Build a voice assistant with OpenAI Whisper and TTS (text to speech) in 5 minutes

Ralf Elfving
15 Nov 202311:24

TLDRIn this tutorial, Ralf demonstrates how to create a voice-based chat assistant using Node.js, the Whisper API for transcription, and OpenAI's text-to-speech (TTS) for audio responses. The process is quick and straightforward, allowing users to have a fully interactive, speech-based conversation with their AI assistant. Ralf provides a link to the code in the video description and walks viewers through the setup, explaining each function and its role in the system. The tutorial also showcases the chat assistant's ability to retain context from previous messages, offering a practical example of OpenAI's API capabilities.

Takeaways

  • 🎤 The video demonstrates how to build a voice-based chat assistant using the Whisper API for transcription and OpenAI's TTS for speech.
  • 🔧 The setup process is quick, taking no more than five minutes, and utilizes Node.js.
  • 📝 The code for the project is available in the video description for viewers to download and experiment with.
  • 🔊 The chat assistant can recognize and maintain context from previous messages in the conversation.
  • 📋 The script involves initializing an OpenAI API client with an API key stored in an environment variable.
  • 📌 The main function sets up a readline interface to control recording start/stop with the Enter key.
  • 🎧 The recording function starts capturing audio and writes it to an output.wav file.
  • 🗣️ The transcribe and chat function sends the recorded audio to the Whisper API and processes the transcribed text.
  • 💬 The chat completions endpoint is used to generate a response based on the transcribed user input and chat history.
  • 🔊 The streamed audio function uses OpenAI's TTS API to convert the chat response text into speech and plays it back.
  • 📋 The tutorial provides a step-by-step guide and a visual diagram to help understand the code and its functions.
  • 👋 The video concludes with an invitation for viewers to engage with the content, ask questions, and subscribe for more tutorials.

Q & A

  • What is the main purpose of the video?

    -The main purpose of the video is to demonstrate how to build a voice-based chat assistant using the Whisper API for transcription and OpenAI's TTS for speech synthesis.

  • Which programming language is used in the tutorial?

    -Node.js is used in the tutorial for creating the chat assistant.

  • How long does it take to set up the chat assistant according to the video?

    -It takes no more than five minutes to set up the chat assistant as per the video instructions.

  • What is the role of the Whisper API in this process?

    -The Whisper API is used for transcribing the user's spoken input into text.

  • What is TTS in the context of the video?

    -TTS stands for Text-to-Speech, which is used to convert the chat assistant's text response into spoken words.

  • How does the chat assistant maintain context of previous messages?

    -The chat assistant maintains context by including previous chat history in the subsequent API calls to the chat completions endpoint.

  • What is the function of the 'set up a read line interface' in the code?

    -The 'set up a read line interface' function allows the user to start or stop recording using the enter key and to terminate the program with any other key.

  • How is the user's voice recorded in the chat assistant setup?

    -The user's voice is recorded using a microphone, and the output is written to an output.wav file.

  • What model is used for the chat completions endpoint?

    -The video uses the GPT-3.5 Turbo model for the chat completions endpoint.

  • How does the chat assistant play the response back to the user?

    -The chat assistant uses the OpenAI TTS API to convert the text response into an audio stream, which is then played using a speaker and ffmpeg.

  • What is the role of ffmpeg in the chat assistant setup?

    -Ffmpeg is used to pipe the audio data from the TTS API into a speaker to play the response back to the user.

Outlines

00:00

🎥 Introduction to Building a Voice-Based Chat Assistant

Ralf introduces a tutorial on creating a voice-based chat assistant using the Whisper API for transcription, the chat completions endpoint for interaction, and OpenAI's TTS for speech output. The process involves Node.js and is claimed to take only five minutes to set up. Ralf demonstrates the end product, which includes a welcome prompt, voice recognition, and context-aware responses. The code for the project is available in the video description.

05:05

📝 Code Walkthrough and Setup

Ralf provides a detailed walk-through of the code, explaining the setup process, including the necessary modules, environment variable setup for the OpenAI API key, and the use of ffmpeg. The script consists of five main functions, which are explained in sequence. The first function sets up a readline interface for recording and playback control, while the second function handles the start and stop of the recording process. Ralf also discusses the transcribe and chat function, which sends the recorded audio to the Whisper API and processes the response for chat completion.

10:09

🔊 Recording, Transcription, and Response

The script's functionality is further explained, focusing on the recording and processing of audio. When the user hits enter, the script stops recording and processes the audio file. The transcribe and chat function sends the audio file to the Whisper API, which returns a transcribed text. This text is then used in a chat completion API call to generate a response. The response text is sent to the TTS API to generate an audio response, which is played back to the user using the streamed audio function.

