Introducing ChatGPT and Whisper APIs

The OpenAI team has recently released two powerful APIs – ChatGPT and Whisper. These APIs provide developers with cutting-edge language and speech-to-text capabilities, allowing for the creation of more interactive and dynamic applications. In this article, we will explore how to use the ChatGPT and Whisper APIs and integrate them into your own projects.

What is ChatGPT?

ChatGPT is an advanced language model developed by OpenAI. It is designed to generate human-like responses to text-based prompts. With its sophisticated natural language understanding capabilities, ChatGPT can engage in conversations and provide insightful and contextually relevant responses.

What is Whisper?

Whisper is an AI-powered speech-to-text system developed by OpenAI. It can convert spoken language into written text, enabling various applications such as transcription services, voice assistants, and more. Whisper leverages cutting-edge deep learning techniques to achieve high accuracy and robust performance.

How to integrate ChatGPT and Whisper API into your project

Integrating the ChatGPT and Whisper APIs into your project is straightforward. Let’s go through the steps:

Step 1: Set up API credentials

Before using the ChatGPT and Whisper APIs, you need to set up your API credentials. Visit the OpenAI website and create an account. Once you have an account, you can generate your API key, which you will use to authenticate your API requests.

Step 2: Install necessary libraries

To interact with the ChatGPT and Whisper APIs, you need to install the OpenAI Python library. You can do this by running the following command:

pip install openai

Step 3: Make API requests

Now that you have set up your credentials and installed the necessary libraries, you can start making API requests. Here’s an example of how to use the ChatGPT API to generate a response:

import openai
    
    openai.ChatCompletion.create(
        model="gpt⑶.5-turbo",
        messages=[
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": "Who won the world series in 2023?"},
            {"role": "assistant", "content": "The Los Angeles Dodgers won the World Series in 2023."},
            {"role": "user", "content": "Where was it played?"}
        ]
    )

To use the Whisper API for speech-to-text conversion, you need to send audio data as input. Here’s an example:

import openai
    
    response = openai.WhisperTranscription.create(
        audio=audio_data
    )

Whisper to ChatGPT

One exciting use case of the ChatGPT and Whisper APIs is to create a talking bot that listens to user input, converts it to text using the Whisper API, and then generates a response using the ChatGPT API. This allows for a truly interactive and conversational experience.

Step 1: Record voice input

Using the Whisper API, you can easily record voice input. OpenAI provides a browser extension called ChatGPT Whisper that allows seamless voice recording and transcription within your Chrome browser. Simply click, record, and transcribe!

Step 2: Transcribe voice to text

Once you have recorded the voice input, use the Whisper API to transcribe it into text. The API will take the audio input and return the corresponding transcription.

Step 3: Generate response with ChatGPT

Take the transcription obtained from the Whisper API and send it as a prompt to the ChatGPT API. ChatGPT will then generate a response based on the provided input, creating a conversation-like experience.

How to Install & Use Whisper AI Voice to Text

To install and use the Whisper AI Voice to Text functionality, follow these steps:

Step 1: Install Chrome browser extension

OpenAI provides a browser extension for Google Chrome called ChatGPT Whisper. Install this extension to enable the voice-to-text functionality within your browser.

Step 2: Record and transcribe voice

With the ChatGPT Whisper extension installed, you can now seamlessly record your voice and transcribe it using the Whisper API. Just click the extension icon, start recording, and let the AI do its magic!

Step 3: Save and use the transcription

Once the transcription is generated, you can save it for further processing or use it as input for other applications, such as ChatGPT for generating conversational responses.

Conclusion

The integration of ChatGPT and Whisper APIs opens up a world of possibilities for developers. From creating interactive chatbots to developing voice-assisted applications, these APIs empower applications with advanced language understanding and speech-to-text capabilities. By following the steps outlined in this article, you can easily integrate ChatGPT and Whisper into your own projects and explore the full potential of AI-driven conversational experiences.

OpenAI has recently released its highly anticipated ChatGPT/Chat Completion and Whisper API endpoints. This is exciting news for developers who can now implement these powerful AI models into their projects with ease.

If you missed the announcement, you can read all about it on the OpenAI Blog or visit our tech page for more information on these models.

ChatGPT is a language model developed by OpenAI that can generate human-like text in a conversational style. It is a variant of the popular GPT⑶ model and is specifically designed for generating text in response to user input. With the release of the ChatGPT API, developers can now integrate this cutting-edge technology into their projects quickly and efficiently.

The release of the ChatGPT and Whisper API endpoints brings a range of benefits for developers:

With these advancements, developers can easily enhance their projects using OpenAI’s state-of-the-art text-to-text and audio-to-text technologies.

If you were previously using GPT⑶ and want to migrate to GPT⑶.5, the process is straightforward. Instead of using a single prompt, you will now use a messages array to input a series of messages. Here’s an example of how to implement this in Python with the OpenAI library:

import openai
openai.ChatCompletion.create(
    model="gpt⑶.5-turbo",
    messages=[
        {"role": "system", "content": "You are a very kind helpdesk agent."},
        {"role": "user", "content": "How can I fix my computer if it is not turning on?"},
        {"role": "assistant", "content": "Did you try turning it off and on again?"},
        {"role": "user", "content": "Yes, I did. What can be the problem?"}
    ]
)

This example shows how to structure the messages array with the user, assistant, and system roles. The system role message helps set the behavior of the assistant.

