Replies: 4 comments
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vibevoice 7b is not the best model to be used with step audio editx editing because vibevoice is a model that likes to generate long segments with multiple speakers, and editing long segments is either not viable (30s is the limit), and it does not work with multiple speakers. Step Audio EditX is unfortunately a slow model. There isn't much we can do about it. Can you check your VRAM during all of this? VibeVoice should be unloaded and then Step EditX loaded (which shouldn't use more than 10GB of your VRAM) |
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Also max tokens are not the amount of tokens it will generate, it should be stopping way before that. Test using the '🎨 Step Audio EditX - Audio Editor' by itself so you can test it on it's own to see what is happening. |
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I do not know exactly what I did, but I think after uninstall onnxruntime onnxruntime_gpu and reinstall onnxruntime_gpu only: My best result is using prev version of TTS Audio Suite + indextts-2 or newest version of TTS Audio Suite with vibevoice over 30s to avoid post edit of emotion tag. I just have to remove noise from vibevoice somehow. I retested vibevoice, with Step Audio EditX at post, it still use all 24GB of GPU Memory, at 0.2 it/s. in the 90th iteration. It get slower over time. |
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Voice cloning for Step Audio EditX is exactly like f5, it needs a ref text. It won't work with direct audio input. 5it/s is not normal I think... but I don't know what else to do. |
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First thing good work on the functionality of all of this.
I really like the idea of changing emotions or speed with tags.
The problem is I have only 24GB GPU memory, and this inline tag use all of it and I often got CUDA error for out of memory.
with 0.2 it/s I need 81920 seconds for the job, that is 22.7 hours for a 30 seconds audio clip.
I see no option to change those default values:
max 8192 tokens, precision=auto, device=auto,
also I have no clue if the warning "no CUDAExecutionProvider for onnxruntime" is problematic or not.
I run on python 3.13, nightly build.
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