82 lines
2.9 KiB
Markdown
82 lines
2.9 KiB
Markdown
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---
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datasets:
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- bond005/taiga_speech_v2
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- bond005/podlodka_speech
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- bond005/rulibrispeech
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language:
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- ru
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license: apache-2.0
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metrics:
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- wer
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pipeline_tag: automatic-speech-recognition
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library_name: transformers
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widget:
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- example_title: Нейронные сети - это хорошо!
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src: https://huggingface.co/bond005/whisper-large-v3-ru-podlodka/resolve/main/test_sound_ru.flac
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- example_title: К сожалению, система распознавания речи не всегда стабильна, особенно
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в шумных условиях.
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src: https://huggingface.co/bond005/whisper-large-v3-ru-podlodka/resolve/main/test_sound_with_noise.wav
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- example_title: Мимо театра мальчик ходил довольно часто — белое, со взбитыми сливками,
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здание-торт.
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src: https://huggingface.co/bond005/whisper-large-v3-ru-podlodka/resolve/main/anna_matveeva_test.wav
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model-index:
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- name: Whisper Large V3 Russian Podlodka by Ivan Bondarenko
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results:
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- task:
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type: automatic-speech-recognition
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name: Speech Recognition
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dataset:
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name: Podlodka.io
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type: bond005/podlodka_speech
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args: ru
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metrics:
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- type: wer
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value: 20.91
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name: WER (with punctuation and capital letters)
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- type: wer
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value: 10.987
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name: WER (without punctuation)
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- task:
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type: automatic-speech-recognition
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name: Speech Recognition
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dataset:
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name: Russian Librispeech
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type: bond005/rulibrispeech
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args: ru
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metrics:
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- type: wer
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value: 9.795
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name: WER (without punctuation)
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---
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# Whisper Large V3 Russian Podlodka
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This repository contains a fine-tuned Whisper Large V3 model for Russian speech recognition. It serves as the core transcription component of the **Pisets** system, specifically optimized for long audio recordings such as lectures and interviews.
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The model was presented in the paper [Pisets: A Robust Speech Recognition System for Lectures and Interviews](https://huggingface.co/papers/2601.18415).
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## System Architecture
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The Pisets system implements a three-component architecture to improve recognition accuracy while minimizing hallucinations:
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1. **Wav2Vec2**: For primary recognition and segmentation.
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2. **Audio Spectrogram Transformer (AST)**: For filtering non-speech segments.
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3. **Whisper (this model)**: For the final high-quality transcription.
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## Implementation
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The complete source code and instructions for using the system (including generation of SRT and DocX files) can be found in the GitHub repository:
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**GitHub:** [https://github.com/bond005/pisets](https://github.com/bond005/pisets)
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## Citation
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If you use this model or the Pisets system in your research, please cite:
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```bibtex
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@article{bondarenko2026pisets,
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title={Pisets: A Robust Speech Recognition System for Lectures and Interviews},
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author={Ivan Bondarenko},
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journal={arXiv preprint arXiv:2601.18415},
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year={2026}
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}
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```
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