Multi modal Audio + Text Fine tuning and Inference with Qwen
another under-rated Qwen!
Qwen 2 Audio is a 7B model that can take in audio and/or text.
I walk through:
Inference in a FREE Google Colab notebook
Data prep and fine-tuning
Inference using a one click
Of note, I describe in detail how to set up a data collator for a multi-modal model. This can be useful beyond this specific architecture and help you train other multi-model model types.
Cheers, Ronan
More Resources at Trelis.com/about
P.S. Going forward, I’ll provide AI summaries of the video, for those of you who wish to skim. Let me know in the comments if this is useful or just taking up space. Thanks.
Fine-tuning and Deploying Qwen 2 Audio for Multimodal AI Applications
In this comprehensive technical walkthrough, Trelis explores the implementation, fine-tuning, and deployment of Qwen-2 Audio, a powerful multimodal model that processes both audio and text inputs. This integrated approach offers significant advantages over traditional pipeline solutions that combine separate speech-to-text and language models.
Understanding the Architecture
The Qwen-2 Audio model employs a sophisticated architecture that combines a Whisper-based audio encoder with a language model. The audio processing pipeline converts sound into overlapping windows, calculating frequencies and adapting them to human hearing patterns through MEL spectrograms. These are then transformed via a linear layer to match the embedding space of the language model, allowing for seamless integration of both audio and text inputs.
Key Applications
The model's versatility enables several practical applications:
Voice assistant implementations with text output
Transcription with emotional context detection
Acoustic analysis for machinery or wildlife sounds
Zero-shot transcription of unfamiliar words
Combined audio-text assisted transcription
Implementation Benefits
A significant advantage of this integrated approach is its compatibility with vLLM for production deployment. Unlike pieced-together solutions using separate models, Qwen-2 Audio can be efficiently deployed using continuous batching, leading to better GPU utilization and cost-effectiveness in production environments.
Practical Implementation
The tutorial demonstrates implementation using both Google Colab (for testing) and RunPod (for training). The Colab implementation showcases basic inference, while the training environment utilizes an A40 GPU for fine-tuning tasks. The model requires quantization for Colab due to VRAM constraints, converting 16-bit weights to 4-bit format.
Fine-tuning Process
The fine-tuning implementation employs LoRA (Low-Rank Adaptation), which creates small adapter matrices rather than updating all model weights. This approach significantly reduces computational requirements while maintaining effective learning capability. The tutorial uses a bird song classification dataset to demonstrate the fine-tuning process, with particular attention to:
Data collation strategies
Proper audio preprocessing
Chat template formatting
Label preparation for training
Production Deployment
For production deployment, the tutorial showcases vLLM implementation using a custom Docker image that includes necessary audio processing libraries. This setup enables efficient inference with parallel request handling, demonstrating how to:
Configure vLLM for audio processing
Set up API endpoints
Handle audio input constraints
Manage GPU resources effectively
Technical Considerations
Several important technical details are covered, including:
Sampling rate management (16kHz requirement)
Audio length constraints (30-second maximum)
Proper tokenization and chat templating
Attention mask handling for both audio and text inputs
Training Strategy
The demonstrated training approach uses a two-phase strategy:
Initial training with constant learning rate
Follow-up training with cosine annealing schedule
This approach aims to find optimal model parameters while avoiding overfitting, though results suggest that data quality and quantity play crucial roles in fine-tuning success.
Practical Results
The bird song classification example, while not achieving perfect results, illustrates important considerations for real-world applications, including:
Data quantity requirements
Audio quality importance
Background noise challenges
Sample length considerations
Development Resources
The tutorial references several important resources:
Public Colab notebooks for testing
RunPod templates for training
Advanced transcription repository access
One-click deployment templates
This comprehensive guide provides developers with the necessary tools and understanding to implement, fine-tune, and deploy Qwen-2 Audio for various multimodal AI applications, with particular attention to production-grade deployment considerations.