Comments (1)
singaxiong
commented on May 20, 2024
Hi, thanks for your interest in the tool! I think the reason for excessive amount of reverb is probably due to that your RIR file has long RT60 time. The convolution is basically very straightforward, whether it is utterances based or block based.
By the way, do you know why Kaldi uses blockwise convolution? Any computational advantage?
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Related Issues (14)
- Why negative grad_input of LF_MMI loss? HOT 1
- How to use CE regularizer in chain-model training? HOT 1
- Why is the chain loss computation so slow? HOT 8
- RIR format
- Problems about HCLG.fst needed by "train_transformer_se.py" HOT 2
- SETraining is extremely slow HOT 4
- minibatches for LFMMI HOT 1
- Decoding with train_transformer_ce.py HOT 2
- no module named 'kaldi.base._kaldi_error'
- Physical data format
- Selective data simulation
- Targeted simulation
- incompatible pytorch and torchvision version HOT 1
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