haqumei 0.7.0

Features

Phoneme <-> Word mapping: Provides detailed mappings linking morphological analysis results with phonemes (g2p_pairs, g2p_mapping, g2p_mapping_prosody, g2p_mapping_detailed), which were previously unavailable. (See Advanced Features)
Prosody Information Retrieval: Provides phoneme sequences enriched with prosodic symbols as well as near-lossless mappings to surface forms (g2p_prosody, g2p_mapping_prosody). (For more details, see Prosody Features.)
Performance: Enables fast processing through a native Rust implementation. (See Benchmark)
Accuracy: Improves accuracy by incorporating English pronunciation estimation via haqumei-kanalizer and other corrections, alongside various techniques from pyopenjtalk-plus. (See Accuracy)
Output Formats: Provides results in various formats, including a simple phoneme sequence (g2p) and a detailed list including unknown word information (g2p_detailed).
Concurrency: Enables concurrent G2P processing across multiple threads using the *_batch methods.

Examples can be found in haqumei/examples.

Install

Rust

During the initial build of haqumei, the dictionary is downloaded and embedded into the binary due to the file size limits on crates.io. For custom dictionaries, or for environments where network access is unavailable during the build, please refer to here.

cargo add haqumei

Python

pip install haqumei

Supported Platforms

Pre-built wheels are available for the following platforms:

OS	Architecture
Linux	`x86_64`, `aarch64`
macOS	`aarch64` (e.g., Apple Silicon M1/M2/M3)
Windows	`x86_64`

Pre-built wheels bundle the embedded dictionary and require no network access during installation.

If a wheel is unavailable for your platform, installation falls back to building from source, which requires a Rust toolchain. In that case, the dictionary is downloaded and embedded during the build (same as the Rust crate build process).

Command-Line Tool

We also provide haqumei-cli, a command-line interface for text processing from the terminal. For detailed usage, including pipeline processing and JSON output, please see haqumei-cli/README.md

cargo install haqumei-cli

Usage

Rust

use haqumei::Haqumei;

fn main() -> Result<(), Box<dyn std::error::Error>> {
  let mut haqumei = Haqumei::new()?;

  let text = "こんにちは、世界！";

  // Convert to phoneme list
  let phonemes = haqumei.g2p(text)?;
  assert_eq!(phonemes, ["k", "o", "N", "n", "i", "ch", "i", "w", "a", "pau", "s", "e", "k", "a", "i"]);

  // Get phoneme list with prosodic symbols
  let phones = haqumei.g2p_prosody(text)?.join(" ");
  assert_eq!(phones, "^ k o [ N n i ch i w a _ s e ] k a i ! $");

  // Convert to katakana reading
  let kana = haqumei.g2k(text)?;
  assert_eq!(kana, "コンニチワ、セカイ！");

  Ok(())
}

Python

from haqumei import Haqumei

# Initialize Haqumei (the dictionary will be automatically set up)
haqumei = Haqumei()

text = "こんにちは、世界！"

# Convert to a phoneme list
phonemes = haqumei.g2p(text)
print(f"Phonemes: {phonemes}")
# -> Phonemes: ["k", "o", "N", "n", "i", "ch", "i", "w", "a", "pau", "s", "e", "k", "a", "i"]

# Get phoneme list with prosodic symbols
phones = " ".join(haqumei.g2p_prosody(text))
print(f"Prosody-annotated phonemes: {phones}")
# -> Prosody-annotated phonemes: ^ k o [ N n i ch i w a _ s e ] k a i ! $

# Convert to katakana reading
kana = haqumei.g2k(text)
print(f"Katakana reading: {kana}")
# -> Katakana reading: コンニチワ、セカイ！

Advanced Features

Getting Phoneme Mapping with the Original Word String

Haqumei implements g2p_pairs to obtain the correspondence between phonemes and their original words.
This is achieved by traversing the JPCommon structure and tracking the pointers to the words to which each phoneme belongs.

use haqumei::Haqumei;

fn main() -> Result<(), Box<dyn std::error::Error>> {
  let mut haqumei = Haqumei::new()?;

  println!("{:?}", haqumei.g2p_pairs("𰻞𰻞麺＆お冷を頼んだ")?);
  // [WordPhonemePair {
  //     word: "𰻞𰻞",
  //     phonemes: ["pau"]
  // }, WordPhonemePair {
  //     word: "麺",
  //     phonemes: ["m", "e", "N"]
  // }, WordPhonemePair {
  //     word: "＆",
  //     phonemes: ["a", "N", "d", "o"]
  // }, WordPhonemePair {
  //     word: "お冷",
  //     phonemes: ["o", "h", "i", "y", "a"]
  // }, ... ]

  Ok(())
}

Detailed G2P Output

In Open JTalk (pyopenjtalk), unknown words are treated as pau (pauses), and Haqumei's standard g2p function follows this behavior.
However, by using the g2p_**_detailed functions, you can detect otherwise ignored unknown words and spaces as unk and sp respectively.

