NVIDIA releases the Nemotron 3.5 Transcribe speech transcription model, tying for first among open-source models with 2.8% WER
NVIDIA has released the open-weight speech transcription model Nemotron 3.5 Transcribe, with 30B total parameters and 4B activated parameters. On Artificial Analysis's non-streaming speech transcription leaderboard, it achieves a 2.8% AA-WER, tying for first among open-source models with Mistral's Voxtral Small. On the speech agent dialogue dataset AA-AgentTalk, it leads Voxtral Small's 2.3% with 1.8%. Its activated parameters are 4B, while Voxtral Small's are 24B; other open-source models of comparable scale have a WER of about 4%.
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AI score 59
Artificial Analysis's evaluation shows that this model, with 4B activated parameters, matches the recognition accuracy of larger models, which can be used to gauge the efficiency frontier of open-source speech transcription models.
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NVIDIA's Nemotron 3.5 Transcribe ties for #1 among open weights Speech to Text models with 2.8% WER, using just 4B active parameters
Nemotron 3.5 Transcribe is NVIDIA's new open weights Speech to Text model for non-streaming transcription, with 30B total and 4B active parameters, and the most accurate NVIDIA model on our non-streaming Speech to Text leaderboard.
Key takeaways
➤ Accuracy: Nemotron 3.5 Transcribe scores 2.8% on the AA-WER Index, tied with Mistral's Voxtral Small for #1 among open-weight models. It leads Voxtral Small on AA-AgentTalk, our dataset of voice agent conversations, at 1.8% vs. 2.3%.
➤ Model size: With 4B active parameters (30B total), Nemotron 3.5 Transcribe sits on the open weights frontier of AA-WER vs. active parameters. It matches the accuracy of Mistral's Voxtral Small with 6x fewer active parameters (4B vs. 24B), while other open weights models of a similar size sit at around 4% WER.
Congratulations to @NVIDIAAI on the release!
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