Update tasks/audio.py

tasks/audio.py

@@ -66,22 +66,25 @@ async def evaluate_audio(request: AudioEvaluationRequest):
             _waveform = _resampler(_waveform)
         return _waveform
 
-    def preprocess_audio(sample):
-        waveform = torch.tensor(sample['audio']['array'], dtype=torch.float32).unsqueeze(0)
-        resized_waveform = resize_audio(waveform, target_length=72000)
-        return amplitude_to_db(mel_transform(resampler(resized_waveform)))
+    resized_waveforms = [
+        resize_audio(torch.tensor(sample['audio']['array'], dtype=torch.float32).unsqueeze(0), target_length=72000)
+        for sample in test_dataset
+    ]
 
-    waveforms = [preprocess_audio(sample) for sample in test_dataset]
-    labels = torch.tensor(true_labels)
+    waveforms, labels = [], []
+    for waveform, label in zip(resized_waveforms, true_labels):
+        waveforms.append(amplitude_to_db(mel_transform(resampler(waveform))))
+        labels.append(label)
 
     waveforms = torch.stack(waveforms)
+    labels = torch.tensor(labels)
 
     test_loader = DataLoader(
         TensorDataset(waveforms, labels),
         batch_size=128,
         shuffle=False,
         pin_memory=True,
-        num_workers=4
+        num_workers=4 
     )
 
     scripted_model = torch.jit.load("./optimized_qat_blazeface_model.pt", map_location=torch.device('cpu'))