English to Katakana Translator

e2k is a Python library that translates English to Katakana using lightweight RNN models trained on data from Wiktionary and JMdict/EDICT. It only requires numpy as a runtime dependency.

We also provide pretrained models and dictionaries in the Releases section (not included in the PyPI package).

Installation

pip install e2k

Usage

1. Basic Translation

Two models are available:

from e2k import P2K, C2K
from g2p_en import G2p # any g2p library with CMUdict phonemes can work

p2k = P2K()
g2p = G2p()

word = "geogaddi"
print(p2k(g2p(word)))  # ジオダギ

c2k = C2K()
print(c2k(word))       # ジオガディ

c2k(word, "top_p", p=0.9, t=2)
c2k(word, "top_k", k=5)

2. Pitch Accent Prediction

The library includes an RNN-based pitch accent predictor trained on Unidic.

from e2k import AccentPredictor as Ap, C2K

ap = Ap()
c2k = C2K()

word = "geogaddi"
katakana = c2k(word)
accent = ap(katakana)

print(f"Katakana: {katakana}, Accent: {accent}")
# Katakana: ジオガディ, Accent: 3

3. Pronunciation Validity (N-Gram Model)

You can check if a word should be transliterated or spelled as-is.

from e2k import NGram
ngram = NGram()

word = "ussr"
if ngram(word):
    print(c2k(word))
else:
    print(ngram.as_is(word))

Example output:

• ussr → ユーエスエスアール
• doggy → ドギー

4. Input/Output Symbol Tables

c2k.in_table  # valid characters
p2k.in_table  # valid phonemes
c2k.out_table # katakana output symbols
p2k.out_table # katakana output symbols
accent.in_table # valid katakana symbols for accent prediction

Performance

Develop

Dependencies

We use uv for dependency management:

• Root package (e2k in src/) is the PyPI library and only depends on numpy.
• Training code lives in the e2k_train/ workspace member (e2k_train) and carries the heavy ML/tooling dependencies.

For root package:

uv sync

Testing

To make it run for debug, you need to download the following files from Releases and put them into src/models.

• c2k and p2k model files (model-c2k.npz and model-p2k.npz)
• ngrams model ngram.json.zip
• accent model accent.npz

# test
uv run src/inference.py

Training

See here for instructions related to data preparation, training, evaluation and exporting.

License

• Code: Unlicense
• Dictionaries: CC-BY-SA (Wiktionary, JMdict/EDICT)
• Accent Predictor: Unidic (GPLv2.0/LGPLv2.1/BSD)
• N-Gram Model: CMUDict (BSD 2-Clause)

Machine learning weights may be considered derivative works, check the upstream licenses when distributing.

Credits

• Wiktionary
• JMdict / EDICT
• Unidic
• CMUDict