Common Voice Utils

This repository collects together basic linguistic processing data for using dataset dumps from the Common Voice project. It aims to provide a one-stop-shop for utilities and data useful in training ASR and TTS systems.

Tools

• Phonemiser:
  • A rudimentary grapheme to phoneme (g2p) system based on either:
    • a deterministic longest-match left-to-right replacement of orthographic units; or
    • a weighted finite-state transducer
• Validator:
  • A validation script that can be used with import_cv2.py from coqui-ai/STT
  • It checks a sentence to see if it can be converted and if possible normalises the encoding, removes punctuation and returns it
• Alphabet:
  • The relevant alphabet of the language, appropriate for use in training ASR
• Segmenter:
  • A deterministic sentence segmentation algorithm tuned for segmenting paragraphs from Wikipedia

Language support

How to use it

Alphabet

>>> from cvutils import Alphabet
>>> a = Alphabet('cv')
>>> a.get_alphabet()
' -абвгдежзийклмнопрстуфхцчшщыэюяёҫӑӗӳ'

Grapheme to phoneme

>>> from cvutils import Phonemiser
>>> p = Phonemiser('ab')
>>> p.phonemise('гӏапынхъамыз')
'ʕapənqaməz'

>>> p = Phonemiser('br')
>>> p.phonemise("implijout")
'impliʒut'

Validator

>>> from cvutils import Validator
>>> v = Validator('ab')
>>> v.validate('Аллаҳ хаҵеи-ԥҳәыси иеилыхны, аҭыԥҳацәа роума иалихыз?')
'аллаҳ хаҵеи-ԥҳәыси иеилыхны аҭыԥҳацәа роума иалихыз'

>>> v = Validator('br')
>>> v.validate('Ha cʼhoant hocʼh eus da gendercʼhel da implijout ar servijer-mañ ?')
"ha c'hoant hoc'h eus da genderc'hel da implijout ar servijer-mañ"

Sentence segmentation

>>> from cvutils import Segmenter 
>>> s = Segmenter('br')
>>> para = "Peurliesañ avat e kemm ar vogalennoù e c'hengerioù evit dont da vezañ heñvel ouzh ar vogalennoù en nominativ (d.l.e. ar stumm-meneg), da skouer e hungareg: Aour, tungsten, zink, uraniom, h.a., a vez kavet e kondon Bouryatia. A-bouez-bras evit armerzh ar vro eo al labour-douar ivez pa vez gounezet gwinizh ha legumaj dreist-holl. A-hend-all e vez gounezet arc'hant dre chaseal ha pesketa."
>>> for sent in s.segment(para):
...     print(sent)
... 
Peurliesañ avat e kemm ar vogalennoù e c'hengerioù evit dont da vezañ heñvel ouzh ar vogalennoù en nominativ (d.l.e. ar stumm-meneg), da skouer e hungareg: Aour, tungsten, zink, uraniom, h.a., a vez kavet e kondon Bouryatia.
A-bouez-bras evit armerzh ar vro eo al labour-douar ivez pa vez gounezet gwinizh ha legumaj dreist-holl.
A-hend-all e vez gounezet arc'hant dre chaseal ha pesketa.

Frequently asked questions

Why not use [insert better system] for [insert task here] ?

There are potentially a lot of better language-specific systems for doing these tasks, but each one has a slightly different API, so if you want to support all the Common Voice languages or even a reasonable subset you have to learn and use the same number of language-specific APIs.

The idea of these utilities is to provide adequate implementations of things are are likely to be useful when working with all the languages in Common Voice. If you are working on a single language or have a specific setup or are using more data than just Common Voice, maybe this isn't for you. But if you want to just train coqui-ai/STT on Common Voice, then maybe it is :)

Why not just make the alphabet from the transcripts ?

Depending on the language in Common Voice, the transcripts can contain a lot of random punctuation, numerals, and incorrect character encodings (for example Latin ç instead of Cyrillic ҫ for Chuvash). These may look the same but will result in bigger sparsity for the model. Additionally some languages may have several encodings of the same character, such as the apostrophe. These will ideally be normalised before training.

Also, if you are working with a single language you probably have time to look through all the transcripts for the alphabetic symbols, but if you want to work with a large number of Common Voice languages at the same time it's useful to have them all in one place.

See also

• epitran: Great grapheme to phoneme system that supports a wide range of languages.

Acknowledgements

• Grapheme to phoneme correspondences for the following languages from epitran:
  • vi, uk, kk, ky, ta
• Code for transducer lookup from Måns Huldén.