AI: Integration - RyanL2004/teamlyse GitHub Wiki

Steps to Create and Implement AI for Meeting Companion Using DeepThink and OpenAI Whisper

1. Introduction

This document provides a comprehensive step-by-step guide for creating and implementing AI into the Meeting Companion project. It leverages DeepThink for advanced summarization and reasoning, and OpenAI Whisper for transcription. Additionally, the use of TensorFlow or PyTorch is explained for training or fine-tuning any additional models required for enhancing AI functionalities.

2. Prerequisites

Before starting, ensure the following:

  • A Node.js backend to handle real-time communication and integrate APIs.
  • A Python environment for AI-related tasks (e.g., summarization and transcription).
  • Libraries installed: openai, transformers, torch, tensorflow, pandas, numpy, etc.
  • Access to DeepThink APIs and OpenAI Whisper models.

3. Overview of Components

  1. Speech-to-Text with OpenAI Whisper:

    • Transcribe audio input from meetings into text.
    • OpenAI Whisper is open-source and offers high accuracy.

  2. Summarization with DeepThink:

    • Use pre-trained DeepThink summarization models for abstractive summaries.
    • Handles reasoning and contextual understanding to generate actionable insights.

  3. TensorFlow or PyTorch:

    • Fine-tune any additional summarization or NLP models if necessary.
    • Use for custom tasks like sentiment analysis, topic modeling, or enhancing accuracy of summarization.

**4.

🚀 Finalized AI/ML Stack

Feature Technology Used
Speech-to-Text (Transcription) OpenAI Whisper
Summarization (Text Processing) Hugging Face Transformers (T5, Pegasus)
Frameworks for Model Handling TensorFlow, PyTorch
Fine-Tuning & Customization (Future Enhancement) PyTorch or TensorFlow (if training is needed)

We will use Hugging Face Transformers to handle summarization efficiently. DeepThink can still be an option for reasoning-based text processing if needed in the future.

🛠 Tools & Technologies We Will Integrate

Category Tool/Framework Purpose
Backend API Flask (Python) Handles /transcription & /summarization APIs
Speech-to-Text OpenAI Whisper Converts meetings into text
Summarization Model Hugging Face Transformers Uses T5/Pegasus for text summarization
ML Framework PyTorch/TensorFlow Supports model fine-tuning in the future
Frontend Integration React (via API calls) Displays summaries in the UI
Database (If Needed) MongoDB/PostgreSQL Stores transcriptions & summaries
Deployment AWS/GCP/Azure Deploys the AI backend

📌 How We’ll Implement Summarization

  • Integrate Hugging Face Transformers for summarization in Flask.
  • Enhance the /transcription endpoint to include automatic summarization.
  • Expose a standalone /summarization endpoint for external text input.
  • Optimize performance using TensorFlow/PyTorch for potential fine-tuning.
  • Prepare for frontend integration via API calls to return summaries.

5. Step-by-Step Implementation

Step 1: Setup Environment

  1. Install Required Libraries:

    pip install openai torch torchvision transformers tensorflow
pip install pydub whisper

  2. Set Up Python Virtual Environment:

    python -m venv venv
source venv/bin/activate

  3. Integrate Whisper and DeepThink:

    • Clone the Whisper repository for local transcription: 
      git clone https://github.com/openai/whisper.git
cd whisper
pip install -e .
    • Obtain access to DeepThink’s API or download their latest models.

Step 2: Speech-to-Text with OpenAI Whisper

  1. Load the Whisper Model:

    import whisper

# Load pre-trained Whisper model
model = whisper.load_model("base")

# Transcribe audio input
result = model.transcribe("path_to_audio_file.mp3")
print("Transcription:", result['text'])

  2. Real-Time Audio Input (Optional):

    • Use libraries like pydub or speech_recognition for real-time audio input.

Step 3: Summarization with DeepThink

  1. Integrate DeepThink’s API:

    • Example API Request: 
      import requests

url = "https://api.deepthink.ai/summarize"
headers = {"Authorization": "Bearer YOUR_API_KEY"}
payload = {"text": result['text'], "max_length": 100}

response = requests.post(url, headers=headers, json=payload)
summary = response.json().get("summary")
print("Summary:", summary)

  2. Batch Processing for Long Meetings:

    • Split long transcriptions into smaller chunks: 
      def chunk_text(text, max_chars=500):
    return [text[i:i+max_chars] for i in range(0, len(text), max_chars)]

chunks = chunk_text(result['text'])
summaries = [requests.post(url, headers=headers, json={"text": chunk}).json().get("summary") for chunk in chunks]
print("Combined Summary:", " ".join(summaries))

Step 4: Use TensorFlow or PyTorch for Model Customization

  1. Fine-Tune Models (Optional):

    • If DeepThink or Whisper’s outputs require customization, fine-tune using TensorFlow or PyTorch.

