Update app.py

app.py

@@ -7,7 +7,12 @@ config = AutoConfig.from_pretrained("SeyedAli/Persian-Speech-Emotion-HuBert-V1")
 model = Wav2Vec2FeatureExtractor.from_pretrained("SeyedAli/Persian-Speech-Emotion-HuBert-V1")
 
 def speech_file_to_array_fn(path, sampling_rate):
-    speech_array, _sampling_rate = torchaudio.load(path)
+   with tempfile.NamedTemporaryFile(suffix=".wav") as temp_audio_file:
+    # Copy the contents of the uploaded audio file to the temporary file
+    temp_audio_file.write(open(path, "rb").read())
+    temp_audio_file.flush()
+    # Load the audio file using torchaudio
+    speech_array, _sampling_rate = torchaudio.load(temp_audio_file.name)
     resampler = torchaudio.transforms.Resample(_sampling_rate)
     speech = resampler(speech_array).squeeze().numpy()
     return speech
@@ -24,8 +29,8 @@ def predict(path, sampling_rate):
     outputs = [{"Label": config.id2label[i], "Score": f"{round(score * 100, 3):.1f}%"} for i, score in enumerate(scores)]
     return outputs
 
-def SER(Audio):
-    return predict(Audio,model.sampling_rate)
+def SER(audio):
+    return predict(audio,model.sampling_rate)
 
 iface = gr.Interface(fn=SER, inputs="audio", outputs="text")
 iface.launch(share=False)