use my own model

app.py

@@ -36,9 +36,10 @@ def demo_predict(df_row):
 
 def cache_model():
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    model_name_or_path = 'm3hrdadfi/wav2vec2-xlsr-greek-speech-emotion-recognition'
+    model_name_or_path = 'khizon/greek-speech-emotion-classifier-demo'
+    generic_greek_model = 'lighteternal/wav2vec2-large-xlsr-53-greek'
     config = AutoConfig.from_pretrained(model_name_or_path)
-    processor = Wav2Vec2Processor.from_pretrained(model_name_or_path)
+    processor = Wav2Vec2Processor.from_pretrained(generic_greek_model)
     model = Wav2Vec2ForSpeechClassification.from_pretrained(model_name_or_path).to(device)
     return config, processor, model, device