texterrors 1.1.1

For WER

texterrors

For calculating WER, CER, other metrics, getting detailed statistics and comparing outputs.

Meant to replace older tools like sclite by being easy to use, modify and extend.

Features:

• Character aware, standard (default) and ctm based alignment
• Metrics by group (for example speaker)
• Comparing two hypothesis files to reference
• Oracle WER
• NEW Simple entity accuracy from reference-side casing
• Sorting most common errors by frequency or count
• Measuring performance on keywords
• Measuring OOV-CER (see https://arxiv.org/abs/2107.08091 )
• Colored output to inspect errors

Example of colored output below (use -c flag). Read the white and green words to read the reference. Read the white and red words to read the hypothesis.

See here for background motivation.

Installing

Requires minimum python 3.9!

pip install texterrors

The package will be installed as texterrors and there will be a texterrors script in your path.

For development, create a local environment and install build/test dependencies, then build in place:

$ uv venv
$ env UV_CACHE_DIR=/tmp/uv-cache uv pip install --python .venv/bin/python -r requirements.txt
$ .venv/bin/cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DPython_EXECUTABLE=$PWD/.venv/bin/python -Dnanobind_DIR=$PWD/.venv/lib/python3.12/site-packages/nanobind/cmake
$ .venv/bin/cmake --build build --config Release
$ .venv/bin/cmake --install build --config Release --prefix $PWD

Example

The -s option means there will be no detailed output. Below ref and hyp are files with the first field equalling the utterance ID (therefore the isark flag).

$ texterrors -isark -s ref hyp  
WER: 83.33 (ins 1, del 1, sub 3 / 6)  

You can specify an output file to save the results, probably what you want if you are getting detailed output (not using -s). Here we are also calculating the CER, the OOV-CER to measure the performance on the OOV words inside the oov_list file, and using colored output (therefore the -c flag).

$ texterrors -c -isark -cer -oov-list-f oov_list ref hyp detailed_wer_output  

Use less -R to view the colored output. Skip the -c flag to not use color.

Check texterrors/__init__.py to see functions that you may be interested in using from python.

Direct Python alignment accepts plain token lists and uses keyword-only options:

from texterrors import align_texts

ref_aligned, hyp_aligned, cost = align_texts(
    ["speedbird", "eight", "six", "two"],
    ["hello", "speedbird", "six", "two"],
    use_chardiff=True,
)

Benchmarking

A small benchmark harness lives in benchmarks/alignment_benchmark.py. By default it runs:

• fast default alignment (use_chardiff=False, skip_detailed=True)
• character-aware alignment (use_chardiff=True, skip_detailed=True)
• detailed output mode (use_chardiff=True, skip_detailed=False)

Run it from the repo root like this:

$ .venv/bin/python benchmarks/alignment_benchmark.py --repeat 7

You can also point it at other ark-like files:

$ .venv/bin/python benchmarks/alignment_benchmark.py --ref my_ref.txt --hyp my_hyp.txt

Options you might want to use

Call texterrors -h to see all options.

-cer, -isctm - Calculate CER, Use ctms for alignment

-utt-group-map - Should be a file which maps uttids to group, WER will be output per group (could use
to get per speaker WER for example).

-second-hyp-f - Use to compare the outputs of two different models to the reference.

-w - Calculate simple entity accuracy using reference-side casing, then lowercase text for scoring.

-freq-sort - Sort errors by frequency rather than count

-oov-list-f - The CER between words aligned to the OOV words will be calculated (the OOV-CER).

-keywords-list-f - Will calculate precision & recall of words in the file.

-oracle-wer - Hypothesis file should have multiple entries for each utterance, oracle WER will be calculated.

Why is the WER slightly higher than in kaldi if I use -use_chardiff?

You can make it equal by not using the -use_chardiff argument.

This difference is because this tool can do character aware alignment. Across a normal sized test set this should result in a small difference.

In the below example a normal WER calculation would do a one-to-one mapping and arrive at a WER of 66.67%.

test	sentence	okay	words	ending	now
test	a	sentenc	ok	endin	now

But character aware alignment would result in the following alignment:

test	-	sentence	okay	words	ending	now
test	a	sentenc	ok	-	endin	now

This results in a WER of 83.3% because of the extra insertion and deletion. And I think one could argue this is the actually correct WER.

Changelog

Recent changes:

• 18.03.26 Migrated the extension module from pybind11 to nanobind and moved builds to CMake/scikit-build-core.
• 24.03.26 Removed weighted WER and added simple entity accuracy from reference-side casing.
• 11.11.25 Weighted WER for English
• 26.02.25 Faster alignment, better multihyp support, fixed multihyp bug.
• 22.06.22 refactored internals to make them simpler, character aware alignment is off by default, added more explanations
• 20.05.22 fixed bug missing regex dependency
• 16.05.22 fixed bug causing wrong detailed output when there is utterance with empty reference, and utts with empty reference are not ignored
• 21.04.22 insertion errors on lower line and switching colors so green is reference
• 27.01.22 oracle WER and small bug fixes
• 26.01.22 fixed bug causing OOV-CER feature to not work
• 22.11.21 new feature to compare two outputs to reference; lots of small changes
• 04.10.21 fixed bug, nocolor option, refactoring, keywords feature works properly, updated README
• 22.08.21 added oracle wer feature, cost matrix creation returns cost now
• 16.07.21 improves alignment based on ctms (much stricter now).