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NVIDIA released Nemotron 3.5 Transcribe, an open-weight speech model available through Hugging Face and ModelScope that combines transcription, speaker attribution, English translation, and audio question answering in one checkpoint. It transcribes 40 languages, translates 31 languages to or from English, supports speaker attribution in 13 languages, processes recordings up to one hour, and labels as many as eight speakers. Natural-language prompts can request outputs such as speaker-labeled transcripts, translations, meeting decisions, or specified formatting for technical vocabulary. Audio question answering is trained in English, and NVIDIA advises users to verify names, speaker changes, and answers against recordings. NVIDIA reports that the model ranks first among open-weight models on the Hugging Face English Open ASR Leaderboard with 5.33% average word error rate, and ties for first on Artificial Analysis’s open-weight ranking at 2.8% AA-WER while activating about 4 billion of its 30 billion parameters per token. Granary v2 supplies an open data-curation pipeline and public manifests covering 463,062 hours of speech across 48 languages. In an NVIDIA medical-transcription case study using Eka Care recordings, fine-tuning reduced held-out-speaker word error rate from 22.44% with the best prompt to 12.17%, a 45.8% relative reduction. NVIDIA said forthcoming quantized versions will support local deployment, while the current checkpoint can be served with vLLM and is designed for recorded rather than live-streaming audio.