Silero TTS: Text-to-Speech in Genuinely One Line of Code

The Silero team's own tagline for this project is "pre-trained text-to-speech models made embarrassingly simple," and it's not an exaggeration — a working voice comes from a single torch.hub.load call, no training pipeline, no multi-file configuration, no separate vocoder to wire up. Silero TTS has quietly stayed in active development since 2020, and its combination of minimal setup with genuinely strong coverage of Russian, other CIS languages, and several Indic languages makes it a distinct option among lightweight pretrained TTS models.

What Is Silero TTS?

Silero TTS is a collection of open-source, MIT-licensed pretrained text-to-speech models, maintained on GitHub under snakers4/silero-models since its initial release around September 2020, with active updates continuing through V3, V4, and the current V5 generation released in October 2025. The models are end-to-end, run on either CPU or GPU, and depend on very little beyond PyTorch itself — a deliberate design choice that keeps both the model files and the deployment footprint small.

Functional Advantages of Silero TTS

• Genuinely one-line usage, loading directly through PyTorch Hub or the silero pip package with no separate setup steps.
• Minimal dependencies, requiring only PyTorch 1.12+ and the Python standard library for standalone use, with torchaudio and omegaconf needed only for specific optional features.
• Runs on CPU or GPU interchangeably, with the same code path working either way by simply changing the target device.
• Portable, standalone model packaging via PyTorch's torch.package.PackageImporter, letting a model ship as a single .pt file with no external dependency tree to manage at deployment time.
• Built-in SSML support across the V3, V4, and V5 generations, giving direct markup-based control over pauses, emphasis, and prosody.
• Genuinely strong Russian, CIS, and Indic language coverage — a linguistic niche that most lightweight open-source TTS projects don't prioritize.
• A five-year, continuously updated track record, which is a meaningfully longer and more consistent maintenance history than many newer open-source TTS releases can claim.

Language Coverage: Where Silero Actually Shines

Rather than chasing the broadest possible language count, Silero has built particular depth in a specific region: Russian is its flagship language, alongside Ukrainian, Kazakh, Uzbek, Tatar, Armenian, Azerbaijani, Belarusian, Georgian, Kyrgyz, and Tajik — a genuinely comprehensive sweep of Commonwealth of Independent States languages that few competing open-source projects cover with this level of care. Separately, Silero also supports several Indic languages, including Hindi, Tamil, and Bengali, though these typically require romanized input text rather than accepting native script directly. If your project needs any of these specific languages, Silero's depth here is a real differentiator against broader-but-shallower multilingual models built primarily around Western European and East Asian languages.

A Dedicated Tool for Russian Pronunciation Accuracy

Beyond the core TTS models, the project includes a companion extension called silero-stress, built specifically to handle two notoriously tricky aspects of Russian text: automated stress marking and homograph disambiguation (correctly reading words that are spelled identically but pronounced differently depending on meaning). It covers roughly 4 million words with a reported F1 score of 0.85 on homograph disambiguation specifically, and processes text at around 0.5 milliseconds per word on a single CPU thread — fast enough to run as a lightweight preprocessing pass immediately before synthesis rather than a separate offline step. This is the kind of narrow, language-specific engineering that a broad multilingual model rarely bothers with, and it directly improves output naturalness for Russian text specifically.

Getting Started with Silero TTS

1. Install via pip for the simplest path. pip install silero gets you the package; models themselves download on demand the first time you request them, either through pip or PyTorch Hub, and can be cached locally to avoid repeated downloads.
2. Load a model with a single torch.hub.load call. torch.hub.load(repo_or_dir='snakers4/silero-models', model='silero_tts', language='ru', speaker='v5_ru') is the documented pattern — swap language and speaker for your target language and desired model version.
3. Generate audio with apply_tts. Once loaded, model.apply_tts(text=your_text, speaker=speaker_name, sample_rate=sample_rate) returns the synthesized audio directly; supported sample rates are 8000, 24000, and 48000 Hz depending on your quality needs.
4. Check models.yml in the repository for the current voice and language list. Since new versions and speakers are added over time, this file is the authoritative, up-to-date source rather than relying on older tutorials that may reference deprecated model IDs.
5. Use SSML markup for pacing and emphasis control. V3, V4, and V5 all support it directly in the input text — the project's Colab examples are the fastest way to see the specific tag syntax in action before writing your own.
6. Package a model as a standalone .pt file for constrained deployment environments. Using torch.package.PackageImporter, you can bundle a model so it only needs PyTorch and the standard library at runtime, without carrying the full repository's dependency tree into production.

Tips for Better Results

• Use the silero-stress extension for Russian content specifically. Given how much stress placement and homograph ambiguity affect Russian pronunciation naturalness, running this lightweight preprocessing step before synthesis is worth the minimal added latency.
• Check whether your target language needs romanized input. For the supported Indic languages in particular, confirm the expected input format before assuming native-script text will work directly.
• Pick your sample rate based on your actual output destination. 8000 Hz is fine for constrained bandwidth or embedded scenarios, while 48000 Hz is the better choice when the output feeds into a production audio pipeline where quality will be scrutinized.
• Don't expect voice cloning. Silero works from a fixed roster of pretrained voices per language rather than zero-shot cloning from a reference clip — if reproducing a specific real voice is your goal, a dedicated cloning model is the right tool instead.
• Revisit models.yml periodically if you're on an older integration. Given the project's active update cadence through V5, newer language or voice additions may not be reflected in documentation or tutorials written against earlier versions.

Silero TTS vs. Other Lightweight Pretrained Models

Silero's specific niche is genuine depth in a language region — Russian and the broader CIS space — that most other lightweight open-source TTS projects treat as a minor addition at best, combined with a setup process that's hard to make any simpler than a single function call.

Frequently Asked Questions

Is Silero TTS really usable in one line of code?

Yes — a single torch.hub.load call (or the equivalent from silero import silero_tts via pip) loads a working model, with no separate training pipeline or multi-step configuration required.

What languages does Silero TTS support best?

Russian is its primary focus, alongside strong coverage of other CIS languages — Ukrainian, Kazakh, Uzbek, Tatar, Armenian, Azerbaijani, Belarusian, Georgian, Kyrgyz, and Tajik — plus several Indic languages including Hindi, Tamil, and Bengali, which typically require romanized input.

Does Silero TTS support voice cloning?

No. It works from a fixed set of pretrained voices per language and version rather than zero-shot cloning from a reference audio clip.

Is Silero TTS free for commercial use?

Yes. It's released under the MIT license, which permits commercial use without royalties or separate licensing agreements.

What is silero-stress, and do I need it?

It's a companion tool specifically for Russian text, handling automated stress marking and homograph disambiguation before synthesis. It's optional but recommended if pronunciation accuracy on Russian content matters to your project, since Russian stress placement significantly affects how natural the output sounds.

Create Speech Online with Echora

For a browser-based workflow with a similar goal, use Echora’s Text to Speech. Enter up to 5,000 characters, choose a built-in or saved custom voice, add supported delivery tags when needed, and adjust voice stability. You can then preview the result and download the finished MP3.

Create Speech from Text →