix and WSJ0-3mix data, you can have a look at the files in padertorch/padertorch/contrib/data/wsj0_mix/
. In general, the JSON structure has to look like this:
{
"datasets": {
"<dataset_name>": {
"<example_id>": {
"audio_path": {
"observation": "<path/to/observation.wav>",
"speech_source": [
"path/to/first/speech/source.wav",
"path/to/second/speech/source.wav",
"... (for three speakers)"
]
},
},
"...": {},
}
}
To run the training on both two- and three-speaker mixtures, you have to include all datasets in the JSON file (or provide multiple JSON files) and set train_datasets
to contain all datasets you want to train on. For WSJ0-2mix, the (simplest) command to run this (after installing padertorch
and its dependencies) is
$ python -m padertorch.contrib.examples.source_separation.or_pit.train with database_json=/path/to/the/json(s) trainer.storage_dir=/path/to/store/experiment/checkpoints
This will run the training without fine-tuning (so, it only performs one iteration). If you want to perform fine-tuning, you can run
python -m padertorch.contrib.examples.source_separation.or_pit.train with database_json=/path/to/the/json(s) trainer.storage_dir=/path/to/store/experiment/checkpoints trainer.model.finetune=True load_model_from=/path/to/the/pre-trained/model
I hope this helps. Let me know if you have any troubles
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Thanks a lot. We can run this!
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Related Issues (11)
- ToDo: Support data parallel in validate
- Error in tbx_utils.py HOT 1
- restructure and update the contrib examples
- Configurable: Support positional only arguments HOT 5
- Move backwards step into model HOT 1
- STFT inverse, stacked representation HOT 3
- Support dataclass default_factory
- Add review visualization utilities
- Drop Python 3.6? HOT 2
- Segmentation Fault when Training Model HOT 2
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