Update app.py

app.py

@@ -1,45 +1,23 @@
 from transformers import pipeline
+import gradio as gr
 
 model_id = "Teapack1/model_KWS"  # update with your model id
-pipe = pipeline("automatic-speech-recognition", model=model_id)
+pipe = pipeline("audio-classification", model=model_id)
 
-import gradio as gr
 
 title = "Keyword Spotting Wav2Vec2"
 description = "Gradio demo for finetuned Wav2Vec2 model on a custom dataset to perform keyword spotting task. Classes are scene 1, scene 2, scene 3, yes, no and stop."
 
 
-def transcribe_speech(filepath):
-    output = pipe(
-        filepath,
-        max_new_tokens=256,
-        generate_kwargs={
-            "task": "transcribe",
-            "language": "sinhalese",
-        },  # update with the language you've fine-tuned on
-        chunk_length_s=30,
-        batch_size=8,
-    )
-    return output["text"]
-
-demo = gr.Blocks()
+def classify_audio(filepath):
+    preds = pipe(filepath)
+    outputs = {}
+    for p in preds:
+        outputs[p["label"]] = p["score"]
+    return outputs
 
-mic_transcribe = gr.Interface(
-    fn=transcribe_speech,
-    inputs=gr.Audio(sources="microphone", type="filepath"),
-    outputs=gr.outputs.Textbox(),
-)
 
-file_transcribe = gr.Interface(
-    fn=transcribe_speech,
-    inputs=gr.Audio(sources="upload", type="filepath"),
-    outputs=gr.outputs.Textbox(),
+demo = gr.Interface(
+    fn=classify_audio, inputs=gr.Audio(type="filepath"), outputs=gr.outputs.Label()
 )
-
-with demo:
-    gr.TabbedInterface(
-        [mic_transcribe, file_transcribe],
-        ["Transcribe Microphone", "Transcribe Audio File"],
-    )
-
 demo.launch(debug=True)