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Hindi voice typing on a PC, fully offline

Short answer: YazSes is free, open-source dictation software that types Hindi into any application on your PC while completely disconnected from the internet. You hold a key, speak, release, and the Devanagari text appears where your cursor is. Nothing is uploaded, there is no account, and there is no per-minute charge.

Most Hindi voice typing tools are websites or Android keyboards: you dictate into their text box and then copy the result out, and they stop working the moment your connection does. YazSes is a background program on your own computer, so it types into Word, a browser, a code editor, WhatsApp Web or a terminal directly, and it works on a train, in a village with patchy data, or on a machine that never touches the internet.

Which Indian languages are supported

The speech model shipped with YazSes is multilingual. Every one of these is supported by the installed model and can be set as your dictation language:

Language Code Language Code
Hindi (हिन्दी) hi Kannada (ಕನ್ನಡ) kn
Bengali (বাংলা) bn Malayalam (മലയാളം) ml
Tamil (தமிழ்) ta Punjabi (ਪੰਜਾਬੀ) pa
Telugu (తెలుగు) te Urdu (اردو) ur
Marathi (मराठी) mr Assamese (অসমীয়া) as
Gujarati (ગુજરાતી) gu Nepali (नेपाली) ne
Sanskrit (संस्कृतम्) sa Sindhi (سنڌي) sd

Accuracy is not the same across all of them — see honest limits below. Hindi, Bengali, Tamil and Urdu have more training data behind them than Assamese or Sanskrit.

Setting it up for Hindi

Two settings matter. The default model is English-only, so you must switch to a multilingual one:

[stt]
model = "small"        # NOT "small.en" — the .en models cannot do Hindi at all
language = "hi"

Then restart:

yazses restart

That is the whole configuration. The model downloads itself the first time you dictate — there is no separate download step, and after that it never needs the network again.

For another Indian language, change language to the code from the table above:

# Tamil
[stt]
model = "small"
language = "ta"

Which model size to choose

Indic languages benefit disproportionately from a larger model — more than English does. If Hindi output looks roughly right but keeps garbling word endings, the model size is usually the cause, not your microphone.

Model Suitability for Indian languages
small.en, base.en Will not work. English-only, no language tokens.
small Usable starting point for Hindi. Weak on the smaller languages.
medium Noticeably better across Indic languages. A good default if your CPU allows.
large-v3 Best available quality; slowest on CPU.

See performance tuning for the latency trade-off.

Hinglish — sentences that mix Hindi and English

Mixing English words into a Hindi sentence is normal Indian speech, and it is the case that trips up most dictation tools. YazSes has a routing layer for exactly this:

[polyglot]
enabled = true
pair = "hi-en"

Be clear about what this does: it routes between the two languages of the pair over the general multilingual model. It is not a purpose-trained code-switching model. A dedicated adapter can be supplied via [polyglot] adapter_path, but that is trained separately and the slot stays empty until you set it.

Will Devanagari actually type into my applications?

This is a fair question, because transcribing Hindi and typing Hindi are two different problems. YazSes has to synthesise keystrokes into whatever window has focus, and not every method can produce characters that are absent from your physical keyboard layout.

  • Linux on X11 — verified working. Devanagari types correctly through the xdotool backend, which maps each character to an X keysym rather than to a physical key. This was tested end-to-end: नमस्ते हिंदी dictated in, नमस्ते हिंदी received by the target application, byte for byte.
  • Any platform — the clipboard backend works by construction. It passes UTF-8 text straight through, so script is irrelevant to it:
[injection]
backend = "clipboard"
  • Linux on Wayland — verify it yourself before relying on it. The default Wayland backend, ydotool, injects at the kernel input layer below the keyboard layout, which is exactly the layer where non-Latin characters are most likely to be dropped. We have not been able to confirm Devanagari output through it. If you are on GNOME or KDE under Wayland, dictate one Hindi sentence into a text editor first; if nothing appears, switch [injection] backend to clipboard as above.

If your text is transcribed but nothing lands in the application, that is an injection problem, not a language problem — troubleshooting covers it.

Honest limits

  • Test it on your own voice before depending on it. Whisper's accuracy varies by language and by accent, and Indian-language performance is well behind its English performance. This page is not claiming parity.
  • Smaller Indian languages are genuinely weaker. Assamese, Sindhi, Sanskrit and Nepali have far less training data than Hindi. A larger model helps but does not erase the gap.
  • Multilingual models are slower than the English-only ones. On a modest CPU, medium in Hindi is meaningfully slower than base.en in English.
  • Wayland + Devanagari is unverified, as described above. X11 and clipboard injection are the paths we have actually tested.