Samuel R.

Looking at earlier speech models, their training data, and how people use them in products and everyday use cases, one thing stood out: the same recording can need different outputs. Sometimes you want every word and filler. Sometimes you want clean, formatted text. Sometimes you want to add context, like names or technical terms, to help the model recognize them. Or you want a translation or an answer to a question about the recording. Users should be able to ask for what they need. This idea helped shape #Granary-v2. We expanded from 25 to 48 languages and improved label quality through better ASR labeling, transcript refinement, and quality filtering. We also built labels and instruction pairs for different transcription styles, contextual ASR, code-switching, translation, transcript-based captions, and question answering. #Granary-v2 is part of the training data behind #Nemotron-3.5-Transcribe, which lets users control transcription style, provide context, translate speech, and ask questions about audio. We’re sharing model weights, training recipes, a public subset of #Granary-v2 labels, and the curation pipeline built with NeMo Curator. A lot of work from the NeMo Speech team. Excited to finally share it! 🤗 Model: huggingface.co/nvidia/NVIDIA… 📊 Granary-v2 dataset: huggingface.co/datasets/nvid… 📝 Granary-v2 technical report: research.nvidia.com/labs/nem… 🛠️ Granary-v2 pipeline: github.com/NVIDIA-NeMo/Curat… #NVIDIA #NeMo #SpeechAI #SpeechRecognition #SpeechData #OpenSource