app.py

import whisper

model = whisper.load_model("base")

from transformers import pipeline
en_fr_translator = pipeline("translation_en_to_fr")

import gradio as gr 
import time

def transcribe(audio):
    
    #time.sleep(3)
    # load audio and pad/trim it to fit 30 seconds
    audio = whisper.load_audio(audio)
    audio = whisper.pad_or_trim(audio)

    # make log-Mel spectrogram and move to the same device as the model
    mel = whisper.log_mel_spectrogram(audio).to(model.device)

    # detect the spoken language
    _, probs = model.detect_language(mel)
    lang=(f"Detected language: {max(probs, key=probs.get)}")

    
    
   

    # decode the audio
    options = whisper.DecodingOptions(fp16 = False,task= "translate")
    result = whisper.decode(model, mel, options)
    word= result.text
    trans = en_fr_translator(word)
    Trans = trans[0]['translation_text']
    result=f"{lang}\n{word}\n\nFrench translation: {Trans}"
    return result


gr.Interface(
title = 'OpenAI Whisper ASR Gradio Web UI',
fn=transcribe,
inputs=[
    gr.inputs.Audio(source="microphone", type="filepath")
],
outputs=[
    "textbox"
],
live=True).launch()