Add Whisper V3 Gradio demo

- Import gradio and transformers libraries
- Load Whisper V3 large model pipeline for automatic speech recognition
- Define transcribe_audio function to run pipeline on input audio
- Create gradio interface with audio input and text output
- Launch gradio demo

app.py

@@ -1,7 +1,11 @@
 import gradio as gr
+# Use a pipeline as a high-level helper
+from transformers import pipeline
 
-def greet(name):
-    return "Hello " + name + "!!"
+pipe = pipeline("automatic-speech-recognition", model="openai/whisper-large-v3")
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
+def transcribe_audio(audio):
+    return pipe(audio)
+
+demo = gr.Interface(fn=transcribe_audio, inputs="audio", outputs="text")
 demo.launch()

requirements.txt

@@ -0,0 +1,2 @@
+gradio
+transformers