jafarahmadi/negara-g2p-v2-homorich-disambiguated

Negara-G2P-v2 (Homograph-Disambiguated & Natural Pronunciation)

This model is an advanced, robust fine-tuned version of negara-g2p-clean-v2 specifically optimized for Persian Grapheme-to-Phoneme (G2P) conversion. It achieves state-of-the-art results in Contextual Homograph Disambiguation (رفع ابهام هم‌نگاشت‌ها) while consistently maintaining superior baseline phonetic precision without catastrophic forgetting.

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📊 Scientific Benchmark & Evaluation

To evaluate the model rigorously without synthetic bias, we benchmarked the fine-tuned checkpoint against the base model using Cleaned Human Ground Truth (derived from the human-annotated HomoRich benchmark) alongside a specialized Homograph Disambiguation Test Suite (19 Contextual Pairs):

Model Name	Homograph Accuracy (%) ⬆️	True PER (%) ⬇️	True WER (%) ⬇️	Latency (ms) ⚡
Base Model (negara-g2p-clean-v2)	63.16%	2.59%	8.62%	145.40 ms
Our Fine-Tuned Model (Optimal)	94.74% (+31.58%)	2.41% (Best)	8.33% (Best)	175.89 ms

🌟 Key Highlights:

• +31.58% Absolute Leap in Disambiguation: Resolves highly ambiguous Persian homographs depending on syntax and surrounding semantic context (e.g., maskan [home] vs. mosakken [painkiller], keSti [ship] vs. koSti [wrestling], deh [village] vs. dah [ten], mohr [stamp] vs. mehr [affection]).
• Zero Catastrophic Forgetting: Not only preserves base phonetic capabilities, but lowers the overall Phoneme Error Rate (PER: 2.41%) and Word Error Rate (WER: 8.33%) across general Persian text.
• Optimized for Persian TTS Pipelines: Generates clean, consistent phonetic notation ideal for acoustic models (such as VITS, FastSpeech2, and Tacotron2).

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🔍 Qualitative Comparison (Base vs. Fine-Tuned)

#	Input Sentence (Persian)	Base Model (negara-g2p-clean-v2)	Fine-Tuned Model (Ours)	Target Context / Meaning
1	کشتی بزرگی در دریا غرق شد.	kaStiye bozorgi... ❌	keStiye bozorgi... ✅	Ship (keSti) vs. Wrestling (koSti)
2	از مسکن بیرون آمدم تا یک قرص مسکن خوردم.	az mosakken ... qorse mosakken ❌	az maskan ... qorse mosakken ✅	Home (maskan) vs. Painkiller (mosakken)
3	فضای ده بسیار آرام بود و ده نفر بیدار بودند.	fazAye dah ... faqat dah nafar ❌	fazAye deh ... faqat dah nafar ✅	Village (deh) vs. Ten (dah)
4	به سمت مزرعه رفت، مردی که سمت کدخدایی داشت.	be samte ... samte kadkhodAi ❌	be samte ... semate kadxodAyi ✅	Direction (samt) vs. Job Title (semat)
5	حیوان درنده بود؛ شاید یک ببر! وسایلت را ببر!	... SAyad yek bebar! ... bebar ❌	... SAyad yek babr! ... bebar ✅	Tiger (babr) vs. To Take (bebar)
6	در کشور قطر بود و لوله‌ای به قطر چند متر ساخت.	keSvare qatar ... be qatar Cand metr ❌	keSvare qatar ... be qotre Cand metr ✅	Country Qatar (qatar) vs. Diameter (qotr)
7	او با کمال مهر به نامه‌ای مهر تایید زد.	kamAle mehr ... mehre tayid ❌	kamAle mehr ... mohre ta'yid ✅	Affection (mehr) vs. Stamp/Seal (mohr)
8	به شدت مراقب عرض و آبروی دیگران بود.	morAqebe arz va Abru ❌	morAqebe erz o Abru ✅	Honor/Dignity (erz) vs. Expressing (arz)

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💻 Usage Example

import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

device = "cuda" if torch.cuda.is_available() else "cpu"
model_id = "jafarahmadi/negara-g2p-v2-homorich-disambiguated"

# Load tokenizer and fine-tuned model
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSeq2SeqLM.from_pretrained(model_id).to(device)
model.eval()

# Sample text with homograph challenges
text = "کشتی بزرگی در دریا غرق شد و مسافران از مسکن خود دور شدند."

inputs = tokenizer(text, return_tensors="pt").to(device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_length=256,
        num_beams=4,
        early_stopping=True
    )

phonemes = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(phonemes)
# Expected Output: keStiye bozorgi dar daryA qarq Sod va mosAferAn az maskane xod dur Sodand