Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-10-19 00:04 UTC

Wordcab / wordcab-transcribe

💬 ASR FastAPI server using faster-whisper and Multi-Scale Auto-Tuning Spectral Clustering for diarization.

PythonMIT★ 220 stars⑂ 31 forkssince Mar 2023View on GitHub ↗

Wordcab/wordcab-transcribe holds a health index of 50 out of 100, placing it in the Moderate band. It scores highest on Engineering Quality (84/100) and lowest on Security (21/100). It was last updated 718 days ago. A single contributor accounts for most of its recent work.

50
overall / 100
Moderate

Software health index

Metrics are grouped into weighted categories on one standardized 1–100 scale. Overall starts as their weighted mean, calibrated against the distribution of the public record so bands carry percentile meaning; when public evidence triggers the High-Risk Jurisdiction Policy, the rating is adjusted and receives an At Risk ceiling of 34.

50
Exceptional93-100The record's top tier (≈ top 5%); essentially all checked criteria met
Excellent80-92Strong across the board; minor gaps
Good65-79Healthy; gaps are limited and manageable
Moderate50-64Acceptable with notable gaps; review recommended
Weak35-49Material weaknesses across several areas
At Risk20-34Significant weaknesses; adoption warrants caution
Critical1-19Severe problems (abandoned, single-maintainer, no hygiene)
VitalityCommunity &AdoptionSustainability &GovernanceEngineeringQualitySecurityAI Readiness

Score profile

Each axis is a category. The shape matters more than the average — a healthy subject fills the whole shape, while a spike-and-crater profile means strength in one dimension is masking risk in another.

The weighted overall 50 is calibrated to 50 on the published index scale (record calibration 2026-08-02).

Ownership

WordcabOrganization
12 followers16 public repossince Dec 2021

This repository is backed by an organization — shared, accountable stewardship that can outlive any single maintainer.

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIfaster-whisperpoints to another repo — not scored1.2.19,614,22221352 days agoopenaiwhisperspeechctranslate2inferencequantizationtransformer

Metrics by category

Vitality

Is the project alive — is code being written and are releases shipping?

21At Risk · 21% of overall

Development activity

1Critical
How it's scored
0/36Push recency — last push 718 days ago
0/36Commit cadence — 0/52 weeks with commits
0/18Commit volume — 0 commits in the last year
0/10OpenSSF Scorecard: Maintained — no data
Inputs used
commits_last_year0
human_commit_share1
days_since_last_push718
active_weeks_last_year0

Release discipline

52Moderate
How it's scored
27/27Ships releases — 9 releases published
0/36Release recency — latest release 930 days ago
19.8/27Release cadence — a release every ~45.9 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count9
latest_release_tagv0.5.3
releases_from_tagsno
days_since_latest_release930
mean_days_between_releases45.9

Community & Adoption

Does the project have users, downloads, attention, and a welcoming setup for contributors?

62Moderate · 17% of overall

Popularity & adoption

55Moderate
How it's scored
38/60Stars — 220 stars
12.3/25Forks — 31 forks
4.3/15Watchers — 7 watchers
Inputs used
forks31
stars220
watchers7
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

70Good
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (MIT)
18/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges3
has_contributingyes
has_issue_templateno
has_code_of_conductno
readme_badge_servicesgithub.com, shields.io
has_pull_request_templateno

Sustainability & Governance

Will the project survive its people — bus factor, responsiveness, who backs it, and package upkeep?

59Moderate · 23% of overall

Maintainer resilience (bus factor)

28At Risk
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
3.9/22.5Commit distribution — top contributor authored 83% of commits
12.2/13.5Contributor breadth — 9 contributors
0/10OpenSSF Scorecard: Contributors — no data
Inputs used
bus_factor1
contributors_sampled9
top_contributor_share0.828

Issue & PR responsiveness

97Exceptional
How it's scored
40.9/42Issue resolution — 97% of issues closed
29.1/30PR acceptance — 135/139 decided PRs merged
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-Review — no data
Inputs used
merged_prs135
open_issues5
closed_issues184
prs_merged_7d0
prs_decided_7d0
prs_merged_30d0
prs_decided_30d0
issue_closed_ratio0.974
closed_unmerged_prs4
first_time_authors_30d0
first_time_prs_merged_30d0
first_time_prs_decided_30d0
Excluded from scoring (no data or not applicable): Newcomer PR acceptance. Remaining weights renormalized.

