app.py

from transformers import pipeline
import gradio as gr
import torch

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

asr = pipeline(
    "automatic-speech-recognition",
    model="MaximilianChen/Casper",
    chunk_length_s=30,
    device=device,
)

def transcribe_audio(mic=None, file=None):
    if mic is not None:
        audio = mic
    elif file is not None:
        audio = file
    else:
        return "You must either provide a mic recording or a file"
    transcription = asr(audio)["text"]
    return transcription


gr.Interface(
    fn=transcribe_audio,
    inputs=[
        gr.Audio(source="microphone", type="filepath", optional=True),
        gr.Audio(source="upload", type="filepath", optional=True),
    ],
    outputs="text",
).launch()