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Decision brief
A Python library for assessing speech-to-text systems with word error rate measures.
Good fit when
- For accurate speech recognition evaluation needing direct WER calculations
- When working on projects where the precision of speech transcription is critical
Avoid when
- Avoid if your project requires only basic, non-word level performance indicators
- Not suitable for evaluating text-to-speech systems, as it focuses exclusively on speech-to-text output assessment
Observed Jul 16, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Slowing (105d since push)
- As of 3w
- Provenance
- Not a fork · Organization account
- As of 3w
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install jiwerSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Provides evaluation metrics for speech-to-text applications, focusing on the calculation and analysis of word error rate.
Capability facts
- CLI
- CLI entrypoint
Source: pyproject.toml:[project.scripts] · Jul 31, 2026
- Languages
- python
Source: github.language+pyproject.toml · Jul 31, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Jul 31, 2026)
Or, if you prefer old-fashioned pip and you're using Python >= `3.8`:Source link
Tags
README
Installation
You should be able to install this package using uv:
$ uv add jiwer
Or, if you prefer old-fashioned pip and you're using Python >= 3.8:
$ pip install jiwer
For agents
This page has a .md twin and JSON over the API.