Comprehensive collection of transformer and foundation models for audio, vision, multimodal, and NLP use cases.
Table of Contents
Audio Processing
Speech Recognition and Classification
Audio Generation and Synthesis
- Moshi - Speech-to-speech generation
- MusicGen - Text-to-audio generation
- Bark - Text-to-speech synthesis
Computer Vision
Image Understanding
- SAM - Automatic mask generation
- DepthPro - Depth estimation
- DINO v2 - Image classification
Object Detection and Recognition
Pose and Segmentation
Multimodal
Audio-Text Integration
Image-Text Processing
Advanced Multimodal
Natural Language Processing
Text Understanding
Text Generation and Processing
- BART - Summarization
- T5 - Translation
- Llama - Text generation
- Qwen - Text classification
- Megatron-LM - Large-scale transformer training framework by NVIDIA
Model Selection Guide
| Task Type |
Recommended Models |
Typical Use Case |
| Speech Recognition |
Whisper, Moonshine |
Multilingual transcription |
| Image Understanding |
SAM, DINO v2 |
Visual analysis |
| Multimodal Tasks |
Qwen-VL, Llava, MiniCPM-o |
Cross-modal reasoning |
| Text Processing |
BART, T5, Qwen |
Language tasks |
| Audio Generation |
MusicGen, Bark |
Audio synthesis |
Best Practices
Model Selection
- Choose task-specific models first.
- Check resource constraints early.
- Verify licensing for your deployment.
- Prefer models with active maintenance.
- Use quantization for lower cost inference.
- Batch requests for better throughput.
- Cache repeated prompts and embeddings.
- Use GPU acceleration when available.