Ownership & stewardship

57Moderate
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain
8/25Owner reach — 12 followers of Wordcab
18.6/25Track record — 16 public repos, account ~4 yr old
Inputs used
followers12
owner_typeOrganization
is_verifiedno
owner_loginWordcab
public_repos16
account_age_days1,765

Engineering Quality

Are baseline engineering and documentation practices in place?

84Excellent · 19% of overall

Engineering practices

80Excellent
How it's scored
24/24CI workflows — 1 workflow(s)
24/24Tests present
16/16Linter config — faster-whisper/setup.cfg ([flake8], [isort]), pyproject.toml ([tool.ruff])
0/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests — no data
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configno

Documentation

90Excellent
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://wordcab.github.io/wordcab-transcribe/
10/10Repository description
0/10Topics
10/10Wiki
Inputs used
topics—
has_wikiyes
homepagehttps://wordcab.github.io/wordcab-transcribe/
docs_sitehttps://wordcab.github.io/wordcab-transcribe/
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

Are visible security and supply-chain practices strong, without unresolved high-risk jurisdiction exposure?

21At Risk · 16% of overall

Security posture

1Critical
How it's scored
0/30Security policy (SECURITY.md)
0/25Dependabot config
0/25Dependency lockfiles
0/20CodeQL workflow
Inputs used
sourcefile_signals
lockfiles—
manifestsfaster-whisper/requirements.conversion.txt, faster-whisper/requirements.txt, faster-whisper/setup.cfg, faster-whisper/setup.py, pyproject.toml, requirements.txt
has_codeql_workflowno
has_security_policyno
has_dependabot_configno

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisories — no direct dependency carries a known advisory
25/25Indirect dependencies free of known advisories — no indirect dependency carries a known advisory
0/40No advisories left outstanding — no advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages22
unassessed_packages0
affected_by_severitynone
direct_affected_packages0
Excluded from scoring (no data or not applicable): No advisories left outstanding. Remaining weights renormalized. Matched the pypi:faster-whisper@1.2.1 runtime dependency closure — what installing the published package pulls in — 22 packages. Reachability is not analyzed.

AI Readiness

How well is the repo equipped to be developed and maintained with AI coding agents? Carries a deliberately small weight (4%): agent tooling is a real maintenance signal, but a repository with none can still reach 100/100.

51Moderate · 4% of overall

Agent context & guidance

30At Risk
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
15/15Machine-readable docs (llms.txt) — llms.txt served by the project website (https://docs.docker.com/llms.txt)
14.9/40Legible commit history — 28 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtyes
llms_txt_urlhttps://docs.docker.com/llms.txt
legible_history_share0.28
agent_instruction_files—
agent_instruction_max_bytes—

Verify loop (build / test / typecheck)

48Weak
How it's scored
0/18One-command bootstrap
22/22Automated tests
11/11Lint / format config — faster-whisper/setup.cfg ([flake8], [isort]), pyproject.toml ([tool.ruff])
0/11Static type checking
10/10Reproducible environment — Dockerfile
0/10Demonstrated agent practice — no agent-authored commits among the last 100
0/8Automated maintenance — no automated dependency updates observed
0/10OpenSSF Scorecard: Pinned-Dependencies — no data
Inputs used
has_nixno
has_testsyes
lockfiles—
has_dockerfileyes
typed_languageno
bootstrap_files—
has_devcontainerno
has_linter_configyes
typecheck_configs—
agent_commit_share0
toolchain_manifests—
dependency_bot_commit_share0

