app.py

# from transformers import pipeline
# import gradio as gr


# # Load the pipeline with the cache_dir parameter
# pipe = pipeline(model="tarteel-ai/whisper-base-ar-quran")

# def transcribe(audio):
#     text = pipe(audio)["text"]
#     return text

# iface = gr.Interface(
#     fn=transcribe,
#     inputs=gr.Audio(source="upload", type="filepath"),
#     outputs="text",
# )

# iface.launch()
from transformers import pipeline

model_id = "tarteel-ai/whisper-base-ar-quran"  # update with your model id
pipe = pipeline("automatic-speech-recognition", model=model_id)

def transcribe(filepath):
    output = pipe(
        filepath,
        max_new_tokens=10000,
        chunk_length_s=30,
        batch_size=8,
    )
    return output["text"]

import gradio as gr

iface = gr.Interface(
    fn=transcribe,
    inputs=gr.Audio(source="upload", type="filepath"),
    outputs="text",
)

iface.launch()