Comments (2)
Hi Rikorose, Thanks for your sharing!
This means I can make 9 .hdf5 (3 categories and 3 splits), and set the sampling factor to 1.
I will try this way to augment data first, and I will continue to work on DeepFilterNet.
Best regards,
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Hi there,
thanks for you interest in DeepFilterNet. Your example seems mostly correct.
- Yes, this should work. I have not tested combining noise/speech/rir datasets into one hdf5.
- If you don't need RIR augmentation, you can only provide noise and speech datasets. The sum of the sampling factors do not need to sum up to 1. the 60/20/20% split should be done beforehand. This oversampling factor states how often this dataset should be used within one epoch. If it is smaller than 1, not all samples will be used. If it is larger than 1, samples will be used also more then once. Tldr: Set it to 1, if you don't have a good reason for a different sampling factor.
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Related Issues (20)
- How to use the pretrain-model? HOT 1
- audio super resolution HOT 1
- Clarification on Training Configuration for DeepFilterNet3 (dataset.cfg) HOT 1
- Cannot use DeepFilterNet2 model (CLI) HOT 1
- Pre-built binaries of `libdeepfilter` (C API) HOT 2
- Can't reproduce DeepFilterNet2 and DeepFilterNet3 results on Voicebank Demand test set HOT 6
- Unable to use libDF due to debug checks failing: duplicate name /convt3/Conv.bias HOT 1
- Error(s) in loading state_dict for DfNet: Unexpected key(s) in state_dict: "erb_comp.c", "erb_comp.mn". HOT 1
- Out-of-memory when running deepFilter on long audio files HOT 2
- Using ladspa with mono input produces an error HOT 1
- Pipewire filter-chain source support in Windows HOT 1
- Fine Tuning DeepFilterNet HOT 1
- Installation problem HOT 1
- requirement files installation problem HOT 1
- GPU Memory torch.cuda.OutOfMemoryError HOT 1
- [BUG] Only works with sample rate == 48000 HOT 3
- Multiple GPU training via DistributedDataParallel
- How to quantify the model
- 16k model training configuration and 16k pre-trained model
- RuntimeError: Couldn't find appropriate backend to handle uri <noisy_audio_file> and format None. HOT 1
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