Issue regarding cleaners

[JRMeyer]

>>> Immabot
[January 12, 2020, 4:33am]

Can someone explain what cleaners are and updates to be made for
languages other than English(step by step changes)... slash
Plzz

[This is an archived TTS discussion thread from discourse.mozilla.org/t/issue-regarding-cleaners]

[JRMeyer]

>>> nmstoker
[January 12, 2020, 12:52pm]

Have you read the comments in the code and looked through the code? slash

github.com

#### mozilla/TTS/blob/master/utils/text/cleaners.py

'''
Cleaners are transformations that run over the input text at both training and eval time.

Cleaners can be selected by passing a comma-delimited list of cleaner names as the 'cleaners'
hyperparameter. Some cleaners are English-specific. You'll typically want to use:
1. 'english_cleaners' for English text
2. 'transliteration_cleaners' for non-English text that can be transliterated to ASCII using
the Unidecode library (https://pypi.python.org/pypi/Unidecode)
3. 'basic_cleaners' if you do not want to transliterate (in this case, you should also update
the symbols in symbols.py to match your data).
'''

import re
from unidecode import unidecode
from .number_norm import normalize_numbers

# Regular expression matching whitespace:
_whitespace_re = re.compile(r' slash s+')

# List of (regular expression, replacement) pairs for abbreviations:

This file has been truncated. show
original

You'll likely get a better sense of what they do by experimenting with
inputting some text and seeing what comes out of the functions.

Essentially they're taking the raw transcript and processing it to take
out things the TTS system won't work with well or to make it easier for
it to learn the association between the (processed) text and the audio.

For instance if we look at the abbreviations cleaner it would be quite
hard for a system to realise that input of 'Mr.' should sound like the
sounds of 'mister' so one of the cleaners unabbreviates various titles.
This one is easy to understand but in fact, depending on the phoneme
backend used (such as espeak), you may find this is done for you (ie
espeak can handle 'Mr Jones' giving the phonemes for 'mister Jones').

Other cleaners do things like normalise the numbers (again as it's often
not obvious how digits relate to what is actually spoken) or filter out
characters that have no/limited impact on pronunciation.

As to a step by step list of what to change, it will depend on the
language so you will need to use common sense and knowledge of the
language but you'd probably want to:

accented characters) got accepted by the cleaning and weren't
removed

likely to be submitted by users of the model (assuming your backend
doesn't handle them as mentioned above)

[Archived Post]

[JRMeyer]

>>> Immabot
[January 12, 2020, 8:45pm]

Thanks! slash
I have about 30 hrs of data... slash
How long should i train to get decent output?

[Archived Post]

[JRMeyer]

>>> nmstoker
[January 12, 2020, 10:03pm]

If you use tensorboard you can monitor audio output as it proceeds.
Broadly the simplest rule is keep running until it stops improving and
it's best to give it a go.

Actual time to train will depend on your hardware so I can't really
advise, but I've typically done training runs of somewhere between one
and three days on a 1080Ti but it often has acceptable results somewhat
earlier.

[Archived Post]

[JRMeyer]

>>> Immabot
[January 26, 2020, 2:23pm]

19-46-56.filename}[1366×768 114

slash
I've trained the model and i have the .pth file. slash
I tried to use the Benchmark notebook under notebooks folder for
testing. slash
But I'm confused about the paths that we have to specify,especially
VOCODER_MODEL_PATH and VOCODER_CONFIG_PATH and where do i get the
VOCODER related files... slash
Can you plzz help?

[Archived Post]