# Is Shruti SOTA?

Pending benchmarks. Announced Oct 19, 2026; no independent results yet.

As of Nov 3, 2026, Shruti is pending benchmarks on isitsota: announced Oct 19, 2026, with no independent results on the boards we track yet.

Udgam Labs' speech-to-text model for Hindi, English and Hinglish in one 29.5 MB file that runs offline on a phone, laptop or in a browser tab. Built on Cactus Compute's Cactus Whistle: Udgam fine-tuned all 55M weights on 2,732 hours of openly licensed Indian speech and added 714 Devanagari vocabulary pieces. Apache-2.0.

Canonical: https://isitsota.com/m/shruti
Last updated: 2026-10-24
Tracking since: 2026-10-24

## Announced facts

| Fact | Value |
| --- | --- |
| Lab | Udgam Labs |
| Announced | Oct 19, 2026 |
| API | Not yet |
| Open weights | Published |
| Parameters | 55M (29.5 MB file, 4-bit weights / 8-bit activations) |
| Base model | Cactus-Compute/whistle (Cactus Whistle, Apache-2.0); fine-tuned derivative by Udgam Labs, not trained from scratch |
| Training data | 2,732 hours of openly licensed Indian speech (IndicVoices, Vaani, Shrutilipi, Common Voice 17 and others) |
| Languages | Hindi in Devanagari, English, and Hinglish with English words kept in Latin script |
| Runtime | One .cact file for Cactus's open-source needle engine (Mac, Linux, Windows, Android, iOS, WebAssembly); no server needed |
| Input | 16 kHz mono audio; one pass up to 30 s, longer audio in 30 s windows |
| License | Apache-2.0 (weights), keeping Cactus's notice |

Tags marked unsure are not stated on the lab's own pages.

## What would let us call it

Verdicts use only independent boards we already track. Two consecutive snapshots are required before any crown.

- [x] Publicly announced with a source
- [x] Usable by the public (API or weights)
- [ ] Listed on an independent board we track
- [ ] Enough results for a rank in a category
- [ ] Holds across two snapshots (crowns only)

## What the lab claims

Self-reported. Shown for context; not used in any verdict.

- Lab claim: word error rate on Hindi of 20.46 (Monsoon), 19.92 (FLEURS) and 26.28 (Lahaja), against Whisper large-v3's 33.29, 28.88 and 31.19 in Udgam's harness; Udgam reports Whisper large-v3 clearly ahead on English.
- Lab claim: 35.21 WER on MUCS 2021 Hinglish, the fewest errors among the open models Udgam measured; the larger Hindi-only Koyal-hi-120M and IndicConformer are more accurate on pure Hindi.
- Lab claim: about 1 second per sentence and 80 MB peak memory offline on a Samsung Galaxy F62.

## Sources

- LAB: [Shruti release post (Udgam Labs blog)](https://udgamlabs.com/blog/shruti) (2026-10-19)
- LAB: [Shruti model page (Udgam Labs)](https://udgamlabs.com/models/shruti) (2026-10-19)
- REPO: [udgamlabs/Shruti model card (Hugging Face)](https://huggingface.co/udgamlabs/Shruti)
- REPO: [udgamlabs/shruti (GitHub)](https://github.com/udgamlabs/shruti)

Tracking continues until an independent board lists it.

## Related

- Lab: [Udgam Labs](https://isitsota.com/labs/udgam-labs.md)
- All pending releases: https://isitsota.com/releases.md
- JSON: https://api.isitsota.com/v1/models/shruti