app.py

import os
import tempfile

import torch
import gradio as gr
from transformers import pipeline


MODEL_NAME = "openai/whisper-large-v3"
BATCH_SIZE = 8

device = 0 if torch.cuda.is_available() else "cpu"

pipe = pipeline(
    task="automatic-speech-recognition",
    model=MODEL_NAME,
    chunk_length_s=30,
    device=device,
)


def transcribe(inputs, task="transcribe"):
    if inputs is None:
        raise gr.Error("No audio file submitted!")

    output = pipe(
        inputs, 
        batch_size=BATCH_SIZE, 
        generate_kwargs={"task": task}, 
        return_timestamps=True
    )
    return output["text"]

demo = gr.Interface(
    fn=transcribe,
    inputs=["audio"],
    outputs="text",
    title="Transcribe Audio to Text", # Give our demo a title
)

demo.launch()