altmetrics 0.1.0

Evaluate ASR and MT output with alternative-aware metrics.

altmetrics

altmetrics is a Python toolkit for evaluating speech and text generation systems when multiple orthographically valid references exist. It extends classic metrics such as WER, CER, BLEU, and chrF by expanding bracketed reference alternatives and picking the combination that yields the best score for each hypothesis.

Why altmetrics?

Traditional metrics assume a single canonical reference. In practice, many languages and transcription guidelines permit several spellings (matta vs matten), optional fillers, or regional variants. altmetrics lets you encode these choices in square brackets and automatically evaluates with the most favourable reference for each sentence.

[jenta|jenten] [jogga|jogget] på [broa|broen|brua|bruen]

Installation

pip install altmetrics

Usage

from altmetrics import wer, cer, bleu, chrf

references = [
    "[jenta|jenten] [jogga|jogget] på [broa|broen|brua|bruen]",
    "[katten|katta] ligger på [matta|matten]",
    "Det var en fin dag."
]
hypotheses = [
    "jenta jogga på broa",
    "katten ligger på matta",
    "Det var en fin dag."
]

print("WER :", wer(references, hypotheses, lowercase=True))
print("CER :", cer(references, hypotheses))
print("BLEU:", bleu(references, hypotheses))
print("chrF:", chrf(references, hypotheses))

Features

• Accepts both modern [optionA|optionB] and legacy ["optionA","optionB"] reference syntax.
• Works with multiple metrics via a shared expansion and optimisation pipeline.
• Compatible with recent versions of jiwer and sacrebleu.
• Optional preprocessing: lowercase, punctuation removal, and placeholder control for empty hypotheses.

License

MIT