app.py

from transformers import pipeline

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

import gradio as gr
demo = gr.Blocks()

def transcribe_speech(filepath):
    if filepath is None:
        gr.Warning("No Audio found, please retry")
        return ""

    output = asr(filepath)
    return output["text"]

mic_transcribe = gr.Interface(
    fn = transcribe_speech,
    inputs = gr.Audio(sources="microphone",
                     type="filepath"), 
    outputs=gr.Textbox(label="Transcription", 
                      lines=3),
    allow_flagging="never"
)

file_transcribe = gr.Interface(
    fn = transcribe_speech,
    inputs = gr.Audio(sources="upload",
                     type="filepath"), 
    outputs=gr.Textbox(label="Transcription", 
                      lines=3),
    allow_flagging="never"
)

with demo:
    gr.TabbedInterface(
        [mic_transcribe, 
        file_transcribe], 
        ["Transcribe Microphone", 
        "Transcribe Audio File"], 
    )

demo.launch(debug=True)