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.
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.
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_оÑбÑвание.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.
Russian text intended for learners sometimes contains marks that indicate the syllabic stress. It is usually rendered as a vowel + a combining diacritical mark, typically the combining acute accent \u301. Here are a couple ways of stripping these marks on the command line:
First is a version using Perl
#!/bin/bash f='покупа́ешья́'; echo $f | perl -C -pe 's/\x{301}//g;' And then another using the sd tool:
#!/bin/bash f='покупа́ешья́'; echo $f | sd "\u0301" "" Both rely on finding the combining diacritical mark and removing it with regex.
In the process of writing and maintaining a service that checks Russian word frequencies, I noticed peculiar phenomenon: certain words could not be located in a sqlite database that I knew actually contained them. For example, a query for the word - английский consistently failed, whereas other words would succeed. Eventually the commonality between the failures became obvious. All of the failures contained the letter й , which led me down a rabbit hole of character encoding and this specific case where it can go astray.