Package Health Score

68 / 100

security
Security review needed
popularity
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maintenance
Sustainable
community
Sustainable

Popularity

Recognized

GitHub Stars: 666
Forks: 123
Contributors: 40

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package epitran, we found that it has been starred 666 times.

Security

Security review needed

Security and license risk for latest version

Release Date: Jul 30, 2024
Direct Vulnerabilities: 0
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0
H
0
M
0
L
Indirect Vulnerabilities: 0
C
0
H
0
M
0
L
License Risk: 1
H
1
M
0
L

All security vulnerabilities belong to production dependencies of direct and indirect packages.

License

MIT

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Maintenance

Sustainable

Commit Frequency

Open Issues: 34
Open PR: 0
Last Release: 5 months ago
Last Commit: 4 months ago

Further analysis of the maintenance status of epitran based on released PyPI versions cadence, the repository activity, and other data points determined that its maintenance is Sustainable.

We found that epitran demonstrates a positive version release cadence with at least one new version released in the past 12 months.

In the past month we didn't find any pull request activity or change in issues status has been detected for the GitHub repository.

Community

Sustainable

Readme: Yes
Contributing.md: No
Code of Conduct: No
Contributors: 40
Funding: No

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Package

Python Versions Compatibility: ==3.*

Age: 9 years
Latest Release: 5 months ago
Dependencies: 6 Direct / 29 Total
Versions: 99
Maintainers: 1
Wheels: OS Independent

Not sure how to use epitran?

To help you get started, we've collected the most common ways that epitran is being used within popular public projects.

dmort27 / epitran / epitran / bin / testvectorgen.py View on Github

def main(code, space, infile):
    vec = epitran.vector.VectorsWithIPASpace(code, space)
    with codecs.open(infile, 'r', 'utf-8') as f:
        for line in f:
            fields = line.split('\t')
            if len(fields) > 1:
                word = fields[0]
                print(u"WORD: {}".format(word).encode('utf-8'))
                segs = vec.word_to_segs(word)
                for record in segs:
                    cat, case, orth, phon, id_, vector = record
                    print(u"Category: {}".format(cat).encode('utf-8'))
                    print(u"Case: {}".format(case).encode('utf-8'))
                    print(u"Orthographic: {}".format(orth).encode('utf-8'))
                    print(u"Phonetic: {}".format(phon).encode('utf-8'))
                    print(u"Vector: {}".format(vector).encode('utf-8'))

View more ways to use epitran

Epitran

A library and tool for transliterating orthographic text as IPA (International Phonetic Alphabet).

Usage

The Python modules epitran and epitran.vector can be used to easily write more sophisticated Python programs for deploying the Epitran mapping tables, preprocessors, and postprocessors. This is documented below.

If you wish to use Epitran to convert English to IPA, you must install the Flite (including lex_lookup) as detailed below.

Using the `epitran` Module

The Epitran class

The most general functionality in the epitran module is encapsulated in the very simple Epitran class:

Epitran(code, preproc=True, postproc=True, ligatures=False, cedict_file=None).

Its constructor takes one argument, code, the ISO 639-3 code of the language to be transliterated plus a hyphen plus a four letter code for the script (e.g. 'Latn' for Latin script, 'Cyrl' for Cyrillic script, and 'Arab' for a Perso-Arabic script). It also takes optional keyword arguments:

preproc and postproc enable pre- and post-processors. These are enabled by default.
ligatures enables non-standard IPA ligatures like "ʤ" and "ʨ".
cedict_file gives the path to the CC-CEDict dictionary file (relevant only when working with Mandarin Chinese and which, because of licensing restrictions cannot be distributed with Epitran).
tones allows IPA tones (˩˨˧˦˥) to be included and is needed for tonal languages like Vietnamese and Hokkien. By default, this value is false and will remove IPA tones from the transcription.
For more options, type help(epitran.Epitran.__init__) into a Python terminal session

&gt;&gt;&gt; import epitran
&gt;&gt;&gt; epi = epitran.Epitran('uig-Arab')  # Uyghur in Perso-Arabic script

It is now possible to use the Epitran class for English and Mandarin Chinese (Simplified and Traditional) G2P as well as the other langugages that use Epitran's "classic" model. For Chinese, it is necessary to point the constructor to a copy of the CC-CEDict dictionary:

