How to Fine-Tune Nemotron 3.5 ASR for Your Language, Domain, or Accent

The original content of this article is not provided, so for now its positioning can only be inferred from the title and source: it should be a technical tutorial published by NVIDIA on the Hugging Face Blog, on the topic of how to fine-tune Nemotron 3.5 ASR to make the automatic speech recognition model better suited to a specific language, a specific professional domain, or a specific accent. For Taiwanese readers, the practical value of this kind of content lies in filling the gaps commonly seen in general-purpose speech recognition models, such as when facing Taiwanese Mandarin, Mandarin-Taiwanese code-mixing, industry jargon, customer-service recordings, meeting recordings, or medical and financial vocabulary, where pretrained ASR may produce mishearings, misspelled proper nouns, and poor punctuation and sentence segmentation due to a different corpus distribution. The core concept of fine-tuning is to use audio and verbatim transcript data closer to the target scenario, so the model learns the pronunciation, wording, and acoustic conditions of that context. From the title, the article may cover three customization directions: language adaptation, domain adaptation, and accent adaptation. Language adaptation suits low-resource or non-mainstream language scenarios; domain adaptation leans toward enterprise internal data, professional terminology, or specific tasks; and accent adaptation is especially important for cross-regional services, voice assistants, and customer-service systems. However, because no source details are available, one cannot presume whether the article provides complete code, data format examples, training parameters, model weight links, or NeMo or other framework settings, nor can one confirm its magnitude of performance improvement. Overall, this is a tutorial on ASR fine-tuning that leans toward engineering implementation, with medium-to-high importance, suitable for ML engineers and developers who are working on speech-to-text, meeting transcription, voice agents, subtitle generation, or enterprise voice-data processing to follow.