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preprocess.py

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+import torch
+import torchaudio
+import torchvision
+
+resample_rate = 16000
+
+def process_audio_data(waveform, sample_rate):
+    try:
+        waveform = waveform[0]  # 使用左声道
+        waveform = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=resample_rate)(waveform)
+
+        if waveform.size(0) < 3 * resample_rate:
+            waveform = torch.nn.functional.pad(waveform, (0, 3 * resample_rate - waveform.size(0)))
+        else:
+            waveform = waveform[: 3 * resample_rate]
+
+        mfcc = torchaudio.transforms.MFCC(
+            sample_rate=resample_rate,
+            n_mfcc=13,
+            melkwargs={
+                "n_fft": 256,
+                "win_length": 256,
+                "hop_length": 128,
+                "n_mels": 40,
+            }
+        )(waveform)
+
+        return mfcc
+    except Exception as e:
+        print(f"ERR!: Error in audio processing: {e}")
+        return None
+
+def process_image_data(image):
+    try:
+        image = torchvision.transforms.Resize((1080, 1080))(image)
+        image = image / 255.0
+        image = torchvision.transforms.Normalize(
+            mean=[0.485, 0.456, 0.406],
+            std=[0.229, 0.224, 0.225]
+        )(image)
+
+        return image
+    except Exception as e:
+        print(f"ERR!: Error in image processing: {e}")
+        return None