Datasets:
Usage
The most simple use-case is computing the word error rate between two strings:
from jiwer import wer
reference = "hello world"
hypothesis = "hello duck"
error = wer(reference, hypothesis)
Similarly, to get other measures:
import jiwer
reference = "hello world"
hypothesis = "hello duck"
wer = jiwer.wer(reference, hypothesis)
mer = jiwer.mer(reference, hypothesis)
wil = jiwer.wil(reference, hypothesis)
# faster, because `compute_measures` only needs to perform the heavy lifting once:
output = jiwer.process_words(reference, hypothesis)
wer = output.wer
mer = output.mer
wil = output.wil
You can also compute the WER over multiple sentences:
from jiwer import wer
reference = ["hello world", "i like monthy python"]
hypothesis = ["hello duck", "i like python"]
error = wer(reference, hypothesis)
We also provide the character error rate:
import jiwer
reference = ["i can spell", "i hope"]
hypothesis = ["i kan cpell", "i hop"]
error = jiwer.cer(reference, hypothesis)
# if you also want the alignment
output = jiwer.process_characters(reference, hypothesis)
error = output.cer
Alignment
With jiwer.process_words
, you also get the alignment between the reference and hypothesis.
We provide the alignment as a list of (op, ref_start_idx, ref_idx_end, hyp_idx_start, hyp_idx_end)
, where op
is one of
equal
, replace
, delete
, or insert
.
This looks like the following:
import jiwer
out = jiwer.process_words("short one here", "shoe order one")
print(out.alignments)
# [[[AlignmentChunk(type='insert', ref_start_idx=0, ref_end_idx=0, hyp_start_idx=0, hyp_end_idx=1), ...]]
To visualize the alignment, you can use jiwer.visualize_alignment()
For example:
import jiwer
out = jiwer.process_words(
["short one here", "quite a bit of longer sentence"],
["shoe order one", "quite bit of an even longest sentence here"],
)
print(jiwer.visualize_alignment(out))
Gives the following output
sentence 1
REF: **** short one here
HYP: shoe order one ****
I S D
sentence 2
REF: quite a bit of ** **** longer sentence ****
HYP: quite * bit of an even longest sentence here
D I I S I
number of sentences: 2
substitutions=2 deletions=2 insertions=4 hits=5
mer=61.54%
wil=74.75%
wip=25.25%
wer=88.89%
Note that it also possible to visualize the character-level alignment, simply use the output of jiwer.process_characters()
instead.