diff --git a/examples/tutorials/audio_resampling_tutorial.py b/examples/tutorials/audio_resampling_tutorial.py
index adca5073c9..4071599d7f 100644
--- a/examples/tutorials/audio_resampling_tutorial.py
+++ b/examples/tutorials/audio_resampling_tutorial.py
@@ -16,6 +16,13 @@
 print(torch.__version__)
 print(torchaudio.__version__)
 
+if torch.accelerator.is_available():
+    acc = torch.accelerator.current_accelerator()
+    device = torch.device(acc)
+else:
+    device = torch.device("cpu")
+print(f"Using device: {device}")
+
 ######################################################################
 # Preparation
 # -----------
@@ -41,7 +48,7 @@ def _get_log_freq(sample_rate, max_sweep_rate, offset):
 
     """
     start, stop = math.log(offset), math.log(offset + max_sweep_rate // 2)
-    return torch.exp(torch.linspace(start, stop, sample_rate, dtype=torch.double)) - offset
+    return torch.exp(torch.linspace(start, stop, sample_rate, dtype=torch.double, device=device)) - offset
 
 
 def _get_inverse_log_freq(freq, sample_rate, offset):
@@ -91,6 +98,7 @@ def plot_sweep(
     freq_y = [f for f in freq if f in y_ticks and 1000 <= f <= sample_rate // 2]
 
     figure, axis = plt.subplots(1, 1)
+    waveform = waveform.cpu() if torch.is_tensor(waveform) else waveform
     _, _, _, cax = axis.specgram(waveform[0].numpy(), Fs=sample_rate)
     plt.xticks(time, freq_x)
     plt.yticks(freq_y, freq_y)
@@ -144,7 +152,7 @@ def plot_sweep(
 waveform = get_sine_sweep(sample_rate)
 
 plot_sweep(waveform, sample_rate, title="Original Waveform")
-Audio(waveform.numpy()[0], rate=sample_rate)
+Audio(waveform.cpu().numpy()[0], rate=sample_rate)
 
 ######################################################################
 #
@@ -157,11 +165,11 @@ def plot_sweep(
 # an explanation of how it happens, and why it looks like a reflection.
 
 resample_rate = 32000
-resampler = T.Resample(sample_rate, resample_rate, dtype=waveform.dtype)
+resampler = T.Resample(sample_rate, resample_rate, dtype=waveform.dtype).to(device)
 resampled_waveform = resampler(waveform)
 
 plot_sweep(resampled_waveform, resample_rate, title="Resampled Waveform")
-Audio(resampled_waveform.numpy()[0], rate=resample_rate)
+Audio(resampled_waveform.cpu().numpy()[0], rate=resample_rate)
 
 ######################################################################
 # Controling resampling quality with parameters
@@ -182,13 +190,13 @@ def plot_sweep(
 sample_rate = 48000
 resample_rate = 32000
 
-resampled_waveform = F.resample(waveform, sample_rate, resample_rate, lowpass_filter_width=6)
+resampled_waveform = F.resample(waveform, sample_rate, resample_rate, lowpass_filter_width=6).to(device)
 plot_sweep(resampled_waveform, resample_rate, title="lowpass_filter_width=6")
 
 ######################################################################
 #
 
-resampled_waveform = F.resample(waveform, sample_rate, resample_rate, lowpass_filter_width=128)
+resampled_waveform = F.resample(waveform, sample_rate, resample_rate, lowpass_filter_width=128).to(device)
 plot_sweep(resampled_waveform, resample_rate, title="lowpass_filter_width=128")
 
 ######################################################################
@@ -208,13 +216,13 @@ def plot_sweep(
 sample_rate = 48000
 resample_rate = 32000
 
-resampled_waveform = F.resample(waveform, sample_rate, resample_rate, rolloff=0.99)
+resampled_waveform = F.resample(waveform, sample_rate, resample_rate, rolloff=0.99).to(device)
 plot_sweep(resampled_waveform, resample_rate, title="rolloff=0.99")
 
 ######################################################################
 #
 
-resampled_waveform = F.resample(waveform, sample_rate, resample_rate, rolloff=0.8)
+resampled_waveform = F.resample(waveform, sample_rate, resample_rate, rolloff=0.8).to(device)
 plot_sweep(resampled_waveform, resample_rate, title="rolloff=0.8")
 
 
@@ -234,13 +242,13 @@ def plot_sweep(
 sample_rate = 48000
 resample_rate = 32000
 
-resampled_waveform = F.resample(waveform, sample_rate, resample_rate, resampling_method="sinc_interp_hann")
+resampled_waveform = F.resample(waveform, sample_rate, resample_rate, resampling_method="sinc_interp_hann").to(device)
 plot_sweep(resampled_waveform, resample_rate, title="Hann Window Default")
 
 ######################################################################
 #
 
-resampled_waveform = F.resample(waveform, sample_rate, resample_rate, resampling_method="sinc_interp_kaiser")
+resampled_waveform = F.resample(waveform, sample_rate, resample_rate, resampling_method="sinc_interp_kaiser").to(device)
 plot_sweep(resampled_waveform, resample_rate, title="Kaiser Window Default")
 
 
@@ -267,7 +275,7 @@ def plot_sweep(
     rolloff=0.9475937167399596,
     resampling_method="sinc_interp_kaiser",
     beta=14.769656459379492,
-)
+).to(device)
 plot_sweep(resampled_waveform, resample_rate, title="Kaiser Window Best (torchaudio)")
 
 ######################################################################
@@ -282,7 +290,7 @@ def plot_sweep(
     rolloff=0.85,
     resampling_method="sinc_interp_kaiser",
     beta=8.555504641634386,
-)
+).to(device)
 plot_sweep(resampled_waveform, resample_rate, title="Kaiser Window Fast (torchaudio)")
 
 ######################################################################
diff --git a/examples/tutorials/forced_alignment_tutorial.py b/examples/tutorials/forced_alignment_tutorial.py
index 7fa7c86dc3..969af1fde5 100644
--- a/examples/tutorials/forced_alignment_tutorial.py
+++ b/examples/tutorials/forced_alignment_tutorial.py
@@ -47,7 +47,13 @@
 print(torchaudio.__version__)
 
 
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+if torch.accelerator.is_available():
+    acc = torch.accelerator.current_accelerator()
+    device = torch.device(acc)
+    backend = torch.distributed.get_default_backend_for_device(device)
+else:
+    device = torch.device("cpu")
+    backend = "gloo"
 print(device)
 
 
diff --git a/examples/tutorials/speech_recognition_pipeline_tutorial.py b/examples/tutorials/speech_recognition_pipeline_tutorial.py
index 2c8dfc752b..f5654ba7d4 100644
--- a/examples/tutorials/speech_recognition_pipeline_tutorial.py
+++ b/examples/tutorials/speech_recognition_pipeline_tutorial.py
@@ -42,7 +42,13 @@
 print(torchaudio.__version__)
 
 torch.random.manual_seed(0)
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+if torch.accelerator.is_available():
+    acc = torch.accelerator.current_accelerator()
+    device = torch.device(acc)
+    backend = torch.distributed.get_default_backend_for_device(device)
+else:
+    device = torch.device("cpu")
+    backend = "gloo"
 
 print(device)