Text-to-Speech
Transformers
ONNX
Safetensors
multilingual
t5
text2text-generation
p2g
phoneme-to-grapheme
ipa
accessibility
byt5
text-generation-inference
Instructions to use willwade/byt5-p2g-multilingual-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use willwade/byt5-p2g-multilingual-tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="willwade/byt5-p2g-multilingual-tiny")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("willwade/byt5-p2g-multilingual-tiny") model = AutoModelForSeq2SeqLM.from_pretrained("willwade/byt5-p2g-multilingual-tiny", device_map="auto") - Notebooks
- Google Colab
- Kaggle
ByT5 multilingual P2G (tiny) โ 17M params
Phoneme-to-grapheme inversion of the harmonized retrain. Input:
<lang>: phoneme tokens. Output: word spelling. Trained on a
4.12M-pair harmonized corpus.
Results (4k stratified test sample)
| score | |
|---|---|
| micro exact | 0.397 |
| macro exact | 0.557 |
Files
- HF-format weights at root (~70 MB)
onnx/โ validated encoder+decoder pair
Licence
CC BY-SA 4.0
- Downloads last month
- 39
Model tree for willwade/byt5-p2g-multilingual-tiny
Base model
google/byt5-small