Revolutionary Chat with AI: Langchain, Chroma DB, OpenAI, and Assembly AI

Table of Contents

1. Introduction
2. Project Overview
3. Working with Assembly AI
4. Overview of Speech-to-Text Techniques
5. Utilizing Language Models for Chat with Audio
6. Integrating Streamlit for Application Development
7. Getting the Video File and Extracting Audio
8. Sending a POST Request to Assembly AI
9. Embeddings with LangChain and Vector Storage
10. Utilizing LangChain for Question Answering
11. Summary and Next Steps

Chat with Audio: Building an Application for Speech-to-Text Transcription

In this tutorial, we will walk through the process of building an application called "Chat with Audio" using Assembly AI's speech-to-text transcription service and large language models (LLMs). The application will allow users to upload a video file, extract the audio, and obtain a transcription using Assembly AI's API. We will then use embedded documents and a vector store to perform question answering on the transcription. The application will be developed using Streamlit, a Python library for building interactive web applications.

1. Introduction

Speech-to-text transcription has become increasingly popular, with various tools and services available that utilize large language models (LLMs) to convert audio into text. In this tutorial, we will explore the process of building an application that allows users to upload a video file, extract the audio, and obtain a transcription using Assembly AI's speech-to-text transcription service. We will then leverage Lang Chain and a vector store to perform question answering on the transcription, providing answers to user queries.

2. Project Overview

The "Chat with Audio" application is designed to provide users with the ability to Interact with audio files by extracting transcriptions and performing question answering on the content. The application will be built using Streamlit, a Python library that simplifies the process of developing web applications.

The project will involve the following steps:

1. Getting the video file and extracting the audio using PiTube.
2. Sending a POST request to Assembly AI to obtain the transcription.
3. Generating embeddings using Lang Chain and storing them in a vector store.
4. Utilizing Lang Chain and the vector store for question answering.

3. Working with Assembly AI

Assembly AI is a company that provides various AI and machine learning services, including speech-to-text transcription. In this project, we will utilize Assembly AI's speech-to-text transcription service to obtain the transcription of the audio extracted from the video file. We will make a POST request to the Assembly AI API to obtain the transcription file, which will be saved as a .txt file.

Pros:

• Assembly AI provides speech-to-text transcription with high accuracy.
• API integration simplifies the process of obtaining transcriptions.

Cons:

• Assembly AI's transcription service may have limitations depending on the API plan selected.
• Dependency on an external service that may be subject to changes or disruptions.

4. Overview of Speech-to-Text Techniques

Speech-to-text techniques involve converting spoken language into written text. In this project, we will utilize Assembly AI's speech-to-text transcription service to perform this conversion. Assembly AI uses machine learning models to accurately transcribe the audio from the video file.

Pros:

• Speech-to-text techniques provide a convenient way to convert audio into text.
• Assembly AI's transcription service provides accurate and reliable results.

Cons:

• Speech-to-text transcription may have limitations, such as difficulty understanding accents or background noise.
• Output accuracy may vary depending on the quality of the audio and the complexity of the language.

5. Utilizing Language Models for Chat with Audio

In this project, we will utilize large language models (LLMs) to perform question answering on the transcriptions obtained using Assembly AI's speech-to-text service. LLMs, such as OpenAI's GPT-3, have the ability to understand and generate human-like text, making them suitable for generating responses to user queries.

Pros:

• Large language models provide powerful natural language processing capabilities.
• They can understand and generate human-like text, allowing for sophisticated question answering.

Cons:

• Utilizing large language models may require additional computational resources.
• Generating responses using LLMs may result in lengthy or verbose answers.

6. Integrating Streamlit for Application Development

Streamlit is a Python library that simplifies the development of interactive web applications. In this project, we will utilize Streamlit to build the "Chat with Audio" application, providing users with a user-friendly interface for uploading video files, obtaining transcriptions, and performing question answering.

Pros:

• Streamlit simplifies the development of web applications with its intuitive API.
• It provides built-in components for handling user inputs and displaying data.

Cons:

• Streamlit's capabilities may be limited compared to more complex web frameworks.
• Advanced customization may require additional web development knowledge.

7. Getting the Video File and Extracting Audio

To begin the "Chat with Audio" application, we need to obtain the video file from the user. We will use the PiTube library to extract the audio from the video file, as a video file contains both visual and audio components. The extracted audio will be saved as an MP3 file, which will then be used for transcription.

