-
Notifications
You must be signed in to change notification settings - Fork 37
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
going forward + question #28
Comments
Hi @tig3rmast3r, This is insane!! I wanted to say this looks and sounds super cool! I'm really happy to see vampnet in a full creative interface like this one. Training from scratch requires a large dataset (50k hours of audio, more or less), and enough GPUs to fit a batch size of ~32 for the duration of audio context you'd like to train for. You can have a look a the settings used to train the model in |
Hi Hugo, |
yeah, doubling that value could work. You could also try changing the number of layers and heads, though that might require a bit more finetuning to get it working. |
is it normal that training with identical parameters and dataset on linux gives different results than windows? here's the test wavs.zip comparison between the linux and windows trainings. thanks |
i def have a problem in linux |
i did a quick test and looks that the problem is with torch.compile command. EDIT: i did more test and unfortunately the problem is not with torch.compile, while i've noticed that with torch.compile i get different results both results are bad. |
i've finally found a working combination, honestly i haven't found the root issue but i can use linux now!
i've trained several combinations to understand the impact of torch.compile and python versions on speed and quality
i've finally found a working torch.compile config for multi-gpu, using python 3.10 and latest pytorch!! tested on 4 x rtx4090 About the trick to fix bad audio i have attached a zip containing the following file Basically one of the modules inside the requirements file is causing bad audio. i still haven't identified which one. EDIT: i have updated my fork with installation instructions to get this working on Windows and linux (single and multi gpu) succesfully. i will no longer update this thread and i've removed installation instructions. i've used the compare trick on clean conda envs both locally and on vast.ai containers. hopefully you will find the root cause so we can define the version during pip install -e ./vampnet (or update the code to work with the latest version of whatever it is) Here's the zip files.zip Lastly, while i was testing i've found the time to report some "TimeToTrain" values, it may help finding the perfect server to train and save some (or a lot) of $$
EDIT May 9: updated infos and zipped file, will update more as soon as i have more info. |
Hi Hugo, this project rocks i'm having lot of fun, this is the best AI based audio tool available right now, at least for what i'm looking for.
Would like to share where i'm going from this awesome project..
i've made an app in c# that takes care of keeping loops overtime and send generation presets to gradio like you did with unloop, losing hours just listening to generations and i'm making tons of brand new audio loops too!!
working also on a vst plug-in that send generated wavs into daw combined with demucs for separation, made also a very primitive liveset using 3 c# apps simultaneously in realtime and sending demucsed streams to vst -> reaktor and mixing them, what a blast!!
Not sure if there's interest on what i'm doing, i may share the projects but i'm nothing special with coding, i just know how to use chatgpt properly :)
this is the c# app
this is the liveset setup using vst + bidule + reaktor (+ ipad and midi controller)
930707408-VID-20231230-WA0001.mp4
now for the question, is there a way to start a new training and make a bigger size model ?
is it an easy task ? i mean i have no idea what i have to change in the code to set model size for training like 2x or 3x bigger, i only did (huge) fine-tunings till now. would like to test starting training from scratch and making a bigger model too to see what happens :)
thanks a lot!
The text was updated successfully, but these errors were encountered: