What are the main steps to build this application?

The project involves three main steps: 1) Real-time speech-to-text transcription with AssemblyAI, 2) Passing the transcript to a large language model for analysis using Lemur, and 3) Writing the LLM output to a Google Document via its API.

How is the real-time transcription handled?

The application uses the AssemblyAI API and its real-time transcriber. It configures event handlers and connects to the microphone stream to capture audio and transcribe it into text as you speak.

How does the application use a large language model?

It utilizes AssemblyAI's Lemur framework, passing the real-time transcript and a custom prompt to the LLM. Lemur then analyzes the text based on the prompt's instructions, such as creating bullet points.

How is the transcript buffered and sent to the LLM?

A 'Transcript Accumulator' class collects transcript segments and sends them to the Lemur function for processing every 15 seconds (or a customizable interval), preventing constant API calls.

How is the output written to Google Docs?

The application integrates with the Google Docs API. After obtaining credentials from Google Cloud Console, it uses the API to append the analyzed text from the LLM to a specified Google Document.

What are the advantages of using Lemur with a prompt?

Using Lemur with a prompt allows you to precisely control the type of analysis and output format. You can instruct it to take notes, summarize, extract specific information, and avoid hallucinations or unnecessary preamble.

Key Moments

Live Speech-to-Text With Google Docs Using LLMs (Python Tutorial)

AssemblyAI

Science & Technology3 min read36 min video

Jan 24, 2024|12,318 views|270|11

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Key Moments

TL;DR

Real-time speech-to-text transcriptions are sent to an LLM for analysis and then written to Google Docs.

Key Insights

The project combines real-time speech-to-text transcription (AssemblyAI API) with Large Language Model (LLM) analysis (AssemblyAI's Lemur framework).

The output from the LLM is automatically written to a Google Document using the Google Docs API.

Key steps include setting up AssemblyAI API credentials, configuring real-time transcription with microphone input, and processing transcript segments.

The LLM analysis is controlled via a detailed prompt specifying the desired output format (e.g., bullet points) and constraints (e.g., avoiding preamble).

A transcript accumulator class manages the buffering of speech segments and triggers LLM calls at specified intervals (e.g., every 15 seconds).

Google Cloud Console is used to create credentials and enable the Google Docs API, requiring a downloaded JSON key file for authentication.

PROJECT OVERVIEW AND USE CASES

This project demonstrates how to build a Python application that performs real-time speech-to-text transcription and integrates large language model (LLM) analysis. As users speak, their words are transcribed in real-time and then fed into an LLM for analysis. The LLM's output is subsequently written directly into a Google Document. This real-time, automated process has numerous applications, such as generating meeting or interview notes, filling forms based on customer calls, and many other possibilities enabled by LLM capabilities.

REAL-TIME TRANSCRIPTION SETUP WITH ASSEMBLYAI

The project begins with setting up real-time speech-to-text transcription using the AssemblyAI API. This involves installing necessary dependencies like 'portaudio' and the 'assemblyai' Python package. Users need to obtain a free API key from AssemblyAI's website. The Python script is configured to connect to the AssemblyAI API using this key. Event handlers are defined for 'on_open', 'on_error', and 'on_close' to manage the transcription session. A crucial 'on_data' handler processes incoming transcriptions, distinguishing between final and partial transcripts, and is modified to only capture complete sentences.

INTEGRATING ASSEMBLYAI'S LEMUR FRAMEWORK FOR LLM ANALYSIS

The second major step involves passing the transcribed text to AssemblyAI's Lemur framework, an LLM for analysis. A dedicated 'lemur_call' function is created, which takes the transcript and previous responses as input. This function initializes a Lemur object and defines an input text for the LLM. A detailed prompt is crafted to guide the LLM, instructing it to act as a note-taking assistant, create bullet points from the live transcript (updated every 15 seconds), avoid preambles, and refrain from generating information not present in the transcript. The LLM's response is then captured.

ACCUMULATING TRANSCRIPTS AND TRIGGERING LLM ANALYSIS

To manage the flow of data to the LLM, a 'TranscriptAccumulator' class is implemented. This class stores transcript segments and tracks the time since the last LLM interaction. The 'add_transcript' method appends incoming transcriptions to an internal buffer. If the accumulated transcript exceeds a predefined time interval (e.g., 15 seconds), it triggers the 'lemur_call' function, sending the accumulated text and previous LLM outputs for analysis. The class then clears the transcript buffer, updates the list of previous responses, and resets the last update timestamp, ensuring continuous processing.

CONFIGURING GOOGLE CLOUD AND THE GOOGLE DOCS API

The final stage involves integrating with Google Docs to write the LLM's output. This requires setting up credentials on the Google Cloud Platform. A new project is created, and an OAuth consent screen is configured. An OAuth 2.0 Client ID is generated for a desktop application, and the resulting JSON credentials file is downloaded and saved in the project directory. The Google Docs API must then be enabled for the project. Additionally, specific Google API client libraries for Python are installed using pip.

WRITING LLM OUTPUT TO GOOGLE DOCUMENTS

A Python function, 'update_google_docs', is developed to handle writing content to a Google Document. This function uses the downloaded credentials and the defined API scope to authenticate with Google Docs. It constructs requests to the Google Docs API, specifically using a 'batchUpdate' request with an 'insertText' action to append the LLM-generated content at the end of the document. The 'lemur_call' function is modified to invoke 'update_google_docs' with the LLM's response before the application exits, ensuring that the analyzed text is systematically saved to the designated Google Document.

Mentioned in This Episode

●Products

●Software & Apps

●Companies

Real-Time Speech-to-Text to Google Docs Workflow

Practical takeaways from this episode

Do This

Install necessary dependencies like port audio and assemblyai.

Configure your AssemblyAI API key.

Implement event handlers for real-time transcription (on_open, on_error, on_close, on_data).

Use the microphone stream to feed audio to the transcriber.

Set up a Lemur prompt to define LLM analysis tasks.

Create a Transcript Accumulator class to manage transcript segments and trigger LLM calls.

Generate Google Cloud credentials and enable the Google Docs API.

Download the client JSON file and store it in your project.

Install Google API client libraries.

Implement the update_google_docs function to write content.

Call the update_google_docs function within the Lemur call logic.

Run the Python script to start the end-to-end process.

Avoid This

Do not print partial transcripts to avoid messy output.

Avoid making up information not present in the transcript (LLM hallucinations).

Remove preamble text formatting from LLM responses for cleaner Google Docs output.

Ensure correct spelling for 'insert_text' and capitalization for 'document ID'.

Do not forget to enable the Google Docs API for your project.

Common Questions

This application transcribes speech in real-time using AssemblyAI's API, analyzes the text with a large language model (Lemur), and writes the analyzed output to a Google Document. It's an end-to-end solution for automated note-taking and analysis.