Upload folder using huggingface_hub

README.md

@@ -5,7 +5,8 @@ This is a model I trained to mimic a JCM 800 AMP. It doesn't sound very good, bu
 
 Download `GuneAmp.exe` and try running your own conversion.
 
-Read my notes 
+Read my notes `GuneAmp.pdf`
+
 
 ```python
 import torch
@@ -17,6 +18,48 @@ model.load_state_dict(ckpt["model"])
 model.eval()
 ```
 
+## Using the TorchScript Model from Hugging Face
+
+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.
+
+First, ensure you have the necessary libraries installed:
+```bash
+pip install torch huggingface_hub
+```
+
+Then, use the following Python code to load and use the model:
+
+```python
+import torch
+from huggingface_hub import hf_hub_download
+
+model_id = 'sgune/gune-amp'
+model_filename = 'metal_amp_v2_ts.pt'
+
+model_path = hf_hub_download(repo_id=model_id, filename=model_filename)
+
+#LOAD the model on GPU or CPU
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+print(f"Loading model on device: {device}")
+
+model = torch.jit.load(model_path, map_location=device)
+model.eval()
+
+print("Model loaded successfully!")
+
+input_size = 1024
+dummy_input = torch.randn(1, input_size, dtype=torch.float32).to(device)
+
+print(f"Running inference with dummy input of shape: {dummy_input.shape}")
+
+with torch.no_grad(): # Disable gradient calculations for inference
+    output = model(dummy_input)
+
+print("Inference complete!")
+print("Example output shape:", output.shape)
+print("Example output values:", output)
+```
+
 
 ## COMING SOON
-You can look at `infer.py`, `model.py` and `config.py` for deep-dive.
+`infer.py`, `model.py`, `train.py` and `config.py` deepdives.

config.py

@@ -0,0 +1,14 @@
+import torch
+
+# Path to your trained model checkpoint
+CHECKPOINT_PATH = "metalampnet_ckpt.pth"
+
+# Device to run inference on
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
+# Sliding window size and prediction length
+WINDOW = 2048
+PRED_SAMPLES = 512
+
+# Output sample rate
+SR = 44100