🚀 Conclusion and Future Tutorials

Ralf concludes the tutorial by inviting the audience to interact with the chat assistant and asks it to say goodbye. He emphasizes the simplicity of the setup and encourages viewers to explore OpenAI's APIs further. Ralf also invites feedback and questions in the comments section and promises more tutorials on OpenAI functionalities in the future. The video ends with a call to action for viewers to like, subscribe, and engage with the content.

Mindmap

Keywords

💡Whisper API

The Whisper API is a service provided by OpenAI that enables the transcription of spoken language into text. In the video, it is used to convert the user's voice input into text, which is then processed by the chat assistant. This is a crucial component for creating a voice-based interaction system, as it allows the chat assistant to understand and respond to spoken commands.

💡Chat completions endpoint

The chat completions endpoint is a part of OpenAI's API that generates responses to user inputs in a conversational context. It is used in the video to process the transcribed text and generate a response that the chat assistant can speak back to the user. This endpoint is essential for creating a natural and interactive dialogue between the user and the chat assistant.

💡Text-to-Speech (TTS)

Text-to-Speech, or TTS, is a technology that converts written text into spoken words. In the context of the video, OpenAI's TTS is used to convert the chat assistant's response into an audio format that can be played back to the user. This is a key feature for creating a fully speech-based interaction, as it allows the user to receive responses without having to read any text.

💡Node.js

Node.js is a cross-platform, open-source JavaScript runtime environment that allows developers to run JavaScript code outside of a web browser. In the video, Node.js is used as the programming environment to build the voice-based chat assistant. It provides the necessary tools and libraries to interact with OpenAI's APIs and manage the conversation flow.

💡Environment variable

An environment variable is a dynamic, named value that can affect the way running processes will behave on a computer. In the video, the OpenAI API key is stored in an environment variable to securely manage the credentials required to access OpenAI's services. This is a common practice in software development to keep sensitive information like API keys hidden from the codebase.

💡Readline interface

The readline interface is a feature in Node.js that allows for input/output operations in a terminal-based application. It enables the user to interact with the program by pressing keys, such as 'enter' to start or stop recording, and 'return' to send a command. In the video, the readline interface is set up to manage user input for the voice-based chat assistant.

💡FFmpeg

FFmpeg is a free and open-source software project consisting of a vast software suite of libraries and programs for handling video, audio, and other multimedia files and streams. In the video, FFmpeg is used to play the audio response from the chat assistant. It is a crucial tool for converting and streaming audio data in the Node.js environment.

💡API key

An API key is a unique code that is used to authenticate requests to an API. It is a security measure that ensures only authorized users can access the services provided by the API. In the video, the API key is used to authenticate calls to OpenAI's Whisper and TTS APIs.

💡Streaming audio

Streaming audio refers to the process of delivering audio content over the internet in a continuous flow, allowing users to listen to the content as it is being transmitted, rather than waiting for the entire file to download. In the video, streaming audio is used to play the chat assistant's response in real-time, enhancing the user experience by providing immediate feedback.

💡GPT-3.5 Turbo

GPT-3.5 Turbo is a language model developed by OpenAI, which is a part of the GPT (Generative Pre-trained Transformer) series. It is designed to generate human-like text based on the input it receives. In the video, GPT-3.5 Turbo is used as the model for the chat completions endpoint, generating responses to the user's questions in a conversational manner.

💡Contextual awareness

Contextual awareness refers to the ability of a system to understand and remember the context of previous interactions, allowing it to provide more relevant and coherent responses. In the video, the chat assistant maintains context by including previous chat history in subsequent API calls, ensuring that the responses are contextually appropriate.

Highlights

Ralf demonstrates how to build a voice-based chat assistant using Whisper API and OpenAI's TTS.

The process involves transcribing user input and generating a spoken response.

Node.js is used for the setup, which takes about five minutes.

The end product allows for a fully speech-based interaction with the chat assistant.

The tutorial includes a demonstration of the chat assistant in action.

The chat assistant maintains context of previous messages.

All code is available in a link in the video description.

The setup involves importing required modules and initializing an OpenAI API client.

The OpenAI API key is stored in an environment variable for security.

The main function sets up a readline interface for user input.

Recording starts with a press of the enter key and stops with another press.

The recorded audio is written to an output.wav file.

The transcribe and chat function handles the transcription and chat completion.

The Whisper API is called with the recorded audio file.

The chat completion endpoint is used to generate a response to the user's input.

The response text is then sent to OpenAI's TTS API for audio generation.

The streamed audio function plays the generated response to the user.

The application continues running in the background, allowing for further interaction.

Ralf invites viewers to like, subscribe, and comment with their questions or projects.

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