Talk locally with your documents | PrivateGPT + Whisper + Coqui TTS

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Talk locally with your documents | PrivateGPT + Whisper + Coqui TTS

Table of Contents

  1. Introduction
  2. Setting up the Environment
  3. Cloning the Private GPT Repository
  4. Installing the Requirements
  5. Adding Source Documents
  6. Adding the Base Model
  7. Running the Private GPT Script
  8. Adding Whisper for Input Speech Recognition
  9. Adding Koki TTS for Spoken Synthesized Output
  10. Running the Enhanced Private GPT Script
  11. Conclusion

Introduction

In this tutorial, we will explore how to use private GPT to query the content of your documents on your local computer without the need for any cloud or internet connection. We will also add two additional features to enhance the experience: spoken output using CoqTTS and open AI's open-source speech recognition technology, Whisper, for input processing. This tutorial aims to provide you with the necessary steps to set up and run private GPT, as well as integrating voice technology capabilities. So let's get started!

Setting up the Environment

Before diving into the implementation, it is essential to set up the environment correctly. We will be using Visual Studio Code on a MacBook Air M1, but You can use any Python environment of your choice. Start by creating a new directory called "private GPT" and Create a Python virtual environment inside it to keep this experiment separated from your global Python configuration. Activate the virtual environment using the command source bin/activate.

Cloning the Private GPT Repository

To begin, we need to clone the private GPT GitHub repository. You can find the link in the description box below. This project is relatively new, with the first commit done about a month ago, providing ample room for improvement. There is another project inspired by private GPT called Local GPT, but for this tutorial, we will focus on the original private GPT implementation.

Installing the Requirements

Once the repository is cloned, navigate to the private GPT subfolder inside the cloned repository. Here, you will find a "requirements.txt" file containing all the dependencies needed for this project. To install the requirements, use the command pip install -r requirements.txt. This will ensure that all the necessary packages are installed in your Python virtual environment.

Adding Source Documents

To enable querying of your local documents, you need to add them to the "Source/documents" folder in the private GPT Directory. Private GPT supports multiple file types, including CSV, Microsoft Office files, emails, Markdowns, and PDFs. Make sure to place all your related documents in this folder for indexing and future querying.

Adding the Base Model

In order to utilize the large language model for document querying, we need to add the base model. The private GPT repository provides a link to one of the supported models. Download this model and create a new folder called "models" in the private GPT directory. Place the downloaded model file inside this folder. If you have downloaded a different model or placed it in another location, make sure to adjust the model path accordingly in the ".env" file.

Running the Private GPT Script

With the environment set up and the necessary files in place, it's time to run the private GPT script. Ensure that you are still in your private GPT Python virtual environment and run the command python privateGPT.py. This will process the documents in the "Source/documents" folder and create an index. Once the indexing is complete, you can enter queries to search for Relevant information in your documents.

Adding Whisper for Input Speech Recognition

To enhance the user experience, we can add Whisper, an open-source speech recognition technology developed by OpenAI, for input speech processing. To install Whisper, use the command pip install --upgrade openai-whisper. This will add the necessary dependencies to your Python virtual environment, allowing you to utilize Whisper for speech recognition.

Adding Koki TTS for Spoken Synthesized Output

In addition to input speech recognition, we can also add Koki TTS, a Package for speech synthesis, to create spoken output for the queried results. To install Koki TTS, use the command pip install -U tts. This will provide you with the capability to generate synthesized audio output Based on the generated response from Private GPT.

Running the Enhanced Private GPT Script

With the dependencies installed, we can now run the enhanced Private GPT script that incorporates Whisper for input speech recognition and Koki TTS for spoken synthesized output. The script takes a WAV file as the spoken request and processes it using Whisper for speech recognition. The generated query is then passed to Private GPT for document querying, and the response is synthesized into audio using Koki TTS. To run the script, use the command python privateGPT_voice.py. This will provide you with a hands-free, voice-enabled document querying experience.

Conclusion

In this tutorial, we have explored the usage of private GPT for local document querying. We have seen how to set up the environment, add source documents, and run the Private GPT script for document querying. To enhance the user experience, we have integrated Whisper for input speech recognition and Koki TTS for spoken synthesized output. This voice-enabled document querying capability opens up a world of potential for voice assistants and interactive applications. With further improvements and enhancements, this project can provide a seamless and hands-free document search experience. Give it a try and let us know your thoughts in the comments below!

Highlights

  • Query your local documents using private GPT on your local computer without the need for a cloud or internet connection.
  • Enhance the user experience with spoken output using CoqTTS and input speech recognition using Whisper.
  • Use Private GPT, a large language model, to process and search through your documents for relevant information.
  • Access the full potential of private GPT by adding additional features such as Whisper and Koki TTS.
  • Create a voice-enabled document querying experience with hands-free input and synthesized audio output.

FAQ

Q: Can Private GPT handle different file types?

Yes, Private GPT supports various file types, including CSV, Microsoft Office files, emails, Markdowns, and PDFs. Simply place the relevant documents in the "Source/documents" folder for indexing and querying.

Q: What models does Private GPT support?

Private GPT supports models based on the llama architecture. You can download the base model and place it in the "models" folder in the Private GPT directory. Make sure to adjust the model path accordingly if using a different model.

Q: Can I customize the speech recognition and synthesis capabilities?

Yes, the tutorial demonstrates the integration of Whisper for input speech recognition and Koki TTS for spoken synthesized output. You can explore further customization options as per your requirements.

Q: What are the potential applications of voice-enabled document querying?

Voice-enabled document querying can be used in various applications, including voice assistants, interactive systems, and accessibility tools. The capability to search through documents using voice commands provides a convenient and hands-free experience.

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