&gt;&gt;&gt; import epitran
&gt;&gt;&gt; epi = epitran.Epitran('cmn-Hans', cedict_file='cedict_1_0_ts_utf-8_mdbg.txt')

The most useful public method of the Epitran class is transliterate:

Epitran.transliterate(text, normpunc=False, ligatures=False). Convert text (in Unicode-encoded orthography of the language specified in the constructor) to IPA, which is returned. normpunc enables punctuation normalization and ligatures enables non-standard IPA ligatures like "ʤ" and "ʨ". Usage is illustrated below (Python 2):

&gt;&gt;&gt; epi.transliterate(u'Düğün')
u'dy\u0270yn'
&gt;&gt;&gt; print(epi.transliterate(u'Düğün'))
dyɰyn

Epitran.word_to_tuples(word, normpunc=False): Takes a word (a Unicode string) in a supported orthography as input and returns a list of tuples with each tuple corresponding to an IPA segment of the word. The tuples have the following structure:

(
    character_category :: String,
    is_upper :: Integer,
    orthographic_form :: Unicode String,
    phonetic_form :: Unicode String,
    segments :: List
)

Note that word_to_tuples is not implemented for all language-script pairs.

The codes for character_category are from the initial characters of the two character sequences listed in the "General Category" codes found in Chapter 4 of the Unicode Standard. For example, "L" corresponds to letters and "P" corresponds to production marks. The above data structure is likely to change in subsequent versions of the library. The structure of segments is as follows:

(
    segment :: Unicode String,
    vector :: List
)

Here is an example of an interaction with word_to_tuples (Python 2):

&gt;&gt;&gt; import epitran
&gt;&gt;&gt; epi = epitran.Epitran('tur-Latn')
&gt;&gt;&gt; epi.word_to_tuples(u'Düğün')
[(u'L', 1, u'D', u'd', [(u'd', [-1, -1, 1, -1, -1, -1, -1, -1, 1, -1, -1, 1, 1, -1, -1, -1, -1, -1, -1, 0, -1])]), (u'L', 0, u'u\u0308', u'y', [(u'y', [1, 1, -1, 1, -1, -1, -1, 0, 1, -1, -1, -1, -1, -1, 1, 1, -1, -1, 1, 1, -1])]), (u'L', 0, u'g\u0306', u'\u0270', [(u'\u0270', [-1, 1, -1, 1, 0, -1, -1, 0, 1, -1, -1, 0, -1, 0, -1, 1, -1, 0, -1, 1, -1])]), (u'L', 0, u'u\u0308', u'y', [(u'y', [1, 1, -1, 1, -1, -1, -1, 0, 1, -1, -1, -1, -1, -1, 1, 1, -1, -1, 1, 1, -1])]), (u'L', 0, u'n', u'n', [(u'n', [-1, 1, 1, -1, -1, -1, 1, -1, 1, -1, -1, 1, 1, -1, -1, -1, -1, -1, -1, 0, -1])])]

The Backoff class

Sometimes, when parsing text in more than one script, it is useful to employ a graceful backoff. If one language mode does not work, it can be useful to fall back to another, and so on. This functionality is provided by the Backoff class:

Backoff(lang_script_codes, cedict_file=None)

Note that the Backoff class does not currently support parameterized preprocessor and postprocessor application and does not support non-standard ligatures. It also does not support punctuation normalization. lang_script_codes is a list of codes like eng-Latn or hin-Deva. For example, if one was transcribing a Hindi text with many English loanwords and some stray characters of Simplified Chinese, one might use the following code (Python 3):

from epitran.backoff import Backoff
&gt;&gt;&gt; backoff = Backoff(['hin-Deva', 'eng-Latn', 'cmn-Hans'], cedict_file=‘cedict_1_0_ts_utf-8_mdbg.txt')
&gt;&gt;&gt; backoff.transliterate('हिन्दी')
'ɦindiː'
&gt;&gt;&gt; backoff.transliterate('English')
'ɪŋɡlɪʃ'
&gt;&gt;&gt; backoff.transliterate('中文')
'ʈ͡ʂoŋwən'

Backoff works on a token-by-token basis: tokens that contain mixed scripts will be returned as the empty string, since they cannot be fully converted by any of the modes.

