Practical tutorial on meeting summary generation system based on Vosk and Transformers

1. Project background and value

In modern office scenarios, meeting minutes and summary generation are important links to improve work efficiency. Traditional manual recording methods have problems such as inefficiency and easy omission, while AI-based solutions can transcribe the content of the conference in real time and generate structured summary. This tutorial will guide developers to use the two major tools of Vosk (voice recognition) and Transformers (natural language processing) in the Python ecosystem to build a offline real-time conference transcription and summary system. Through this project, you will learn:

1. Configuration and optimization methods for offline voice recognition;
2. Fine-tuning technology for pre-trained language models;
3. Real-time audio stream processing architecture;
4. Development ideas for multimodal interactive systems.

2. Technology stack analysis

III. System architecture design

4. Detailed implementation steps

4.1 Environment configuration

# Create a virtual environment
 python -m venv venv
 source venv/bin/activate
 
 # Install core dependencies
 pip install vosk transformers torch pydub flask-socketio
 
 # Download the pretrained model
 wget /vosk/models/vosk-model-cn-0.
 unzip vosk-model-cn-0. -d model/vosk
 
 wget /facebook/bart-large-cnn/resolve/main/
 tar -xzvf -C model/transformers

4.2 Implementation of voice recognition module

# audio_processor.py
 import vosk
 import pyaudio
 from pydub import AudioSegment
 
 class AudioRecognizer:
     def __init__(self, model_path="model/vosk/vosk-model-cn-0.22"):
          = (model_path)
          = (, 16000)
        
     def process_chunk(self, chunk):
         if .accept_waveform(chunk):
             Return ()
         else:
             return .partial_result()
 
 class AudioStream:
     def __init__(self):
          = ()
          = (
             format=pyaudio.paInt16,
             channels=1,
             rate=16000,
             input=True,
             frames_per_buffer=8000
         )
        
     def read_stream(self):
         While True:
             data = (4096)
             yield data
 
 #User Example
 recognizer = AudioRecognizer()
 audio_stream = AudioStream()
 
 for chunk in audio_stream.read_stream():
     text = recognizer.process_chunk(chunk)
     if text:
         print(f"Result: {text}")

4.3 BART abstract model fine-tuning

# bart_finetune.py
 from transformers import BartTokenizer, BartForConditionalGeneration, Trainer, TrainingArguments
 import torch
 from datasets import load_dataset
 
 # Load the pretrained model
 model_name = "facebook/bart-large-cnn"
 tokenizer = BartTokenizer.from_pretrained(model_name)
 model = BartForConditionalGeneration.from_pretrained(model_name)
 
 # Prepare the meeting dataset
 dataset = load_dataset("csv", data_files="meeting_data.csv")
 def preprocess(examples):
     inputs = tokenizer(
         examples["text"],
         max_length=1024,
         truncation=True,
         padding="max_length"
     )
     outputs = tokenizer(
         examples["summary"],
         max_length=256,
         truncation=True,
         padding="max_length"
     )
     return {
         "input_ids": inputs["input_ids"],
         "attention_mask": inputs["attention_mask"],
         "labels": outputs["input_ids"]
     }
 
 tokenized_dataset = (preprocess, batched=True)
 
 # Define training parameters
 training_args = TrainingArguments(
     output_dir="./results",
     num_train_epochs=3,
     per_device_train_batch_size=4,
     save_steps=500,
 )
 
 # Start fine adjustment
 trainer = Trainer(
     model=model,
     args=training_args,
     train_dataset=tokenized_dataset["train"],
     eval_dataset=tokenized_dataset["test"],
 )
 ()

4.4 Real-time system integration

#
 from flask import Flask, render_template
 from flask_socketio import SocketIO, emit
 import threading
 
 app = Flask(__name__)
 socketio = SocketIO(app)
 
 # Initialize the recognizer
 recognizer = AudioRecognizer()
 audio_stream = AudioStream()
 
 # Real-time processing of threads
 def audio_processing():
     meeting_text = []
     for chunk in audio_stream.read_stream():
         text = recognizer.process_chunk(chunk)
         if text:
             meeting_text.append(text)
             # Trigger summary generation every 30 seconds
             if len(meeting_text) % 15 == 0:
                 summary = generate_summary(" ".join(meeting_text))
                 ("update_summary", {"summary": summary})
 
