fossasia/voxbento - Open Source PR Review Scorecard

Open Source AI powered Interpretation Platform https://voxbento.com

C-Rank Grade: S (Elite) - 76/100

External PR Merge Rate: 88%

Response Time: 20h

First Timer Success: 63%

Frequently Asked Questions

Is fossasia/voxbento welcoming to first-time open-source contributors?

fossasia/voxbento has a recorded first-timer success rate of 62.5%. Repositories ranked S typically provide actionable feedback during code reviews and actively nurture new community contributors.

How fast can I expect code review feedback on my pull request?

Maintainers in fossasia/voxbento respond to incoming external pull requests in approximately 20.2 hours on average. Keeping PRs focused on single tasks and ensuring tests pass helps maintainers review faster.

What does the 76.4 C-Rank™ score (S Tier) represent?

The C-Rank™ system evaluates GitHub projects on a 0–100 scale using real data: PR merge rates, review turnaround time, active maintainer presence, and first-time contributor success. A score of 76.4 places fossasia/voxbento in the S tier.

What is the external contributor pull request merge rate for fossasia/voxbento?

The external contributor pull request merge rate for fossasia/voxbento is 87.6%, based on public PR activity from non-core contributors.

Are there Good First Issues available in fossasia/voxbento?

fossasia/voxbento currently has 4 active issue(s) tagged with beginner-friendly labels like "good first issue", "beginner", or "up-for-grabs".

fossasia
fossasia/voxbentoSElite1.5k
GitHub
Back to Explorer
fossasia

fossasia/voxbento

1,482
SElite(76/100)Python

Open Source AI powered Interpretation Platform https://voxbento.com

Compare
Jump to:

AI Maintainer Review Guidelines

Review Persona

Strict Architecture Gatekeeper

Warmth Score
7.2/10
Patience Score
8.0/10
Nitpick Rate
25%

Strict review standards in fossasia/voxbento. Ensure PR scope matches issue requirements closely before requesting review.

Top PR Submission Do's

  • Ensure code complies with the project coding style
  • Keep PRs scoped to a single concern
  • Include context and link to the related issue

Top PR Friction Pitfalls (Don'ts)

  • Do not submit PRs without linking an issue
  • Do not break existing tests without fixing them
  • Do not mix unrelated refactors in a single PR
Response Velocity
20 hours
Standard maintainer review cycle

Average Response Latency

Tracks hours until a maintainer leaves a review, comment, or PR response.

Merge Efficiency
87.6%
High acceptance rate for external PRs

External Acceptance Rate

Percentage of community pull requests successfully merged into main.

First-Timer Success
62.5%
Strong first-timer PR acceptance rate

First PR Conversion

Rate at which developers submitting their first repository PR succeed.

Active Maintainers
12 core
Highly collaborative maintainer core
Diagnostic Health HUD
87.6%
Merge Gauge
62.5%
1st-Timer
Community Vibe69/100

Embed C-Rank Badge

Show contributors that your repository actively reviews and merges external pull requests.

GetMerged C-Rank badge for fossasia/voxbento
[![GetMerged C-Rank](https://getmerged.abhishekco.de/api/badge/fossasia/voxbento)](https://getmerged.abhishekco.de/fossasia/voxbento?utm_source=github&utm_medium=badge)

Active Good First Issues (5)

View on GitHub

Summary As VoxBento evolves, we are increasingly relying on running intensive Machine Learning models locally. While we currently run Speech-to-Text (faster-whisper) and Text-to-Text (ctranslate2), our future roadmap may include local Text-to-Speech (TTS), local LLMs, or audio/video analysis models. Currently, we run these models directly within the main web server process. This approach does not scale. It requires immense manual engineering to prevent models from crashing the server, leaves hardware underutilized, and makes adding new models incredibly difficult. This Epic tracks the creation of a Generalized ML Serving Architecture using Ray serve By migrating to Ray Serve, we establish a robust, scalable foundation for all future local ML models. We will decouple ML inference from the web server, enabling massive throughput, autoscaling, and out-of-the-box memory management for any model we deploy. The Problems : To understand why a generalized ML architecture is required, the eng

📅 Opened Aug 4, 2026💬 0 comments
Quality: 50/100Contribute

Summary portal/translations/worker.py _translate_and_broadcast is called once per target language per segment. Each invocation independently: Opens a new DB session for vocabulary lookup (line 152) Creates a new httpx.AsyncClient for the LLM call (line 218) Opens another DB session to save the translation (line 177) With 5 target languages, one transcription segment triggers 15 DB session opens and 5 httpx client creates. Evidence # Called N times via asyncio.gather (line 120) async def _translate_and_broadcast(self, ...): # Session 1: vocabulary lookup async with get_session() as vocab_session: # line 152 vocab_entries = await resolve_vocabulary_entries(...) # Session 2 (inside _call_llm): new httpx client async with httpx.AsyncClient(timeout=timeout) as client: # line 218 ... # Session 3: save translation async with get_session() as local_session: # line 177 ... Impact Connection pool pressure on both the database

