Shruti Marathi
Speech to text for Marathi and English, on your device, with no server.
Try it now · in your browser · free, no login
Speak Marathi, English or both. Shruti Marathi 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 Marathi, English or both.
The language is detected for you. English words stay in English (Latin script) and are underlined here; Marathi 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 Marathi · 29.7 MB · Marathi and English · 2 November 2026
The most accurate open Marathi speech-to-text model under 100 MB that we measured.
Shruti Marathi writes Marathi in Devanagari and keeps English words in English, the way people type: उद्या सकाळी नऊ वाजता physics ची exam आहे. Every other open model under 100 MB we measured gets more Marathi words wrong than right. It is within 1.33× of IndicConformer Marathi’s error rate on FLEURS (29.9 against 22.4) at about a fifth of its size, and on conversations that mix in English it makes fewer errors than that model when English words are written in English. It runs offline on a five-year-old phone, 12× faster than real time in 64 MB of memory with the native engine, and it invented no words on 100 silent clips.
| Model | File | FLEURS | Kathbath | DiarBench | IndicVoices | SPRING-INX | Vaani |
|---|---|---|---|---|---|---|---|
| Shruti Marathi | 29.7 MB | 29.9 | 24.5 | 38.2 | 22.2 | 42.9 | 32.2 |
| Whisper tiny | 44 MB | 273.1 | 278.1 | 373.9 | 321.8 | 375.6 | 338.7 |
| Whisper base | 82 MB | 177.9 | 193.2 | 402.9 | 450.2 | 420.6 | 412.3 |
| marathi-conformer-33m-moe | 136.7 MB | 41.0 | 32.2 | 76.7 | 66.0 | 77.3 | 64.0 |
| IndicConformer Marathi | 137.7 MB | 22.4 | 14.1 | 41.5 | 27.6 | 53.0 | 44.0 |
Word error rate %, lower is better; 200 clips per set, the same scorer for every model, measured 30 October to 2 November 2026. FLEURS and Kathbath are read Marathi; the other four are conversations that mix in English, scored with English words written in English. With the English words dropped from those references, IndicConformer Marathi is ahead (DiarBench 32.3 against 40.9). Whisper tiny and base add words, so their rates pass 100%.
Where it falls short
- On pure Marathi, IndicConformer Marathi (about 5× larger) makes about a quarter fewer errors on FLEURS and about 40% fewer on Kathbath.
- Spelling variants (उशिरा/उशीरा) and joined words count as errors and are common; some English loanwords come out in Devanagari.
- It does not do Hindi: pick Hindi above for Shruti Hindi.
- Noise, music and speaker-played audio were not tested.
Try Shruti Marathi Shruti Marathi 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-marathi.cact, from Shruti Marathi's Hugging Face repo
./needle --model shruti-marathi.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-marathi.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 Marathi’s model card on Hugging Face lists its training datasets and their licences.