✨ | Added app.py script so the model can be used.

app.py

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+import torch
+import torch.nn as nn
+import torch.optim as optim
+
+import torch.nn.functional as F
+import torchaudio
+import tqdm
+
+import argparse
+import math
+import os
+
+import AudioUtils
+from generator import SISUGenerator
+
+
+# Init script argument parser
+parser = argparse.ArgumentParser(description="Training script")
+parser.add_argument("--device", type=str, default="cpu",  help="Select device")
+parser.add_argument("--model", type=str, help="Model to use for upscaling")
+parser.add_argument("--clip_length", type=int, default=256, help="Internal clip length, leave unspecified if unsure")
+parser.add_argument("-i", "--input", type=str, help="Input audio file")
+parser.add_argument("-o", "--output", type=str, help="Output audio file")
+
+args = parser.parse_args()
+
+device = torch.device(args.device if torch.cuda.is_available() else "cpu")
+print(f"Using device: {device}")
+
+generator = SISUGenerator()
+
+models_dir = args.model
+clip_length = args.clip_length
+input_audio = args.input
+output_audio = args.output
+
+if models_dir:
+    generator.load_state_dict(torch.load(f"{models_dir}", map_location=device, weights_only=True))
+else:
+    print(f"Generator model (--model) isn't specified. Do you have the trained model? If not you need to train it OR acquire it from somewhere (DON'T ASK ME, YET!)")
+
+generator = generator.to(device)
+
+def start():
+    # To Mono!
+    audio, original_sample_rate = torchaudio.load(input_audio, normalize=True)
+    audio = AudioUtils.stereo_tensor_to_mono(audio)
+
+    splitted_audio = AudioUtils.split_audio(audio, clip_length)
+    splitted_audio_on_device = [t.to(device) for t in splitted_audio]
+    processed_audio = []
+
+    for clip in tqdm.tqdm(splitted_audio_on_device, desc="Processing..."):
+        processed_audio.append(generator(clip))
+
+    reconstructed_audio = AudioUtils.reconstruct_audio(processed_audio)
+    print(f"Saving {output_audio}!")
+    torchaudio.save(output_audio, reconstructed_audio.cpu().detach(), original_sample_rate)
+
+start()