Testing

app.py

@@ -4,22 +4,20 @@ import torch
 import torchaudio
 from transformers import Wav2Vec2FeatureExtractor, Wav2Vec2ForSequenceClassification
 
-device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+device = "cuda" if torch.cuda.is_available() else "cpu"
 
 model_name = "Hemg/human-emotion-detection"
 feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_name)
-model = Wav2Vec2ForSequenceClassification.from_pretrained(model_name).to(device) 
+model = Wav2Vec2ForSequenceClassification.from_pretrained(model_name)
+model.to(device)
 
 def preprocess_audio(audio):
-    print('hallo')
     waveform, sampling_rate = torchaudio.load(audio)
     resampled_waveform = torchaudio.transforms.Resample(orig_freq=sampling_rate, new_freq=16000)(waveform)
     return {'speech': resampled_waveform.numpy().flatten(), 'sampling_rate': 16000}
 
 @spaces.GPU
 def inference(audio):
-    print('hello')
-    
     example = preprocess_audio(audio)
     inputs = feature_extractor(example['speech'], sampling_rate=16000, return_tensors="pt", padding=True)
     inputs = inputs.to(device)  # Move inputs to GPU