---
id: nemo-toolkit
version: "3.0.0"
license: Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for… (full text in the JSON record)
license_treatment: permissive
maintenance: active
---

# nemo-toolkit — NeMo - a toolkit for Conversational AI

License: permissive · Maintenance: active · Downloads: 1.5M/mo

## What it is and what it does

nemo-toolkit is NVIDIA's framework for speech AI, built on PyTorch to help researchers and developers train and deploy models for automatic speech recognition, text-to-speech synthesis, and speech-based language models. It ships with pre-trained checkpoints (Parakeet, Canary, MagpieTTS, Nemotron-Speech) covering multiple languages and use cases, and supports both offline and streaming inference with configurable latency-accuracy tradeoffs.

The package installs as a pure-Python wheel over your existing PyTorch and CUDA setup without replacing them. It depends on 15 runtime packages including torch, numba, scikit-learn, huggingface_hub, and tensorboard. Training requires an NVIDIA GPU; inference can run on CPU but GPU is recommended. The repository is actively maintained (last commit 2026-08-14) and carries no known vulnerabilities.

Use it for:

- Build automatic speech recognition systems for English or 25+ European languages using pre-trained Parakeet or Canary checkpoints
- Deploy streaming ASR with configurable latency (80ms–1s) using Nemotron-3.5-ASR-Streaming for real-time transcription
- Generate multilingual speech synthesis using MagpieTTS with support for 9 languages including English, Spanish, French, and Mandarin
- Fine-tune or customize speech models on your own audio data using the modular PyTorch-based training pipeline
- Integrate speech recognition and translation into conversational AI applications using Nemotron VoiceChat or speech LLM components

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

nemo-toolkit provides a PyTorch framework for building, training, and deploying speech AI models including automatic speech recognition (ASR), text-to-speech (TTS), and speech-based language models.

Yes. nemo-toolkit is actively maintained, carries no known vulnerabilities, and offers low install friction. It is well-suited for researchers and developers building speech AI systems. Requires PyTorch 2.7+, Python 3.10+, and ideally an NVIDIA GPU; if your environment already meets these, installation is straightforward. The Apache 2.0 license permits commercial use. Install if you need to work with ASR, TTS, or speech-based language models; skip if you need only inference on pre-trained models without customization or if you lack GPU access.

## Install

pip install nemo-toolkit
uv add nemo-toolkit
poetry add nemo-toolkit

## Installing nemo-toolkit

Before you install: Low install friction with a pure-Python wheel. Actively maintained with a release 7 days old and 18128 repository stars. Requires PyTorch 2.7 or above and Python 3.10 or above; GPU with CUDA is required for training but optional for inference.

License in practice: Apache License 2.0 permits commercial use, modification, and distribution with attribution. You may use this in proprietary projects provided you include a copy of the license and document any changes to the source.

Quickstart:

pip install nemo-toolkit
import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.EncDecCTCModel.from_pretrained('nvidia/parakeet-ctc-0.6b')
transcriptions = asr_model.transcribe(['audio.wav'])

Requires PyTorch 2.7 or above and Python 3.10 or above. NVIDIA GPU with CUDA is required for training; for inference, CPU is possible but GPU is recommended. Some model checkpoints may require setting TORCH_FORCE_NO_WEIGHTS_ONLY_LOAD=1 environment variable.

Verify before relying:

- Whether all 15 runtime dependencies (including torch, numba, cuda-bindings) install without version conflicts in typical environments
- Performance characteristics and latency of inference on CPU versus GPU
- Compatibility matrix with specific CUDA versions beyond the stated 12.6/13.2 support

## Package facts

- License: Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for… (full text in the JSON record) (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 1.5M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

speech recognition ASR models, text to speech TTS synthesis, pytorch speech AI framework, automatic speech recognition toolkit, speech language models, nvidia speech processing, audio AI model training, speech-recognition, text-to-speech, gpu-accelerated

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