@thecollabagepatch Thank you very much for doing this :)I really appreciate it:)
I am on Win 11 but I also use cloud Linux instances. So, yes, if you can do the Win one, I would be happy to try it out and give you feedback.
Thanks again!
Alex
asigalov61
AI & ML interests
Music AI
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replied to
thecollabagepatch's
post
about 7 hours ago
replied to
thecollabagepatch's
post
1 day ago
@thecollabagepatch Yes, if you can add seamless native support for Audacity, it will be really cool :)
Audacity is very popular and it is also open source so I think it will be a nice addition to your project and it will also be very useful to me.
Thanks again for your great work.
Alex
replied to
thecollabagepatch's
post
2 days ago
@thecollabagepatch Thank you for your response. This is cool :)
Yes, if you can make one for latest stable-audio, that would be very nice.
Also, I wanted to ask you if any of your plugins compatible with Audacity?
Alex.
replied to
thecollabagepatch's
post
2 days ago
@thecollabagepatch Great job! But magenta-realtime kinda sucks IMHO. Can you make the same thing for stable-audio or FB musicgen?
Thank you.
reacted to
thecollabagepatch's
post with ❤️
2 days ago
Post
343
hey musicians
hf continues to make the anti-suno device possible with gary4juce, the VST for your DAW that doesn't try to replace you.
v2 just released. https://thepatch.gumroad.com/l/gary4juce (pay what you want)
now you can use google's magenta-realtime model to generate 48k samples based on your input audio (or other model outputs...there's 4 to play with now).
just duplicate my hf space, turn on an L4/L40s and throw the url into the plugin.
i've got a few finetunes you can switch to as well. or you can push your finetune to the hub and play around.
the space: thecollabagepatch/magenta-retry (you can also use the html web tester to play around with realtime generation on the L40s)
hf continues to make the anti-suno device possible with gary4juce, the VST for your DAW that doesn't try to replace you.
v2 just released. https://thepatch.gumroad.com/l/gary4juce (pay what you want)
now you can use google's magenta-realtime model to generate 48k samples based on your input audio (or other model outputs...there's 4 to play with now).
just duplicate my hf space, turn on an L4/L40s and throw the url into the plugin.
i've got a few finetunes you can switch to as well. or you can push your finetune to the hub and play around.
the space: thecollabagepatch/magenta-retry (you can also use the html web tester to play around with realtime generation on the L40s)
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- 10 replies
reacted to
their
post with 🤗❤️
3 months ago
Post
4888
🔥Check out new SOTA Orpheus Auto-Continuations Generator🔥
asigalov61/Orpheus-Music-Transformer
Now you can generate good music with Orpheus without supervision!!!
@Timzoid @John6666 @alvanalrakib
asigalov61/Orpheus-Music-Transformer
Now you can generate good music with Orpheus without supervision!!!
@Timzoid @John6666 @alvanalrakib
posted
an
update
3 months ago
Post
4888
🔥Check out new SOTA Orpheus Auto-Continuations Generator🔥
asigalov61/Orpheus-Music-Transformer
Now you can generate good music with Orpheus without supervision!!!
@Timzoid @John6666 @alvanalrakib
asigalov61/Orpheus-Music-Transformer
Now you can generate good music with Orpheus without supervision!!!
@Timzoid @John6666 @alvanalrakib
replied to
their
post
3 months ago
@a13ph Yes, thank you. I starred it :)
reacted to
tomaarsen's
post with 🤗🚀
3 months ago
Post
4402
😎 I just published Sentence Transformers v5.1.0, and it's a big one. 2x-3x speedups of SparseEncoder models via ONNX and/or OpenVINO backends, easier distillation data preparation with hard negatives mining, and more:
1️⃣ Faster ONNX and OpenVINO backends for SparseEncoder models
Usage is as simple as
2️⃣ New
This new output format is immediately compatible with the MarginMSELoss and SparseMarginMSELoss for training SentenceTransformer, CrossEncoder, and SparseEncoder losses.
3️⃣ Gathering across devices
When doing multi-GPU training using a loss that has in-batch negatives (e.g. MultipleNegativesRankingLoss), you can now use
4️⃣ Trackio support
If you also upgrade
5️⃣ MTEB Documentation
We've added some documentation on evaluating SentenceTransformer models properly with MTEB. It's rudimentary as the documentation on the MTEB side is already great, but it should get you started.
Plus many more smaller features & fixes (crash fixes, compatibility with datasets v4, FIPS compatibility, etc.).
See the full release notes here: https://github.com/UKPLab/sentence-transformers/releases/tag/v5.1.0
Big thanks to all of the contributors for helping with the release, many of the features from this release were proposed by others. I have a big list of future potential features that I'd love to add, but I'm
1️⃣ Faster ONNX and OpenVINO backends for SparseEncoder models
Usage is as simple as
backend="onnx" or backend="openvino" when initializing a SparseEncoder to get started, but I also included utility functions for optimization, dynamic quantization, and static quantization, plus benchmarks.2️⃣ New
n-tuple-scores output format from mine_hard_negativesThis new output format is immediately compatible with the MarginMSELoss and SparseMarginMSELoss for training SentenceTransformer, CrossEncoder, and SparseEncoder losses.
3️⃣ Gathering across devices
When doing multi-GPU training using a loss that has in-batch negatives (e.g. MultipleNegativesRankingLoss), you can now use
gather_across_devices=True to load in-batch negatives from the other devices too! Essentially a free lunch, pretty big impact potential in my evals.4️⃣ Trackio support
If you also upgrade
transformers, and you install trackio with pip install trackio, then your experiments will also automatically be tracked locally with trackio. Just open up localhost and have a look at your losses/evals, no logins, no metric uploading.5️⃣ MTEB Documentation
We've added some documentation on evaluating SentenceTransformer models properly with MTEB. It's rudimentary as the documentation on the MTEB side is already great, but it should get you started.
