Construye una IA parlante en tiempo real - SIN CÓDIGO - OpenAI GPT-4 Turbo & Whisper

Table of Contents:

1. Introduction
2. Understanding the Whisper API
3. Integration of Transcriptions API in Bubble
4. Setting Up the Authorization Headers
5. Making a POST Request to the Whisper API
6. Configuring Parameters and Sending Files
7. Real-Time Speech-to-Text Transcription
8. Implementing a Real-Time Speech-to-Text Plugin
9. Processing and Generating AI Response
10. Playback of the AI Voice
11. Adding Loading Indications
12. Conclusion

Introduction

In this article, we will explore the capabilities of OpenAI's Whisper model and the Transcriptions API. We will learn how to integrate the API within the Bubble platform and build a real-time speech-to-text transcription application. We will also dive into the process of generating responses using AI models and implementing a playback system for the AI voice. So, let's get started with understanding the Whisper API and its features.

1. Understanding the Whisper API

The Whisper API, powered by OpenAI, enables developers to convert speech into accurate text transcriptions. It utilizes the Whisper model, which has been trained on a vast amount of multilingual and multitask Supervised data. With its advanced language processing capabilities, the Whisper API can transcribe audio files and detect languages in more than 100 languages. However, it does not offer speaker separation or identification features out of the box.

2. Integration of Transcriptions API in Bubble

To start using the Whisper API in Bubble, we need to integrate the Transcriptions API using the Bubble platform's API connector. By adding the API connector plugin and configuring the necessary headers, we can make valid requests to the Whisper API for speech-to-text transcriptions.

3. Setting Up the Authorization Headers

The Whisper API requires two authorization headers: the authorization token and the content Type. By correctly setting these headers in the API connector, we ensure secure and authenticated access to the API.

4. Making a POST Request to the Whisper API

To transcribe audio files using the Whisper API, we need to make a POST request. By configuring the API connector's action and endpoint, we can send files and receive transcription text responses from the API.

5. Configuring Parameters and Sending Files

While making the POST request, we need to include parameters such as the file, model, and response format. These parameters determine the input audio file, the Whisper model to be used, and the desired output format. By correctly setting these parameters and sending the audio file, we can obtain accurate transcriptions from the Whisper API.

6. Real-Time Speech-to-Text Transcription

To achieve real-time speech-to-text transcription, we can utilize a browser-Based speech-to-text plugin available in the Bubble plugin store. This plugin uses the browser's built-in speech recognition functionality to transcribe speech in real-time. By integrating this plugin into our application, we can enable users to speak and see their transcriptions appear in real-time.

7. Implementing a Real-Time Speech-to-Text Plugin

After integrating the speech-to-text plugin, we can set up buttons and actions to start and stop the transcription process. By toggling the listening state of the plugin, we can capture and transcribe the user's speech. Additionally, we can add conditions to reset and initialize the transcription inputs.

8. Processing and Generating AI Response

Once we have the real-time transcription text, we can process it and generate an AI response using OpenAI's gp4 model. By sending the transcription text to the model and obtaining the response, we can enable conversational interactions with the AI. It is important to consider the response temperature and adjust it accordingly to achieve the desired tone and style of the AI's replies.

9. Playback of the AI Voice

To make the AI's responses more interactive, we can implement a playback system to convert the generated text into synthesized speech. By using the OpenAI Speech API, we can send the text and receive an AI-generated voice file in return. This voice file can then be played back to the user, bringing the AI conversations to life.

10. Adding Loading Indications

To enhance the user experience, we can add loading indications during the transcription and response generation processes. These loading indications will provide feedback to the user, indicating that the AI system is processing their input and generating a response. By incorporating loading animations or messages, we can make the user interface more engaging and interactive.

11. Conclusion

In this article, we have explored the capabilities of OpenAI's Whisper model and the Transcriptions API. We have learned how to integrate the API within the Bubble platform, enabling us to build a real-time speech-to-text transcription application. Furthermore, we have implemented AI-generated responses and a playback system to make the AI interactions more conversational and engaging. By adding loading indications, we have enhanced the user experience. With these techniques, developers can leverage OpenAI's powerful AI models to Create interactive applications that allow users to have real-time conversations with AI agents.

Highlights:

• Understand the capabilities of OpenAI's Whisper model and Transcriptions API.
• Integrate the Transcriptions API in Bubble to enable speech-to-text transcription.
• Configure the authorization headers for secure access to the Whisper API.
• Make a POST request and send audio files for transcription.
• Utilize a browser-based speech-to-text plugin for real-time transcription.
• Process the real-time transcription and generate AI responses using the gp4 model.
• Implement a playback system to convert text responses into AI-generated voices.
• Enhance the user experience by adding loading indications during the transcription and response generation processes.

FAQ:

Q: What is the Whisper API? A: The Whisper API is powered by OpenAI and allows developers to convert speech into text transcriptions using the advanced Whisper model.

Q: Can the Whisper API detect languages in real-time? A: Yes, the Whisper API can detect languages in real-time and transcribe audio files in over 100 languages.

Q: Does the Whisper API offer speaker separation or identification features? A: No, the Whisper API does not offer speaker separation or identification features by default. Third-party services like Gladi or Deep cram can be used for such functionalities.

Q: Can real-time speech-to-text transcription be achieved in Bubble? A: Yes, Bubble offers plugins for real-time speech-to-text transcription that utilize the browser's built-in speech recognition capabilities.

Q: How can AI responses be generated using the gp4 model? A: By sending the transcription text to the gp4 model, developers can obtain AI-generated responses to user input.

Q: Can the AI-generated responses be converted into synthesized speech? A: Yes, the OpenAI Speech API can be used to convert AI-generated text into synthesized speech for playback to the user.