Voice Chat with AI : OpenAI Whisper

Introduction

In recent times, voice chat applications powered by AI have gained immense popularity due to their interactive and user-friendly experience. In this article, we will explore how to build a voice chat application using OpenAI's Whisper model for transcription and the GPT-4 Mini model for generating responses. By integrating these models with a Streamlit application, we can create a seamless conversational experience that remembers context and provides real-time feedback in audio format.

Overview of the Application

This application will allow users to record their audio queries, which will then be transcribed to text using OpenAI's Whisper model. After converting speech to text, we will utilize the GPT-4 Mini model to generate a suitable response. Finally, the response will be converted back into audio using another OpenAI text-to-speech model, enabling users to interact solely through voice.

Key Components of the Application:

1. Audio Recording: We will use the Streamlit Audio Recorder library to capture user's audio input.
2. Speech-to-Text Conversion: The recorded audio will be sent to the OpenAI Whisper API, which will transcribe the audio into text.
3. Response Generation: The transcribed text will be fed into the GPT-4 Mini model, which will generate a response based on the input.
4. Text-to-Speech Conversion: The response text will be converted back into audio format using an OpenAI text-to-speech model.
5. Playback: The audio reply will be played back to the user through Streamlit.

Step-by-Step Implementation

Setup

To get started, we first set up a virtual environment and install the necessary libraries, including Streamlit and OpenAI's Python client.

pip install streamlit openai

Next, we create an app.py file where we will write our application code. In this file, we will configure the Streamlit page and layout.

import streamlit as st

## Introduction
st.set_page_config(page_title="Voice Chat", page_icon=":speech_balloon:")

Audio Recording

Using the Streamlit Audio Recorder library, we create a function for recording audio, which will save the captured audio as a file.

def record_audio():
    # Function logic for recording audio
    pass

Transcribe Speech to Text

We will create a function that takes the audio file and uses OpenAI's Whisper API to convert the speech to text.

def speech_to_text(audio_file):
    # Use OpenAI's Whisper API to transcribe audio to text
    pass

Generate Response

Next, we will create a function to interact with the GPT-4 Mini model to fetch a response based on the transcribed text.

def get_response(user_input):
    # Use OpenAI's GPT-4 Mini for generating a response
    pass

Convert Text to Speech

After obtaining the response, we will convert it back into audio using OpenAI's text-to-speech functionalities.

def text_to_speech(response_text):
    # Use OpenAI's text-to-speech model to convert text to audio
    pass

Play the Response

Finally, we will configure the application to play back the generated audio response.

def play_audio(audio_file):
    # Logic to play audio using Streamlit
    pass

Example Interaction

To demonstrate the functionality, a user might start by asking, "Hey, what's the weather today?" The audio will be recorded, transcribed, and passed through the OpenAI models. The response, like "The weather is sunny," will then be converted back to audio and played for the user.

Conclusion

Integrating OpenAI Whisper and GPT-4 Mini in a voice chat application within Streamlit not only provides an engaging user experience but also showcases the powerful capabilities of AI in natural language processing and speech recognition. As we continue to explore more about voice interactions, we might also look for alternative models to reduce costs and expand functionalities.

Keywords

OpenAI, Whisper, GPT-4 Mini, Voice Chat, Speech-to-Text, Text-to-Speech, Streamlit, AI, Audio Recorder

FAQ

Q1: What is OpenAI Whisper?
A1: OpenAI Whisper is a speech recognition model that converts spoken language into written text.

Q2: How does the voice chat application work?
A2: Users record their audio questions, which are transcribed with Whisper, processed by GPT-4 Mini for responses, and converted back into audio for playback.

Q3: What is GPT-4 Mini?
A3: GPT-4 Mini is a smaller, faster variant of OpenAI's GPT-4 model, designed for generating text-based responses.

Q4: Can the application handle different voice commands?
A4: Yes, as the application is built to process various audio inputs, it can handle different types of user queries.

Q5: Is the application scalable?
A5: Yes, the application can be scaled to include more features and functionalities, such as conversation history and multi-user support.