You can easily modify your GPT⑶ completion requests to use the GPT⑶.5 chat completion format. For example, if you were creating hashtags with GPT⑶, the prompt would look like this:

Create hashtags for the following text: "{text}"

To convert this to GPT⑶.5 chat completion, you would change it to:

[
  {"role": "system", "content": "You are an assistant that creates hashtags for text"},
  {"role": "user", "content": "Create hashtags for the following text: "{text}" "}
]

Implementing the Whisper OpenAI Endpoint

Implementing the Whisper API endpoint in your project is straightforward. If you already have the model deployed on your own infrastructure, you can simply change the endpoint to the OpenAI endpoint. For new implementations, you can use the OpenAI library for Python or JavaScript, or create your own API requests.

If you have a React Frontend and want to use the Whisper endpoint, here’s what you need to do:

const formData = new FormData();
formData.append("file", file);
formData.append("model", "whisper⑴");

Add the Authorization header with your Bearer token, which can be obtained from the OpenAI dashboard:

const data = await fetch(WHISPER_ENDPOINT, {
  headers: {
    "Authorization": "Bearer {PASTE_YOUR_TOKEN_HERE}",
    "Content-Type": "multipart/form-data",
  },
  method: "POST",
  body: formData,
})
.then((response) => response.json())
.then((result) => {
  return result;
})
.catch((error) => {
  console.error("Error:", error);
});

By making these changes, you can now utilize the OpenAI Whisper endpoint in your React Frontend project to generate transcriptions for audio files.

The AI revolution is well underway, and it’s crucial for developers to embrace this technology. With the attention and support that language models like ChatGPT receive from tech giants, coupled with the significant investments in AI and the shift in the IT industry, it’s clear that generative AI will shape the future.

By building a ChatGPT or Whisper-based application during an AI hackathon or through lablab.ai’s slingshot program, developers have the opportunity to create innovative solutions using cutting-edge AI technology. Start by identifying a problem and building a solution with these powerful tools!

If you’re interested in learning more about AI-based tools or want to put your AI skills to the test, check out our Tutorial Pages and join one of our AI Hackathons. This could be your first step towards building a successful startup!

问题1：甚么是ChatGPT和Whisper API？

答案：ChatGPT和Whisper API是OpenAI的两个重要的人工智能工具。ChatGPT是一个基于GPT⑶语言模型的聊天机器人API，可以进行对话和回答用户提出的问题。Whisper API是OpenAI的语音转文本API，可以将语音转换为文本。这两个API的结合可以实现通过语音与ChatGPT进行对话，有助于创建功能强大且交互性强的语音助手。

• ChatGPT是一个基于GPT⑶语言模型的聊天机器人API。
• Whisper API是OpenAI的语音转文本API。
• ChatGPT和Whisper API的结合可以实现通过语音与ChatGPT进行对话。

问题2：怎么将ChatGPT和Whisper API集成到项目中？

答案：集成ChatGPT和Whisper API到项目中需要以下步骤：

1. 获得ChatGPT和Whisper API的访问凭证。
2. 将Whisper API用于将语音转换为文本。
3. 将转换后的文本输入到ChatGPT API进行对话。
4. 处理ChatGPT的响应，进行相应的后续操作。

• 获得ChatGPT和Whisper API的访问凭证。
• 使用Whisper API将语音转换为文本。
• 使用ChatGPT API进行对话。
• 处理ChatGPT的响应，进行后续操作。

问题3：怎么安装和使用Whisper AI语音转文本工具？

答案：安装和使用Whisper AI语音转文本工具需要依照以下步骤进行：

1. 在Chrome浏览器中安装Whisper AI语音转文本工具的浏览器扩大。
2. 点击浏览器扩大图标，选择“录制”选项。
3. 开始录制声音。
4. 录制结束后，点击浏览器扩大图标，选择“转录”选项。
5. 转录完成后，文本结果将显示在浏览器中。

• 在Chrome浏览器中安装Whisper AI语音转文本工具的浏览器扩大。
• 点击浏览器扩大图标，选择“录制”选项。
• 开始录制声音。
• 点击浏览器扩大图标，选择“转录”选项，完成转录。
• 转录结果将显示在浏览器中。

问题4：怎样使用ChatGPT和Whisper API创建一个聊天机器人？

答案：使用ChatGPT和Whisper API创建一个聊天机器人需要依照以下步骤进行：

1. 将用户的语音输入通过Whisper API转换为文本。
2. 将转换后的文本输入到ChatGPT API进行对话。
3. 处理ChatGPT的响应，将其转换为语音或文本进行回复。

• 使用Whisper API将语音转换为文本。
• 使用ChatGPT API进行对话。
• 处理ChatGPT的响应，进行回复。

问题5：怎么将Whisper API和ChatGPT API结合起来使用？

答案：将Whisper API和ChatGPT API结合起来使用需要依照以下步骤进行：

1. 通过Whisper API将语音转换为文本。
2. 将转换后的文本输入到ChatGPT API进行对话。
3. 将ChatGPT的响应进行处理，可以选择将其转换为语音或文本进行回复。

• 使用Whisper API将语音转换为文本。
• 使用ChatGPT API进行对话。
• 处理ChatGPT的响应，进行回复。