app.py

import gradio as gr
import whisper

# Load the smallest model variant for fast CPU inference
model = whisper.load_model("base")

def transcribe_audio(audio_file):
    """
    Transcribe the audio file using Whisper
    """
    try:
        # Transcribe the audio
        result = model.transcribe(audio_file)
        return result["text"]
    except Exception as e:
        return f"Error during transcription: {str(e)}"

# Create the Gradio interface
interface = gr.Interface(
    fn=transcribe_audio,
    inputs=gr.Audio(type="filepath"),  # Updated syntax
    outputs="text",
    title="Speech to Text Converter",
    description="Upload an audio file to convert speech to text using Whisper",
    examples=[["sample1.mp3"], ["sample2.wav"]],
    cache_examples=True
)

# Launch the app
if __name__ == "__main__":
    interface.launch(share=True)