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Russian Word Stress by Silero

Project description

Mailing list : test Mailing list : test License: MIT

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Silero Stress


Silero Stress — pre-trained enterprise-grade automated stress and homograph disambiguation for the Russian language.


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Fast start


Dependencies

System requirements to run python examples on x86-64 systems:

  • python 3.8+;
  • 1G+ RAM;
  • A modern CPU with AVX, AVX2, AVX-512 or AMX instruction sets.

Dependencies:

  • torch>=1.12.0;

Using pip: pip install silero-stress

from silero_stress import load_accentor
accentor = load_accentor()
sample_sent = "Меня зовут Лева Королев. Я из готов. И я уже готов открыть все ваши замки любой сложности!"
print(accentor(sample_sent))

Using torch.hub:

import torch
torch.set_num_threads(1)

accentor = torch.hub.load(repo_or_dir='snakers4/silero-stress)
sample_sent = "Меня зовут Лева Королев. Я из готов. И я уже готов открыть все ваши замки любой сложности!"
print(accentor(sample_sent))

Key Features


  • Wide coverage

    Silero Stress covers ~4M known Russian words and word forms with 100% accuracy. It also covers ~2K homographs with F1 score of 0.85, per-word accuracy of 91% and total dataset accuracy of 93%.

  • Fast

    On 1 CPU thread every ordinary word takes ~0.5ms to process, every 400-char sentence with two homographs (common prod case) takes ~30ms.

  • Lightweight and portable

    Total package is about 50MB in size, boasting ~400x compression ratio.

  • General and generalizable

    Silero Stress was trained on a large dataset, containing ~4M known words and word forms and ~120M annotated sentences with homographs. It also works with unknown (and invented) words with 60-70% accuracy.

  • No Strings Attached

    Published under permissive license (MIT) Silero Stress has zero strings attached - no telemetry, no keys, no registration, no built-in expiration, no keys or vendor lock.

  • Minimal and minified

    Code bloat, unnecessary dependencies and libraries are removed. Silero Stress depends only on PyTorch as a neural engine and the Python standard library.


Typical Use Cases


  • Academic research
  • Text-to-speech applications
  • Telephony and call-center automation, voice bots

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Get In Touch


Try our models, create an issue, start a discussion, join our telegram chat, email us, read our news.

Please see our wiki for relevant information and email us directly.

Citations

@misc{Silero Stress,
  author = {Silero Team},
  title = {Silero Stress: pre-trained enterprise-grade automated stress and homograph disambiguation for the Russian language},
  year = {2025},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/snakers4/silero-stress}},
  commit = {insert_some_commit_here},
  email = {hello@silero.ai}
}

Vocabulary issues


If you see any problems with any particular words, please send your examples as an issue or a discussion.

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