The Backoff class has the following public methods:

transliterate: returns a unicode string of IPA phonemes
trans_list: returns a list of IPA unicode strings, each of which is a phoneme
xsampa_list: returns a list of X-SAMPA (ASCII) strings, each of which is phoneme

Consider the following example (Python 3):

&gt;&gt;&gt; backoff.transliterate('हिन्दी')
'ɦindiː'
&gt;&gt;&gt; backoff.trans_list('हिन्दी')
['ɦ', 'i', 'n', 'd', 'iː']
&gt;&gt;&gt; backoff.xsampa_list('हिन्दी')
['h\\', 'i', 'n', 'd', 'i:']

DictFirst

The DictFirst class provides a simple alternative to the Backoff class. It requires a dictionary of words known to be of Language A, one word per line in a UTF-8 encoded text file. It accepts three arguments: the language-script code for Language A, that for Language B, and a path to the dictionary file. It has one public method, transliteration, which works like Epitran.transliterate except that it returns the transliteration for Language A if the input token is in the dictionary; otherwise, it returns the Language B transliteration of the token:

&gt;&gt;&gt; import dictfirst
&gt;&gt;&gt; df = dictfirst.DictFirst('tpi-Latn', 'eng-Latn', '../sample-dict.txt')
&gt;&gt;&gt; df.transliterate('pela')
'pela'
&gt;&gt;&gt; df.transliterate('pelo')
'pɛlow'

Preprocessors, postprocessors, and their pitfalls

In order to build a maintainable orthography to phoneme mapper, it is sometimes necessary to employ preprocessors that make contextual substitutions of symbols before text is passed to a orthography-to-IPA mapping system that preserves relationships between input and output characters. This is particularly true of languages with a poor sound-symbols correspondence (like French and English). Languages like French are particularly good targets for this approach because the pronunciation of a given string of letters is highly predictable even though the individual symbols often do not map neatly into sounds. (Sound-symbol correspondence is so poor in English that effective English G2P systems rely heavily on pronouncing dictionaries.)

Preprocessing the inputs words to allow for straightforward grapheme-to-phoneme mappings (as is done in the current version of epitran for some languages) is advantageous because the restricted regular expression language used to write the preprocessing rules is more powerful than the language for the mapping rules and allows the equivalent of many mapping rules to be written with a single rule. Without them, providing epitran support for languages like French and German would not be practical. However, they do present some problems. Specifically, when using a language with a preprocessor, one must be aware that the input word will not always be identical to the concatenation of the orthographic strings (orthographic_form) output by Epitran.word_to_tuples. Instead, the output of word_to_tuple will reflect the output of the preprocessor, which may delete, insert, and change letters in order to allow direct orthography-to-phoneme mapping at the next step. The same is true of other methods that rely on Epitran.word_to_tuple such as VectorsWithIPASpace.word_to_segs from the epitran.vector module.

For information on writing new pre- and post-processors, see the section on "Extending Epitran with map files, preprocessors and postprocessors", below.

Using the `epitran.vector` Module

The epitran.vector module is also very simple. It contains one class, VectorsWithIPASpace, including one method of interest, word_to_segs:

The constructor for VectorsWithIPASpace takes two arguments:

code: the language-script code for the language to be processed.
spaces: the codes for the punctuation/symbol/IPA space in which the characters/segments from the data are expected to reside. The available spaces are listed below.

Its principle method is word_to_segs:

VectorWithIPASpace.word_to_segs(word, normpunc=False). word is a Unicode string. If the keyword argument normpunc is set to True, punctuation discovered in word is normalized to ASCII equivalents.

