my_jiwer/docs/usage.md

# Usage

The most simple use-case is computing the word error rate between two strings:

```python
from jiwer import wer

reference = "hello world"
hypothesis = "hello duck"

error = wer(reference, hypothesis)
```

Similarly, to get other measures:

```python
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:

```python
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:

```python
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:

```python3
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:

```python3
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
```text
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.