This repository serves as a comprehensive hub for Ukrainian language speech processing, offering a curated collection of models, datasets, and tools for both Speech-to-Text (STT) and Text-to-Speech (TTS). It targets researchers, developers, and power users working with Ukrainian audio data, providing readily accessible resources and benchmarks to accelerate development and evaluation.

How It Works

The project aggregates links to numerous pre-trained models from platforms like Hugging Face, covering various architectures such as wav2vec2, HuBERT, Citrinet, Conformer, and Whisper. It also highlights custom fine-tuned versions and quantized variants for improved efficiency. The repository emphasizes practical application by providing links to interactive demos and detailed benchmarks (WER, CER, Accuracy) on the Common Voice 10 test split, allowing users to compare model performance directly.

Quick Start & Requirements

• Models are primarily accessed via Hugging Face model IDs (e.g., Yehor/w2v-bert-uk-v2.1).
• Many models require PyTorch and Hugging Face transformers library.
• Specific models may have additional dependencies (e.g., NVIDIA NeMo for Citrinet/ContextNet).
• GPU acceleration is highly recommended for efficient inference and training.
• Links to demos and specific model repositories are provided for immediate testing.

Highlighted Details

• Extensive collection of STT models, including fine-tuned Whisper variants and NVIDIA's Citrinet/ContextNet.
• Comprehensive benchmarks comparing various STT architectures on Ukrainian data.
• Curated list of Ukrainian TTS models (StyleTTS2, RAD-TTS, Coqui TTS, FastPitch) with associated datasets.
• Links to useful related tools like Ukrainian language models, IPA converters, and forced aligners.

Maintenance & Community

• Active community support via Discord and Telegram channels.
• Regular updates and additions of new models and datasets are implied by the breadth of content.
• Links to related projects and initiatives like "Speech-UK" are provided.

Licensing & Compatibility

• Licenses vary by individual model and dataset; users must verify each resource.
• Many models are released under permissive licenses (e.g., MIT, Apache 2.0), facilitating commercial use.
• Some datasets may have specific usage terms.

Limitations & Caveats

• This repository is primarily a curated list of links, not a unified framework; integration may require custom scripting.
• Performance can vary significantly between models and datasets, requiring careful selection based on specific use cases.
• Some older models (e.g., DeepSpeech) may have lower performance compared to state-of-the-art architectures.