Pros:

• PiTube simplifies the process of extracting audio from video files.
• Extracting audio allows for speech-to-text processing and question answering.

Cons:

• Dependency on PiTube may require additional management and potential compatibility issues.
• Audio extraction may affect audio fidelity or quality.

8. Sending a POST Request to Assembly AI

Once we have the audio file extracted from the video, we will send a POST request to the Assembly AI transcription model API. This API call will transcribe the audio file, providing us with the text transcription. The transcription will be saved as a .txt file, which will be used for further processing.

Pros:

• Sending a POST request to the Assembly AI API simplifies the transcription process.
• Assembly AI provides accurate speech-to-text transcriptions.

Cons:

• Dependency on an external API may incur additional costs or limitations.
• Proper management of API keys and authentication is necessary.

9. Embeddings with LangChain and Vector Storage

Next, we will utilize the Lang Chain library to Create embeddings from the transcription text. Embeddings are numerical representations of text that capture its meaning and Context. The embeddings will be stored in a vector store using Chroma DB, a vector database.

Pros:

• Lang Chain simplifies the process of creating text embeddings.
• Using embeddings allows for efficient similarity matching and question answering.

Cons:

• Setting up Chroma DB and managing the vector store may require additional configuration.
• Embeddings may have limitations when capturing nuanced or context-specific information.

10. Utilizing LangChain for Question Answering

With the embeddings stored in the vector store, we can utilize Lang Chain's question answering capabilities to generate responses to user queries. Lang Chain leverages the embeddings and the index created in the previous step to perform question answering on the transcription text.

Pros:

• Lang Chain provides question answering capabilities, making it suitable for interactive applications.
• Question answering can provide valuable insights and information Based on user queries.

Cons:

• Question answering accuracy may be affected by the quality and complexity of the transcription text.
• Handling complex queries or questions requiring contextual understanding may be challenging.

11. Summary and Next Steps

In this project, we have built the "Chat with Audio" application, allowing users to upload video files, extract audio, obtain transcriptions, and perform question answering on the transcriptions. We have utilized Assembly AI's speech-to-text service, Lang Chain for embedding creation, and Streamlit for web application development.

Next, we will explore the integration of OpenAI's Whisper model and Sentence Transformers to replace Assembly AI and Lang Chain, respectively. By incorporating these open source alternatives, we can create a complete open source-backed chat with audio application. Stay tuned for the next part of this series!

Highlights

• We built the "Chat with Audio" application using Assembly AI's speech-to-text service and large language models.
• The application allows users to upload video files, extract audio, obtain transcriptions, and perform question answering.
• We utilized Streamlit for web application development, simplifying the development process.
• Assembly AI's speech-to-text service provided accurate transcriptions.
• Lang Chain and Chroma DB were used for creating embeddings and storing them in a vector store.
• Streamlit's interactive components provided a user-friendly interface for the application.
• Next, we will explore the integration of OpenAI's Whisper model and Sentence Transformers to create a complete open source-backed chat with audio application.

FAQs

Q: Can I use the "Chat with Audio" application for any video file?

A: The application is designed to work with audio from video files, but certain limitations may exist depending on the quality and format of the input video.

Q: Is the Assembly AI transcription service free?

A: Assembly AI provides free trials and limited free usage, but certain usage restrictions may Apply. It is recommended to review Assembly AI's pricing and licensing policies for more information.

Q: Can the application handle different languages?

A: Yes, the application can handle different languages based on the capabilities of the underlying transcription service and language models. However, it may perform better with common languages.

Q: How accurate are the transcriptions obtained using Assembly AI?

A: Assembly AI's speech-to-text service provides accurate transcriptions, but the accuracy may depend on factors such as audio quality, speaker Clarity, and language complexity.

Q: Can I replace Assembly AI with an alternative transcription service?

A: Yes, the application can be modified to use alternative speech-to-text transcription services. However, the integration process may vary depending on the chosen service.

Q: Can I perform question answering on transcriptions in real-time?

A: The application can perform question answering on transcriptions in near real-time, but the response time may depend on the complexity of the question and the processing capabilities of the underlying models.

Q: Can I extend the functionality of the application to handle more complex tasks?

A: Yes, the application can be extended to include additional functionality based on individual requirements, such as sentiment analysis, summarization, or entity recognition.

Q: Is the source code for the application available?

A: Yes, the source code for the "Chat with Audio" application is available on the GitHub repository Mentioned in the tutorial. Feel free to explore and modify it according to your needs.