NVIDIA-NeMo/Speech

A scalable generative AI framework built for researchers and developers working on Large Language Models, Multimodal, and Speech AI (Automatic Speech Recognition and Text-to-Speech)

What it solves

NVIDIA NeMo Speech provides a comprehensive toolkit for researchers and developers to create, customize, and deploy advanced speech AI models. It simplifies the process of building complex audio systems by providing pre-trained model checkpoints and a standardized framework for development, reducing the need to build speech architectures from scratch.

How it works

Built on PyTorch, the toolkit allows users to leverage existing code and pre-trained weights to develop models across three primary areas:

  • Automatic Speech Recognition (ASR): Converting spoken audio to text, including streaming capabilities for real-time applications.
  • Text to Speech (TTS): Generating natural-sounding speech from text, including multilingual support.
  • Speech LLMs: Integrating speech capabilities into large language models for natural, interruptible, and low-latency voice conversations.

Who it’s for

It is designed for AI researchers and PyTorch developers who are building speech-centric AI applications, ranging from high-accuracy offline transcription to real-time streaming voice assistants.

Highlights

  • Diverse Model Collection: Includes specialized models like Nemotron-3.5-ASR for streaming, Parakeet for unified offline/streaming inference, and MagpieTTS for multilingual speech generation.
  • Hardware Optimization: Deeply integrated with NVIDIA GPUs and CUDA, with optional accelerated backends (like Transformer Engine and FlashAttention) for high-performance inference and training.
  • Flexible Deployment: Supports various installation paths, including turnkey Docker containers and flexible pip installations that preserve existing PyTorch environments.
  • Multilingual Support: Offers models capable of handling dozens of languages across ASR and TTS tasks.