Fine-Tuning Parakeet for Hebrew and Your Own Jargon

A sloth, a donkey, and a curious human send a prompt and see what they're doing. One hits a record with a question on his mind. Two brothers pick it up, see what they find. Corn takes his time. Herman's got the facts. Back and forth, they never hold back. My Weird Prompts, come on in. That's where we begin. My Weird Prompts, glad you're here.

This episode's ID is nine zero three six one three: Fine-tuning Parakeet for Hebrew and Your Own Jargon. Listen to the whole show at stream.myweirdprompts.com. This episode was generated using artificial intelligence. Please verify facts. What would it actually take to teach a small on-device speech model a language it's never heard?

And whether you'd even need to.

That's the two-part question Daniel sent in this week, and it comes out of something he's been living with for a while. He's been running NVIDIA's Parakeet models on his Android, and he says it's a qualitative step-up from Whisper, which from somebody who uses dictation all day every day isn't a casual compliment.

No, it isn't.

So here's his situation. He uses Parakeet V2, the English-only one, because most of what he dictates is English. But he lives in Israel, so sometimes he wants Hebrew, and he's made an argument I think is worth taking seriously. If you only ever speak one or two languages, a multilingual model is a crapshoot at guessing which one you're in. Two models, one per language, makes more sense to him. Then there's the second thing, which is a personal vocabulary fine-tuned for English to catch his tech jargon and the code-switching he does even in English. His example, and it's a good one: I'm going out to pick up milk at the makolet. Makolet is an everyday Hebrew word for a corner shop. English ASR models butcher it almost every time.

And every time.

He also flags a stubborn myth he keeps running into, that only Whisper can be fine-tuned, and says he's seen linguistic fine-tunes for smaller on-device models, but none in Hebrew. So two questions, both scoped to the NVIDIA models with inference on Android. First, what's entailed in creating a Hebrew fine-tune? Second, can you fine-tune these smaller models for personal vocabulary, including the code-switching case? And for both, data prep, resources, time, and whether it'll actually run on Android when you're done.

There's a lot in there, and one of those questions has a much happier answer than the other.

Which one?

The makolet one, but let's set the stage first because Parakeet deserves a proper introduction.

Yo.

Parakeet is a family of ASR models from NVIDIA built on what's called a fast conformer encoder paired with one of three decoders: CTC, RNN-T, or TDT, which is token and duration transducer. They're trained and shipped through NVIDIA NeMo, and the checkpoints live on Hugging Face under the NVIDIA namespace.

And the specific ones Daniel's using.

Parakeet TDT W.6B V2 is the English-only one. It sat at number one on the Hugging Face Open ASR leaderboard as of May twenty twenty-five. Then V3 is the multilingual successor covering twenty-five European languages. There are also CTC and RNN-T siblings at .6 and one point one billion parameters, a unified English model, a streaming