app.py

from transformers import pipeline

model_id = 'carlfeynman/whisper-small-tamil'

pipe = pipeline('automatic-speech-recognition', model=model_id)

def transcribe_speech(filepath):
  pred = pipe(
    filepath,
        max_new_tokens=256,
        generate_kwargs={
            "task": "transcribe",
            "language": "tamil",
        },  
        chunk_length_s=30,
        batch_size=8,
  )
  return pred['text']


import gradio as gr


demo = gr.Blocks()

mic_transcribe = gr.Interface(
    fn=transcribe_speech,
    inputs=gr.Audio(source='microphone',type='filepath'),
    outputs="textbox"
)

file_transcribe =  gr.Interface(
    fn=transcribe_speech,
    inputs=gr.Audio(source='upload', type='filepath'),
    outputs="textbox"
)

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

demo.launch(share=True)