Please note that sp does not refer to raw space characters in the input, but rather the "記号,空白" (symbol, space) part-of-speech output by Mecab, which is normally ignored in pyopenjtalk. Therefore, symbols that Mecab itself ignores (e.g., \t, \n) are not included in sp.

Known words: Regular phoneme sequence (punctuation marks become pau).
Unknown words: unk
Spaces, etc.: sp (Space)

Using g2p_mapping, you can obtain the phoneme-to-word mapping along with flags indicating whether a word is unknown (is_unknown) and whether it would normally be ignored in the original pipeline (is_ignored). In addition, using g2p_mapping_detailed allows you to retrieve not only the mapping but also part-of-speech information and accent details.

use haqumei::Haqumei;

fn main() -> Result<(), Box<dyn std::error::Error>> {
  let mut haqumei = Haqumei::new()?;

  println!("{:?}", haqumei.g2p_detailed("こんにちは 𰻞𰻞麺")?);
  // ["k", "o", "N", "n", "i", "ch", "i", "w", "a", "sp", "unk", "m", "e", "N"]

  println!("{:?}", haqumei.g2p_mapping("𰻞𰻞麺 お冷を頼んだ")?);
  // [WordPhonemeMap {
  //     word: "𰻞𰻞",
  //     phonemes: ["unk"],
  //     is_unknown: true,
  //     is_ignored: false,
  // },
  // WordPhonemeMap {
  //     word: "麺",
  //     phonemes: ["m", "e", "N"],
  //     is_unknown: false,
  //     is_ignored: false,
  // },
  // WordPhonemeMap {
  //     word: "\u{3000}",
  //     phonemes: ["sp"],
  //     is_unknown: false,
  //     is_ignored: true,
  // },
  // WordPhonemeMap {
  //     word: "お冷",
  //     phonemes: ["o", "h", "i", "y", "a"],
  //     is_unknown: false,
  //     is_ignored: false,
  // }, ... ]

  println!("{:?}", haqumei.g2p_mapping_detailed("薄明")?);
  // [WordPhonemeDetail {
  //    word: "薄明",
  //    phonemes: ["h","a","k","u","m","e","e"],
  //    features: [
  //        "薄明",
  //        "名詞",
  //        "一般",
  //        "*",
  //        "*",
  //        "*",
  //        "*",
  //        "薄明",
  //        "ハクメイ",
  //        "ハクメー",
  //        "0/4",
  //        "C2",
  //    ],
  //    pos: "名詞",
  //    pos_group1: "一般",
  //    pos_group2: "*",
  //    pos_group3: "*",
  //    ctype: "*",
  //    cform: "*",
  //    orig: "薄明",
  //    read: "ハクメイ",
  //    pron: "ハクメー",
  //    accent_nucleus: 0,
  //    mora_count: 4,
  //    chain_rule: "C2",
  //    chain_flag: -1,
  //    is_unknown: false,
  //    is_ignored: false,
  // }]

  Ok(())
}

Modifying Output with G2P Options

You can customize the behavior of Haqumei by using Haqumei::with_options. For details on the default behavior and available options, please refer to HaqumeiOptions.

In the following example, normalize_unicode (which is disabled by default) is enabled to apply Unicode NFC normalization to the input text.

use haqumei::{Haqumei, HaqumeiOptions, UnicodeNormalization};

fn main() -> Result<(), Box<dyn std::error::Error>> {
  let mut haqumei = Haqumei::with_options(HaqumeiOptions {
    normalize_unicode: UnicodeNormalization::Nfc,
    ..Default::default()
  })?;

  let text = &[
    "\u{304B}\u{3099}", // か + ゙ (が)
    "\u{306F}\u{309A}", // は + ゚ (ぱ)
    "\u{30B3}\u{3099}", // コ + ゙ (ゴ)
  ];

  println!("{:?}", haqumei.g2p_detailed_batch(text)?);
  // Output: [["g", "a"], ["p", "a"], ["g", "o"]]

  Ok(())
}

Prosody Features (`g2p_prosody` / `g2p_mapping_prosody`)

Specification of `g2p_prosody_with_options`

Converts the input text into a phoneme list annotated with prosodic symbols based on the ProsodyFormat setting. (The g2p_prosody method behaves identically to specifying ProsodyFormat::Default.)