Code legibility for models

51Moderate
How it's scored
0/45Type-checkable code — Python without a type-check config
51/55Manageable file sizes — 7/97 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes376,109
source_files_sampled97
oversized_source_files7

Machine-readable interfaces

100Exceptional
How it's scored
0/40API schema (OpenAPI/GraphQL/proto) — not applicable to this kind of software
0/20MCP server — not applicable to this kind of software
40/40Runnable examples — notebooks
Inputs used
example_dirsnotebooks
has_mcp_signalno
api_schema_files—
interfaces_expected_of—
Excluded from scoring (no data or not applicable): API schema (OpenAPI/GraphQL/proto), MCP server. Remaining weights renormalized.

Key facts

220GitHub stars
9contributors
0commits, last 12 months
718days since last push
9releases
1bus factor
5open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • Could not fetch pypi package 'wordcab-transcribe' from its registry
  • pypi package 'faster-whisper' points at a different repository (https://github.com/SYSTRAN/faster-whisper); excluded from ecosystem scoring
  • OpenSSF Scorecard did not return a usable result (killed by SIGKILL — most likely the container's memory limit; 2026/10/19 00:03:29 Warning: PATs stored in env variables GITHUB_AUTH_TOKEN and GITHUB_TOKEN differ. Scorecard will use the former.); skipping Scorecard checks

More detail

Star and fork history 0 ★ / 31 ⇿
0Stars
31Forks
8Releases

When each star and fork was added, collected from GitHub and bucketed by day. Cumulative growth sits directly above the daily additions it is made of, so the two read against each other: steady organic accretion looks nothing like an abrupt, short-lived burst. Where that difference is measurable, it is reported as growth authenticity.

01325383132023-042024-082026-01
Major 0Minor 4Patch 4

Each point covers 3 days.

Direct dependencies 32
RegistryPackageVersion constraintManifest
PyPItensorrt_llm==0.9.0.dev2024032600pyproject.toml
PyPICython==3.0.10pyproject.toml
PyPIyoutokentome@ git+https://github.com/gburlet/YouTokenToMe.git@dependenciespyproject.toml
PyPIdeepmultilingualpunctuation==1.0.1pyproject.toml
PyPInemo_toolkit==1.23.0pyproject.toml
PyPIaiohttp==3.9.3pyproject.toml
PyPIaiofiles==23.2.1pyproject.toml
PyPIboto3—pyproject.toml
PyPIfaster-whisper@ https://github.com/SYSTRAN/faster-whisper/archive/refs/heads/master.tar.gzpyproject.toml
PyPIffmpeg-python==0.2.0pyproject.toml
PyPItransformers==4.38.2pyproject.toml
PyPIlibrosa==0.10.1pyproject.toml
PyPIloguru==0.7.2pyproject.toml
PyPInltk==3.8.1pyproject.toml
PyPInumpy==1.26.4pyproject.toml
PyPIonnxruntime==1.17.1pyproject.toml
PyPIpandas==2.2.1pyproject.toml
PyPIpydantic==2.6.4pyproject.toml
PyPIpython-dotenv==1.0.1pyproject.toml
PyPItensorshare==0.1.1pyproject.toml
PyPItorch==2.2.2pyproject.toml
PyPItorchaudio==2.2.2pyproject.toml
PyPIwget==3.2.0pyproject.toml
PyPIyt-dlp==2024.3.10pyproject.toml
PyPItiktoken==0.6.0pyproject.toml
PyPIdatasets==2.18.0pyproject.toml
PyPIkaldialign==0.9.0pyproject.toml
PyPIopenai-whisper==v20231117pyproject.toml
PyPIsoundfile==0.12.1pyproject.toml
PyPIsafetensors==0.4.2pyproject.toml
PyPIjanus==1.0.0pyproject.toml
PyPIbackports.lzma==0.0.14pyproject.toml
All dependencies 51

