Learn how to implement and fine-tune multimodal audio and text models using Qwen 2 Audio in this comprehensive 57-minute technical tutorial. Explore the model's architecture, capabilities, and practical applications ranging from customer support voice assistants to emotion detection in transcriptions. Follow along with a detailed Google Colab implementation that covers model loading, audio processing, dataset preparation, LoRA fine-tuning setup, and VLLM inference deployment. Master the technical aspects of data collation, training configuration, and hyperparameter optimization while understanding the advantages of integrated multimodal approaches over separate models. Access hands-on examples using provided resources including Colab notebooks, datasets, and complete scripts to develop production-ready audio-text processing solutions.
Multimodal Audio and Text Fine-tuning with Qwen - Implementation Guide
Overview
Syllabus
- Introduction to multimodal audio plus text models
- Overview of Qwen 2 Audio model capabilities and applications
- Technical details of the Qwen 2 Audio model architecture
- Benefits of integrated multimodal model vs separate models
- Applications and use cases
- Key advantages of the integrated model
- Specific applications:
- Introduction to LoRA fine-tuning approach
- Google Colab implementation walkthrough
- Model loading and configuration
- Testing audio processing capabilities
- Audio input examples and testing
- Dataset preparation for fine-tuning
- Detailed data collation process
- Processing audio and text inputs
- Setting up the data collator
- Training configuration and LoRA setup
- Training process and hyperparameters
- VLLM inference setup
- Production deployment considerations
- Fine-tuning results and analysis
- Conclusion and summary
Taught by
Trelis Research
