cy_GB-bu_tts

cy_GB-bu_tts - Welsh Neural Text-to-Speech

This is a Welsh (Cymraeg) neural text-to-speech model trained using Piper, a fast, local neural TTS system optimized for Raspberry Pi and other low-end devices.

Developed by: Uned Technolegau Iaith (Language Technologies Unit), Bangor University Model type: Neural TTS (VITS-based architecture) Language: Welsh (cy_GB) License: CC0-1.0 Format: ONNX

Model Details

• Architecture: Based on Piper's VITS (Variational Inference with adversarial learning for end-to-end Text-to-Speech)
• Speakers: Multi-speaker model with 3 speaker variants
• Quality: Medium quality (suitable for screen readers and assistive technology)
• Model Size: Approximately 77 MB
• Inference Speed: Optimized for real-time synthesis on CPU
• Sample Rate: 22050 Hz
• Training Framework: Piper training pipeline

Training Data

This model was trained on the bu-tts-cy-en dataset (Bangor University Text to Speech Welsh-English dataset).

Dataset characteristics:

• Size: 10,000-100,000 samples
• Languages: Welsh and English (bilingual dataset)
• License: CC0 1.0 (Public Domain)
• Content: Audio recordings with corresponding text transcriptions
• Source: Language Technologies Unit, Bangor University

Training data limitations:

• Dataset consists of freely available recordings (public domain audiobooks and research-quality recordings)
• Coverage is not comprehensive across all Welsh vocabulary and contexts
• Some pronunciation patterns may be influenced by the limited speaker diversity in the training data
• Quality improvements would be possible with larger, more diverse, professionally-recorded datasets

Intended Use

Primary use cases:

• Screen readers and assistive technology (particularly NVDA integration)
• Accessibility tools for Welsh speakers with visual impairments
• Welsh language learning applications
• Local, offline Welsh TTS applications
• Research in Welsh speech synthesis

Supported platforms:

• Compatible with Piper TTS runtime
• Works with Sonata TTS engine
• ONNX Runtime on x86/x64 architectures
• Raspberry Pi and other resource-constrained devices

Usage

With Piper

# Download model files
wget https://huggingface.co/techiaith/cy_GB-bu_tts/resolve/main/cy_GB-bu_tts.onnx
wget https://huggingface.co/techiaith/cy_GB-bu_tts/resolve/main/cy_GB-bu_tts.onnx.json

# Run synthesis
echo "Bore da, sut wyt ti?" | piper \
  --model cy_GB-bu_tts.onnx \
  --output_file output.wav

With NVDA Screen Reader

Install the techiaith Welsh Neural Voices addon for NVDA:

1. Download the addon from the releases page
2. Install and restart NVDA
3. Voices will download automatically on first run (77 MB)
4. Select "Uned Technolegau Iaith - Welsh Neural Voices" in NVDA's speech settings

With Python (ONNX Runtime)

import onnxruntime as ort
import numpy as np
import json
import wave

# Load model
session = ort.InferenceSession("cy_GB-bu_tts.onnx")

# Load config
with open("cy_GB-bu_tts.onnx.json") as f:
    config = json.load(f)

# For complete implementation, refer to:
# https://github.com/rhasspy/piper/blob/master/src/python_run/piper/voice.py

With Sonata Engine

from sonata import tts_engine

engine = tts_engine.TTSEngine()
engine.load_voice("cy_GB-bu_tts.onnx")

# Synthesize speech
audio = engine.synthesize("Bore da!")
engine.save_audio(audio, "output.wav")

Sample Audio

Listen to voice samples at: Piper Welsh samples

Limitations

• Pronunciation: May exhibit incorrect or unusual pronunciation for some words, particularly:
  • Technical terms and neologisms
  • Place names not represented in training data
  • Words with ambiguous pronunciation rules
• Audio Quality: Medium quality - suitable for assistive technology but not studio-grade
• Domain Coverage: Best performance on general conversational text; may struggle with specialized domains
• Expressivity: Limited emotional range (neutral/informative tone)
• Platform: Optimized for CPU inference on x86/x64; ARM64 Windows not supported
• Language Mixing: While trained on bilingual data, best results when using pure Welsh text

Performance

• Real-time Factor: < 1.0 on modern CPUs (faster than real-time synthesis)
• Latency: Low latency suitable for interactive applications
• Memory Usage: ~100 MB RAM during inference
• Supported Platforms: Windows 10/11 (x86/x64), Linux (x86/x64), Raspberry Pi

Model Files

This repository contains:

• cy_GB-bu_tts.onnx - The neural TTS model in ONNX format
• cy_GB-bu_tts.onnx.json - Model configuration file (phoneme mapping, sample rate, etc.)

Citation

If you use this model, please cite:

@misc{cy_GB_bu_tts_2025,
  author = {{Language Technologies Unit, Bangor University}},
  title = {cy\_GB-bu\_tts: Welsh Neural Text-to-Speech Model},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/techiaith/cy_GB-bu_tts}}
}

@dataset{bu_tts_cy_en_2025,
  author = {{Language Technologies Unit, Bangor University}},
  title = {Bangor University Text to Speech Welsh-English Dataset},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/techiaith/bu-tts-cy-en}}
}

@misc{piper_tts,
  author = {{Rhasspy Community}},
  title = {Piper: A fast, local neural text to speech system},
  year = {2023},
  publisher = {GitHub},
  howpublished = {\url{https://github.com/rhasspy/piper}},
  note = {Now maintained at \url{https://github.com/OHF-Voice/piper1-gpl}}
}

Acknowledgments

This work builds upon contributions from the wider open-source TTS community:

• Piper TTS and the Rhasspy community for developing the training framework and TTS architecture that makes high-quality, local neural TTS accessible
• Musharraf Omer for creating Sonata TTS engine and the Sonata-NVDA addon, which enables seamless integration with screen readers
• Contributors to the Welsh language TTS training data
• The broader open-source speech synthesis community for advancing accessible voice technology

License

This model is released under CC0-1.0 (Public Domain). You are free to use, modify, and distribute this model for any purpose without restriction.

The training code (Piper) is licensed under MIT License.

Contact & Support

Organization: Uned Technolegau Iaith / Language Technologies Unit, Bangor University Issues: Report issues at GitHub Issues Project Page: NVDA Welsh Neural Voices

Version History

• 2025.11.0 (Beta): Initial public release with 3 speaker variants, medium quality

Related Resources

• NVDA Welsh Neural Voices Addon - Screen reader integration
• Piper TTS - Training and inference framework
• Sonata Engine - Cross-platform TTS engine
• Training Dataset - Welsh-English TTS corpus

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This model was developed to support Welsh language accessibility and to preserve and promote the Welsh language through modern speech technology.