 # Start the thread
 (target=audio_processing, daemon=True).start()
 
 @('/')
 def index():
     return render_template('')
 
 if __name__ == '__main__':
     (app, debug=True)

4.5 Web front-end implementation

<!-- templates/ -->
 <!DOCTYPE html>
 <html>
 <head>
     <title>Conference Summary System</title>
     <script src="/ajax/libs//4.0.1/"></script>
 </head>
 <body>
     <div style="display: flex; gap: 20px">
         <div style="flex: 1">
             <h2>Real-time transcription</h2>
             <div style="height: 400px; overflow-y: auto; border: 1px solid #ccc"></div>
         </div>
         <div style="flex: 1">
             <h2>Conference Summary</h2>
             <div style="height: 400px; overflow-y: auto; border: 1px solid #ccc"></div>
         </div>
     </div>
 
     <script>
         const socket = io();
         ('update_summary', (data) => {
             ('summary').innerHTML = ;
         });
     </script>
 </body>
 </html>

V. Performance optimization strategy

1. Audio preprocessing optimization：

def preprocess_audio(file_path):
     audio = AudioSegment.from_wav(file_path)
     # Noise reduction processing
     audio = audio.low_pass_filter(3000)
     # Standardized volume
     audio = (headroom=10)
     return audio.set_frame_rate(16000)

2.Model reasoning acceleration：

# Use ONNX Runtime to speed up reasoning
 import onnxruntime as ort
 
 def convert_to_onnx(model_path):
     # You need to install transformers[onnx] first
     pipeline = pipeline("summarization", model=model_path)
     pipeline.save_pretrained("onnx_model")
 
 # Load the optimized model
 ort_session = ("onnx_model/")

3.Streaming optimization：

# Use double buffered queues
 from collections import deque
 
 class AudioBuffer:
     def __init__(self):
          = deque(maxlen=5)
        
     def add_chunk(self, chunk):
         (chunk)
        
     def get_full_buffer(self):
         return b"".join()

VI. Deployment Plan

1. Local deployment：

# Install system-level dependencies
 sudo apt-get install portaudio19-dev
 
 # Use systemd to manage services
 sudo nano /etc/systemd/system/meeting_summary.service

2.Cloud native deployment：

# Kubernetes deployment configuration example
 apiVersion: apps/v1
 kind: Deployment
 metadata:
   name: meeting-summary-app
 spec:
   replicas: 2
   selector:
     matchLabels:
       app: meeting-summary
   template:
     metadata:
       labels:
         app: meeting-summary
     spec:
       containers:
       - name: app
         image: your_docker_image:latest
         Ports:
         - containerPort: 5000
         resources:
           limits:
             /gpu: 1

7. Expansion direction

1. Multimodal fusion：

• Integrated OpenCV to achieve lip recognition assistance
• Combined with action recognition to analyze spokesperson emotions

2.Knowledge graph integration：

from transformers import AutoModelForQuestionAnswering
 
 # Build a domain knowledge graph
 knowledge_graph = {
     "Technical Architecture": ["Microservice", "Serverless", "Containerization"],
     "Project Management": ["Agile Development", "Kanban Method", "Scrum"]
 }
 
 # Implement context-aware summary
 def contextual_summary(text):
     model = AutoModelForQuestionAnswering.from_pretrained("bert-base-chinese")
     # Add knowledge graph query logic
     return enhanced_summary

3.Personalized summary：

# Use Sentence-BERT to calculate text similarity
 from sentence_transformers import SentenceTransformer
 
 def personalized_summary(user_profile, meeting_text):
     model = SentenceTransformer('paraphrase-multilingual-MiniLM-L12-v2')
     embeddings = (meeting_text)
     # Select relevant paragraphs based on user portrait
     return custom_summary

8. Summary

This tutorial fully presents the entire process from environment configuration to system deployment. Developers can adjust the following parameters according to actual needs:

• Speech recognition model: supports switching to different language models;
• Abstract Generative Model: It can be replaced with T5, PEGASUS and other models;
• Front-end framework: can be replaced with Vue/Angular and other frameworks;
• Deployment plan: Support Docker/Kubernetes cluster deployment.

Through the practice of this project, developers will have an in-depth understanding of the integration methods of voice technology and NLP models and master the core skills of building intelligent conference systems. It is recommended to iterate from the basic functions and gradually add advanced functions such as personalization and multimodality.