A typical interaction with the VectorsWithIPASpace object via the word_to_segs method is illustrated here (Python 2):

&gt;&gt;&gt; import epitran.vector
&gt;&gt;&gt; vwis = epitran.vector.VectorsWithIPASpace('uzb-Latn', ['uzb-Latn'])
&gt;&gt;&gt; vwis.word_to_segs(u'darë')
[(u'L', 0, u'd', u'd\u032a', u'40', [-1, -1, 1, -1, -1, -1, -1, -1, 1, -1, -1, 1, 1, 1, -1, -1, -1, -1, -1, 0, -1]), (u'L', 0, u'a', u'a', u'37', [1, 1, -1, 1, -1, -1, -1, 0, 1, -1, -1, -1, -1, -1, -1, -1, 1, 1, -1, 1, -1]), (u'L', 0, u'r', u'r', u'54', [-1, 1, 1, 1, 0, -1, -1, -1, 1, -1, -1, 1, 1, -1, -1, 0, 0, 0, -1, 0, -1]), (u'L', 0, u'e\u0308', u'ja', u'46', [-1, 1, -1, 1, -1, -1, -1, 0, 1, -1, -1, -1, -1, 0, -1, 1, -1, -1, -1, 0, -1]), (u'L', 0, u'e\u0308', u'ja', u'37', [1, 1, -1, 1, -1, -1, -1, 0, 1, -1, -1, -1, -1, -1, -1, -1, 1, 1, -1, 1, -1])]

(It is important to note that, though the word that serves as input--darë--has four letters, the output contains five tuples because the last letter in darë actually corresponds to two IPA segments, /j/ and /a/.) The returned data structure is a list of tuples, each with the following structure:

(
    character_category :: String,
    is_upper :: Integer,
    orthographic_form :: Unicode String,
    phonetic_form :: Unicode String,
    in_ipa_punc_space :: Integer,
    phonological_feature_vector :: List
)

A few notes are in order regarding this data structure:

character_category is defined as part of the Unicode standard (Chapter 4). It consists of a single, uppercase letter from the set {'L', 'M', 'N', 'P', 'S', 'Z', 'C'}.. The most frequent of these are 'L' (letter), 'N' (number), 'P' (punctuation), and 'Z' (separator [including separating white space]).
is_upper consists only of integers from the set {0, 1}, with 0 indicating lowercase and 1 indicating uppercase.
The integer in in_ipa_punc_space is an index to a list of known characters/segments such that, barring degenerate cases, each character or segment is assignmed a unique and globally consistant number. In cases where a character is encountered which is not in the known space, this field has the value -1.
The length of the list phonological_feature_vector should be constant for any instantiation of the class (it is based on the number of features defined in panphon) but is--in principle--variable. The integers in this list are drawn from the set {-1, 0, 1}, with -1 corresponding to '-', 0 corresponding to '0', and 1 corresponding to '+'. For characters with no IPA equivalent, all values in the list are 0.

