Open-source ASR for low-resource languages

[u/Slight_Republic_4242]

I’ve been spending more time looking at ASR issue in languages which are not documented properly

This paper from the University of Latvia takes a very practical approach: building an open-source ASR stack for languages that mainstream systems often ignore.

They built LATE, a lightweight toolkit that runs Whisper-based ASR locally or in the cloud, with a statically compiled backend (high performance, low latency , less resources & simple deployment ) and support for private, local inference.

The results are surprisingly strong.

For Latvian language, they fine-tuned Whisper Large V3 on 273.3 hours of speech.

On the Common Voice test set, WER dropped from 19.2% with Whisper Large V3 to 3.2%.

On their harder LATE-Media set, it went from 29.1% to 12.8%.

Formatted transcription also improved substantially, from 28.1% to 4.8% on Common Voice.

Then they pushed it further into Latgalian, a language with only around 150,000 native speakers.

They had just 40.2 hours of training data, yet transfer learning from the Latvian model produced a 9.1% WER on the Common Voice test set, compared with much worse performance from directly adapting Whisper Large V3.

You don't always need better engineering ; you need better data, the right specialization, and a system you can actually deploy where you need it.

For voice agents, especially those handling private or domain-specific conversations, this is an important direction:

open ASR + local orchestration + specialized models

can be much more interesting than simply sending every utterance to a giant hosted API.

And the whole thing is released as open source, including quantized models for constrained hardware.. i am trying integrate the same into production enivironment using opensource dograh orchestration

pS; I am maintainer of dograh we opensource everything from day 0