GraphCanon updated 2w · GitHub synced 2w
Decision brief
NVIDIA-NeMo/Speech - A scalable toolkit for speech AI tasks such as ASR, TTS, and speaker recognition built on PyTorch with CUDA support.
Good fit when
- When working on projects that require extensive GPU utilization for training large models due to its support for efficient CUDA usage.
- For researchers and developers looking for a comprehensive solution that supports not only Automatic Speech Recognition (ASR) but also includes speaker diarization, recognition, speech synthesis (TTS)
Avoid when
- For environments where GPU access is limited or unavailable since the toolkit highly recommends a GPU setup for both training and recommended for inference.
- If your Python/PyTorch/CUDA versions fall below the specified requirements (Python 3.12+, PyTorch 2.7+), as lower versions will not be compatible with NeMo Speech.
Observed Jul 11, 2026 · Source: enrich:decision_facts
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Very active (0d since push)
- As of 2w
- Provenance
- Not a fork · Organization account
- As of 2w
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install SpeechSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
NVIDIA-NeMo/Speech is a comprehensive toolkit for Automatic Speech Recognition (ASR), speaker diarization, speaker recognition, speech synthesis (TTS), and more. It is built on PyTorch and supports CUDA for efficient GPU utilization.
Capability facts
- Languages
- python
Source: github.language+pyproject.toml · Aug 7, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 7, 2026)
NeMo Speech works with the **Python, PyTorch, and CUDA versions of your choosing**:Source link
Tags
README
Requirements
NeMo Speech works with the Python, PyTorch, and CUDA versions of your choosing:
- Python 3.12 or above
- PyTorch 2.7 or above (CPU, CUDA, etc. — your choice)
- NVIDIA GPU + CUDA (required for training; recommended for inference)
If you already have a Python/PyTorch/CUDA stack that satisfies those minimums, NeMo Speech installs on top of it without replacing it, so your existing PyTorch build is kept (see the install options below). The versions pinned in uv.lock and shipped in the official container — Python 3.13, PyTorch 2.11 with CUDA 12.9 or PyTorch 2.12 with CUDA 13.2 — are simply the combinations we actively test and support. They make setup turnkey and reproducible, but they are not a hard requirement.
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.
Install NeMo Speech
The recommended way to install NeMo Speech is from source with uv, which reproduces our actively-tested stack from the committed uv.lock. If you need different Python/PyTorch/CUDA versions, NeMo also installs over your existing environment via pip — see the pip fallback below.
Docker (turnkey, our supported stack)
NGC container: Coming soon — the pull command for the prebuilt NeMo Speech container image will be published here.
To build the container from source (CUDA 13 / H100+ by default):
git clone https://github.com/NVIDIA-NeMo/NeMo.git
cd NeMo
docker buildx build -f docker/Dockerfile -t nemo-speech . # CUDA 13 / H100+ (default)
docker run --rm -it --gpus all -v "$PWD:/workspace" nemo-speech bash
For A100, set GPU_TARGET=a100 — A100 works with both CUDA 12 and CUDA 13 (CUDA 13, the default base image, is recommended; the CUDA 12 base is a convenience). See the header of docker/Dockerfile for all build arguments (BASE_IMAGE, GPU_TARGET).
For agents
This page has a .md twin and JSON over the API.