Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Optimize downscale including using numba #795

Merged
merged 1 commit into from
Sep 11, 2022

Conversation

oyvindln
Copy link
Contributor

Informal test using timeit indicates it's like 100x faster (doing loops in python directly is VERY inefficient). It was one of the functions identified via the profiler.

Should give a small speedup on samples with analog audio.

was a little unsure about what some of the arrays represent so the function docs needs looking over

Also, noted that the 16-bit PCM values are limited to +/- 32766 rather than the typical -32,768 to 32,767, wasn't sure if this is intentional and maybe required by the format or if it's an oversight.

@oyvindln
Copy link
Contributor Author

should be downscale_audio, haven't touced downscale

@happycube
Copy link
Owner

I think it's intentional so that dropouts could be assigned to one of the boundaries.

@happycube happycube merged commit 5d8ae7b into happycube:master Sep 11, 2022
@oyvindln oyvindln deleted the downscale_audio_numba branch September 12, 2022 12:28
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

Successfully merging this pull request may close these issues.

None yet

2 participants