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):

🌟 Key Highlights:

• +35 % 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: 4.04%) and Word Error Rate (WER: 10.83%) 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)

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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-finetuned"

# 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