Full resolved dependency set from the GitHub dependency graph: 33 direct and 18 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIaiofiles23.2.1direct
PyPIaiohttp3.9.3direct
PyPIbackports-lzma0.0.14direct
PyPIboto3—direct
PyPIcython3.0.10direct
PyPIdatasets—direct
PyPIdatasets2.18.0direct
PyPIdeepmultilingualpunctuation1.0.1direct
PyPIffmpeg-python0.2.0direct
PyPIjanus1.0.0direct
PyPIkaldialign0.9.0direct
PyPIlibrosa0.10.1direct
PyPIloguru0.7.2direct
PyPInltk3.8.1direct
PyPInumpy1.26.4direct
PyPIonnxruntime—direct
PyPIonnxruntime1.17.1direct
PyPIpandas2.2.1direct
PyPIpydantic2.6.4direct
PyPIpython-dotenv1.0.1direct
PyPIsafetensors0.4.2direct
PyPIsoundfile0.12.1direct
PyPItensorrt-llm0.9.0.dev2024032600direct
PyPItensorshare0.1.1direct
PyPItiktoken0.6.0direct
PyPItorch—direct
PyPItorch2.2.2direct
PyPItorchaudio—direct
PyPItorchaudio2.2.2direct
PyPItransformers—direct
PyPItransformers4.38.2direct
PyPIwget3.2.0direct
PyPIyt-dlp2024.3.10direct
PyPIargon2-cffi23.1.0indirect
PyPIctranslate2—indirect
PyPIevaluate—indirect
PyPIfastapi0.110.0indirect
PyPIhuggingface-hub—indirect
PyPIipython8.24.0indirect
PyPIjiwer—indirect
PyPImemory-profiler—indirect
PyPIpy3nvml—indirect
PyPIpyannote-audio—indirect
PyPIpyannote-audio3.2.0indirect
PyPIpython-jose3.3.0indirect
PyPIpython-multipart0.0.9indirect
PyPIshortuuid1.0.13indirect
PyPIsvix1.21.0indirect
PyPItokenizers—indirect
PyPIuvicorn0.29.0indirect
PyPIwebsockets12.0indirect
Dependency advisories 0

Installing pypi:faster-whisper@1.2.1 pulls in 22 packages, direct and transitive: 0 carry known advisories, of which 0 are direct dependencies.

No known advisories affect the assessed dependencies.

An advisory means the version recorded in the dependency graph falls inside an advisory’s affected range. Reachability is not analysed, and the graph includes development and test pins — a finding may concern tooling rather than shipped software.

Raw JSON report machine-readable

Feedback

Spotted something off in this report, or have thoughts to share? Wrong measurements, missed tooling, ideas, questions — anything is welcome. Every message is read and gets a response.

The message is kept through sign-in.

Related records

Inspected repositories sharing catalogue tags or ecosystems with Wordcab/wordcab-transcribe. Read them side by side →

100matplotlib/matplotlibmatplotlib: plotting with PythonExceptional100apache/airflowApache Airflow - A platform to programmatically author, schedule, and monitor workflowsExceptional100deepset-ai/haystackOpen-source AI orchestration framework for building context-engineered, production-ready LLM applications. Design modular pipelines and agent workflows with explicit control over retrieval, routing, memory, and generation. Built for scalable agents, RAG, multimodal applications, semantic search, and conversational systems.Exceptional100risingwavelabs/risingwaveEvent streaming platform for agentic AI. Continuously ingest, transform, and serve event streams in real time, at scale.Exceptional100onnx/onnxOpen standard for machine learning interoperabilityExceptional100run-llama/llama_indexLlamaIndex is the document processing platform for AIExceptional

Scores are signals, not warranties. They reflect publicly visible practices on GitHub — not a code audit, and not a security guarantee.

Missing data is excluded and weights renormalized, never scored as zero. Methodology is versioned and open: metrics v2.10.0, schema v0.34.0 — full methodology · metrics wiki.

How one result sits in the wider record: aggregate statistics — PyPI.