The output commonly includes the following prosodic symbols:

Symbol	Meaning	Position
`^`	Beginning of utterance (BOS)	Sentence-initial
`$`	End of utterance (EOS)	Sentence-final
`?`	End of interrogative (？)	Sentence-medial
`!`	End of exclamation (Custom extension)	Sentence-medial
`_`	Pause / Comma (、)	Sentence-medial
`#`	Accent phrase boundary	Sentence-medial
`{...}`	Unknown word	Sentence-medial

For more information on Japanese accents, please refer to the tdmelodic User Manual / Preliminary Knowledge (Japanese).

ProsodyFormat::Default

In addition to the above, the output includes the following prosodic symbols:

Symbol	Meaning	Position
`[`	Pitch rise (Phrase head)	Near the beginning of a phrase
`]`	Pitch fall (Accent nucleus)	Right after the nuclear mora

The symbols [ and ] are based on the accent notation commonly used in tdmelodic and similar tools. They correspond to ^ and ! in the algorithm described by Kurihara et al. (2021) in "Prosodic Features Control by Symbols as Input of Sequence-to-Sequence Acoustic Modeling for Neural TTS".

ProsodyFormat::Prefix

Instead of using pitch rise/fall symbols ([ and ]), pitch high/low is attached as a prefix to each phoneme:

H_ : High pitch
L_ : Low pitch

The pitch is explicitly indicated for each phoneme.

Example: "青い空" -> ["^", "L_a", "H_o", "L_i", "#", "H_s", "H_o", "L_r", "L_a", "$"]

ProsodyFormat::Numeric

Pitch high/low is attached as a suffix to each phoneme as a numeric value:

:1 : High pitch
:0 : Low pitch

Example: "青い空" -> ["^", "a:0", "o:1", "i:0", "#", "s:1", "o:1", "r:0", "a:0", "$"]

Example

use haqumei::Haqumei;

fn main() -> Result<(), Box<dyn std::error::Error>> {
  let mut haqumei = Haqumei::new()?;

  let phones = haqumei.g2p_prosody("こんにちは、世界！")?;
  assert_eq!(phones.join(" "), "^ k o [ N n i ch i w a _ s e ] k a i ! $");

  let phones = haqumei.g2p_prosody("青い空、広がる。")?;
  assert_eq!(phones.join(" "), "^ a [ o ] i # s o ] r a _ h i [ r o g a r u _ $");

  Ok(())
}

Specification of `g2p_mapping_prosody`

On the other hand, g2p_mapping_prosody analyzes the input text and retrieves an alignment between detailed linguistic information for each morpheme (word) and phonemes with prosodic symbols.

While [Haqumei::g2p_prosody] and [Haqumei::g2p_prosody_with_options] return a flat list of strings (Vec<String>), this function returns structured data (Vec<WordPhonemeProsody>) annotated with part-of-speech, accent type, reading, and pitch information.

This is suitable for speech synthesis frontend processing when you want to maintain the correspondence between morphemes and phonemes, individually retrieve and manipulate pitch high/low ([PitchAccent]), or handle unknown words.

Information included in `WordPhonemeProsody`

The following information is included as data for each morpheme:

Field	Description	Example
`word`	Surface form of the morpheme	`"空"`
`pos`, `pos_group1`~`3`	Part-of-speech and its subdivisions	`"名詞"`, `"一般"`
`orig`, `read`, `pron`	Original form, reading, pronunciation form	`"空"`, `"ソラ"`, `"ソラ"`
`accent_nucleus`	Accent nucleus position (0: Heiban type, 1~: n-th mora)	`1`
`mora_count`	Number of moras	`2`
`is_unknown`	Whether it was judged as an unknown word by MeCab	`false`
`is_ignored`	Whether no phoneme was assigned	`false`

Prosodic Phoneme (`ProsodicPhoneme`)

The phonemes field contains a list of the following elements:

Variant	Meaning	Output symbol in `g2p_prosody`, etc.
`Phoneme`	The phoneme itself and its pitch (`High` / `Low`)	`a`, `a:0`, `H_a`, etc.
`AccentPhraseBoundary`	Accent phrase boundary	`#`
`Pause`	Regular pause / comma	`_`
`Interrogative`	End of interrogative / Pause	`?`
`Exclamatory`	End of exclamation / Pause	`!`

Example

use haqumei::{Haqumei, PitchAccent, ProsodicPhoneme};

fn main() -> Result<(), Box<dyn std::error::Error>> {
  let mut haqumei = Haqumei::new()?;

  // Retrieve text as structured data per morpheme
  let mapping = haqumei.g2p_mapping_prosody("青い空が、好きだ！")?;

  // Morpheme information for "青い"
  let aoi = &mapping[0];
  assert_eq!(aoi.word, "青い");
  assert_eq!(aoi.pos, "形容詞");
  assert_eq!(aoi.read, "アオイ");
  assert_eq!(aoi.accent_nucleus, 2); // 中高型

  // Phoneme and pitch information for "青い" (a: Low, o: High, i: Low)
  assert!(matches!(
      aoi.phonemes[0],
      ProsodicPhoneme::Phoneme { pitch: Some(PitchAccent::Low), .. }
  ));

  let da = mapping.last().unwrap();
  assert_eq!(da.word, "！");
  assert!(da.phonemes.contains(&ProsodicPhoneme::Exclamatory));

  Ok(())
}

Accuracy

We evaluated the accuracy using the haqumei-eval crate. Below are the results:

Phoneme Error Rate (PER) evaluated on prj-beatrice/jsut-label, a fork of jsut-label providing annotations for the Basic5000 subset of the JSUT corpus.
Katakana Error Rate (Katakana ER) evaluated on ROHAN.

jsut-label

Phoneme Error Rate (S+D+I / N_expected): 1.24% (Substitute=2244, Delete=572, Insert=889, N=297843)

HaqumeiOptions:

HaqumeiOptions {
  use_unidic_yomi: true,
  normalize_iu: Some(IuPronunciation::Yuu),
  ..Default::default()
}

ROHAN

Katakana Error Rate (S+D+I / N_expected): 1.64% (Substitute=1689, Delete=493, Insert=288, N=150637)

HaqumeiOptions:

HaqumeiOptions {
  revert_long_vowels: true,
  revert_yotsugana: true,
  ..Default::default()
}

Benchmark

Here are the comparison results between pyopenjtalk (Baseline) and haqumei using approximately 318,000 characters of Japanese text.

Input data: I Am a Cat (吾輩は猫である) 318,407 chars / 8,451 lines (Average 37 chars/line) (Ruby characters have been removed)

Execution Mode	Execution Time (Mean)	Throughput	Speedup
pyopenjtalk (Baseline)	2.358 s	135k chars/s	1.00x
haqumei (Default)	1.303 s	244k chars/s	1.81x
haqumei (`g2p_batch`, Default)	0.098 s	3.24M chars/s	24.04x
haqumei (Heavy)	2.101 s	151k chars/s	1.12x
haqumei (`g2p_batch`, Heavy)	0.268 s	1.18M chars/s	8.80x

The detailed benchmark code can be found in haqumei-bench/pyopenjtalk.

Additionally, Rust-layer benchmarks for Haqumei using Criterion.rs can be run via cargo bench in the haqumei-bench crate. The comparison benchmark with pyopenjtalk-plus is located in haqumei-bench/pyopenjtalk-plus.

Performance Notes

Throughput Variation by Input Structure:
Especially in the *_batch APIs, throughput (chars/s) tends to increase as the number of characters per line grows (up to approximately 4KB), compared with pyopenjtalk. This efficiency stems from an implementation that directly extracts labels from Open JTalk's internal structures, combined with minimal FFI overhead. When processing large volumes of text, it is most efficient to pass content in substantial chunks rather than splitting it into excessively short lines.
Difference Between Default and Heavy:
In the table, "Default" represents the configuration using Haqumei::new as is, while "Heavy" shows the results when predict_nani and use_unidic_yomi are enabled in HaqumeiOptions.

Building with a Custom Embedded Dictionary

By default, haqumei downloads the dictionary at build time and embeds it into the binary. This allows the crate to be published to crates.io while still producing a self-contained binary.

