#55th of 23 in Open models
speech
PyTorch toolkit for training and deploying ASR, TTS and speech LLM models
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- Stars
- 18.6k
- License
- Apache-2.0
- Last commit
- Oct 2026
- Last release
- Aug 2026
- Language
- Python
Missed the latest GitHub check; these facts may be older.
Overview
NVIDIA NeMo Speech is a Python/PyTorch toolkit for building speech models: automatic speech recognition, text-to-speech and speech LLMs. It ships training code and pre-trained checkpoints such as Parakeet, Canary, Nemotron-Speech-Streaming and MagpieTTS, published on Hugging Face. Install from source with uv, from PyPI as nemo-toolkit, or use the NGC container.
Who it is for: Researchers and PyTorch developers training ASR, TTS and speech LLMs
Strengths
- Covers ASR, TTS and speech LLMs in one codebase with pre-trained open checkpoints
- Streaming ASR checkpoint with 80ms-1s configurable latency and 40 languages
- MagpieTTS v2607 supports 12 languages; Parakeet V3 and Canary V2 cover 25 European languages
- Installs over an existing PyTorch 2.7+ build via pip without replacing it
Weaknesses
- NVIDIA GPU with CUDA required for training; only recommended for inference
- Requires Python 3.12 or newer
- Tested stack is pinned to PyTorch 2.11/2.12 with CUDA 12.9 or 13.2
- Not an inference server; no HTTP API or web UI described in the README
What it needs
- GPU required
- Needs PyTorch 2.7+, CUDA, Python 3.12+
- Models: Parakeet, Canary, Nemotron-Speech-Streaming, MagpieTTS, Canary-Qwen-2.5B
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