Update app.py

app.py

@@ -0,0 +1,47 @@
+import torch
+import gradio
+from transformers import Wav2Vec2FeatureExtractor
+from datasets import Dataset
+import librosa
+
+feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("superb/hubert-large-superb-er")
+
+def get_emotion(microphone, file_upload, task):
+        warn_output = ""
+    
+        if (microphone is not None) and (file_upload is not None):
+            warn_output = (
+                "WARNING: You've uploaded an audio file and used the microphone. "
+                "The recorded file from the microphone will be used and the uploaded audio will be discarded.\n"
+            )
+        elif (microphone is None) and (file_upload is None):
+            return "ERROR: You have to either use the microphone or upload an audio file"
+
+    file = microphone if microphone is not None else file_upload
+    test = feature_extractor(file, sampling_rate=16000, padding=True, return_tensors="pt" ).to(torch.float32)       
+    logits = model(**test).logits
+    predicted_ids = torch.argmax(logits, dim=-1)
+    labels = [model.config.id2label[_id] for _id in predicated_ids.tolist()]
+    return labels
+
+demo = gr.Blocks()
+
+mf_transcribe = gr.Interface(
+    fn=get_emotion,
+    inputs=[
+        gr.inputs.Audio(source="microphone", type="filepath", optional=True),
+        gr.inputs.Audio(source="upload", type="filepath", optional=True),,
+    ],
+    outputs="text",
+    layout="horizontal",
+    theme="huggingface",
+    title="AER",
+    description=(
+        "get the emotion"
+    ),
+    allow_flagging="never",
+)
+
+with demo:
+    gr.TabbledInterface([mf_transcribe],'Trancribe')
+demo.launch(enable_queue=True)