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Reza2kn/negara-g2p-clean-v7.1
negara-g2p-clean-v7.1 is a other model from Reza2kn. Use it for the other task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
Negara v7.1 is the Persian grapheme-to-phoneme frontend used by Gouya. It keeps the complete Negara v7 checkpoint and adds a deliberately small, exact-match repair overlay learned from 470 human-reviewed Gooya-5 votes…
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From the Hugging Face model README
Negara v7.1 is the Persian grapheme-to-phoneme frontend used by Gouya. It keeps the complete Negara v7 checkpoint and adds a deliberately small, exact-match repair overlay learned from 470 human-reviewed Gooya-5 votes, with Gemini 3.6 Flash used only as a conservative agreement signal.
The checkpoint at the repository root is directly loadable with AutoModelForSeq2SeqLM.from_pretrained(). The v7.1 behavior is produced by applying overlay.json to aligned (Persian surface word, raw v7 phone word) pairs. If alignment or an exact pair is absent, the original v7 output is preserved.
pip install 'transformers==4.57.6' torch
git clone https://huggingface.co/Reza2kn/negara-g2p-clean-v7.1
cd negara-g2p-clean-v7.1
python run_negara_v71.py --text 'سلام الاغ عزیز حالت چطوره؟ خوب و خوش و سلامتی؟ احوالت چطوره؟'
Expected v7_1 output:
salAm olAqe aziz hAlet Cetoreh xub o xoS o salAmati ahvAlet Cetoreh
You can also load the underlying v7 checkpoint normally:
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
repo = "Reza2kn/negara-g2p-clean-v7.1"
tokenizer = AutoTokenizer.from_pretrained(repo)
model = AutoModelForSeq2SeqLM.from_pretrained(repo)
سلامتی -> salAmati pronunciation is preserved.The bundled evaluation_report.json records a five-fold, record-disjoint out-of-fold check. Each learned rule excludes labels from its held-out record.
| Metric | v7 baseline | v7.1 overlay |
|---|---|---|
| Exact phone choices | 190 / 291 | 195 / 291 |
| Exact corrections | 0 / 101 | 7 / 101 |
| False changes to human-kept phones | 0 / 190 | 2 / 190 |
This is a precision-oriented lexical repair release, not a fully retrained G2P model. Its gains are intentionally narrow, and exact rules will not generalize to every inflection or unseen word.
model.safetensors and tokenizer/config files: unchanged Negara v7 checkpointoverlay.json: v7.1 exact-match repair layer and provenancerun_negara_v71.py: reference inference implementationevaluation_report.json: frozen out-of-fold evaluation receiptThis model is licensed under the Apache License, Version 2.0. See LICENSE.