Shruti Sindhi
Speech to text for Sindhi and English, on your device, with no server.
Try it now · in your browser · free, no login
Speak Sindhi, English or both. Shruti Sindhi 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 Sindhi, English or both.
Speak Sindhi, English or both. English words can stay in English (Latin script) and are underlined here; Sindhi is written in Devanagari, not Perso-Arabic script. 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 Sindhi · 29.7 MB · Sindhi and English · 7 November 2026
Sindhi, written in Devanagari, and English in one 29.7 MB file that runs on your device.
Shruti Sindhi writes Sindhi in Devanagari, and it keeps English words in English letters, as here: छोकिरे जो biodata ऐं photo मोकिलियो. It writes Sindhi in Devanagari, not Perso-Arabic script. It is one small file, and we make no comparative claim for it: the conversation test is only 27 turns. It runs offline on a five-year-old phone, about 11× 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 Sindhi | 29.7 MB | 33.0 | 34.2 | 38.0 |
| IndicConformer Sindhi | 137.7 MB | 33.1 | 38.7 | 35.8 |
Word error rate %, lower is better; DiarBench 27 turns, the same scorer for every model, measured 3 November 2026. Whisper is not in this table: it hardly writes Devanagari on these sets, so its error rates would measure script, not accuracy. 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: 27 turns from 17 conversations, 971 reference words, 51 of them English, in 12 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 Sindhi speech models exist that we have not measured; these results cover only the models listed here.
Where it falls short
- Devanagari only. Readers of Perso-Arabic Sindhi can’t use the output. FLEURS Sindhi is in Arabic script and was left out for this reason, so there is no independent read test.
- A small test. DiarBench Sindhi is 27 turns from 17 conversations with 51 English words, too few to say much.
- The level result is on the flattering set. IndicVoices valid (-2.45 to +2.39) shares a source with our training data. Character errors there 19.9% vs 12.41%, and on DiarBench 21.64% vs 14.33%.
- English words in Devanagari: about 7 of DiarBench’s 51 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 2 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 Sindhi 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 Sindhi).
- Not tested on Sindhi: noise, music, far-away or speaker-played audio, Linux, Windows and iOS. No punctuation; numbers come out as words.
Try Shruti Sindhi Shruti Sindhi 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-sindhi.cact, from Shruti Sindhi's Hugging Face repo
./needle --model shruti-sindhi.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-sindhi.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 Sindhi’s model card on Hugging Face lists its training datasets and their licences.