Language Support

Transliteration Language/Script Pairs

Code	Language (Script)
aar-Latn	Afar
aii-Syrc	Assyrian Neo-Aramaic
amh-Ethi	Amharic
amh-Ethi-pp	Amharic (more phonetic)
amh-Ethi-red	Amharic (reduced)
ara-Arab	Literary Arabic
ava-Cyrl	Avaric
aze-Cyrl	Azerbaijani (Cyrillic)
aze-Latn	Azerbaijani (Latin)
ben-Beng	Bengali
ben-Beng-red	Bengali (reduced)
bxk-Latn	Bukusu
cat-Latn	Catalan
ceb-Latn	Cebuano
ces-Latn	Czech
cjy-Latn	Jin (Wiktionary)
cmn-Hans	Mandarin (Simplified)*
cmn-Hant	Mandarin (Traditional)*
cmn-Latn	Mandarin (Pinyin)*
ckb-Arab	Sorani
csb-Latn	Kashubian
deu-Latn	German
deu-Latn-np	German†
deu-Latn-nar	German (more phonetic)
eng-Latn	English‡
epo-Latn	Esperanto
fas-Arab	Farsi (Perso-Arabic)
fra-Latn	French
fra-Latn-np	French†
fra-Latn-p	French (more phonetic)
ful-Latn	Fulah
gan-Latn	Gan (Wiktionary)
got-Latn	Gothic
hak-Latn	Hakka (pha̍k-fa-sṳ)
hau-Latn	Hausa
hin-Deva	Hindi
hmn-Latn	Hmong
hrv-Latn	Croatian
hsn-Latn	Xiang (Wiktionary)
hun-Latn	Hungarian
ilo-Latn	Ilocano
ind-Latn	Indonesian
ita-Latn	Italian
jam-Latn	Jamaican
jav-Latn	Javanese
kaz-Cyrl	Kazakh (Cyrillic)
kaz-Cyrl-bab	Kazakh (Cyrillic—Babel)
kaz-Latn	Kazakh (Latin)
kbd-Cyrl	Kabardian
khm-Khmr	Khmer
kin-Latn	Kinyarwanda
kir-Arab	Kyrgyz (Perso-Arabic)
kir-Cyrl	Kyrgyz (Cyrillic)
kir-Latn	Kyrgyz (Latin)
kmr-Latn	Kurmanji
kmr-Latn-red	Kurmanji (reduced)
lao-Laoo	Lao
lij-Latn	Ligurian
lsm-Latn	Saamia
ltc-Latn-bax	Middle Chinese (Baxter and Sagart 2014)
mal-Mlym	Malayalam
mar-Deva	Marathi
mlt-Latn	Maltese
mon-Cyrl-bab	Mongolian (Cyrillic)
mri-Latn	Maori
msa-Latn	Malay
mya-Mymr	Burmese
nan-Latn	Hokkien (pe̍h-oē-jī)
nan-Latn-tl	Hokkien (Tâi-lô)
nld-Latn	Dutch
nya-Latn	Chichewa
ood-Lat-alv	Tohono O'odham
ood-Latn-sax	Tohono O'odham
ori-Orya	Odia
orm-Latn	Oromo
pan-Guru	Punjabi (Eastern)
pol-Latn	Polish
por-Latn	Portuguese
quy-Latn	Ayacucho Quechua / Quechua Chanka
ron-Latn	Romanian
run-Latn	Rundi
rus-Cyrl	Russian
sag-Latn	Sango
sin-Sinh	Sinhala
sna-Latn	Shona
som-Latn	Somali
spa-Latn	Spanish
spa-Latn-eu	Spanish (Iberian)
sqi-Latn	Albanian
srp-Latn	Serbian
swa-Latn	Swahili
swa-Latn-red	Swahili (reduced)
swe-Latn	Swedish
tam-Taml	Tamil
tam-Taml-red	Tamil (reduced)
tel-Telu	Telugu
tgk-Cyrl	Tajik
tgl-Latn	Tagalog
tgl-Latn-red	Tagalog (reduced)
tha-Thai	Thai
tir-Ethi	Tigrinya
tir-Ethi-pp	Tigrinya (more phonemic)
tir-Ethi-red	Tigrinya (reduced)
tpi-Latn	Tok Pisin
tuk-Cyrl	Turkmen (Cyrillic)
tuk-Latn	Turkmen (Latin)
tur-Latn	Turkish (Latin)
tur-Latn-bab	Turkish (Latin—Babel)
tur-Latn-red	Turkish (reduced)
ukr-Cyrl	Ukranian
urd-Arab	Urdu
uig-Arab	Uyghur (Perso-Arabic)
uzb-Cyrl	Uzbek (Cyrillic)
uzb-Latn	Uzbek (Latin)
vie-Latn	Vietnamese
wuu-Latn	Shanghainese Wu (Wiktionary)
xho-Latn	Xhosa
yor-Latn	Yoruba
yue-Latn	Cantonese
zha-Latn	Zhuang
zul-Latn	Zulu

*Chinese G2P requires the freely available CC-CEDict dictionary.

†These language preprocessors and maps naively assume a phonemic orthography.

‡English G2P requires the installation of the freely available CMU Flite speech synthesis system.

Languages with limited support due to highly ambiguous orthographies

Some the languages listed above should be approached with caution. It is not possible to provide highly accurate support for these language-script pairs due to the high degree of ambiguity inherent in the orthographies. Eventually, we plan to support these languages with a different back end based on WFSTs or neural methods.

Code	Language (Script)
ara-Arab	Arabic
cat-Latn	Catalan
ckb-Arab	Sorani
fas-Arab	Farsi (Perso-Arabic)
fra-Latn	French
fra-Latn-np	French†
mya-Mymr	Burmese
por-Latn	Portuguese

Language "Spaces"

Code	Language	Note
amh-Ethi	Amharic
deu-Latn	German
eng-Latn	English
nld-Latn	Dutch
spa-Latn	Spanish
tur-Latn	Turkish	Based on data with suffixes attached
tur-Latn-nosuf	Turkish	Based on data with suffixes removed
uzb-Latn-suf	Uzbek	Based on data with suffixes attached

Note that major languages, including French, are missing from this table due to a lack of appropriate text data.

