Audio RAG with ColQwen2.5-Omni

Hey There!

Welcome back to another little-scripts adventure! After diving deep into multimodal document retrieval with Colnomic in the first project, I've been poking around for new ColPali updates when I stumbled across something interesting: ColQwen-Omni. It got me thinking about another frontier in RAG systems: audio retrieval. What if we could ask questions about video content and get answers that understand not just the words, but the actual audio content?

That's what I've been experimenting with in colqwen_omni - a simple script that combines ColQwen2.5-Omni's multimodal understanding with OpenAI's audio capabilities. You can feed it a video URL, and it'll process the audio directly and respond with both text and speech. Nothing too fancy, but it's been fun to play with!

The Audio Challenge

Traditional RAG systems work great for text documents, but what about when your knowledge lives in audio format? Podcasts, lectures, interviews, educational videos - there's a lot of valuable content in audio that's traditionally been tricky to search and query effectively.

Most existing solutions rely on speech-to-text conversion, which has some drawbacks:

Lossy transcription: Important context like tone, emphasis, and audio quality gets lost
Preprocessing bottlenecks: Every audio file needs to be transcribed before it can be queried
Context fragmentation: Audio chunks lose their temporal and contextual relationships

Enter ColQwen2.5-Omni

ColQwen2.5-Omni takes an interesting approach to this problem. Unlike traditional methods that convert audio to text first, ColQwen2.5-Omni can directly process and understand audio content, creating semantic embeddings that capture both the linguistic and acoustic properties of speech.

What Makes It Interesting?

Direct Audio Processing: No transcription needed - the model works directly with audio waveforms, preserving the contextual information that gets lost in text conversion.

Multimodal Architecture: Built on the ColPali foundation, it understands the relationships between different modalities and can handle audio content nicely.

Semantic Understanding: Creates embeddings that capture not just what's being said, but how it's being said, including some emotional context and speaker characteristics.

Efficient Chunking: Processes audio in configurable chunks, allowing for retrieval while keeping things computationally reasonable.

How It Works

Let me walk you through the basic flow:

Step 1: Audio Acquisition

# Process video URL and extract audio
audio_path = rag.extract_audio(video_url)

The system uses yt-dlp to process video URLs and extract high-quality audio in WAV format. This gives us clean audio data to work with, without any compression artifacts that might affect the model's understanding.

Step 2: Intelligent Chunking

# Split audio into semantic chunks
audio_chunks = rag.chunk_audio(audio_path, chunk_length_seconds=30)

Unlike simple time-based chunking, the system splits audio into manageable segments that preserve contextual boundaries. The configurable chunk length (10-120 seconds) allows you to optimize for your specific use case.

Step 3: Embedding Generation

# Create semantic embeddings with ColQwen2.5-Omni
embeddings = rag.create_embeddings(audio_chunks)

This is where ColQwen2.5-Omni processes each audio chunk and generates semantic embeddings that capture both the content and the acoustic properties of the speech.

Step 4: Semantic Retrieval

# Find relevant audio chunks for a query
top_indices = rag.query_audio("What is the main topic discussed?", k=5)

When you ask a question, the system finds the most relevant audio chunks using semantic similarity, not just keyword matching. This means it can understand context and relationships between concepts.

Step 5: Audio-Aware Response

# Generate both text and audio responses
response = rag.answer_query(query, k=5)

In the final step, instead of just returning text, the system sends the relevant audio chunks directly to OpenAI's audio model, which can listen to the original audio and provide both text and spoken responses.

The Interface

I've wrapped this in a simple Gradio interface with four main tabs:

🔧 Setup

Initialize the system with your OpenAI API key and load the ColQwen2.5-Omni model. The setup is straightforward, but the model loading can take a few minutes the first time.

🎥 Process Video

Simply paste a video URL, configure your chunk length, and let the system do its work. The processing time depends on video length, but you'll get real-time status updates.

💬 Ask Questions

Ask questions about your processed video and get both text and audio responses. You can adjust how many audio chunks to use for context.

ℹ️ Help

Comprehensive usage instructions and troubleshooting tips to get you up and running quickly.

Real-World Applications

Listing a few, but possibilites could be endless:

Educational Content: Transform lectures and tutorials into searchable, queryable knowledge bases
Podcast Analysis: Extract insights from podcast episodes and create interactive summaries
Interview Processing: Quickly find specific topics discussed in long-form interviews
Meeting Analysis: Process recorded meetings and extract key decisions and action items
Content Research: Analyze competitor content or industry discussions for insights

Technical Deep Dive

Architecture Overview

The system combines several components:

ColQwen2.5-Omni: For direct audio embedding generation
OpenAI Audio API: For question answering with audio responses
Gradio: For the web interface
PyTorch: For model inference with GPU acceleration
yt-dlp: For video URL processing

Performance Optimizations

Batch Processing: Audio chunks are processed in configurable batches for optimal GPU utilization
Memory Management: Efficient handling of large audio files without memory overflow
GPU Acceleration: Full CUDA support with Flash Attention 2 for faster inference
Caching: Embeddings are cached in memory for rapid retrieval

Flexibility Features

Configurable Chunking: Adjust chunk length based on your content type
Batch Size Control: Optimize processing speed vs. memory usage
Model Selection: Choose the appropriate OpenAI model for your use case
Multi-format Support: Works with any audio format supported by yt-dlp

Getting Started

The setup is surprisingly straightforward. This is part of my little-scripts monorepo, which houses various utility scripts, tools, and AI-powered applications for development and automation.

# Clone the repository
git clone https://github.com/athrael-soju/little-scripts.git
cd little-scripts/colqwen_omni

# Set up your environment
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
pip install -r requirements.txt

# Set your OpenAI API key
export OPENAI_API_KEY="your-api-key-here"

# Launch the interface
python run_ui.py

That's it! The interface will open in your browser at http://localhost:7860, and you can start processing videos immediately.

Why This Is Interesting

Audio RAG opens up some interesting possibilities for how we interact with audio content. Instead of just passive listening, we can have interactive conversations with videos, lectures, and podcasts. It's a neat way to make audio content more searchable and accessible.

The combination of ColQwen2.5-Omni's direct audio processing with OpenAI's audio response capabilities creates something pretty cool: a system that doesn't just understand what was said, but can engage with it conversationally.

What's Next?

Some exciting possibilities:

Speaker Recognition/Diarization: Identifying and tracking different speakers in conversations
Emotional Analysis: Understanding sentiment and emotional context in audio
Live Processing: Real-time audio analysis for streaming content

The Little-Scripts Collection

This is just another project in my little-scripts monorepo, which is growing into a nice collection of AI-powered tools and experiments. Current projects include:

colnomic_qdrant_rag: Multimodal document retrieval with Colnomic and Qdrant
colqwen_omni: This audio RAG system with ColQwen2.5-Omni
eomt_panoptic_seg: Panoptic segmentation experiments

Try It Yourself!

The little-scripts series is all about making things accessible, easy to run and most importantly, fun! Give this one a try and let me know what you think!

Remember: This is research-grade software designed for exploration and learning. Always consider your specific requirements and constraints when deploying in production environments.

What will you ask your first video? I'm curious to hear about your audio RAG adventures!