18 citations · 20 across the 6 of their papers we have counts for
6 papers
Nemotron 3 Ultra: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning
NVIDIA, :, Aaron Blakeman +571
We introduce Nemotron 3 Ultra, a 550 billion total and 55 billion active parameter Mixture-of-Experts Hybrid Mamba-Attention language model. We pre-trained Nemotron 3 Ultra on 20 t…
Nemotron 3 Super: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning
NVIDIA, :, Aakshita Chandiramani +544
We describe the pre-training, post-training, and quantization of Nemotron 3 Super, a 120 billion (active 12 billion) parameter hybrid Mamba-Attention Mixture-of-Experts model. Nemo…
Less is More: Accurate Speech Recognition & Translation without Web-Scale Data
Krishna C. Puvvada, Piotr Żelasko, He Huang +9
Recent advances in speech recognition and translation rely on hundreds of thousands of hours of Internet speech data. We argue that state-of-the art accuracy can be reached without…
Fast Context-Biasing for CTC and Transducer ASR models with CTC-based Word Spotter
Andrei Andrusenko, Aleksandr Laptev, Vladimir Bataev +2
Accurate recognition of rare and new words remains a pressing problem for contextualized Automatic Speech Recognition (ASR) systems. Most context-biasing methods involve modificati…
LibriSpeech-PC: Benchmark for Evaluation of Punctuation and Capitalization Capabilities of end-to-end ASR Models
Aleksandr Meister, Matvei Novikov, Nikolay Karpov +3
Traditional automatic speech recognition (ASR) models output lower-cased words without punctuation marks, which reduces readability and necessitates a subsequent text processing mo…
Conformer-based Target-Speaker Automatic Speech Recognition for Single-Channel Audio
Yang Zhang, Krishna C. Puvvada, Vitaly Lavrukhin +1
We propose CONF-TSASR, a non-autoregressive end-to-end time-frequency domain architecture for single-channel target-speaker automatic speech recognition (TS-ASR). The model consist…