Pashto Conformer G2P

The first open-source G2P model for Pakistani Pashto

A lightweight Conformer CTC grapheme-to-phoneme model for converting Pakistani Pashto text into broad IPA-style phoneme strings.

Repository: TBOGamer22/nemo-pashto-g2p-conformer

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Overview

Pashto Conformer G2P is a character-level grapheme-to-phoneme model trained for Pakistani Pashto speech technology. It converts Pashto-script text into broad IPA-style phoneme strings and is designed for TTS preprocessing, phoneme-label generation, and pronunciation-quality auditing.

To the best of our knowledge, this is the first open-source G2P model specifically released for Pakistani Pashto, including local Pakistani Pashto writing patterns commonly seen in real-world speech datasets and TTS pipelines.

This repository is intended to be the first model in a broader multilingual G2P collection. Future language models can follow the same structure: one portable NeMo artifact, local tokenizers, a small inference script, and a clear evaluation summary.

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Highlights

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Quick Start

Install dependencies in an environment with PyTorch and NVIDIA NeMo:

Quick Start

Install dependencies in an environment with PyTorch and NVIDIA NeMo:

pip install -r requirements.txt

Run inference:

python inference.py --text "دا یو مثال دی"

Run inference with JSON output:

python inference.py --text "که موږ غواړو چې د پښتو ژبې لپاره يو قوي او باوري ټکنالوژيکي نظام جوړ کړو، نو اړينه ده چې د متن، غږ او تلفظ تر منځ اړيکې په ډېر دقت سره وڅېړو." --json

Example JSON output:

{
  "text": "که موږ غواړو چې د پښتو ژبې لپاره يو قوي او باوري ټکنالوژيکي نظام جوړ کړو، نو اړينه ده چې د متن، غږ او تلفظ تر منځ اړيکې په ډېر دقت سره وڅېړو.",
  "phonemes": "ka muŋ gwɑɽu t͡ʃe də paxto ʒəbe ləpɑra jəw qawi aw bɑwri ʈaknɑlod͡ʒiki nizɑm d͡ʒoɽ kɽu no aɽina da t͡ʃe də matan ɣag aw talafuz tar mand͡z aɽike pə ɖer daxt sara wat͡seɽo"
}

Batch inference from a text file with one Pashto sentence per line:

python inference.py --text-file inputs.txt --batch-size 32

The script automatically uses CUDA when available. To force CPU:

python inference.py --text "دا یو مثال دی" --device cpu

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Model Files

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Intended Use

This model is intended for:

• Pakistani Pashto text-to-phoneme generation
• Pashto TTS preprocessing
• G2P label bootstrapping
• Dataset quality auditing
• Pronunciation consistency checks
• Low-resource Pashto speech technology research

This model is not intended to be:

• A general Pashto language model
• An ASR model
• A translation model
• A dialect classifier
• A narrow phonetic transcription system

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Architecture

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Training Data

The model was trained on a Human-Validated Pashto TTS G2P Dataset.

The labels were cleaned through language filtering, G2P disagreement checks, and Gemini + human-assisted validation for suspicious rows.

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Evaluation

Internal Validation

The selected checkpoint is:

pashto-conformer-g2p-epoch=10-val_per=0.0847.ckpt

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PLDST External Audit

An additional external audit was run on a 5,000-row sample from the first four speakers of PLDST.

For this audit, Gemini generated IPA-style reference labels from audio plus Pashto text, while this model predicted phonemes from Pashto text only.

Per-speaker weighted PER:

The external audit is intentionally conservative because the reference labels were generated from audio-plus-text and may include pronunciation, dialect, recording, or Gemini-labeling effects that are not always recoverable from text alone.

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Why This Model Matters

Pakistani Pashto is underrepresented in open-source speech technology. Many existing tools either do not support Pashto well or do not target the writing patterns found in Pakistani Pashto speech datasets.

This model provides a practical open-source baseline for:

• Building Pashto TTS systems
• Creating phoneme-based speech datasets
• Auditing Pashto pronunciation labels
• Supporting future Pashto ASR, TTS, and speech-to-speech systems

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Limitations

• Outputs are broad IPA-style phoneme strings, not narrow phonetic transcriptions.
• The model may struggle with code-switching, names, abbreviations, numbers, punctuation-heavy text, and rare spellings.
• Pashto dialect variation is not fully captured.
• The model predicts from text only; it does not use audio during inference.
• Some labels were LLM-assisted and should be treated as high-quality practical labels, not a gold linguistic standard.

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Ethical and Practical Notes

This model is intended for speech technology, dataset preparation, and linguistic tooling.

For production TTS, ASR, or research use, outputs should be reviewed carefully, especially for:

• Difficult names
• Dialectal words
• Code-switched utterances
• Noisy or non-standard spellings
• High-stakes linguistic claims

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Author

Developed and released by:

Talha Bin Omar

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Citation

If you use this model, please cite it as:

@misc{binomar2026pashto_conformer_g2p,
  title        = {Pashto Conformer G2P: An Open-Source Grapheme-to-Phoneme Model for Pakistani Pashto},
  author       = {Bin Omar, Talha},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/TBOGamer22/nemo-pashto-g2p-conformer}},
  note         = {Conformer CTC G2P model for broad IPA phoneme generation}
}

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License

This model is released under the MIT License.

Copyright (c) 2026 Talha Bin Omar