Large models know English and Russian, and past that the map is mostly blank: for the languages of Russia there is very little ready to use, and what exists was built by small teams and enthusiasts. This collection gathers those models: speech recognition, synthesis, translation, text analysis. Quality varies, so test on your own recordings, but this is the only way to build a service in a native language without waiting for the big labs to get there.
Russian to Tuvan translation that runs on your own machine: ready GGUF builds, a LoRA adapter and a standalone Windows app. The author warns that names, numbers and important text should be double-checked with a native speaker.
The most complete set of models for the Sakha (Yakut) language in one place: language models of several sizes, translation to and from Russian, embeddings and text recognition from images. Quality varies, so test it on your own data.
A small toolkit for Bashkir text: a fill-mask language model, a Bashkir language detector, fastText embeddings, a Bashkir-Russian pair scorer and a letter restorer. All published in autumn 2026, so check quality on your own data.
Years of work by one enthusiast around the Bashkir Common Voice project: Whisper, wav2vec2, w2v-BERT and GigaAM fine-tuned for Bashkir, Tatar and Mari. Quality differs between models, so test them on your own recordings.
A community that extends NLLB and mBART to low-resource languages. Its models cover Erzya, Tuvan, Bashkir, Tatar, Chuvash, Buryat, Mari, Khakas and Karachay-Balkar. Quality differs by language, so test it on your own texts.
A recent set of models for Tatar: morphological analysis on several base models, question answering about Tatar and Russian place names, and Mistral and GPT-2 fine-tunes for Tatar.
About two dozen Whisper, wav2vec2 and WavLM fine-tunes for Karelian, including recordings where the speaker switches from Karelian to Russian. The model cards are empty, so quality must be checked on your own recordings.
A multilingual encoder trained on more than 1800 languages. The list includes Tatar, Bashkir, Chuvash, Udmurt, Buryat, Komi, Ingush and other languages of Russia. A base for classifiers and search over such texts.
A set of LaBSE fine-tunes that turn sentences into vectors: Buryat, Mari, Udmurt, Ingush, Chuvash, Sakha and Kalmyk. Trained on pairs with Russian; quality differs by language.
Chuvash speech synthesis: an F5-TTS fine-tune on top of the Russian version, trained on 24 hours of Common Voice recordings. It can copy a voice from a sample — only with the voice owner’s consent. The author calls it an experiment, so check quality on your own texts.
A project around the Udmurt language: encoders based on mBERT, rubert-tiny2 and Glot500, part-of-speech taggers, a typo correction model and Udmurt-English embeddings.
Detects which language a text is written in: the third version covers more than two thousand labels, including Tatar, Bashkir, Chuvash, Udmurt, Mari, Erzya, Komi and other languages of Russia.
A language model for Tatar: Mistral 7B converted to a Tatar tokenizer, plus a version for translating between Tatar and a dozen other languages, including Russian. Check quality on your own texts.
Meta speech models covering more than a thousand languages. The supported list includes Bashkir, Tatar, Chuvash, Sakha, Ossetian, Chechen, Avar, Udmurt, Mari, Erzya, Moksha, Kalmyk and Karelian. Quality varies a lot by language, so test it on your own recordings.
Separate mGPT versions, one per language: Bashkir, Buryat, Kalmyk, Mari, Ossetian, Tatar, Tuvan, Chuvash and Sakha. Each card also lists Russian and English.
An open model can run on your own server: data stays in-house, there is no per-request fee, and the model can be fine-tuned on your documents.
