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# AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/C. Word error rate metrics.ipynb.
# %% auto 0
__all__ = ['librispeech_data', 'DfBuilder', 'WERStats']
# %% ../nbs/C. Word error rate metrics.ipynb 2
import jiwer
from whisper_normalizer.english import EnglishTextNormalizer
import torchaudio
from pathlib import Path
import pandas as pd
# %% ../nbs/C. Word error rate metrics.ipynb 3
engnorm = EnglishTextNormalizer()
def whisper_normalize(x):
if type(x) == list:
return [engnorm(y) for y in x]
else:
return engnorm(x)
default_transform = jiwer.transforms.Compose([
jiwer.transforms.ToLowerCase(),
jiwer.transforms.ExpandCommonEnglishContractions(),
whisper_normalize,
jiwer.transforms.RemoveMultipleSpaces(),
jiwer.transforms.Strip(),
jiwer.transforms.RemovePunctuation(),
jiwer.transforms.ReduceToListOfListOfWords(),
])
# %% ../nbs/C. Word error rate metrics.ipynb 5
def librispeech_data(datadir, sample_rate=16000):
for file in Path(datadir).rglob('*.txt'):
for line in file.read_text().split('\n'):
if not line: continue
idx, text = line.split(" ", 1)
x, sr = torchaudio.load((file.parent/idx).with_suffix('.flac'))
if sr != sample_rate:
x = torchaudio.transforms.Resample(sr, self.sample_rate)(x)
yield x, text
# %% ../nbs/C. Word error rate metrics.ipynb 6
class DfBuilder:
def __init__(self):
self.data = {}
def push(self, **kwargs):
for k,v in kwargs.items():
if k not in self.data:
self.data[k] = [v]
else:
self.data[k].append(v)
def df(self):
return pd.DataFrame(self.data)
# %% ../nbs/C. Word error rate metrics.ipynb 7
class WERStats(DfBuilder):
def __init__(self, transform=default_transform):
super().__init__()
self.reference_transform = transform
self.hypothesis_transform = transform
def push_sample(self, snd, gt_text, text, idx=None):
if snd is not None: self.push(secs = snd.shape[-1]/16000)
diff = jiwer.process_words(gt_text, text, reference_transform=self.reference_transform, hypothesis_transform=self.hypothesis_transform)
self.push(
idx = idx,
gt_text = gt_text,
text = text,
wer = diff.wer,
mer = diff.mer,
wil = diff.wil,
wip = diff.wip,
)
return diff
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