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AI BriefWire / Briefing
Hugging Face published a guide on fine-tuning Nemotron 3.5, an automatic speech recognition (ASR) model. The tutorial explains how to adapt the model for specific languages, domains, or accents. This helps improve speech recognition accuracy for diverse user needs.
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A Blog post by NVIDIA on Hugging Face
If you've ever built a product that needs to transcribe speech, you've probably hit one of these walls:
Nemotron 3.5 ASR was built to collapse all four of those problems into one model.
One model, 40 language-locales. A single 600M-parameter checkpoint transcribes English (US/GB), Spanish (US/ES), German, French (FR/CA), Italian, Arabic, Japanese, Korean, Portuguese (BR/PT), Russian, Hindi, Turkish, Vietnamese, Dutch, Ukrainian, Polish, Finnish, Mandarin, Czech, Bulgarian, Slovak, Swedish, Croatian, Romanian, Estonian, Danish, Hungarian, Norwegian Bokmål, Norwegian Nynorsk, Hebrew, Greek, Lithuanian, Latvian, Maltese, Slovenian, and Thai. No per-language deployment, no model-swapping.
Real-time streaming, done right. The model is built on a Cache-Aware FastConformer encoder. Traditional "buffered" streaming re-processes overlapping chunks of audio at every step, doing the same work many times over. This model instead caches the encoder's internal state and reuses it — every audio frame is processed exactly once, with no overlap. The result is dramatically lower compute and end-to-end latency, with no accuracy penalty.
Punctuation and capitalization, natively. The output is production-ready text — proper casing, commas, periods, question marks — straight from the model. No separate punctuation-restoration step.
A Cache-Aware FastConformer encoder (24 layers). FastConformer is an efficient evolution of the Conformer architecture with linearly scalable attention. The "cache-aware" part is the streaming magic: the encoder keeps a cache of its self-attention and convolution activations from previous frames, so as new audio arrives it only computes what's genuinely new. Nothing is recomputed.