Vak Conformer targets speech recognition in six Indic languages

Shunya Labs has published Vak Conformer, a Conformer-based automatic speech recognition model built to transcribe six Indic languages. The model is available now on Hugging Face.

The Conformer architecture, which combines convolutional layers with self-attention, has become a common backbone for modern ASR systems because it captures both local acoustic detail and longer-range context. Applying it to Indic speech is a practical choice: these languages present rich phonetic variety and, in many cases, limited high-quality training data compared with English.

Why it matters

Speech recognition for South Asian languages remains thin relative to the size of the population that speaks them. A model tuned specifically for a group of Indic languages can lower the barrier for developers building voice interfaces, transcription tools, and accessibility features in regional markets.

• Architecture: Conformer, a widely used ASR backbone
• Coverage: six Indic languages
• Availability: open weights on Hugging Face under a custom license

The release record lists few hard specifications — parameter count, training data, and benchmark figures are not detailed in the metadata — so teams evaluating it should consult the model card directly and test against their own audio. As an initial release, it establishes a foundation the developers can build on rather than a finished, benchmarked system.