  2. Example with Hugging Face and PyTorch:

    from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("t5-small")
model = AutoModelForSeq2SeqLM.from_pretrained("t5-small")

text = "Meeting transcription input text here."
inputs = tokenizer.encode("summarize: " + text, return_tensors="pt", max_length=512, truncation=True)
summary_ids = model.generate(inputs, max_length=150, min_length=40, length_penalty=2.0, num_beams=4, early_stopping=True)

print("Custom Summary:", tokenizer.decode(summary_ids[0], skip_special_tokens=True))

  3. TensorFlow Example:

    import tensorflow as tf
from transformers import TFAutoModelForSeq2SeqLM, AutoTokenizer

model = TFAutoModelForSeq2SeqLM.from_pretrained("t5-small")
tokenizer = AutoTokenizer.from_pretrained("t5-small")

inputs = tokenizer("summarize: Meeting transcription input", return_tensors="tf", max_length=512, truncation=True)
outputs = model.generate(inputs.input_ids, max_length=150, min_length=40, length_penalty=2.0, num_beams=4, early_stopping=True)

print("Custom Summary:", tokenizer.decode(outputs[0], skip_special_tokens=True))

Step 5: Integration with Node.js Backend

  1. Expose Python Scripts as APIs:

    • Use Flask or FastAPI to expose Whisper and DeepThink functionalities. 
      from flask import Flask, request, jsonify
app = Flask(__name__)

@app.route('/transcribe', methods=['POST'])
def transcribe():
    audio_file = request.files['audio']
    result = model.transcribe(audio_file)
    return jsonify(result['text'])

@app.route('/summarize', methods=['POST'])
def summarize():
    text = request.json.get("text")
    summary = requests.post(url, headers=headers, json={"text": text}).json().get("summary")
    return jsonify(summary)

if __name__ == '__main__':
    app.run(debug=True)

  2. Call Python APIs from Node.js:

    • Use axios in Node.js to send requests to the Flask/FastAPI endpoints.

6. Deployment

  1. Host Backend APIs:

    • Deploy Flask/FastAPI app on AWS, Google Cloud, or Heroku.

  2. Optimize Model Performance:

    • Use TensorFlow Lite or ONNX Runtime for faster inference if required.

  3. Test the Full Workflow:

    • Run end-to-end tests to validate transcription, summarization, and integration with meeting platforms.

7. Conclusion

By leveraging OpenAI Whisper for transcription and DeepThink for summarization, along with TensorFlow or PyTorch for customization, you can create a robust and cost-effective AI Meeting Companion. This setup ensures scalability, ease of integration, and high performance while keeping costs minimal. Let me know if you'd like further guidance or specific troubleshooting tips!

📌 Next Steps: What’s Next for the AI Meeting Companion?

Now that transcription & summarization work flawlessly, we can enhance the AI Meeting Companion with new features. Here are some exciting next steps:

1️⃣ Optimize Summarization for Key Action Points

Right now, the summary provides a general overview.
➡️ We can extract key insights from meetings using NLP, such as:

  • Decisions made
  • Actionable tasks
  • Deadlines and follow-ups

🔹 Solution: Use Hugging Face’s text2text-generation models for action item extraction.

2️⃣ Live Transcription (Real-Time Processing)

Instead of processing recorded files only, we can:

  • Integrate with live meeting platforms (Zoom, Teams)
  • Transcribe in real-time while the meeting is happening

🔹 Solution: Implement WebSockets to handle live streams.

3️⃣ Multilingual Support

Some users might have multilingual meetings.
➡️ Next step: Support real-time translation alongside transcription.

🔹 Solution: Use Hugging Face mT5 or OpenAI models for multi-language summarization.

4️⃣ Frontend & User Interface

Right now, everything runs via APIs.
The next step is to integrate with the React frontend to display:

✅ Live transcriptions
✅ Summaries in a structured format
✅ User customization (e.g., adjust summarization length, languages, etc.)

