app.py

import gradio as gr
from transformers import pipeline

# Load the model and processor
model_id = "openai/whisper-small"

device = "cpu"
BATCH_SIZE = 8
pipe = pipeline(
    task="automatic-speech-recognition",
    model=model_id,
    chunk_length_s=30,
    device=device,
)

def transcribe(inputs, task):
    if inputs is None:
        raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.")

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

def transcribelocal(microphone, file_upload):
  # Check which input is not None
  if microphone is not None:
    audio = microphone
  else:
    audio = file_upload

  return transcribe(audio, "transcribe")

# Create a Gradio interface with two modes: realtime and file upload
iface = gr.Interface(
  fn=transcribelocal,
  inputs=[
    gr.inputs.Audio(source="microphone", type="filepath", label="Realtime Mode"),
    gr.inputs.Audio(source="upload", type="filepath", label="File Upload Mode")
  ],
  outputs=[
    gr.outputs.Textbox(label="Transcription")
  ],
  title="Whisper Transcription App",
  description="A Gradio app that uses OpenAI's whisper model to transcribe audio"
)

# Launch the app
iface.launch()