Add description

model.py

@@ -1,8 +1,7 @@
+import torch
 from typing import Tuple
-
 from transformers import AutoTokenizer, AutoModelForCausalLM
 
-
 # Initialize the model and tokenizer variables as None
 tokenizer = None
 model = None
@@ -17,9 +16,16 @@ def get_model_and_tokenizer() -> Tuple[AutoModelForCausalLM, AutoTokenizer]:
     """
     global model, tokenizer
     if model is None or tokenizer is None:
+        # Set device
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
         # Load the tokenizer and the model
         tokenizer = AutoTokenizer.from_pretrained("juancopi81/lmd_8bars_tokenizer")
         model = AutoModelForCausalLM.from_pretrained(
             "juancopi81/lmd-8bars-2048-epochs20_v3"
         )
+
+        # Move model to device
+        model = model.to(device)
+
     return model, tokenizer

packages.txt

@@ -1,4 +1,4 @@
-libfluidsynth2
+libfluidsynth
 build-essential
 libasound2-dev
 libjack-dev

requirements.txt

@@ -1,4 +1,5 @@
 note-seq
 matplotlib
 transformers
-pyfluidsynth
+pyfluidsynth
+torch

utils.py

@@ -71,6 +71,9 @@ def generate_new_instrument(
         # Encode the conditioning tokens.
         input_ids = tokenizer.encode(seed, return_tensors="pt")
 
+        # Move the input_ids tensor to the same device as the model
+        input_ids = input_ids.to(model.device)
+
         # Generate more tokens.
         eos_token_id = tokenizer.encode("TRACK_END")[0]
         generated_ids = model.generate(