Installation of Flite (for English G2P)

For use with most languages, Epitran requires no special installation steps. It can be installed as an ordinarary python package, either with pip or by running python setup.py install in the root of the source directory. However, English G2P in Epitran relies on CMU Flite, a speech synthesis package by Alan Black and other speech researchers at Carnegie Mellon University. For the current version of Epitran, you should follow the installation instructions for lex_lookup, which is used as the default G2P interface for Epitran.

`t2p`

Not recommended This interface to Flite is now deprecated; Use lex_lookup.

`lex_lookup`

Recommended

t2p does not behave as expected on letter sequences that are highly infrequent in English. In such cases, t2p gives the pronunciation of the English letters of the name, rather than an attempt at the pronunciation of the name. There is a different binary included in the most recent (pre-release) versions of Flite that behaves better in this regard, but takes some extra effort to install. To install, you need to obtain at least version 2.0.5 of Flite. We recommend that you obtain the source from GitHub (https://github.com/festvox/flite). Untar and compile the source, following the steps below, adjusting where appropriate for your system:

$ tar xjf flite-2.0.5-current.tar.bz2
$ cd flite-2.0.5-current

$ git clone git@github.com:festvox/flite.git
$ cd flite/

then

$ ./configure &amp;&amp; make
$ sudo make install
$ cd testsuite
$ make lex_lookup
$ sudo cp lex_lookup /usr/local/bin

When installing on MacOS and other systems that use a BSD version of cp, some modification to a Makefile must be made in order to install flite-2.0.5 (between steps 3 and 4). Edit main/Makefile and change both instances of cp -pd to cp -pR. Then resume the steps above at step 4.

Usage

To use lex_lookup, simply instantiate Epitran as usual, but with the code set to 'eng-Latn':

&gt;&gt;&gt; import epitran
&gt;&gt;&gt; epi = epitran.Epitran('eng-Latn')
&gt;&gt;&gt; print epi.transliterate(u'Berkeley')
bɹ̩kli

Extending Epitran with map files, preprocessors and postprocessors

Language support in Epitran is provided through map files, which define mappings between orthographic and phonetic units, preprocessors that run before the map is applied, and postprocessors that run after the map is applied. Maps are defined in UTF8-encoded, comma-delimited value (CSV) files. The files are each named -.csv where is the (three letter, all lowercase) ISO 639-3 code for the language and is the (four letter, capitalized) ISO 15924 code for the script. These files reside in the data directory of the Epitran installation under the map, pre, and post subdirectories, respectively. The pre- and post-processor files are text files whose format is described belown. They follow the same naming conventions except that they have the file extensions .txt.

Map files (mapping tables)

The map files are simple, two-column files where the first column contains the orthgraphic characters/sequences and the second column contains the phonetic characters/sequences. The two columns are separated by a comma; each row is terminated by a newline. For many languages (most languages with unambiguous, phonemically adequate orthographies) just this easy-to-produce mapping file is adequate to produce a serviceable G2P system.

The first row is a header and is discarded. For consistency, it should contain the fields "Orth" and "Phon". The following rows by consist of fields of any length, separated by a comma. The same phonetic form (the second field) may occur any number of times but an orthographic form may only occur once. Where one orthograrphic form is a prefix of another form, the longer form has priority in mapping. In other words, matching between orthographic units and orthographic strings is greedy. Mapping works by finding the longest prefix of the orthographic form and adding the corresponding phonetic string to the end of the phonetic form, then removing the prefix from the orthographic form and continuing, in the same manner, until the orthographic form is consumed. If no non-empty prefix of the orthographic form is present in the mapping table, the first character in the orthographic form is removed and appended to the phonetic form. The normal sequence then resumes. This means that non-phonetic characters may end up in the "phonetic" form, which we judge to be better than losing information through an inadequate mapping table.

