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https://git.ecker.tech/ aims to provide a place to share my efforts while maintaining true ownership of my code, as I do not trust GitHub.
XMR: 4B9TQdkAkBFYrbj5ztvTx89e5LpucPeTSPzemCihdDi9EBnx7btn8RDNZTBz2zihWsjMnDkzn5As1LU6gLv3KQy8BLsZ8SG
- Joined on
2022-10-10
ce3866d0cd
added '''estimating''' iterations until milestones (lr=[1, 0.5, 0.1] and final lr, very, very inaccurate because it uses instantaneous delta lr, I'll need to do a riemann sum later
Added a field to reduce the worker size. By default, it's set to 2, as there doesn't seem to be any performance penalties.
If you're running into that memory issue again, regenerate your…
Compromise implemented in commit 3e220ed306c619868fb4195afffd622a013d771d.
Default language is set to en (compatible with both implemented whisper implementations).
When a specialized…
1316331be3
forgot to try and have it try and auto-detect for openai/whisper when no language is specified
3e220ed306
added option to set worker size in training config generator (because the default is overkill), for whisper transcriptions, load a specialized language model if it exists (for now, only english), output transcription to web UI when done transcribing
Just wanting to follow up, as an incidental discovery, the amount of system RAM training can consume can really creep up on you. I haven't gotten any luxurious messages about memory allocation…
I'll compromise and have it automatically use the -en version if a non -en model is selected. desu, I think it's better to have it default to a more universal model over having a more…
5026d93ecd
sloppy fix to actually kill children when using multi-GPU distributed training, set GPU training count based on what CUDA exposes automatically so I don't have to keep setting it to 2
df24827b9a
renamed mega batch factor to an actual real term: gradient accumulation factor, fixed halting training not actually killing the training process and freeing up resources, some logic cleanup for gradient accumulation (so many brain worms and wrong assumptions from testing on low batch sizes) (read the training section in the wiki for more details)