app.py

import whisper
import gradio as gr 
import time
import warnings
import json
import openai
import os
from gtts import gTTS
warnings.filterwarnings("ignore")

openai.api_key = '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'
model = whisper.load_model("base")
model.device
!ffmpeg -f lavfi -i anullsrc=r=44100:cl=mono -t 10 -q:a 9 -acodec libmp3lame Temp.mp3
def chatgpt_api(input_text):
    messages = [
    {"role": "system", "content": "You are a helpful assistant."}]
    
    if input_text:
        messages.append(
            {"role": "user", "content": input_text},
        )
        chat_completion = openai.ChatCompletion.create(
            model="gpt-3.5-turbo", messages=messages
        )
    
    reply = chat_completion.choices[0].message.content
    return reply
    def transcribe(audio):

    language = 'en'

    audio = whisper.load_audio(audio)
    audio = whisper.pad_or_trim(audio)

    mel = whisper.log_mel_spectrogram(audio).to(model.device)

    _, probs = model.detect_language(mel)

    options = whisper.DecodingOptions()
    result = whisper.decode(model, mel, options)
    result_text = result.text
    
    out_result = chatgpt_api(result_text)
    
    audioobj = gTTS(text = out_result, 
                    lang = language, 
                    slow = False)
    
    audioobj.save("Temp.mp3")

    return [result_text, out_result, "Temp.mp3"]
    output_1 = gr.Textbox(label="Speech to Text")
output_2 = gr.Textbox(label="ChatGPT Output")
output_3 = gr.Audio("Temp.mp3")

gr.Interface(
    title = 'OpenAI Whisper and ChatGPT ASR Gradio Web UI', 
    fn=transcribe, 
    inputs=[
        gr.inputs.Audio(source="microphone", type="filepath")
    ],

    outputs=[
        output_1,  output_2, output_3
    ],
    live=True).launch()