app.py

import torch
from transformers import pipeline
import gradio as gr

device = 0 if torch.cuda.is_available() else -1

pipe = pipeline(
    model='Aizazayyubi/khowar-whisper-asr',
    tokenizer='Aizazayyubi/khowar-whisper-asr',
    task='automatic-speech-recognition',
    device=device
)

def transcribe(audio):
    result = pipe(audio)
    return result.get('text', 'Transcription failed.')

iface = gr.Interface(
    fn=transcribe,
    inputs=gr.Audio(sources=['microphone', 'upload'], type='filepath'),
    outputs='text'
)

iface.launch(share=True)