# kaldialign
A small package that exposes edit distance computation functions from [Kaldi](https://github.com/kaldi-asr/kaldi). It uses the original Kaldi code and wraps it using pybind11.
## Installation
```bash
conda install -c kaldialign kaldialign
```
or
```bash
pip install --verbose kaldialign
```
or
```bash
pip install --verbose -U git+https://github.com/pzelasko/kaldialign.git
```
or
```bash
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.
```python
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.
```python
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.
```python
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:
```python
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.
Raw data
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"description": "# kaldialign\n\nA small package that exposes edit distance computation functions from [Kaldi](https://github.com/kaldi-asr/kaldi). It uses the original Kaldi code and wraps it using pybind11.\n\n## Installation\n\n```bash\nconda install -c kaldialign kaldialign\n```\n\nor\n\n```bash\npip install --verbose kaldialign\n```\n\nor\n\n```bash\npip install --verbose -U git+https://github.com/pzelasko/kaldialign.git\n```\n\nor\n\n```bash\ngit clone https://github.com/pzelasko/kaldialign.git\ncd kaldialign\npython3 -m pip install --verbose .\n```\n\n## Examples\n\n### Alignment\n\n`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.\n\n```python\nfrom kaldialign import align\n\nEPS = '*'\na = ['a', 'b', 'c']\nb = ['a', 's', 'x', 'c']\nali = align(a, b, EPS)\nassert ali == [('a', 'a'), ('b', 's'), (EPS, 'x'), ('c', 'c')]\n```\n\n### Edit distance\n\n`edit_distance(ref, hyp)` - used to obtain the total edit distance, as well as the number of insertions, deletions and substitutions.\n\n```python\nfrom kaldialign import edit_distance\n\na = ['a', 'b', 'c']\nb = ['a', 's', 'x', 'c']\nresults = edit_distance(a, b)\nassert results == {\n 'ins': 1,\n 'del': 0,\n 'sub': 1,\n 'total': 2\n}\n```\n\nFor alignment and edit distance, you can pass `sclite_mode=True` to compute WER or alignments\nbased on SCLITE style weights, i.e., insertion/deletion cost 3 and substitution cost 4.\n\n### Bootstrapping method to extract WER 95% confidence intervals\n\n`boostrap_wer_ci(ref, hyp, hyp2=None)` - obtain the 95% confidence intervals for WER using Bisani and Ney boostrapping method.\n\n```python\nfrom kaldialign import bootstrap_wer_ci\n\nref = [\n (\"a\", \"b\", \"c\"),\n (\"d\", \"e\", \"f\"),\n]\nhyp = [\n (\"a\", \"b\", \"d\"),\n (\"e\", \"f\", \"f\"),\n]\nans = bootstrap_wer_ci(ref, hyp)\nassert ans[\"wer\"] == 0.4989\nassert ans[\"ci95\"] == 0.2312\nassert ans[\"ci95min\"] == 0.2678\nassert ans[\"ci95max\"] == 0.7301\n```\n\nIt also supports providing hypotheses from system 1 and system 2 to compute the probability of S2 improving over S1:\n\n```python\nfrom kaldialign import bootstrap_wer_ci\n\nref = [\n (\"a\", \"b\", \"c\"),\n (\"d\", \"e\", \"f\"),\n]\nhyp = [\n (\"a\", \"b\", \"d\"),\n (\"e\", \"f\", \"f\"),\n]\nhyp2 = [\n (\"a\", \"b\", \"c\"),\n (\"e\", \"e\", \"f\"),\n]\nans = bootstrap_wer_ci(ref, hyp, hyp2)\n\ns = ans[\"system1\"]\nassert s[\"wer\"] == 0.4989\nassert s[\"ci95\"] == 0.2312\nassert s[\"ci95min\"] == 0.2678\nassert s[\"ci95max\"] == 0.7301\n\ns = ans[\"system2\"]\nassert s[\"wer\"] == 0.1656\nassert s[\"ci95\"] == 0.2312\nassert s[\"ci95min\"] == -0.0656\nassert s[\"ci95max\"] == 0.3968\n\nassert ans[\"p_s2_improv_over_s1\"] == 1.0\n```\n\n## Motivation\n\nThe 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.\n",
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