Shruti Dogri
Speech to text for Dogri and English, on your device, with no server.
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Speak Dogri, English or both. Shruti Dogri writes it down on your device.
Shruti · checking this device
Audio never leaves your device. Shruti and its open-source engine run inside this page.
(or drop one on the transcript)
Load Shruti, then press Record and speak Dogri, English or both.
Speak Dogri, English or both. English words can stay in English (Latin script) and are underlined here; Dogri is written in Devanagari. While you speak, words appear once they have settled, a second or two behind you; when you stop, Shruti reads the whole recording once more for the final text.
Shruti Dogri · 29.7 MB · Dogri and English · 7 November 2026
Dogri and English in one 29.7 MB file that runs on your device.
Shruti Dogri writes Dogri in Devanagari, and it keeps English words in English letters, as here: कटड़े आह्ली बस हून चलने आह्ली ऐ ticket लेई लैओ. It is one small file, and we make no comparative claim for it: IndicConformer Dogri is ahead on the one sizeable test, and the conversation test is only 24 turns. It runs offline on a five-year-old phone, about 10× faster than real time in 64 MB of memory with the native engine, and it invented no words on 100 silent clips.
| Model | File | IndicVoices valid | DiarBench, English in English | DiarBench, native |
|---|---|---|---|---|
| Shruti Dogri | 29.7 MB | 43.5 | 34.5 | 37.0 |
| IndicConformer Dogri | 137.7 MB | 38.5 | 39.7 | 37.4 |
Word error rate %, lower is better; DiarBench 24 turns, the same scorer for every model, measured 3 November 2026. IndicVoices valid is IndicVoices’ own held-out split: its speakers are kept out of training and its sentences blocked, but it is the same source as our training data, so it flatters us. DiarBench is real conversation from outside it: 24 turns from 17 conversations, 479 reference words, 40 of them English, in 16 turns. The English-in-English column scores against references that write English words in English (Latin script), and English words count only if written in English letters; the native column scores against references that write those English words in Devanagari. Other Dogri speech models exist that we have not measured; these results cover only the models listed here.
Where it falls short
- The test can’t support a claim. DiarBench Dogri is 24 turns from 17 conversations, 479 words, 40 English words. No independent read set was available.
- IndicConformer Dogri is ahead on IndicVoices valid (38.5 vs 43.5; +2.58 to +7.27). Character errors: 26.59% vs 17.91% there, 25.64% vs 16.98% on DiarBench.
- One rival measured. Whisper does not list Dogri. Other Dogri models exist that we have not measured.
- English words in Devanagari: about 12 of DiarBench’s 40 by automatic alignment. DiarBench also glosses some local names in Latin, so a correct native spelling can score as an error.
- In a browser it was tested on 4 lines played into this page on one phone’s Chrome, with the language set. All 4 came out in Devanagari, and 3 kept some English letters. Not yet tested with a live microphone. On the other Shruti languages where we measured it, plain English loses a little accuracy this way.
- No Dogri reader has reviewed this output yet.
- Loud noise: Shruti models sometimes repeat a phrase over and over in loud noise (measured on Shruti Hindi-family models, not on Dogri).
- Not tested on Dogri: noise, music, far-away or speaker-played audio, Linux, Windows and iOS. No punctuation; numbers come out as words.
Try Shruti Dogri Shruti Dogri on Hugging Face the model file, the full results and how each model was run
Use it in your app
Every Shruti model runs in Cactus’s open-source needle engine. Download the engine for your platform and the file for your language, and pass it a WAV file or 16 kHz audio.
# Command line (Mac, Linux, Windows)
# Engine: huggingface.co/Cactus-Compute/needle3, your platform
# Model: shruti-dogri.cact, from Shruti Dogri's Hugging Face repo
./needle --model shruti-dogri.cact --audio clip.wav
# for speech that is only English: --audio-language en
# Python
pip install cactus-needle
import needle
shruti = needle.Whistle(weights="shruti-dogri.cact")
print(shruti.transcribe("clip.wav")["text"])- Android, iOS and the web: call the engine’s C API.
needle_loadtakes the model file,needle_transcribea clip, andneedle_stream_transcribe_processabout a second of live audio at a time. The demo on this page makes the same calls, from WebAssembly in a Web Worker. - Leave the language unset for Hindi and Hinglish: detection does as well as setting it on Hindi, Hinglish, accented Hindi and US English. Set English when the speech is only English with an Indian accent, which detection often takes for Hindi. On a subset of Svarah: 21.13% word errors with English set, 25.75% detected.
- Keywords, one per line, favour the names, places and product words your users say.
- Cactus’s Python package (cactus-needle) sends anonymous usage counts by default; set
NEEDLE_TELEMETRY=0to turn that off. The command-line engine and the WebAssembly engine on this page make no network requests of their own.
Credits
Every Shruti model is built on Cactus Whistle and runs in the needle engine, both by Cactus Compute under Apache-2.0. The engine files on this page are Cactus’s WebAssembly build, unmodified, served with its licence and notice. Shruti Dogri’s model card on Hugging Face lists its training datasets and their licences.