I’ve tried a lot of fonts for Cyrillic. My favourite is Georgia.

As a non-native Russian speaker, there’s something about serif fonts, either on-screen or in print, that makes the text so much more legible.

I alluded to this nuance involving variable scope in my post on automating pdf processing, but I wanted to expand on it a bit.

Consider this little snippet:

i=0
printf "foo:bar:baz:quux" | grep -o '[^:]\+' | while read -r line ; do
   printf "Inner scope: %d - %s\n" $i $line
   ((i++))
   [ $i -eq 3 ] && break;
done
printf "====\nOuter scope\ni = %d\n" $i;

If you run this script - not in interactive mode in the shell - but as a script, what will i be in the outer scope? And why?

I have written previously about stripping syllabic stress marks from Russian text using a Perl-based regex tool. But I needed a means of doing in solely in Python, so this just extends that idea.

#!/usr/bin/env python3

def strip_stress_marks(text: str) -> str:
   b = text.encode('utf-8')
   # correct error where latin accented ó is used
   b = b.replace(b'\xc3\xb3', b'\xd0\xbe')
   # correct error where latin accented á is used
   b = b.replace(b'\xc3\xa1', b'\xd0\xb0')
   # correct error where latin accented é is used
   b = b.replace(b'\xc3\xa0', b'\xd0\xb5')
   # correct error where latin accented ý is used
   b = b.replace(b'\xc3\xbd', b'\xd1\x83')
   # remove combining diacritical mark
   b = b.replace(b'\xcc\x81',b'').decode()
   return b

text = "Том столкну́л Мэри с трампли́на для прыжко́в в во́ду."

print(strip_stress_marks(text))
# prints "Том столкнул Мэри с трамплина для прыжков в воду."

The approach is similar to the Perl-based tool we constructed before, but this time we are working working on the bytes object after encoding as utf-8. Since the bytes object has a replace method, we can use that to do all of the work. The first 4 replacements all deal with edge cases where accented Latin characters are use to show the placement of syllabic stress instead of the Cyrillic character plus the combining diacritical mark. In these cases, we just need to substitute the proper Cyrillic character. Then we just strip out the “combining acute accent” U+301 → \xcc\x81 in UTF-8. After these replacements, we just decode the bytes object back to a str.

For one-off projects that target Anki collections, I often use Python in a standalone application rather than an Anki add-on. Since I’m not going to distribute these little creations that are specific to my own needs, there’s no reason to create an add-on. These are just a few notes - nothing comprehensive - on the process.

One thing to be aware of is that there must be a perfect match between the Anki major and minor version numbers for the Python anki module to work. If you are running Anki 2.1.48 on your desktop application but have the Python module built for 2.1.49, it will not work. This is a huge irritation and there’s no backwards compatibility; the versions must match precisely.

This may be obvious to some, but visually-recognizing character encoding at a glance is not always obvious.

For example, pronunciation files downloaded form Forvo have the following appearance:

pronunciation_ru_оÑ‚бывание.mp3

How can we extact the actual word from this gibberish? Optimally, the filename should reflect that actual word uttered in the pronunciation file, after all.

Step 1 - Extracting the interesting bits

The gibberish begins after the pronunciation_ru_ and ends before the file extension. Any regex tool can tease that out.