Shruti Santali
Speech to text for Santali and English, on your device, with no server.
Try it now · in your browser · free, no login
Speak Santali, English or both. Shruti Santali 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 Santali, English or both.
Speak Santali, English or both. English words can stay in English (Latin script) and are underlined here; Santali is written in Ol Chiki. 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 Santali · 29.7 MB · Santali and English · 7 November 2026
Santali in Ol Chiki and English in one 29.7 MB file that runs on your device.
Shruti Santali writes Santali in Ol Chiki, and some English words stay in English letters, as here: ᱤᱱᱟᱹ ᱵᱚᱭᱦᱟ ᱡᱟᱢᱥᱮᱫᱽᱯᱩᱨ ᱨᱮ ᱢᱤᱫᱴᱟᱝ company ᱨᱮ ᱠᱟᱹᱢᱤᱭᱮᱫᱟᱭ. On our two tests we found no clear difference from IndicConformer Santali, a model almost five times its size: DiarBench native 33.6 against 34.2 (99 turns), IndicVoices valid 40.3 against 42.6, with both intervals including 0. It runs offline on a five-year-old phone, about 13× 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 Santali | 29.7 MB | 40.3 | 33.0 | 33.6 |
| IndicConformer Santali | 137.7 MB | 42.6 | 35.4 | 34.2 |
Word error rate %, lower is better; DiarBench 99 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: 99 turns from 28 conversations, 2,897 reference words, 63 of them English, in 34 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 Ol Chiki. Other Santali speech models exist that we have not measured; these results cover only the models listed here.
Where it falls short
- No lead over the one rival measured. DiarBench native -3.16 to +2.03, DiarBench English in English -4.94 to +0.25, IndicVoices valid -5.29 to +0.52. Character errors are higher: 15.88% vs 11.7% and 16.88% vs 13.3%.
- Ol Chiki only. It does not write Santali in Devanagari, Bengali, Odia or Latin script, and no script-ignored check exists for Ol Chiki.
- A thin English test. 63 English words in 2,897, of which 36.5% come out in English letters.
- English words in Ol Chiki: about 30 of DiarBench’s 63 by automatic alignment (doctor as ᱰᱟᱠᱛᱟᱨ). 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 test clips read by an AI voice, not real speech, played into this page on one phone’s Chrome, with the language set. All 4 came out in Ol Chiki. 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 Santali 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 Santali).
- Not tested on Santali: noise, music, far-away or speaker-played audio, Linux, Windows and iOS. No punctuation; numbers come out as words.
Try Shruti Santali Shruti Santali 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-santali.cact, from Shruti Santali's Hugging Face repo
./needle --model shruti-santali.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-santali.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 Santali’s model card on Hugging Face lists its training datasets and their licences.