How to save all of the recently recorded real-time audio into a .mp3 or a .wav file?

Question

I hope you are all well. I was trying to implement a multimodal sentiment analysis fyp. I was modifying my audio component. I was curious how to save all of a recently recorded real-time audio into a .wav or a .mp3 file?

For example suppose there was a program in which i was performing audio analysis, i dont know how to record all of audio into a .wav or a .mp3 file. Here is my current audio sentiment analysis code:

import pyttsx3
engine = pyttsx3.init()


# Import necessary libraries
import speech_recognition as sr
import pyaudio
import wave
#import sentiment_analysis_library
#import llm_library
#import text_to_speech_library
from transformers import pipeline
from gtts import gTTS





# Initialize recognizer class (for recognizing the speech)
r = sr.Recognizer()

# Function to transcribe audio
def transcribe_audio(audio):
    return r.recognize_google(audio)

# Function to analyze waveform
def analyze_waveform(audio):
    # Your waveform analysis code here
    pass

# Function to analyze sentiment
def analyze_sentiment(text):
    classifier = pipeline("text-classification",model='bhadresh-savani/distilbert-base-uncased-emotion', return_all_scores=False)
    prediction = classifier(text)
    return prediction

# Function to get response from llm
def get_llm_response(text):
    return llm_library.get_response(text)

# Function to convert text to speech
def text_to_speech(text):
    engine.say(str(text))
    engine.runAndWait()
    

# Main function to handle real-time audio
def handle_real_time_audio():
    while True:
        with sr.Microphone() as source:
            print("Listening...")
            audio = r.listen(source)
            
            # Transcribe the audio to text
            text = transcribe_audio(audio)
            
            # Analyze the waveform
            analyze_waveform(audio)
            
            # Analyze the sentiment
            sentiment = analyze_sentiment(text)
            print("Current sentiment is: " + str(sentiment))
            
            # Feed the transcribed speech into an llm
            llm_response = get_llm_response(text)
            
            # Convert the llm response to audio
            text_to_speech(str(llm_response))

# Call the main function
handle_real_time_audio()