diff --git a/examples/demo.py b/examples/demo.py
index e42ae91..b0505c5 100644
--- a/examples/demo.py
+++ b/examples/demo.py
@@ -8,87 +8,140 @@
     noise_shaped_reverberation,
 )
 
-os.makedirs("outputs/demo", exist_ok=True)
+if __name__ == "__main__":
+    use_gpu = torch.cuda.is_available()
 
-if not os.path.exists("examples/demo/di_guitar.wav"):
-    os.system(
-        "wget csteinmetz1.github.io/sounds/assets/short_riff.wav -O outputs/demo/short_riff.wav"
+    if use_gpu:
+        torch.set_default_device("cuda")
+
+    os.makedirs("outputs/demo", exist_ok=True)
+
+    if not os.path.exists("outputs/demo/idmt-rock-input-varying-gain.wav"):
+        os.system(
+            "wget csteinmetz1.github.io/sounds/assets/amps/idmt-rock-input-varying-gain.wav -O outputs/demo/idmt-rock-input-varying-gain.wav"
+        )
+
+    # load DI guitar sample
+    x, sr = torchaudio.load(
+        "outputs/demo/idmt-rock-input-varying-gain.wav", backend="soundfile"
     )
 
-# load DI guitar sample
-x, sr = torchaudio.load("outputs/demo/short_riff.wav", backend="soundfile")
-
-# add batch dim
-x = x.unsqueeze(0)
-
-# process with chain of effects
-y = parametric_eq(
-    x,
-    sr,
-    low_shelf_gain_db=-12.0,
-    low_shelf_cutoff_freq=1000,
-    low_shelf_q_factor=0.5,
-    band0_gain_db=0.0,
-    band0_cutoff_freq=1000,
-    band0_q_factor=0.5,
-    band1_gain_db=0.0,
-    band1_cutoff_freq=1000,
-    band1_q_factor=0.5,
-    band2_gain_db=0.0,
-    band2_cutoff_freq=1000,
-    band2_q_factor=0.5,
-    band3_gain_db=0.0,
-    band3_cutoff_freq=1000,
-    band3_q_factor=0.5,
-    high_shelf_gain_db=12.0,
-    high_shelf_cutoff_freq=4000,
-    high_shelf_q_factor=0.5,
-)
-print(y.shape)
-y = compressor(
-    y,
-    sr,
-    threshold_db=-12.0,
-    ratio=4.0,
-    attack_ms=10.0,
-    release_ms=100.0,
-    knee_db=12.0,
-    makeup_gain_db=0.0,
-)
-print(y.shape)
-y = distortion(y, sr, drive_db=16.0)
-print(y.shape)
-y = noise_shaped_reverberation(
-    y,
-    sr,
-    band0_gain=0.8,
-    band1_gain=0.4,
-    band2_gain=0.2,
-    band3_gain=0.5,
-    band4_gain=0.5,
-    band5_gain=0.5,
-    band6_gain=0.5,
-    band7_gain=0.6,
-    band8_gain=0.7,
-    band9_gain=0.8,
-    band10_gain=0.9,
-    band11_gain=1.0,
-    band0_decay=0.5,
-    band1_decay=0.5,
-    band2_decay=0.5,
-    band3_decay=0.5,
-    band4_decay=0.5,
-    band5_decay=0.5,
-    band6_decay=0.5,
-    band7_decay=0.5,
-    band8_decay=0.5,
-    band9_decay=0.5,
-    band10_decay=0.5,
-    band11_decay=0.5,
-    mix=0.2,
-)
-print(y.shape)
-y = y.squeeze(0)
-print(y.shape)
+    start_idx = int(49.0 * sr)
+    end_idx = start_idx + 441000
+    x = x[0:1, start_idx:end_idx]
+
+    # add batch dim
+    x = x.unsqueeze(0)
+
+    if use_gpu:
+        x = x.cuda()
+
+    # process with chain of effects
+    y = parametric_eq(
+        x,
+        sr,
+        low_shelf_gain_db=torch.tensor([-12.0]),
+        low_shelf_cutoff_freq=torch.tensor([1000]),
+        low_shelf_q_factor=torch.tensor([0.5]),
+        band0_gain_db=torch.tensor([0.0]),
+        band0_cutoff_freq=torch.tensor([1000]),
+        band0_q_factor=torch.tensor([0.5]),
+        band1_gain_db=torch.tensor([0.0]),
+        band1_cutoff_freq=torch.tensor([1000]),
+        band1_q_factor=torch.tensor([0.5]),
+        band2_gain_db=torch.tensor([0.0]),
+        band2_cutoff_freq=torch.tensor([1000]),
+        band2_q_factor=torch.tensor([0.5]),
+        band3_gain_db=torch.tensor([0.0]),
+        band3_cutoff_freq=torch.tensor([1000]),
+        band3_q_factor=torch.tensor([0.5]),
+        high_shelf_gain_db=torch.tensor([12.0]),
+        high_shelf_cutoff_freq=torch.tensor([4000]),
+        high_shelf_q_factor=torch.tensor([0.5]),
+    )
+    y = compressor(
+        y,
+        sr,
+        threshold_db=torch.tensor([-12.0]),
+        ratio=torch.tensor([4.0]),
+        attack_ms=torch.tensor([10.0]),
+        release_ms=torch.tensor([100.0]),
+        knee_db=torch.tensor([12.0]),
+        makeup_gain_db=torch.tensor([0.0]),
+    )
 
