Artificial Intelligence is revolutionizing how we process and analyze audio data. Imagine being able to convert spoken words into text and extract meaningful insights automatically. In this guide, we’ll walk through how to build an Audio Analyst AI Agent using Serena Reasoning Builder, a virtual AI development environment with built-in Retrieval-Augmented Generation (RAG) and audio-to-text capabilities.
This project will allow us to:
✅ Transcribe audio into text
✅ Analyze the transcribed text for key insights
✅ Determine the sentiment of the speaker
✅ Summarize the text into concise information
you can also enjoy learning by watching video
https://www.youtube.com/watch?v=R9p_cRXH72Y
Let’s get started! 🚀
Step 1: Setting Up Serena Reasoning Builder
Before we begin, we need to ensure access to Serena’s built-in AI features by generating an API token:
1️⃣ Sign in to NeuralLead Cloud.
2️⃣ Go to ‘Manage API’ and generate your API token.
3️⃣ Use API.SetToken in your project to authenticate and enable Serena’s features.
NeuralLead Cloud : Serena API Manager
This step is crucial as it allows you to use audio transcription, text analysis, and AI-driven queries without additional installations.
Step 2: Converting Audio to Text
Once the token is set, we need to transcribe an audio file into text. For this, we use the API.ListenFromAudio component.
How to Implement It:
✅ Right-click in Serena Reasoning Builder, search for “Listen,” and select “Listen from file path.”
✅ Provide the file path of the audio you want to analyze.
✅ Use Serena’s built-in transcription to convert the speech into text.
✅ Extract the transcribed text as a string for further processing.
With this, we now have the spoken words in text format! 🎤➡️📝
Step 3: Text Analysis with API.Ask
Now that we have the transcribed text, the next step is analyzing it. We will use API.Ask components to extract useful information.
Three Key Analysis Steps:
🔹 Key Information Extraction — Identifies important points from the text.
🔹 Sentiment Analysis — Determines whether the speaker’s tone is positive, negative, or neutral.
🔹 Text Summarization — Condenses the text into a short summary.
How to Implement API.Ask:
Each API.Ask component takes two prompts:
1️⃣ User Prompt — The transcribed text.
2️⃣ System Prompt — Instructions defining the type of analysis.
For example:
Key Information Extraction Prompt: “From the given meeting transcript, extract key discussion points, action items, and decisions made. Provide concise but informative summaries.”
Sentiment Analysis Prompt: “Analyze the sentiment of each speaker’s statements in the transcript and categorize them as Positive, Neutral, or Negative. Provide an overall sentiment score for the meeting.”
Text Summarization Prompt: “Generate a structured summary of the meeting, including a brief introduction, key points discussed, and assigned action items. Ensure clarity and conciseness”
By running these three API.Ask components, we can automatically extract insights from the audio!
Step 4: Formatting and Displaying the Output
Now that we have extracted key information, sentiment, and a summary, we need to format and display the results.
Steps to Display Results:
✅ Use string concatenation to structure the extracted insights under appropriate headings.
✅ Combine all outputs into one formatted text string.
✅ Use Console.WriteLine to display the final results in an easy-to-read format.
Final Output :
Click on Run Button to Run the Program
You May see similar output by Running the program:
Key Point Extraction :
Here are the key discussion points, action items, and decisions made from the transcript:
**Key Discussion Points:**
* The speaker developed a methodology to help Stanford MBA students feel more comfortable and confident in responding to questions and speaking up in class.
* The methodology was created in response to a problem identified by the deans, where students were struggling to answer cold call questions from professors.
* The methodology draws on research from psychology, anthropology, sociology, improvisation, and neuroscience.
**Action Items:**
* The methodology is now being offered to all Stanford MBA students within the first three weeks of their time at the university.
**Decisions Made:**
* The decision to implement the methodology as a required or optional component of the MBA program is not explicitly stated, but it appears that the methodology is now a standard offering for new students.
* The decision to share the methodology with others is implied, as the speaker is presenting it to an unspecified audience.
_________________________________________________________
Sentiment Analysis :
Here's the sentiment analysis of the speaker's statement:
* The speaker starts by expressing a desire to share a methodology they developed, which implies a sense of pride and enthusiasm (Positive).
* They mention a problem that the deans identified, which is a neutral statement.
* The speaker then describes the methodology they developed, which is a positive statement, as they are sharing their expertise and accomplishments.
* They mention the benefits of the methodology, such as helping students feel more comfortable and confident, which is a positive statement.
* The speaker also mentions specific situations where students can apply the skills they learn, such as interviewing for jobs and giving feedback to employees, which implies a sense of practicality and usefulness (Positive).
Overall sentiment score: Positive (with a tone of enthusiasm and pride)
Categorization: Positive
Note: The speaker's tone is professional and confident, which reinforces the positive sentiment.
_________________________________________________________
Contextual Summarization :
Here is a structured summary of the meeting:
**Introduction**
* The speaker presented a methodology developed to address a specific challenge faced by Stanford MBA students.
* The challenge was that students were struggling to respond to cold call questions from professors.
**Key Points Discussed**
* The speaker conducted research in multiple fields, including psychology, anthropology, sociology, improvisation, and neuroscience to develop the methodology.
* The methodology is designed to help students feel more comfortable and confident in various situations, including:
+ Answering questions in class
+ Standing up in class and giving presentations
+ Interviewing for jobs
+ Giving feedback to employees
* The methodology is offered to all Stanford MBA students within the first three weeks of their program.
**Action Items**
* None explicitly mentioned in the summary, but potential action items could include:
+ Implementing the methodology as a required program for all Stanford MBA students
+ Providing training or support for professors to effectively use the methodology in their teaching
+ Evaluating the effectiveness of the methodology in improving student confidence and performance.
This structured approach makes it easy to interpret insights from any audio file. 🎯
Final Thoughts
Congratulations! 🎉 You’ve successfully built an Audio Analyst AI Agent using Serena Reasoning Builder. This project demonstrates how AI can automate audio analysis by:
✔️ Transcribing speech into text
✔️ Extracting key insights
✔️ Analyzing sentiment
✔️ Summarizing the information
Want to explore more? Check out our GitHub repository for the complete project and join our Discord community to discuss AI innovations!
🔗 GitHub: https://github.com/simonjriddix/SerenaReasoningSamples
💬 Join Discord: https://discord.com/invite/DB9UxSmC8h
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