NeMo (Neural Modules) is a toolkit for creating AI applications built around neural modules, conceptual blocks of neural networks that take typed inputs and produce typed outputs.

collections/

• ASR - Collection of modules and models for building speech recognition networks.
• TTS - Collection of modules and models for building speech synthesis networks.
• Audio - Collection of modules and models for building audio processing networks.
• SpeechLM2 - Collection of modules and models for building multimodal LLM.

core/

Provides fundamental APIs and utilities for NeMo modules, including:

• Classes - Base classes for datasets, models, and losses.
• Config - Configuration management utilities.
• Neural Types - Typed inputs/outputs for module interaction.
• Optim - Optimizers and learning rate schedulers.

lightning/

Integration with PyTorch Lightning for training and distributed execution:

• Strategies & Plugins - Custom Lightning strategies.
• Fabric - Lightweight wrapper for model training.
• Checkpointing & Logging - Utilities for managing model states.

utils/

General utilities for debugging, distributed training, logging, and model management:

• callbacks/ - Hooks for training processes.
• loggers/ - Logging utilities for different backends.
• debugging & profiling - Performance monitoring tools.