Iver Jordal
Iver Jordal
Thanks for the patience. This is still on my TODO list 🙈
I had another look at this now, and gathered some thoughts. Here's what I imagine would be good to have in/for this transform: - [ ] The peak (offset) can...
Closing for inactivity. Feel free to suggest a reopen later if the work gets picked up again
This one has appeared: https://github.com/yoyololicon/torchcomp I haven't tried it yet, but it looks promising at first glance.
Could you write a few sentences that explains what this transform does, what it sounds like and when it is useful?
Thanks for sharing! If I were you, I would "clean" the data, i.e. remove the silent part from the files, and then that would be the fix for the observed...
Good point. AddShortNoises may indeed be better suited for your need. You could set [noise_transform](https://iver56.github.io/audiomentations/waveform_transforms/add_short_noises/#noise_transform) to `Trim()`, and it would trim your noise sound before finding an offset for it...
Also try precision=16 in pytorch-lightning. By default it uses the native amp in pytorch
One of the reasons why pyloudnorm uses a lot of memory is that it creates a float64 copy of the audio internally for filtering, due to the way scipy works....
I can close this issue after we make the final switch. For now it just says this: ``` warnings.warn( "The default value of noise_rms in AddShortNoises will change from" "...