app.py

from transformers import pipeline
import gradio as gr
import os

# Load the ASR model
asr = pipeline(task="automatic-speech-recognition",
               model="distil-whisper/distil-small.en")

# Define the transcription function
def transcribe_long_form(filepath):
    if filepath is None:
        return "No audio file provided, please upload a file or record one."
    output = asr(filepath)
    return output['text']

# Set up the Gradio interface
with gr.Blocks() as demo:
    with gr.Tab("Transcribe Audio"):
        with gr.Row():
            audio_input = gr.Audio(sources=["microphone", "upload"], type="filepath")
            submit_button = gr.Button("Transcribe")
        transcription_output = gr.Textbox(label="Transcription", lines=3)

        submit_button.click(
            transcribe_long_form, 
            inputs=[audio_input],
            outputs=[transcription_output]
        )

# Launch the Gradio app
demo.launch(share=True, server_port=int(os.environ.get('PORT1', 7860)))  # Default port 7860 if PORT1 is not set