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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.