kaldialign 0.9.2

Kaldi alignment methods wrapped into Python

kaldialign

A small package that exposes edit distance computation functions from Kaldi. It uses the original Kaldi code and wraps it using pybind11.

Installation

conda install -c kaldialign kaldialign

or

pip install --verbose kaldialign

or

pip install --verbose -U git+https://github.com/pzelasko/kaldialign.git

or

git clone https://github.com/pzelasko/kaldialign.git
cd kaldialign
python3 -m pip install --verbose .

Examples

Alignment

align(ref, hyp, epsilon) - used to obtain the alignment between two string sequences. epsilon should be a null symbol (indicating deletion/insertion) that doesn't exist in either sequence.

from kaldialign import align

EPS = '*'
a = ['a', 'b', 'c']
b = ['a', 's', 'x', 'c']
ali = align(a, b, EPS)
assert ali == [('a', 'a'), ('b', 's'), (EPS, 'x'), ('c', 'c')]

Edit distance

edit_distance(ref, hyp) - used to obtain the total edit distance, as well as the number of insertions, deletions and substitutions.

from kaldialign import edit_distance

a = ['a', 'b', 'c']
b = ['a', 's', 'x', 'c']
results = edit_distance(a, b)
assert results == {
    'ins': 1,
    'del': 0,
    'sub': 1,
    'total': 2
}

For alignment and edit distance, you can pass sclite_mode=True to compute WER or alignments based on SCLITE style weights, i.e., insertion/deletion cost 3 and substitution cost 4.

Bootstrapping method to extract WER 95% confidence intervals

boostrap_wer_ci(ref, hyp, hyp2=None) - obtain the 95% confidence intervals for WER using Bisani and Ney boostrapping method.

from kaldialign import bootstrap_wer_ci

ref = [
    ("a", "b", "c"),
    ("d", "e", "f"),
]
hyp = [
    ("a", "b", "d"),
    ("e", "f", "f"),
]
ans = bootstrap_wer_ci(ref, hyp)
assert ans["wer"] == 0.4989
assert ans["ci95"] == 0.2312
assert ans["ci95min"] == 0.2678
assert ans["ci95max"] == 0.7301

It also supports providing hypotheses from system 1 and system 2 to compute the probability of S2 improving over S1:

from kaldialign import bootstrap_wer_ci

ref = [
    ("a", "b", "c"),
    ("d", "e", "f"),
]
hyp = [
    ("a", "b", "d"),
    ("e", "f", "f"),
]
hyp2 = [
    ("a", "b", "c"),
    ("e", "e", "f"),
]
ans = bootstrap_wer_ci(ref, hyp, hyp2)

s = ans["system1"]
assert s["wer"] == 0.4989
assert s["ci95"] == 0.2312
assert s["ci95min"] == 0.2678
assert s["ci95max"] == 0.7301

s = ans["system2"]
assert s["wer"] == 0.1656
assert s["ci95"] == 0.2312
assert s["ci95min"] == -0.0656
assert s["ci95max"] == 0.3968

assert ans["p_s2_improv_over_s1"] == 1.0

Motivation

The need for this arised from the fact that practically all implementations of the Levenshtein distance have slight differences, making it impossible to use a different scoring tool than Kaldi and get the same error rate results. This package copies code from Kaldi directly and wraps it using pybind11, avoiding the issue altogether.