allenai/OLMo-2-0325-32B
Text Generation
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Oleg Lavrovsky
loleg
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Models for crowdsourced open data
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Published a stable version of Ukrainian Text-to-Speech library on GitHub and PyPI.
Features:
- Multi-speaker model: 2 female (Tetiana, Lada) + 1 male (Mykyta) voices;
- Fine-grained control over speech parameters, including duration, fundamental frequency (F0), and energy;
- High-fidelity speech generation using the RAD-TTS++ acoustic model;
- Fast vocoding using Vocos;
- Synthesizes long sentences effectively;
- Supports a sampling rate of 44.1 kHz;
- Tested on Linux environments and Windows/WSL;
- Python API (requires Python 3.9 or later);
- CUDA-enabled for GPU acceleration.
Repository: https://github.com/egorsmkv/tts_uk
Features:
- Multi-speaker model: 2 female (Tetiana, Lada) + 1 male (Mykyta) voices;
- Fine-grained control over speech parameters, including duration, fundamental frequency (F0), and energy;
- High-fidelity speech generation using the RAD-TTS++ acoustic model;
- Fast vocoding using Vocos;
- Synthesizes long sentences effectively;
- Supports a sampling rate of 44.1 kHz;
- Tested on Linux environments and Windows/WSL;
- Python API (requires Python 3.9 or later);
- CUDA-enabled for GPU acceleration.
Repository: https://github.com/egorsmkv/tts_uk
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🌟 Progress in the German FineWeb edu reproduction 🌟
We're delighted to share the launch of our new Data Quality Classification Model, designed specifically for evaluating educational content in German. This tool uses advanced machine learning techniques to assess texts across all educational levels, from primary school to university.
🔍 Inspired by Huggingface's fine web edu dataset, we've worked hard to refine data classification methods ensuring educators and learners access top-quality resources.
We're excited about the future as we continue improving our models and expanding our datasets.
Access the model here: pL-Community/GermanEduScorer-Qwen2-1.5b
🙏 A huge thank you to David and Daryoush from Vago Solutions; Björn and Jan from Ellamind / DiscoResearch for their expert insights throughout this project. Your support has been crucial.
This project was made possible by the support of PrimeLine AI.
We're delighted to share the launch of our new Data Quality Classification Model, designed specifically for evaluating educational content in German. This tool uses advanced machine learning techniques to assess texts across all educational levels, from primary school to university.
🔍 Inspired by Huggingface's fine web edu dataset, we've worked hard to refine data classification methods ensuring educators and learners access top-quality resources.
We're excited about the future as we continue improving our models and expanding our datasets.
Access the model here: pL-Community/GermanEduScorer-Qwen2-1.5b
🙏 A huge thank you to David and Daryoush from Vago Solutions; Björn and Jan from Ellamind / DiscoResearch for their expert insights throughout this project. Your support has been crucial.
This project was made possible by the support of PrimeLine AI.
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