🔧 Step 1: Setup & Dependencies

Before we fine-tune the model, we need to install the necessary libraries. Ensure your virtual environment (venv) is activated and install the required dependencies:



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pip install torch transformers datasets accelerate evaluate rouge_score
  • torch → Deep learning framework for training
  • transformers → Pre-trained NLP models (Hugging Face)
  • datasets → Managing large datasets for NLP
  • accelerate → Optimizes training for fast performance
  • evaluate → Metrics for evaluating model performance
  • rouge_score → Measures summarization accuracy

📚 Step 2: Choosing a Training Dataset

To fine-tune our summarization model, we need a high-quality dataset. Hugging Face provides several options:

Dataset | Description -- | -- CNN/DailyMail | News summarization dataset (Long-form summarization) XSum | Single-sentence extreme summarization SAMSum | Summarization of dialogue (best for conversational summaries)

Since your AI Meeting Companion focuses on summarizing long-form transcriptions, we will start with CNN/DailyMail.

🏗 Step 3: Load the Dataset

We'll use the Hugging Face Datasets library to load cnn_dailymail:



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from datasets import load_dataset

# Load the CNN/DailyMail dataset for training
dataset = load_dataset("cnn_dailymail", "3.0.0")


# Split the dataset into train and validation sets
train_data = dataset["train"]
val_data = dataset["validation"]


# Sample
print(train_data[0])

📝 Step 4: Preprocessing the Data

Since our dataset contains long-form text, we need to preprocess it by tokenizing it into input/output sequences.



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from transformers import BartTokenizer

# Load BART tokenizer
tokenizer = BartTokenizer.from_pretrained("facebook/bart-large-cnn")


# Define max input & output length
MAX_INPUT = 1024
MAX_TARGET = 128


# Function to tokenize input and summaries
def preprocess_data(data):
model_inputs = tokenizer(data["article"], max_length=MAX_INPUT, truncation=True, padding="max_length")


<span class="hljs-comment"># Tokenize summaries</span>
<span class="hljs-keyword">with</span> tokenizer.as_target_tokenizer():
    labels = tokenizer(data[<span class="hljs-string">"highlights"</span>], max_length=MAX_TARGET, truncation=<span class="hljs-literal">True</span>, padding=<span class="hljs-string">"max_length"</span>)

model_inputs[<span class="hljs-string">"labels"</span>] = labels[<span class="hljs-string">"input_ids"</span>]
<span class="hljs-keyword">return</span> model_inputs



# Apply processing
tokenized_dataset = dataset.map(preprocess_data, batched=True)

🎯 Step 5: Fine-Tuning the Model

Now, let's set up the training configuration and fine-tune facebook/bart-large-cnn.



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from transformers import BartForConditionalGeneration, TrainingArguments, Trainer

# Load BART model
model = BartForConditionalGeneration.from_pretrained("facebook/bart-large-cnn")


# Training parameters
training_args = TrainingArguments(
output_dir="./bart-summarization",
evaluation_strategy="epoch",
save_strategy="epoch",
logging_dir="./logs",
per_device_train_batch_size=4,
per_device_eval_batch_size=4,
num_train_epochs=3,  # Adjust based on performance
learning_rate=5e-5,
weight_decay=0.01,
save_total_limit=2,
push_to_hub=False
)


# Trainer setup
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_dataset["train"],
eval_dataset=tokenized_dataset["validation"],
)


# Train the model
trainer.train()

🏆 Step 6: Evaluating the Model

After fine-tuning, we need to evaluate how well the model performs using ROUGE Score, which is the standard metric for summarization.



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from evaluate import load

# Load ROUGE evaluator
rouge = load("rouge")


# Generate model summaries on validation set
def compute_metrics(eval_pred):
preds, labels = eval_pred
preds = tokenizer.batch_decode(preds, skip_special_tokens=True)
labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
return rouge.compute(predictions=preds, references=labels)


# Run evaluation
metrics = trainer.evaluate()
print(metrics)

📤 Step 7: Save & Deploy the Model

Once satisfied with the performance, save the fine-tuned model.



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model.save_pretrained("./fine-tuned-bart")
tokenizer.save_pretrained("./fine-tuned-bart")

This will allow us to load it in our Flask API instead of the default facebook/bart-large-cnn.

🚀 Next Steps

  1. 🛠️ Integrate the fine-tuned model into your AI Meeting Companion API.
  2. 📈 Test it with real meetings and analyze performance.
  3. 💡 Optimize hyperparameters to improve results.
  4. 🔥 Deploy the model in production (consider Hugging Face Spaces or AWS Lambda).
- Integrating the summary at the end of the meeting for example "Whenever the user clicks on the end meeting or clove call button , then provide the AI's must do, insights, solutions
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