Comments (1)
Hi. You can see the tensorboard here https://huggingface.co/teticio/audio-diffusion-instrumental-hiphop-256/tensorboard, for example, and see the loss curves and generated audios along the way. It already generates decent results after 10 epochs, which takes about 12 hours on my RTX 2080 Ti with this dataset of around 30,000 samples.
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Related Issues (20)
- train_vae.py report error 'gpu' is not a valid DistributedType HOT 5
- Ask for the losses of vae HOT 1
- Recommended training hyperparameters for 44.1Khz & 48Khz Samplerate HOT 2
- diffusers v-0.12.0 causes import issues HOT 1
- Diffusers v0.12 removed the `ema_model.averaged_model` attribute HOT 1
- Increasing input size HOT 4
- how does the audio_to_images.py file work? HOT 3
- Whether the longer music sample is the repetition of a shorted sample? HOT 1
- NameError: name 'transformers' is not defined upon running model via Gradio HOT 2
- Dataset constriants HOT 16
- High fidelity training? HOT 3
- Training own music samples? HOT 1
- Can I input audio file then generate image HOT 2
- Numpy Error HOT 1
- AttributeError: 'AutoencoderKL' object has no attribute 'sample_size' HOT 3
- teticio/audio-diffusion-256 is really good HOT 1
- multi-gpu training HOT 1
- [Little Feedback] Thank you! :) HOT 2
- is it possible to use the train_unet.py script as a regular ldm? HOT 2
- whats the difference between 256 and 512 dataset HOT 1
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from audio-diffusion.