Plus many more smaller features & fixes (crash fixes, compatibility with datasets v4, FIPS compatibility, etc.).
See the full release notes here: https://github.com/UKPLab/sentence-transformers/releases/tag/v5.1.0
Big thanks to all of the contributors for helping with the release, many of the features from this release were proposed by others. I have a big list of future potential features that I'd love to add, but I'm
reacted to
their
post with 🔥
5 months ago
Post
2716
Check out new symbolic music AI front end and CLI training app
https://webchatappai.github.io/midi-gen/
https://github.com/WebChatAppAi/Orpheus-Midi-Model-Maker
@Timzoid @Csplk @not-lain @victor @bartowski @John6666
https://webchatappai.github.io/midi-gen/
https://github.com/WebChatAppAi/Orpheus-Midi-Model-Maker
@Timzoid @Csplk @not-lain @victor @bartowski @John6666
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- 2 replies
posted
an
update
5 months ago
Post
2716
Check out new symbolic music AI front end and CLI training app
https://webchatappai.github.io/midi-gen/
https://github.com/WebChatAppAi/Orpheus-Midi-Model-Maker
@Timzoid @Csplk @not-lain @victor @bartowski @John6666
https://webchatappai.github.io/midi-gen/
https://github.com/WebChatAppAi/Orpheus-Midi-Model-Maker
@Timzoid @Csplk @not-lain @victor @bartowski @John6666
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- 2 replies
reacted to
their
post with 🔥🤗
5 months ago
Post
501
Hey guys!
I wanted to invite all of you who are interested in symbolic music AI to check out my Orpheus Music Transformer
IMHO the model turned out very well and it plays very well too.
I would really appreciate any feedback and likes. It helps a lot.
Here are the links for your convenience:
1) Orpheus Music Transformer main demo space asigalov61/Orpheus-Music-Transformer
2) Orpheus Music Transformer Collection asigalov61/orpheus-music-transformer-685c3c8e59ed1414c02bb8cd
3) Orpheus Music Transformer Models Repo asigalov61/Orpheus-Music-Transformer
I hope you will enjoy it :)
Sincerely,
Alex
I wanted to invite all of you who are interested in symbolic music AI to check out my Orpheus Music Transformer
IMHO the model turned out very well and it plays very well too.
I would really appreciate any feedback and likes. It helps a lot.
Here are the links for your convenience:
1) Orpheus Music Transformer main demo space asigalov61/Orpheus-Music-Transformer
2) Orpheus Music Transformer Collection asigalov61/orpheus-music-transformer-685c3c8e59ed1414c02bb8cd
3) Orpheus Music Transformer Models Repo asigalov61/Orpheus-Music-Transformer
I hope you will enjoy it :)
Sincerely,
Alex
posted
an
update
5 months ago
Post
501
Hey guys!
I wanted to invite all of you who are interested in symbolic music AI to check out my Orpheus Music Transformer
IMHO the model turned out very well and it plays very well too.
I would really appreciate any feedback and likes. It helps a lot.
Here are the links for your convenience:
1) Orpheus Music Transformer main demo space asigalov61/Orpheus-Music-Transformer
2) Orpheus Music Transformer Collection asigalov61/orpheus-music-transformer-685c3c8e59ed1414c02bb8cd
3) Orpheus Music Transformer Models Repo asigalov61/Orpheus-Music-Transformer
I hope you will enjoy it :)
Sincerely,
Alex
I wanted to invite all of you who are interested in symbolic music AI to check out my Orpheus Music Transformer
IMHO the model turned out very well and it plays very well too.
I would really appreciate any feedback and likes. It helps a lot.
Here are the links for your convenience:
1) Orpheus Music Transformer main demo space asigalov61/Orpheus-Music-Transformer
2) Orpheus Music Transformer Collection asigalov61/orpheus-music-transformer-685c3c8e59ed1414c02bb8cd
3) Orpheus Music Transformer Models Repo asigalov61/Orpheus-Music-Transformer
I hope you will enjoy it :)
Sincerely,
Alex
reacted to
clem's
post with ❤️
8 months ago
Post
4781
We just crossed 1,500,000 public models on Hugging Face (and 500k spaces, 330k datasets, 50k papers). One new repository is created every 15 seconds. Congratulations all!
- 3 replies
reacted to
Jaward's
post with ❤️
11 months ago
Post
3229
nanoBLT: Simplified lightweight implementation of a character-level Byte Latent Transformer model (under 500 lines of code). The model is 2x4x2 (n_layers_encoder, n_layers_latent, n_layers_decoder) layer deep trained on ~1M bytes of tiny Shakespeare with a patch size of 4.
Code: https://github.com/Jaykef/ai-algorithms/blob/main/byte_latent_transformer.ipynb
Code: https://github.com/Jaykef/ai-algorithms/blob/main/byte_latent_transformer.ipynb
@BlinkDL Nice! You did an amazing job with RNNs! :)
reacted to
BlinkDL's
post with 🔥
about 1 year ago
Post
5770
RWKV-7 "Goose" preview rc2 => Peak RNN architecture?😃Will try to squeeze more performance for the final release. Preview code & model: https://github.com/BlinkDL/RWKV-LM/tree/main/RWKV-v7
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- 2 replies