silero
Silero Models: pre-trained enterprise-grade TTS models.
Decision gist · record as of 2026-08-14
Yes. Silero is actively maintained, has no known vulnerabilities, and offers a straightforward pip install with low friction. The permissive MIT license poses no restrictions. It's a good fit if you need multilingual TTS—especially for Russian or CIS languages—and can tolerate the PyTorch dependency. If you need English-only TTS or prefer lighter dependencies, evaluate alternatives first.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires PyTorch-compatible system with AVX2 instruction set support on x86/64 platforms; models download on first use.
- Low install friction with a pure-Python wheel.
- Actively maintained with recent commits and no known vulnerabilities.
License · maintenance · safety
permissive license (permissive) — Licensed under MIT, permissive terms that allow commercial and private use with minimal restrictions.
last release 2026-02-03 (192 days) · last repo commit 2026-07-31 · 6,063 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 138,200 downloads/mo, #11,339 on PyPI
Alternatives
Verify before relying
pip install silero
from silero import silero_tts
model, example_text = silero_tts(language='ru', speaker='v5_ru')
audio = model.apply_tts(text=example_text)- Whether V5 models support languages beyond Russian and CIS region languages listed in the excerpt
- Exact model file sizes and typical memory footprint during inference
- Whether standalone use (without PyTorch) is fully supported or requires manual setup
- Performance characteristics on CPU vs GPU for different model versions
What it is and what it does
Silero is a collection of pre-trained neural text-to-speech models designed for production use. It converts written text into natural-sounding speech across multiple languages, with particular strength in Russian (supporting automated stress and homograph resolution) and CIS region languages. The package provides end-to-end models that work on both CPU and GPU, downloadable on demand and cached locally.
You use it by loading a model for your target language and speaker, then calling apply_tts() with your text. It supports SSML markup for fine-grained control over speech characteristics. The package integrates with PyTorch and can be used either via pip or PyTorch Hub, making it portable for deployment in applications ranging from voice assistants to accessibility tools.
Use it for
- Generate Russian speech with correct stress and homograph disambiguation for voice applications
- Build multilingual chatbots or voice interfaces supporting CIS region languages
- Create accessible audio versions of text content in supported languages
- Synthesize speech with SSML markup for prosody control in production systems
- Integrate offline TTS into applications without cloud API dependencies
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Silero is actively maintained, has no known vulnerabilities, and offers a straightforward pip install with low friction. The permissive MIT license poses no restrictions. It's a good fit if you need multilingual TTS—especially for Russian or CIS languages—and can tolerate the PyTorch dependency. If you need English-only TTS or prefer lighter dependencies, evaluate alternatives first.
Install
silero on PyPI
Before you install
Low install friction with a pure-Python wheel. Actively maintained with recent commits and no known vulnerabilities. Requires torch, omegaconf, and numpy as runtime dependencies.
Requires PyTorch-compatible system with AVX2 instruction set support on x86/64 platforms; models download on first use.
License in practice
Licensed under MIT, permissive terms that allow commercial and private use with minimal restrictions.
Quickstart
pip install silero
from silero import silero_tts
model, example_text = silero_tts(language='ru', speaker='v5_ru')
audio = model.apply_tts(text=example_text)Verify before relying
- Whether V5 models support languages beyond Russian and CIS region languages listed in the excerpt
- Exact model file sizes and typical memory footprint during inference
- Whether standalone use (without PyTorch) is fully supported or requires manual setup
- Performance characteristics on CPU vs GPU for different model versions
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.7 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagestorchomegaconfnumpy |
| Maintenance | Actively maintained 192 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 138,200 / month, #11,339 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: DevelopersIntended Audience :: Science/ResearchLicense :: OSI Approved :: MIT LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.6Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Multimedia :: Sound/AudioTopic :: Scientific/Engineering :: Artificial Intelligence |
Evidence: silero-0.5.5-py3-none-any.whl
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See also silero-vad · mlx-audio · lhotse · coqui-tts · piper-tts · google-cloud-texttospeech · livekit-plugins-soniox · pyttsx3 · kokoro-onnx · TTS