NVIDIA NeMo Speech

Checkout our HuggingFace🤗 collection for the latest open
weight checkpoints and demos!

Updates

The first release of NeMo Speech after NeMo repository split is scheduled for June 2026, as the repo undergoes transformation.
For the latest stable released version, please use the 26.02 NGC container.

2026-04: Parakeet-unified-en-0.6b has been released with high-quality offline and streaming (with a minimum latency of 160ms) inference in one model for English language with punctuation and capitalization support.
2026-03: Nemotron 3 VoiceChat is now released in Early Access. Built on the Nemotron Nano v2 LLM backbone with Nemotron speech and TTS decoder, VoiceChat delivers full-duplex, natural, interruptible conversations with low latency. Try out the demo and apply for early access.
2026-03: Nemotron-Speech-Streaming v2603 has been
updated. It has been trained on a larger and more diverse corpus, resulting in lower WER across all latency modes.
Try out the demo and check out
the NIM.
2026-03: MagpieTTS v2602 has been released with support
for 9 languages(En, Es, De, Fr, Vi, It, Zh, Hi, Ja). Try out
the demo and check out
the NIM.
2026-01: Nemotron-Speech-Streaming was released: One checkpoint that enables users to pick their optimal point
on the latency-accuracy Pareto curve!
2026-01: MagpieTTS was released.
2026: This repo has pivoted to focus on audio, speech, and multimodal LLM. For the last NeMo release with support for more
modalities, see v2.7.0
2025-08: Parakeet V3 and
Canary V2 have been released with speech recognition and translation
support for 25 European languages.
2025-06: Canary-Qwen-2.5B has been released with record-setting
5.63% WER on English Open ASR Leaderboard.

Introduction

NVIDIA NeMo Speech is built for researchers and PyTorch developers working on Speech models including Automatic Speech
Recognition (ASR), Text to Speech (TTS), and Speech LLMs. It is designed to help you efficiently create, customize, and
deploy new AI models by leveraging existing code and pre-trained model checkpoints.

For technical documentation, please see the
NeMo Framework User Guide.

Requirements

Python 3.12 or above
Pytorch 2.6 or above
NVIDIA GPU (if you intend to do model training)

As of Pytorch 2.6,
torch.load defaults to using weights_only=True. Some model checkpoints may require using weights_only=False.
In this case, you can set the env var TORCH_FORCE_NO_WEIGHTS_ONLY_LOAD=1 before running code that uses torch.load.
However, this should only be done with trusted files. Loading files from untrusted sources with more than weights only
can have the risk of arbitrary code execution.

Developer Documentation

Version	Status	Description
Latest		Documentation of the latest (i.e. main) branch.
Stable		Documentation of the stable (i.e. most recent release) - To be added

Install NeMo Speech

NeMo Speech is installable via pip: pip install 'nemo-toolkit[all]'
To install with extra dependencies for CUDA 12.x or 13.x, use pip install 'nemo-toolkit[all,cu12]'
or pip install 'nemo-toolkit[all,cu13]' respectively.