Cache-aware Streaming Conformer with multiple lookaheads.

Streaming/Buffered ASR (CTC/Transducer) - Chunked Inference Examples.

NeMo Original Multi-blank Transducers and Token-and-Duration Transducers (TDT).

Supports the following decoders/losses:.

Squeezeformer-CTC and Squeezeformer-Transducer.

Conformer-CTC, Conformer-Transducer, FastConformer-CTC, FastConformer-Transducer.

Jasper, QuartzNet, CitriNet, ContextNet.

HuggingFace Space for Audio Transcription (File, Microphone and YouTube).

Which can be used to find the optimal model parallel configuration for training on a specific cluster.Īlso see our introductory video for a high level overview of NeMo.

The NM launcher has extensive recipes, scripts, utilities, and documentation for training NeMo LLMs and also has an Autoconfigurator We have a full suite of example scripts that support multi-GPU/multi-node training.įor scaling NeMo LLM training on Slurm clusters or public clouds, please see the NVIDIA NeMo Megatron Launcher. These models can be used to transcribe audio, synthesize speech, or translate text in just a few lines of code.įor advanced users that want to train NeMo models from scratch or finetune existing NeMo models State of the Art pretrained NeMo models are freely available on HuggingFace Hub and

NeMo models can be optimized for inference and deployed for production use-cases with NVIDIA Riva. Training is automatically scalable to 1000s of GPUs.Īdditionally, NeMo Megatron LLM models can be trained up to 1 trillion parameters using tensor and pipeline model parallelism. The primary objective of NeMo is to help researchers from industry and academia to reuse prior work (code and pretrained models)Īnd make it easier to create new conversational AI models.Īll NeMo models are trained with Lightning and Text-to-speech synthesis (TTS), large language models (LLMs), and NVIDIA NeMo is a conversational AI toolkit built for researchers working on automatic speech recognition (ASR),

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