If you want to build with your own dictionary embedded in the binary, you can change the configuration as follows.

Change the Cargo Features

Disable the default download-dictionary feature and enable build-dictionary.

[dependencies]
haqumei = { version = "x.y.z", features = ["embed-dictionary", "build-dictionary"], default-features = false }

Prepare the Dictionary Source and Set the Environment Variable

Prepare a dictionary source directory containing .csv and .def files to be compiled at build time, then set its path to the HAQUMEI_DICT_SRC environment variable before running the build.

On Unix-like systems:

HAQUMEI_DICT_SRC="/path/to/your/dictionary" cargo build --release

On Windows (PowerShell):

& { $env:HAQUMEI_DICT_SRC="C:\path\to\your\dictionary"; cargo build --release }

Note: If the environment variable is not set, the build script falls back to dictionary, relative to the crate root.

Dictionary

Haqumei uses the dictionary included in pyopenjtalk-plus.

License

Haqumei, excluding haqumei-jlabel and haqumei-kanalizer, is distributed under the terms of the Apache License 2.0.

Licenses and Origins of Bundled Software

haqumei includes C/C++ source code and dictionary data from modified versions of Open JTalk to provide its Grapheme-to-Phoneme (G2P) functionality. The origins and licenses of this bundled code are as follows:

Bundled Open JTalk Source Code
- Origin: The code contained in the vendor/open_jtalk directory is based on the tsukumijima/open_jtalk repository, which integrates improvements from various community forks (e.g., VOICEVOX project) into an enhanced version of Open JTalk.
- License: The bundled Open JTalk source code is licensed under the Modified BSD License. This license applies only to the code located in vendor/open_jtalk, and does not apply to the rest of this project. In accordance with redistribution requirements, the full text of the Modified BSD License is included in vendor/open_jtalk/src/COPYING.
Bundled Dictionary Data
- Origin: The dictionary data contained in the haqumei/dictionary directory is based on tsukumijima/pyopenjtalk-plus, a modified fork of r9y9/pyopenjtalk.
- License: The dictionary data is covered by the license notices in haqumei/dictionary/COPYING.
Bundled haqumei-jlabel Source Code
- Origin: The code contained in the haqumei-jlabel directory is based on the jpreprocess/jlabel repository.
- License: The bundled haqumei-jlabel source code is licensed under the BSD 3-Clause License. This license applies only to the code located in haqumei-jlabel, and does not apply to the rest of this project. In accordance with redistribution requirements, the full text of the BSD 3-Clause License is included in haqumei-jlabel/LICENSE.
Bundled haqumei-kanalizer Crate
- Origin: The ONNX models bundled in haqumei-kanalizer are based on VOICEVOX/kanalizer, with model weights from VOICEVOX/kanalizer-model (converted via o24s/kanalizer-onnx).
- License: The entire haqumei-kanalizer crate (both the Rust code and the bundled model weights) is licensed under the MIT License.

Acknowledgements

The fundamental design and API of haqumei are inspired by pyopenjtalk and its highly improved fork, pyopenjtalk-plus. In addition, some implementations are based on jlabel and kanalizer to improve usability and accuracy.

We are deeply grateful to the authors and contributors of these foundational projects.

Features

Install

Rust

Python

Supported Platforms

Command-Line Tool

Usage

Rust

Python

Advanced Features

Getting Phoneme Mapping with the Original Word String

Detailed G2P Output

Modifying Output with G2P Options

Prosody Features (g2p_prosody / g2p_mapping_prosody)

Specification of g2p_prosody_with_options

ProsodyFormat::Default

ProsodyFormat::Prefix

ProsodyFormat::Numeric

Example

Specification of g2p_mapping_prosody

Information included in WordPhonemeProsody

Prosodic Phoneme (ProsodicPhoneme)

Example

Accuracy

jsut-label

ROHAN

Benchmark

Performance Notes

Building with a Custom Embedded Dictionary

Change the Cargo Features

Prepare the Dictionary Source and Set the Environment Variable

Dictionary

License

Licenses and Origins of Bundled Software

Acknowledgements

Prosody Features (`g2p_prosody` / `g2p_mapping_prosody`)

Specification of `g2p_prosody_with_options`

Specification of `g2p_mapping_prosody`

Information included in `WordPhonemeProsody`

Prosodic Phoneme (`ProsodicPhoneme`)