Silero Models: pre-trained TTS for Russian, CIS and Turkic languages

The gap Silero Models fills: TTS for languages the big vendors skip

Most hosted speech APIs cover a few dozen languages well and everything else badly or not at all. Silero Models takes the opposite position. The repository lists speakers for Chuvash, Crimean Tatar, Gagauz, Karakalpak, Kyrgyz, Khakas, Yakut, Siberian Tatar, Tatar, Tajik, Turkmen, Tuvan and Uzbek under the v5_turkic model, plus a separate v5_cis_base and v5_cis_ext group and a v5 group. The README states the models are fully end-to-end, come with a large library of voices, and are meant to be used in one line with minimal, portable setup. For Russian specifically, the README claims automated stress and homograph handling, which is the part that normally forces you to pre-process text before synthesis. The audience is narrow and clear: engineers building voice output for Russian or for Central Asian and Siberian languages who cannot get those voices from a commercial API, and who are willing to accept the licence terms.

How the models are loaded and where the weights come from

There is no server and no daemon. The architecture is a set of pre-trained checkpoints described in models.yml, plus a thin loading layer. The README says models are downloaded on demand both by the pip package and by PyTorch Hub, and that caching is your job: either do it manually or invoke a model once so it lands in a cache folder. The pip package and the PyTorch Hub path are based on the same code, so examples written for torch.hub.load transfer to the pip import with only the import line changed. The package declares two runtime dependencies in requirements.txt, omegaconf and torch, which tells you the config and tensor work is not hidden behind a framework. The repository also ships hubconf.py for the Hub entry point, and a src directory alongside the notebooks. The notebooks are the real documentation of data flow: examples_tts.ipynb, examples_tts_cis.ipynb and examples_tts_turkic_caucasian.ipynb each cover a model family.

Installing Silero Models and generating your first Russian audio file

The README gives two installation routes. The pip route is a single install command, and the README's own snippet shows the call shape for Russian:

pip install silero

After that, the documented usage imports silero_tts, selects a language and speaker, and calls apply_tts. The README uses language='ru' and speaker='v5_ru' in its example, and returns both a model object and an example_text string that the model was built to read.

from silero import silero_tts
model, example_text = silero_tts(language='ru',
                                 speaker='v5_ru')
audio = model.apply_tts(text=example_text)

The audio variable holds the synthesised waveform, and example_text is the sample sentence supplied with the model, which is the fastest way to confirm the checkpoint loaded and the speaker ID is valid. If you prefer the Hub route, the README's third flavour is torch.hub.load(), and it notes the same examples work for both. Nothing in the README documents an output sample rate argument for the pip call; the v5_turkic model section states that model supports 8000, 24000 and 48000 sampling rates, so check the model-specific section before assuming a rate. The first call downloads weights, so expect a delay on a cold machine and a cache folder afterwards.

The licence is the first thing to check, not the last

The README badge points at CC BY-NC 4.0, which excludes commercial use. The repository also carries a separate LICENSE_CIS file at the top level, next to LICENSE, and release v5.4 is titled Change pip-package license. That combination means the licence story is not one document. The README has a Licence section, but the repository does not spell out which model families fall under which file, and the GitHub sidebar reports the repository licence as NOASSERTION, meaning the platform could not classify it automatically. Read both files before you integrate. This is not a formality: a non-commercial restriction on a TTS model used inside a product is a blocking constraint, and the presence of a second licence file suggests at least part of the model set is handled differently. Treat the licence as an engineering input, and get your own legal reading rather than inferring from a badge.

Where Silero Models is the wrong choice

Three cases stand out. First, commercial deployment on a model covered by CC BY-NC 4.0. Second, languages outside the set: the README's tables enumerate specific speakers and language codes, and there is no fallback mechanism described for a language that is not listed. Third, stress and homograph handling. The README states plainly that the v5_turkic model does not support stress at all, while the Russian bullet claims automated stress and homographs. So the quality of prosody is not uniform across the model families, and a pipeline that assumes Russian-level text normalisation will behave differently on a Turkic language. There is also an operational gap: the README describes on-demand download and manual caching but does not document a rollback procedure or a pinned-version workflow, so if a checkpoint changes under you, the README does not tell you how to return to the previous one. The changelog file exists at the repository root, which is where you would look, but the README itself is silent on this.

Silero Models against a general-purpose TTS toolkit

The natural comparison is a toolkit such as Coqui TTS or a hosted cloud speech API. The difference is in what is fixed and what is flexible. A general-purpose toolkit expects you to bring a dataset or pick from a broad multilingual catalogue, and it exposes training and fine-tuning as first-class workflows. Silero Models is the opposite: the checkpoints are pre-trained, the speaker list is enumerated in models.yml, and the README frames the whole thing as one-line usage. You are not training anything. You are picking a language and a speaker ID and calling apply_tts. That is a real advantage when your target language is Chuvash or Tuvan and no toolkit ships a checkpoint for it, and a real disadvantage when you need a voice that is not in the list, because there is no documented path in the README to add one. A cloud API wins on operational simplicity and loses on language coverage for exactly the languages this project targets. The SSML support noted for the v5_turkic model, with details in the project wiki, is the main lever for controlling output without retraining.

Maintenance cadence and what an upgrade costs you

The last push to the repository was on 2026-07-31, and the most recent release is v5.6, tagged SAPI5 for Windows Release, dated 2026-06-04. Before that, v5.5 on 2026-02-03 added numpy to pip-requirements, and v5.4 on 2026-01-30 changed the pip-package licence. Two of the three recent releases are packaging and licence changes rather than new model families, which suggests the maintenance work is concentrated on distribution and platform integration. The SAPI5 release is the one with real operational weight: it means Windows speech interfaces are a supported target, not just Python calls. Upgrading is cheap in code, since the call surface is one function, but not free in behaviour: a speaker ID that exists in one model version may not exist in another, and the README points to models.yml as the file where metadata and newer versions are added. Diff that file, not the README, when you move between versions. The repository is not archived, and the last push is recent enough that the project is still being touched, but the release titles tell you what kind of attention it is getting.

Editorial conclusion

Adopt Silero Models if you need Russian, CIS or Turkic speech synthesis and your use is non-commercial, and verify the licence file that applies to your language group before shipping. Skip it if you need a commercial licence for a v5 model that ships only under LICENSE_CIS, or if you need a language outside the models.yml list. Check models.yml for the current speaker IDs and the release notes for the SAPI5 Windows build before you plan a deployment.

Frequently asked questions

Official sources

1. Issues
2. README
3. Releases
4. snakers4/silero-models on GitHub