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---
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license: mit
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tags:
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- audio
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- pytorch
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- torchscript
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- guitar-amp-simulation
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- real-time
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inference:
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framework: pytorch
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task: audio-to-audio
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inputs:
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- name: input
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type: float[]
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description: "Input waveform or features (e.g. [batch, channels, samples])"
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outputs:
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- name: output
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type: float[]
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description: "Output waveform or processed features"
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---
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## Usage
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This is a model I trained to mimic a JCM 800 AMP. It doesn't sound very good, but as a first pass, I'm glad I have it.
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Download [GuneAmp.exe](GuneAmp.exe) and try running your own conversion.
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Read my notes [GuneAmpNotes](GuneAmpNotes.pdf)
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## Using the TorchScript Model from Hugging Face
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If you wish to use the TorchScript version of the model directly, you can download it from Hugging Face and load it using the following Python code.
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First, ensure you have the necessary libraries installed:
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```bash
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pip install torch huggingface_hub
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```
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Then, use the following Python code to load and use the model:
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```python
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import torch
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from huggingface_hub import hf_hub_download
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model_id = 'sgune/gune-amp'
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model_filename = 'metal_amp_v2_ts.pt'
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model_path = hf_hub_download(repo_id=model_id, filename=model_filename)
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#LOAD the model on GPU or CPU
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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print(f"Loading model on device: {device}")
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model = torch.jit.load(model_path, map_location=device)
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model.eval()
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print("Model loaded successfully!")
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input_size = 1024
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dummy_input = torch.randn(1, input_size, dtype=torch.float32).to(device)
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print(f"Running inference with dummy input of shape: {dummy_input.shape}")
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with torch.no_grad(): # Disable gradient calculations for inference
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output = model(dummy_input)
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print("Inference complete!")
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print("Example output shape:", output.shape)
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print("Example output values:", output)
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```
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## COMING SOON
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`infer.py`, `model.py`, `train.py` and `config.py` deepdives.
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