| --- |
| license: gemma |
| license_name: license |
| license_link: LICENSE |
| base_model: |
| - ModelSpace/GemmaX2-28-2B-v0.1 |
| pipeline_tag: translation |
| library_name: transformers |
| tags: |
| - text-generation |
| language: |
| - de |
| - en |
| - fr |
| - es |
| datasets: |
| - iisys-hof/olaph-data |
| --- |
| ## Model Summary |
|
|
| OLaPhLLM is a large language model for phonemization, finetuned from GemmaX2-28-2B-v0.1. |
| Its tokenizer was extended with phoneme tokens, derived from a BPE tokenizer trained on phoneme sequences generated by the [OLaPh framework](https://github.com/iisys-hof/olaph)). |
|
|
| The model was then finetuned for grapheme-to-phoneme conversion on a multilingual dataset (English, German, French, Spanish), created by phonemizing text from HuggingFaceFW/fineweb and HuggingFaceFW/fineweb-2 using the OLaPh framework as well as with lexicon words taken from OLaPh. |
| The training set comprised 650,000 sentence pairs per target language (English, French, German, and Spanish), supplemented by 100,000 isolated, randomly selected lexicon entries per language, totaling 3 million training examples. |
|
|
| - **Finetuned By**: Institute for Information Systems at Hof University |
| - **Model type**: Text-To-Text |
| - **Dataset**: [OLaPh Phonemization Dataset](https://huggingface.co/datasets/iisys-hof/olaph-data) |
| - **Language(s)**: English, French, German, Spanish |
| - **License**: Gemma (Gemma is provided under and subject to the Gemma Terms of Use found at ai.google.dev/gemma/terms) |
| - **Release Date**: May 8, 2026 |
|
|
| ## Evaluation |
|
|
| The reported values are Phone Error Rate (PER) across the [Wikipron](https://github.com/CUNY-CL/wikipron) dataset. |
|
|
| Compared Models/Frameworks: |
|
|
| - [eSpeak NG](https://github.com/espeak-ng/espeak-ng) |
| - [Gruut](https://pypi.org/project/gruut) |
| - [charsiu/g2p_multilingual_byT5_tiny_16_layers_100](https://huggingface.co/charsiu/g2p_multilingual_byT5_tiny_16_layers_100) |
| - [OLaPh Framework](https://github.com/iisys-hof/olaph) |
| - OLaPh LLM (this) |
|
|
| | Language | espeak | gruut | byt5 | olaph | olaph_llm | |
| | :--- | :--- | :--- | :--- | :--- | :--- | |
| | **de** | 0.17594 | 0.17558 | 0.27864 | 0.04302 | 0.13518 | |
| | **en_uk** | 0.14117 | 0.19174 | 0.13036 | 0.08749 | 0.16321 | |
| | **en_us** | 0.14588 | 0.16545 | 0.16345 | 0.10491 | 0.14713 | |
| | **es** | 0.04324 | 0.04210 | 0.05436 | 0.02582 | 0.03179 | |
| | **fr** | 0.06203 | 0.04045 | 0.09217 | 0.03143 | 0.08591 | |
| |
| ## Usage |
| |
| ```python |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| |
| lang = "English" #German, French, Spanish |
| sentence = "But we are not sorry, for the rain is delightful." |
| |
| model_id = "iisys-hof/OLaPhLLM_v2" |
| tokenizer = AutoTokenizer.from_pretrained(model_id) |
| model = AutoModelForCausalLM.from_pretrained(model_id).to("cuda") |
| |
| prompt = f"Translate this from {lang} to Phones:\n{lang}: " |
| |
| inputs = tokenizer(f"{prompt}{sentence}\nPhones:", return_tensors="pt").to("cuda") |
|
|
| outputs = model.generate(**inputs, max_new_tokens=256) |
| phonemized = tokenizer.decode(outputs[0], skip_special_tokens=True) |
| phonemized = phonemized.split("\n")[-1].replace("Phones:", "") |
| |
| print(phonemized) |
| ``` |
| |
| ### Citation |
| ```bibtex |
| @misc{wirth2026olaphoptimallanguagephonemizer, |
| title={OLaPh: Optimal Language Phonemizer}, |
| author={Johannes Wirth}, |
| year={2026}, |
| eprint={2509.20086}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CL}, |
| url={https://arxiv.org/abs/2509.20086}, |
| } |
| ``` |
