app.py

import gradio as gr
import whisper
from langcodes import *

def speech_to_text(tmp_filename, uploaded, model_size):
    model = whisper.load_model(model_size)
    source = uploaded if uploaded is not None else tmp_filename
    result = model.transcribe(source)
    return f'Detected language: {Language.make(language=result["language"]).display_name()}\n\n You said: {result["text"]}'


gr.Interface(
   
    title="Whisper by OpenAI",
    thumbnail="https://cdn.openai.com/whisper/asr-summary-of-model-architecture-desktop.svg",
    css="""
    .gr-prose p{text-align: center;}
    .gr-button {background: black;color: white}
    """,
    description="Whisper is an automatic speech recognition (ASR) system trained on 680,000 hours of multilingual and multitask supervised data collected from the web.",
    fn=speech_to_text,
    inputs=[
        gr.Audio(label="Record your voice on your mic",source="microphone", type="filepath"),
        gr.Audio(source="upload", type="filepath", label="Upload Audio"), 
        gr.Dropdown(label="Select model size",value="base",choices=["tiny", "base", "small", "medium", "large"])],
    outputs="text").launch()