Create app.py

app.py

@@ -0,0 +1,35 @@
+import gradio as gr
+from transformers import pipeline
+
+# Load the model
+pipe = pipeline("automatic-speech-recognition", model="vargha/whisper-large-v3")
+
+# Define the inference function
+def transcribe_audio(audio):
+    if audio is None:
+        return "No audio file uploaded. Please try again."
+    
+    try:
+        # Perform transcription
+        result = pipe(audio)["text"]
+        return result
+    except Exception as e:
+        return f"Error during transcription: {str(e)}"
+
+# Create a Gradio interface for uploading audio or using the microphone
+with gr.Blocks() as interface:
+    gr.Markdown("# Whisper Large V3 Speech Recognition")
+    gr.Markdown("Upload an audio file or use your microphone to transcribe speech to text.")
+    
+    # Create the input and output components
+    audio_input = gr.Audio(type="filepath", label="Input Audio")
+    output_text = gr.Textbox(label="Transcription")
+    
+    # Add a button to trigger the transcription
+    transcribe_button = gr.Button("Transcribe")
+    
+    # Bind the transcribe_audio function to the button click
+    transcribe_button.click(fn=transcribe_audio, inputs=audio_input, outputs=output_text)
+
+# Launch the Gradio app
+interface.launch()