Word Error Rate for Air Traffic Control
Project description
airwer
Word Error Rate for Air Traffic Control.
Install
pip install airwer
Usage
import airwer
airwer.wer("descend flight level two five zero", "descend FL250") # 0.0
airwer.wer("turn heading two one zero", "turn heading 220") # > 0.0
API
Each takes a single utterance (str) or a corpus (Sequence[str]), plus an
optional WerConfig to override the default CANONICAL profile.
| Function | What it scores |
|---|---|
wer(ref, hyp) |
Corpus Word Error Rate (the default metric) |
cer(ref, hyp) |
Character Error Rate |
numeric_wer(ref, hyp) |
WER over numbers only - safety-critical digits |
agreement(a, b) |
Symmetric [0, 1] overlap of two transcripts (1 = identical), for model-vs-model voting |
ladder(ref, hyp) |
WER at each normalization rung, raw to semantic |
process(ref, hyp) |
Full WerResult - every metric plus per-utterance and distribution stats |
normalize(text) exposes the normalization step on its own. WerConfig, the
profiles presets, and the vocab term lists let you tune phraseology
handling.
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