NVIDIA NeMo — Scalable Speech & LLM Training Framework

NVIDIA NeMo

NeMo is NVIDIA's end-to-end framework for developing and deploying state-of-the-art conversational AI, large language models, and speech models. It is built on PyTorch Lightning and supports distributed training across thousands of GPUs with tensor, pipeline, and data parallelism.

Key Features

• ASR: Conformer, Citrinet, FastConformer — SOTA word error rates
• TTS: FastPitch, HiFi-GAN, Mixer-TTS for natural speech synthesis
• NLP/LLM: GPT-style training, instruction tuning (SFT), RLHF, parameter-efficient fine-tuning
• Multimodal: vision-language alignment pipelines
• Collections: modular model collections for ASR, NLP, TTS, Vision
• Megatron-LM integration for ultra-large-scale training
• Deployment: NVIDIA TRT-LLM, Triton Inference Server export paths

Quick Start

import nemo.collections.asr as nemo_asr

# Load a pre-trained ASR model
asr_model = nemo_asr.models.EncDecCTCModelBPE.from_pretrained(
    model_name="stt_en_conformer_ctc_large"
)

transcriptions = asr_model.transcribe(["podcast.wav"])
print(transcriptions[0])

Install via ai-supply

npx ai-supply add nemo-speech-and-llm-framework

Curated mirror of the open-source NeMo (Apache-2.0). Get it from the source.