app.py

import gradio as gr
from transformers import pipeline

asr = pipeline(task = "automatic-speech-recognition",
               model = "openai/whisper-large-v3")

demo = gr.Blocks()

def transcribe_speech(filepath):
  if filepath is None:
    gr.Warning("No audio file 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()