Preprocesssors and postprocessors

For language-script pairs with more complicated orthographies, it is sometimes necessary to manipulate the orthographic form prior to mapping or to manipulate the phonetic form after mapping. This is done, in Epitran, with grammars of context-sensitive string rewrite rules. In truth, these rules would be more than adequate to solve the mapping problem as well but in practical terms, it is usually easier to let easy-to-understand and easy-to-maintain mapping files carry most of the weight of conversion and reserve the more powerful context sensitive grammar formalism for pre- and post-processing.

The preprocessor and postprocessor files have the same format. They consist of a sequence of lines, each consisting of one of four types:

Symbol definitions
Context-sensitive rewrite rules
Comments
Blank lines

Symbol definitions

Lines like the following

::vowels:: = a|e|i|o|u

define symbols that can be reused in writing rules. Symbols must consist of a prefix of two colons, a sequence of one or more lowercase letters and underscores, and a suffix of two colons. The are separated from their definitions by the equals sign (optionally set off with white space). The definition consists of a substring from a regular expression.

Symbols must be defined before they are referenced.

Rewrite rules

Context-sensitive rewrite rules in Epitran are written in a format familiar to phonologists but transparent to computer scientists. They can be schematized as

a -&gt; b / X _ Y

which can be rewitten as

XaY → XbY

The arrow -> can be read as "is rewritten as" and the slash / can be read as "in the context". The underscore indicates the position of the symbol(s) being rewritten. Another special symbol is the octothorp #, which indicates the beginning or end of a (word length) string (a word boundary). Consider the following rule:

e -&gt; ə / _ #

This rule can be read as "/e/ is rewritten as /ə/ in the context at the end of the word." A final special symbol is zero 0, which represents the empty string. It is used in rules that insert or delete segments. Consider the following rule that deletes /ə/ between /k/ and /l/:

ə　-&gt; 0 / k _ l

All rules must include the arrow operator, the slash operator, and the underscore. A rule that applies in a context-free fashion can be written in the following way:

ch -&gt; x / _

The implementation of context-sensitive rules in Epitran pre- and post-processors uses regular expression replacement. Specifically, it employs the regex package, a drop-in replacement for re. Because of this, regular expression notation can be used in writing rules:

c -&gt; s / _ [ie]

c -&gt; s / _ (i|e)

For a complete guide to regex regular expressions, see the documentation for re and for regex, specifically.

Fragments of regular expressions can be assigned to symbols and reused throughout a file. For example, symbol for the disjunction of vowels in a language can be used in a rule that changes /u/ into /w/ before vowels:

::vowels:: = a|e|i|o|u
...
u -&gt; w / _ (::vowels::)

There is a special construct for handling cases of metathesis (where "AB" is replaced with "BA"). For example, the rule:

(?P[เแโไใไ])(?P.) -&gt; 0 / _

Will "swap" the positions of any character in "เแโไใไ" and any following character. Left of the arrow, there should be two groups (surrounded by parentheses) with the names sw1 and sw2 (a name for a group is specified by ?P appearing immediately after the open parenthesis for a group). The substrings matched by the two groups, sw1 and sw2 will be "swapped" or metathesized. The item immediately right of the arrow is ignored, but the context is not.

To move IPA tones to the end of the word, first ensure that tones=True in the instantiated Epitran object and use the following rule:

(?P[˩˨˧˦˥]+)(?P\w+) -&gt; 0 / _\b

The rules apply in order, so earlier rules may "feed" and "bleed" later rules. Therefore, their sequence is very important and can be leveraged in order to achieve valuable results.

Comments and blank lines

Comments and blank lines (lines consisting only of white space) are allowed to make your code more readable. Any line in which the first non-whitespace character is a percent sign % is interpreted as comment. The rest of the line is ignored when the file is interpreted. Blank lines are also ignored.

A strategy for adding language support

Epitran uses a mapping-and-repairs approach to G2P. It is expected that there is a mapping between graphemes and phonemes that can do most of the work of converting orthographic representations to phonological representations. In phonemically adequate orthogrphies, this mapping can do all of the work. This mapping should be completed first. For many languages, a basis for this mapping table already exists on Wikipedia and Omniglot (though the Omniglot tables are typically not machine readable).

