nemo-toolkit
NeMo - a toolkit for Conversational AI
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- 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.
License · maintenance · safety
permissive license (permissive) — 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.
last release 2026-08-07 (7 days) · last repo commit 2026-08-14 · 18,128 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,538,107 downloads/mo, #3,792 on PyPI
Alternatives
Verify before relying
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'])- 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
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 on this page. Verify before relying on it.
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
nemo-toolkit on PyPI
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.
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.
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'])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 | permissive license permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 15 packagesaistorefsspechuggingface_hubnumbacuda-bindingsnumpyonnxscikit-learnsetuptoolssmart-opentensorboardtext-unidecodetorchtqdmwrapt |
| Maintenance | Actively maintained 7 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 1,538,107 / month, #3,792 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableEnvironment :: ConsoleIntended Audience :: DevelopersIntended Audience :: Information TechnologyIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseNatural Language :: EnglishOperating System :: OS IndependentProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Topic :: Scientific/EngineeringTopic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Scientific/Engineering :: Image RecognitionTopic :: Scientific/Engineering :: MathematicsTopic :: Software Development :: LibrariesTopic :: Software Development :: Libraries :: Python ModulesTopic :: Utilities |
Evidence: nemo_toolkit-3.0.0-py3-none-any.whl
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See also nemo-text-processing · nvdlfw-inspect · nvidia-riva-client · speechbrain · nemoguardrails · data-designer-config · pyctcdecode · nemo-evaluator · onnx-asr · nemo-gym