📅 Opened Jul 6, 2026💬 0 comments
Quality: 90/100Contribute

Summary portal/websockets/manager.py is a 344-line god module that mixes two responsibilities: Connection management (3 classes: ConnectionManager, ListenerConnectionManager, TTSConnectionManager) — lines 25-116 Business logic handlers (8 functions: _handle_join, _handle_leave, _handle_chat, _handle_set_active, _handle_update_state, _handle_set_broadcast_unlocked, _handle_initiate_handoff, _handle_accept_handoff, _handle_cancel_handoff) — lines 137-343 The handlers do DB queries, auth checks, and business logic — this is not connection management. Current Module Layout portal/websockets/ __init__.py (empty) handlers.py (157 lines — route definitions only, imports handlers from manager.py) manager.py (344 lines — connection managers + all handler functions) Proposed Refactor portal/websockets/ __init__.py routes.py (rename handlers.py — WebSocket route definitions) manager.py (keep only connection manager classes + broadcast_t

📅 Opened Jul 6, 2026💬 0 comments
Quality: 90/100Contribute

Summary Nearly every template file uses inline style= attributes instead of CSS classes. This makes the styles impossible to override, theme, or maintain consistently. Key Offenders File Lines What is inline-styled templates/base.html 13-23 Footer (8+ properties), toast container (5 properties) templates/admin/base.html 33-45 Modal margins, flex layout templates/account.html 12, 50 Brand image height, badge background override templates/403.html 10-17 5 separate inline styles for layout templates/listener-event.html 157-187 Flex layouts, display, gaps, borders templates/login.html 14 Image height/margin templates/register.html 14 Image height/margin Impact Inline styles cannot be overridden by CSS classes without !important Breaks theming and dark mode support Ad-hoc styling instead of intentional design system Maintenance burden: every page has its own one-off styles Proposed Fix For each template: Identify inline styles Create or reuse a named CSS class in th

📅 Opened Jul 6, 2026💬 0 comments
Quality: 90/100Contribute

Summary home.html and listener_join.html load the Tailwind CSS CDN in production: <script src="https://cdn.tailwindcss.com"></script> This violates the project convention (AGENTS.md: "No framework. Frontend is plain ES modules") and introduces several problems: 330+ KB JIT compiler loaded and executed in the browser on every page load Flash of unstyled content (FOUC) while Tailwind compiles External CDN dependency — if cdn.tailwindcss.com goes down, pages are unstyled Tailwind docs explicitly say: "Do not use the CDN in production" Files Affected templates/home.html (764 lines, heavy Tailwind usage) templates/listener_join.html (35 lines, light Tailwind usage) Proposed Fix listener_join.html (quick win): Convert ~10 Tailwind utility classes to vanilla CSS classes Add them to an appropriate stylesheet home.html (larger effort): Extract the used Tailwind classes into a compiled/static CSS file Or rewrite the page using the project vanilla CSS approach Move inline JS to static/js

📅 Opened Jul 6, 2026💬 1 comment
Quality: 90/100Contribute
Looking for more Python beginner tasks?Explore Python GFI

Contributor Community Vibe Feedback

Rate what actually matters after opening a pull request here.

Have you contributed to this repo?

Rate your first-hand PR experience (review speed, maintainer responsiveness, and onboarding ease) to help other contributors.

3 ratings required
Maintainer helpfulness
Review speed
Beginner friendliness

Contributor Compatibility & Review Speed Analysis for fossasia/voxbento

When evaluating whether to contribute to fossasia/voxbento, response velocity and maintainer engagement are crucial. GetMerged continuously tracks pull request trajectories, first-comment latency, and code review rounds to help developers avoid submitting pull requests to backlogged repositories.

Currently, maintainers of fossasia/voxbento acknowledge new external contributions in approximately 20 hours. Out of all submitted pull requests from non-core authors in the last 180-day window, 87.6% were successfully merged into the primary branch.

Frequently Asked Questions - Contributing to fossasia/voxbento

01

Is fossasia/voxbento welcoming to first-time open-source contributors?

fossasia/voxbento has a recorded first-timer success rate of 62.5%. Repositories ranked Elite typically provide actionable feedback during code reviews and actively nurture new community contributors.

02

How fast can I expect code review feedback on my pull request?

The initial maintainer response time averages ~20 hours. Keeping PRs scoped to single concerns and ensuring CI checks succeed will optimize review turnaround.

03

What does the 76.4 C-Rank™ score represent?

The C-Rank™ index scores repositories on a 0 to 100 scale using an objective formula: external PR merge rates, initial response speed, active maintainer count, and first-time contributor retention. A score of 76.4 places fossasia/voxbento in the Elite tier.

04

What is the external contributor pull request merge rate for fossasia/voxbento?

The external pull request merge rate is 87.6%. GetMerged isolates non-core community contributions so external developers get an accurate benchmark of PR acceptance probability.

05

Are there beginner Good First Issues open in fossasia/voxbento?

Yes, fossasia/voxbento currently has 4 active issue(s) tagged with beginner-friendly labels. You can inspect these directly from the repository issues tab.

GetMerged C-Rank™ Indexing Standard

All metrics displayed for fossasia/voxbento are automatically retrieved via the public GitHub API and recalculated daily. Insider pull requests submitted by repository owners or organization members are excluded from merge rate calculations to preserve objective external contributor statistics.