On the other hand, many writing systems deviate from the phonemically adequate idea. It is here that pre- and post-processors must be introduced. For example, in Swedish, the letter <a> receives a different pronunciation before two consonants (/ɐ/) than elsewhere (/ɑː/). It makes sense to add a preprocessor rule that rewrites </a><a> as /ɐ/ before two consonants (and similar rules for the other vowels, since they are affected by the same condition). Preprocessor rules should generally be employed whenever the orthographic representation must be adjusted (by contextual changes, deletions, etc.) prior to the mapping step.

One common use for postprocessors is to eliminate characters that are needed by the preprocessors or maps, but which should not appear in the output. A classic example of this is the virama used in Indic scripts. In these scripts, in order to write a consonant not followed by a vowel, one uses the form of the consonant symbol with particular inherent vowel followed by a virama (which has various names in different Indic languages). An easy way of handling this is to allow the mapping to translate the consonant into an IPA consonant + an inherent vowel (which, for a given language, will always be the same), then use the postprocessor to delete the vowel + virama sequence (wherever it occurs).

In fact, any situation where a character that is introduced by the map needs to be subsequently deleted is a good use-case for postprocessors. Another example from Indian languages includes so-called schwa deletion. Some vowels implied by a direct mapping between the orthography and the phonology are not actually pronounced; these vowels can generally be predicted. In most languages, they occur in the context after a vowel+consonant sequence and before a consonant+vowel sequence. In other words, the rule looks like the following:

ə -&gt; 0 / (::vowel::)(::consonant::) _ (::consonant::)(::vowel::)

Perhaps the best way to learn how to structure language support for a new language is to consult the existing languages in Epitran. The French preprocessor fra-Latn.txt and the Thai postprocessor tha-Thai.txt illustrate many of the use-cases for these rules.

Citing Epitran

If you use Epitran in published work, or in other research, please use the following citation:

David R. Mortensen, Siddharth Dalmia, and Patrick Littell. 2018. Epitran: Precision G2P for many languages. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Paris, France. European Language Resources Association (ELRA).

@InProceedings{Mortensen-et-al:2018,
  author = {Mortensen, David R.  and Dalmia, Siddharth and Littell, Patrick},
  title = {Epitran: Precision {G2P} for Many Languages},
  booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},
  year = {2018},
  month = {May},
  date = {7--12},
  location = {Miyazaki, Japan},
  editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and H\'el\`ene Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga},
  publisher = {European Language Resources Association (ELRA)},
  address = {Paris, France},
  isbn = {979-10-95546-00-9},
  language = {english}
  }

epitran dependencies

g2pk jamo marisa-trie regex requests setuptools

FAQs

What is epitran?

Tools for transcribing languages into IPA. Visit Snyk Advisor to see a full health score report for epitran, including popularity, security, maintenance & community analysis.

Is epitran popular?

The python package epitran receives a total of 7,599 weekly downloads. As such, epitran popularity was classified as a recognized. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is epitran well maintained?

We found indications that epitran maintenance is sustainable demonstrating some project activity. We saw a total of 40 open source contributors collaborating on the project. See the full package health analysis to learn more about the package maintenance status.

Is epitran safe to use?

While scanning the latest version of epitran, we found that a security review is needed. A total of 0 vulnerabilities or license issues were detected. See the full security scan results.

Last updated on 25 December-2024, at 04:05 (UTC).

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Not sure how to use epitran?

Epitran

Usage

Using the epitran Module

The Epitran class

The Backoff class

DictFirst

Preprocessors, postprocessors, and their pitfalls

Using the epitran.vector Module

Language Support

Transliteration Language/Script Pairs

Languages with limited support due to highly ambiguous orthographies

Language "Spaces"

Installation of Flite (for English G2P)

t2p

lex_lookup

Usage

Extending Epitran with map files, preprocessors and postprocessors

Map files (mapping tables)

Preprocesssors and postprocessors

Symbol definitions

Rewrite rules

Comments and blank lines

A strategy for adding language support

Citing Epitran

epitran dependencies

FAQs

What is epitran?

Is epitran popular?

Is epitran well maintained?

Is epitran safe to use?

Build a secure application checklist

Select a recommended open source package

Scan your app for vulnerabilities

Source Code

Open Source

Container

Config

Fix identified vulnerabilities

Monitor for new issues

Using the `epitran` Module

Using the `epitran.vector` Module

`t2p`

`lex_lookup`