Bangla Heteronym Reader (MuRIL-base)

Many Bangla words are written the same way but read differently depending on the sentence. This model reads the sentence and picks the right pronunciation, for use in the front end of a Bangla text-to-speech system.

It covers the heteronyms of the bangla-heteronyms dataset. Each reading is a respelling in Bangla script, and a visible hasanta is written as hasanta + ZWNJ (কর্‌).

Results

Test set of the main split: 1,104 sentences, 3 per reading.

Speed per sentence (batch size 1, whole pipeline): 10.3 ms on a Tesla T4 GPU (fp16), 40 ms on 4 CPU threads. On the GPU it reads about 2,000 sentences per second at batch size 32.

By mean test accuracy it ranked eleventh of the 12 models in the benchmark. For most uses we recommend bangla-heteronym-banglabert (99.46%, 12.5 ms on a T4).

Usage

The model has a small custom head, so load it with the code from the GitHub repository:

git clone https://github.com/kawshikbuet17/bangla-heteronym-benchmark
cd bangla-heteronym-benchmark
pip install -r requirements.txt

from huggingface_hub import snapshot_download
from src.predict import Predictor

model = Predictor.load(snapshot_download("kawshikbuet17/bangla-heteronym-muril-base"), "cuda")  # or "cpu"

Sample outputs

These are the real outputs of this checkpoint. The cards of all models in the benchmark use the same sentences, so the outputs can be compared.

phoneticize(text): replaces every heteronym in the text with its reading. Everything else stays unchanged.

model.phoneticize("আগে কর কাজটা, তারপর আবার কর।")
# 'আগে করো কাজটা, তারপর আবার করো।'

model.phoneticize("আজ আকাশ পরিষ্কার।")          # no heteronym
# 'আজ আকাশ পরিষ্কার।'

phoneticize_detailed(text): the same text, plus the position, reading and confidence of every heteronym.

model.phoneticize_detailed("আগে কর কাজটা, তারপর আবার কর।")
# ('আগে করো কাজটা, তারপর আবার করো।',
#  [{'word': 'কর', 'position': 4, 'prediction': 'করো', 'confidence': 0.7326},
#   {'word': 'কর', 'position': 25, 'prediction': 'করো', 'confidence': 0.7334}])

predict(sentence, word): the reading of one heteronym in a sentence (its first occurrence).

model.predict("সময়মতো কর না দিলে জরিমানা গুনতে হবে।", "কর")
# 'কর্‌'

predict_marked(text): the reading of the word you mark with [ ]. Use it when the word occurs more than once.

model.predict_marked("সরকার কর বাড়িয়েছে, তাই হিসাবটা আবার [কর]।")
# 'করো'

phoneticize_marked(text): replaces only the marked words and removes the marks.

model.phoneticize_marked("সরকার কর বাড়িয়েছে, তাই হিসাবটা আবার [কর]।")
# 'সরকার কর বাড়িয়েছে, তাই হিসাবটা আবার করো।'

Batch versions: every method has a _batch version that takes a list and sends all inputs through the model together, which is much faster for many sentences.

model.phoneticize_batch(["আগে কর কাজটা, তারপর আবার কর।", "বিকেলে মাঠে বল নিয়ে যাব।"])
# ['আগে করো কাজটা, তারপর আবার করো।', 'বিকেলে মাঠে বল্‌ নিয়ে যাব।']

model.predict_batch([("সময়মতো কর না দিলে জরিমানা গুনতে হবে।", "কর"), ("বিকেলে মাঠে বল নিয়ে যাব।", "বল")])
# ['কর্‌', 'বল্‌']

model.predict_marked_batch(["তুমি একটু অপেক্ষা [কর]।", "বিকেলে মাঠে [বল] নিয়ে যাব।"])
# ['করো', 'বল্‌']

model.phoneticize_marked_batch(["সরকার [কর] বাড়িয়েছে।", "সে [বল] নিয়ে এল, তুমি কথাটা বল।"])
# ['সরকার কর্‌ বাড়িয়েছে।', 'সে বল্‌ নিয়ে এল, তুমি কথাটা বল।']

For a word that is not in the dataset, predict raises a ValueError. The known words and their readings are in model.hconfig["candidates"], for example model.hconfig["candidates"]["কর"] gives ['কর্‌', 'করো', 'কোরো'].

How it works

The input is the word and its sentence with the word marked: কর + আগে @ কর @ কাজটা, তারপর আবার কর।. MuRIL-base encodes it, and a linear layer on the opening marker scores the word's readings. The highest score wins.

Training: fine-tuned from google/muril-base-cased on 14 sentences per reading, AdamW, learning rate 5e-5, batch size 32, fp16, at most 10 epochs with early stopping on development accuracy, on one Tesla T4.

Limitations

• Only the heteronyms in the dataset. For other words the model has no readings to choose from.
• The sentences were written for the dataset, so accuracy on real text can be lower.

License

Apache 2.0, the license of the base model google/muril-base-cased. The weights were modified by fine-tuning them on the bangla-heteronyms dataset.

Prepared by

Kawshik Kumar Paul
Senior Software Engineer | Researcher
Department of Computer Science and Engineering (CSE)
Bangladesh University of Engineering and Technology (BUET)
Email: kawshikbuet17@gmail.com