-torchaudio.save("outputs/demo/short_riff_output.wav", y, sr, backend="soundfile")
+    y = distortion(y, sr, drive_db=torch.tensor([42.0]))
+
+    y = parametric_eq(
+        y,
+        sr,
+        low_shelf_gain_db=torch.tensor([0.0]),
+        low_shelf_cutoff_freq=torch.tensor([1000]),
+        low_shelf_q_factor=torch.tensor([0.5]),
+        band0_gain_db=torch.tensor([0.0]),
+        band0_cutoff_freq=torch.tensor([1000]),
+        band0_q_factor=torch.tensor([0.5]),
+        band1_gain_db=torch.tensor([0.0]),
+        band1_cutoff_freq=torch.tensor([1000]),
+        band1_q_factor=torch.tensor([0.5]),
+        band2_gain_db=torch.tensor([0.0]),
+        band2_cutoff_freq=torch.tensor([1000]),
+        band2_q_factor=torch.tensor([0.5]),
+        band3_gain_db=torch.tensor([0.0]),
+        band3_cutoff_freq=torch.tensor([1000]),
+        band3_q_factor=torch.tensor([0.5]),
+        high_shelf_gain_db=torch.tensor([-18.0]),
+        high_shelf_cutoff_freq=torch.tensor([4000]),
+        high_shelf_q_factor=torch.tensor([0.5]),
+    )
+
+    y = noise_shaped_reverberation(
+        y,
+        sr,
+        band0_gain=torch.tensor([0.8]),
+        band1_gain=torch.tensor([0.4]),
+        band2_gain=torch.tensor([0.2]),
+        band3_gain=torch.tensor([0.5]),
+        band4_gain=torch.tensor([0.5]),
+        band5_gain=torch.tensor([0.5]),
+        band6_gain=torch.tensor([0.5]),
+        band7_gain=torch.tensor([0.6]),
+        band8_gain=torch.tensor([0.7]),
+        band9_gain=torch.tensor([0.8]),
+        band10_gain=torch.tensor([0.9]),
+        band11_gain=torch.tensor([1.0]),
+        band0_decay=torch.tensor([0.5]),
+        band1_decay=torch.tensor([0.5]),
+        band2_decay=torch.tensor([0.5]),
+        band3_decay=torch.tensor([0.5]),
+        band4_decay=torch.tensor([0.5]),
+        band5_decay=torch.tensor([0.5]),
+        band6_decay=torch.tensor([0.5]),
+        band7_decay=torch.tensor([0.5]),
+        band8_decay=torch.tensor([0.5]),
+        band9_decay=torch.tensor([0.5]),
+        band10_decay=torch.tensor([0.5]),
+        band11_decay=torch.tensor([0.5]),
+        mix=torch.tensor([0.15]),
+    )
+
+    y = y.squeeze(0)
+    x = x.squeeze(0)
+    print(y.shape)
+
+    y = y / torch.max(torch.abs(y))
+    x = x / torch.max(torch.abs(x))
+
+    torchaudio.save(
+        "outputs/demo/idmt-rock-input-varying-input.wav",
+        x.cpu(),
+        sr,
+        backend="soundfile",
+    )
+
+    torchaudio.save(
+        "outputs/demo/idmt-rock-input-varying-output.wav",
+        y.cpu(),
+        sr,
+        backend="soundfile",
+    )
diff --git a/examples/style_transfer.py b/examples/style_transfer.py
index 84374f4..667254c 100644
--- a/examples/style_transfer.py
+++ b/examples/style_transfer.py
@@ -191,7 +191,7 @@ def __init__(
 
                 self.examples.append((filepath, frame_offset))
 
-        self.examples = self.examples * 100
+        self.examples = self.examples
 
     def __len__(self):
         return len(self.examples)
@@ -270,7 +270,7 @@ def validate(
 
 def step(input: torch.Tensor, model: torch.nn.Module):
     # generate reference by randomly processing input
-    torch.manual_seed(1)
+    # torch.manual_seed(1)
     rand_equalizer_params = torch.rand(
         input.shape[0],
         model.equalizer.num_params,
@@ -394,8 +394,8 @@ def train(
     train_filepaths = filepaths[: int(len(filepaths) * 0.8)]
     val_filepaths = filepaths[int(len(filepaths) * 0.8) :]
 
-    train_filepaths = train_filepaths[:1]
-    val_filepaths = train_filepaths[:1]
+    # train_filepaths = train_filepaths[:1]
+    # val_filepaths = train_filepaths[:1]
 
     train_dataset = AudioFileDataset(train_filepaths, length=262144)
     train_dataloader = torch.utils.data.DataLoader(