Whisper Api

D 59 completed
Api
unknown / shell · tiny
6
Files
316
LOC
1
Frameworks
4
Languages

Pipeline State

completed
Run ID
#364630
Phase
done
Progress
1%
Started
Finished
2026-04-13 01:31:02
LLM tokens
0

Pipeline Metadata

Stage
Cataloged
Decision
proceed
Novelty
46.62
Framework unique
Isolation
Last stage change
2026-05-10 03:35:10
Deduplication group #66238
Member of a group with 1 similar repo(s) — this repo is canonical view group →
Top concepts (2)
Project DescriptionWeb Backend
Provenance: Repobility (https://repobility.com) — every score reproducible from /scan/

AI Prompt

Create a RESTful API service for speech-to-text transcription using OpenAI's Whisper model. The service should be built with FastAPI and handle audio file uploads via a POST request to `/transcribe`. It needs to support specifying the audio file, language code, and task (transcribe or translate). Additionally, implement endpoints to check the service health at `/health` and to unload the model from VRAM at `/unload`. The setup should also include shell scripts for environment setup and systemd service installation.
fastapi restful-api speech-to-text whisper python shell audio-processing systemd
Generated by gemma4:latest

Catalog Information

The Whisper API is a RESTful service for speech-to-text based on OpenAI's Whisper, supporting dynamic multi-model and VRAM management, integrated with QBert for shared GPU management.

Description

The Whisper API is a RESTful service that provides speech-to-text functionality using OpenAI's Whisper. It supports dynamic multi-model loading and VRAM management. The API is integrated with QBert for shared GPU management. Users can transcribe audio files, translate text, or unload models from the VRAM.

الوصف

API Whisper هي خدمة RESTful تقوم بتحويل الكلام إلى نص باستخدام OpenAI Whisper، وتدعم تحميل النماذج المتعددة بشكل ديناميكي و إدارة VRAM. وهي متكاملة مع QBert لتعديل إدارة GPU المشتركة. يمكن للمستخدمين تحويل الملفات الصوتية، ترجمة النصوص أو إزالة النماذج من VRAM.

Novelty

7/10

Tags

speech-to-text openai-whisper multi-model vram-management gpu-sharing

Technologies

fastapi openai uvicorn

Claude Models

claude-opus-4.6 claude-opus-4.5

Quality Score

D
58.8/100
Structure
44
Code Quality
90
Documentation
39
Testing
0
Practices
78
Security
100
Dependencies
60

Strengths

  • Consistent naming conventions (snake_case)
  • Good security practices \u2014 no major issues detected

Weaknesses

  • No LICENSE file \u2014 legal ambiguity for contributors
  • No tests found \u2014 high risk of regressions
  • No CI/CD configuration \u2014 manual testing and deployment

Recommendations

  • Add a test suite \u2014 start with critical path integration tests
  • Set up CI/CD (GitHub Actions recommended) to automate testing and deployment
  • Add a linter configuration to enforce code style consistency
  • Add a LICENSE file (MIT recommended for open source)

Security & Health

4.1h
Tech Debt (E)
A
OWASP (100%)
PASS
Quality Gate
A
Risk (10)
Source: Repobility analyzer · https://repobility.com
Unknown
License
0.0%
Duplication
Full Security Report AI Fix Prompts SARIF SBOM

Languages

shell
38.9%
python
32.2%
markdown
27.7%
text
1.3%

Frameworks

FastAPI

Concepts (2)

Data scored by Repobility · https://repobility.com
CategoryNameDescriptionConfidence
Repobility · code-quality intelligence platform · https://repobility.com
auto_descriptionProject DescriptionServizio REST per speech-to-text basato su OpenAI Whisper, con supporto multi-modello dinamico e gestione VRAM. Integrato con QBert per la gestione GPU condivisa.80%
auto_categoryWeb Backendweb-backend70%

Quality Timeline

1 quality score recorded.

View File Metrics

Embed Badge

Add to your README:

![Quality](https://repos.aljefra.com/badge/88798.svg)
Quality BadgeSecurity Badge
Export Quality CSVDownload SBOMExport Findings CSV