kolani 0.3.7
Convert Hebrew text into IPA for TTS systems and learning
Requires Python: <3.13,>=3.8

# Kolani

Grapheme to phoneme in Hebrew

Convert Hebrew text into IPA for TTS systems and learning.

## Features

- Nikud model with phonetic marks 🧠
- Convert nikud text to modern spoken phonemes 🗣️
- Expand dates, numbers, etc 📚
- Handle mixed English/Hebrew with fallback 🌍
- Real time onnx model support 💫

## Install

Due to ongoing development, it is recommend to install from git directly.

```console
pip install git+https://github.com/thewh1teagle/kolani
```

You can find the package as well in `pypi.org/project/kolani-hebrew`

## Play 🕹️

See [Phonemize with Hebrew Space](https://huggingface.co/spaces/thewh1teagle/phonemize-in-hebrew)

## Usage

```python
from kolani import phonemize
phonemes = phonemize('שָׁלוֹם עוֹלָם')
print(phonemes) # ʃalˈom olˈam
```

Note: Kolani expects diacritics and phonetics marks.

Please use [phonikud-onnx](phonikud_onnx) for adding diacritics.

## Examples

See [examples](examples)

## Docs 📚

- It is recommend to add nikud with [phonikud-onnx](phonikud_onnx) model
- Hebrew nikud is normalized
- Most of the Hebrew rules happen in `phonemize.py`
- It is highly recommend to normalize Hebrew using `kolani.normalize('שָׁלוֹם')` when training models

### Nikud set and symbols

- Chars from `\u05b0` to `\u05ea` (Letters and nikud)
- `'"` (Gershaim),
- `\u05ab` (Hat'ama)
- `\u05bd` (Shva Na)
- `|` (Prefix letters)

`\u05ab` and `\u05bd` are not standard - we invented them to mark `Hat'ama` and `Shva Na` clearly.

See [Hebrew UTF-8](https://en.wikipedia.org/wiki/Unicode_and_HTML_for_the_Hebrew_alphabet#Compact_table)

### Hebrew phonemes 🔠

Stress marks (1)

- `ˈ` - stress, visually looks like single quote, but it's `\u02c8`

Vowels (5)

- `a` - Shamar
- `e` - Shemer
- `i` - Shimer
- `o` - Shomer
- `u` - Shumar

Consonants (24)

- `b` - Bet
- `v` - Vet, Vav
- `d` - Daled
- `h` - Hey
- `z` - Zain
- `χ` - Het, Haf
- `t` - Taf, Tet
- `j` - Yud
- `k` - Kuf, Kaf
- `l` - Lamed
- `m` - Mem
- `n` - Nun
- `s` - Sin, Samekh
- `f` - Fey
- `p` - Pey
- `ts` - Tsadik
- `tʃ` - Tsadik with Geresh (`צִ'יפְּס`)
- `w` - Example: `וָואלָה`
- `ʔ` - Alef/Ayin, visually looks like `?`, but it's `\u0294`
- `ɡ` - Gimel, visually looks like `g`, but it's actually `\u0261`
- `ʁ` - Resh `\u0281`
- `ʃ` - Shin `\u0283`
- `ʒ` - Zain with Geresh (`בֵּז׳`) `\u0292`
- `dʒ` - Gimel with Geresh (`גִּ׳ירָפָה`)

### Mixed English 🌎

You can mix the phonemization of English by providing a fallback function that accepts an English string and returns phonemes.
Note: if you use this with TTS, it is recommended to train the model on phonemized English. Otherwise, the model may not recognize the phonemes correctly.
Cool fact: modern Hebrew phonemes mostly exist in English except `ʔ` (Alef/Ayin), Resh `ʁ` and `χ` (Het).

## How It Works 🔧

To train TTS models, it’s essential to represent speech accurately. Plain Hebrew text is ambiguous without diacritics, and even with them, Shva Na and Hat'ama can cause confusion. For example, "אני אוהב אורז" (I like rice) and "אני אורז מזוודה" (I pack a suitcase) share the same diacritics for "אורז" but have different Hat'ama.

The workflow is as follows:

1. Add diacritics using a standard Nakdan.

2. Enhance the diacritics with an enhanced Nakdan that adds invented diacritics for Hat'ama and Shva Na. See [phonikud](phonikud)

3. Convert the text with diacritics to phonemes (alphabet characters that represent sounds) using this library, based on coding rules.

4. Train the TTS model on phonemes, and at runtime, feed the model phonemes to generate speech.

This ensures accurate and clear speech synthesis. Since the output phonemes are similar to English, we can fine tune an English model with as little as one hour of Hebrew data.


## ℹ️ Limitations

- Some of the *nikud* may sound a bit formal - similar to other models  
- Some words get the same *nikud* but different *hatama* - not always accurate  
- Basic support for non-words (gibberish, typos) - not always handled
- Names and non-Hebrew words are sometimes predicted incorrectly  

💡 You can always pass your own phonemes using markdown-like syntax:  
`[...title](/ʔantsiklopˈedja/)`

## 🧠 Future Ideas

- _Multilingual LLM Expander_
  
  Expand numbers, emojis, dates, times, and more using a lightweight multilingual LLM or transformer.  
  The idea is to train a small model on pairs of raw text → expanded text, making it easier to generate speech-friendly inputs.

- _Transformer/LLM G2P_
  
  Skip coding rules - make a dataset with current G2P, then train a model end-to-end on text to phonemes.

## Datasets

- [ILSpeech](https://huggingface.co/datasets/thewh1teagle/ILSpeech) (speech, MIT)
- [RanSpeech](https://huggingface.co/datasets/thewh1teagle/RanSpeech) (speech, non commercial)
- [Saspeech](https://www.openslr.org/134) (speech, non commercial)
- [phonikud-data](https://huggingface.co/datasets/thewh1teagle/phonikud-data) (nikud and phonetics, cc-4.0)

### Notes

- The default schema is `modern`. you can use `plain` schema for simplicify (eg. `x` instead of `χ`). use `phonemize(..., schema='plain')`
- There's no secondary stress (only `Milel` and `Milra`)
- The `ʔ`/`h` phonemes trimmed from the suffix
- Stress placed usually on the last syllable - `Milra`, sometimes on one before - `Milel` and rarely one before `Milel`
- Stress should be placed in the syllable always **before vowel** and _NOT_ in the first character of the syllable
- See [Unicode Hebrew table](https://en.wikipedia.org/wiki/Unicode_and_HTML_for_the_Hebrew_alphabet#Compact_table)
- See [Modern Hebrew phonology](https://en.m.wikipedia.org/wiki/Modern_Hebrew_phonology)

### Paper 📑

See [kolani-paper](https://github.com/thewh1teagle/kolani-paper)

### Testing 🧪

Run `uv run pytest`

## Credits

Special thanks ❤️ to [dicta-il](https://huggingface.co/dicta-il/dictabert-large-char-menaked) for their amazing Hebrew diacritics model ✨ and the dataset that made this possible!