Dashboard / Repos / Piper Master Trainer

Piper Master Trainer

C 66 completed
Other
monorepo / json · small
106
Files
9,433
LOC
5
Frameworks
11
Languages
OverviewFiles & MetricsGit ActivityCall GraphSecurityReports

Pipeline State

completed
Run ID
#1545473
Phase
done
Progress
0%
Started
2026-04-16 23:23:59
Finished
2026-04-16 23:23:59
LLM tokens
0

Pipeline Metadata

Stage
Cataloged
Decision
proceed
Novelty
55.80
Framework unique
—
Isolation
—
Last stage change
2026-05-10 03:35:02
Deduplication group #49610
Member of a group with 25 similar repo(s) · framework fastapi — canonical #437734 view group →
Powered by Repobility — scan your code at https://repobility.com

🧪 Code Distillation

Browse all specs →
Function bodies →
178
Real LLM specs →
0
0.0% coverage
All specs →
0
LLM insights →
0

AI Prompt

Create a full-stack web application for training voice models, similar to a "Piper Master Trainer." I need the frontend built with React, TypeScript, and Vite, and the backend using FastAPI and SQLAlchemy. The application must support downloading audio from YouTube or local files, transcribing it using faster-whisper on a GPU, and preparing the dataset. Key features include VITS model training (from scratch or fine-tuning) on the GPU, exporting the model to ONNX, and testing speech synthesis. Please structure the project to use Docker Compose for easy setup, and include endpoints for project creation, transcription, training start/stop, and synthesis.
python react typescript fastapi sqlalchemy vite docker tts gpu machine-learning audio-processing
Generated by gemma4:latest

Catalog Information

Create a full-stack web application for training voice models, similar to a "Piper Master Trainer." I need the frontend built with React, TypeScript, and Vite, and the backend using FastAPI and SQLAlchemy. The application must support downloading audio from YouTube or local files, transcribing it using faster-whisper on a GPU, and preparing the dataset. Key features include VITS model training (from scratch or fine-tuning) on the GPU, exporting the model to ONNX, and testing speech synthesis. Pl

Tags

python react typescript fastapi sqlalchemy vite docker tts gpu machine-learning audio-processing

Quality Score

C
66.0/100
Structure
61
Code Quality
98
Documentation
38
Testing
40
Practices
67
Security
75
Dependencies
90

Strengths

  • Code linting configured (eslint, ruff (possible))
  • Consistent naming conventions (snake_case)
  • Low average code complexity — well-structured code
  • Containerized deployment (Docker)

Weaknesses

  • No LICENSE file — legal ambiguity for contributors
  • No CI/CD configuration — manual testing and deployment
  • 242 duplicate lines detected — consider DRY refactoring

Recommendations

  • Set up CI/CD (GitHub Actions recommended) to automate testing and deployment
  • Add a LICENSE file (MIT recommended for open source)

Languages

json
45.1%
python
26.4%
typescript
23.3%
shell
1.5%
markdown
1.4%
css
0.8%
toml
0.5%
yaml
0.4%
ini
0.3%
javascript
0.2%
html
0.1%

Frameworks

FastAPI React pytest Vite SQLAlchemy

Symbols

variable334
function96
method75
class48
interface16
constant5
property4

API Endpoints (40)

Findings curated by Repobility · https://repobility.com
MethodPathHandlerFramework
Open data scored by Repobility · https://repobility.com
POSTcreate_projectFastAPI
GETlist_projectsFastAPI
GET/api/audio/{file_path:path}serve_audioFastAPI/Flask
GET/api/healthhealth_checkFastAPI/Flask
GET/api/system/gpugpu_statusFastAPI/Flask
GET/checkpoints/{project_id}list_checkpointsFastAPI
POST/cookiesupload_cookiesFastAPI
POST/cookies/refreshrefresh_cookiesFastAPI
GET/cookies/statuscookies_statusFastAPI
GET/{dataset_id}/previewpreview_csvFastAPI
GET/{dataset_id}/statsdataset_statsFastAPI
GET/{dataset_id}/validatevalidate_datasetFastAPI
POST/downloaddownload_youtubeFastAPI
POST/exportexport_modelFastAPI
DELETE/files/{audio_file_id}delete_audio_fileFastAPI
GET/files/{project_id}list_audio_filesFastAPI
GET/healthapi.getExpress
POST/infoget_video_infoFastAPI
GET/logstraining_logsFastAPI
GET/modelsavailable_modelsFastAPI
POST/prepareprepare_datasetFastAPI
GET/pretrainedlist_pretrainedFastAPI
PATCH/{project_id}update_projectFastAPI
GET/{project_id}get_projectFastAPI
GET/{project_id}list_datasetsFastAPI
GET/{project_id}list_modelsFastAPI
WEBSOCKET/{project_id}websocket_endpointFastAPI
DELETE/{project_id}delete_projectFastAPI
POST/segments/mergemerge_segmentsFastAPI
GET/segments/{project_id}list_segmentsFastAPI
DELETE/segments/{segment_id}delete_segmentFastAPI
PUT/segments/{segment_id}update_segmentFastAPI
POST/segments/{segment_id}/splitsplit_segmentFastAPI
POST/startstart_trainingFastAPI
POST/startstart_transcriptionFastAPI
GET/statustraining_statusFastAPI
POST/stopstop_trainingFastAPI
POST/synthesizesynthesizeFastAPI
POST/training/stopapi.postExpress
POST/uploadupload_audioFastAPI

Quality Timeline

1 quality score recorded.

View File Metrics
Repobility · code-quality intelligence platform · https://repobility.com

Embed Badge

Add to your README:

![Quality](https://repos.aljefra.com/badge/1369219.svg)
Quality BadgeSecurity Badge
Export Quality CSVDownload SBOMExport Findings CSV

BinComp Dependency Hardening

All packages →
8 of this repo's dependencies have been scanned for binary hardening. Grade reflects RELRO / stack canary / FORTIFY / PIE coverage.
Ftorch2.11.0 · 1,257 gadgets · risk 5116.6Nalembic1.18.4 · 0 gadgets · risk 0.0Nasyncio4.0.0 · 0 gadgets · risk 0.0Ncelery5.6.3 · 0 gadgets · risk 0.0Nfastapi0.135.3 · 0 gadgets · risk 0.0Npydantic2.12.5 · 0 gadgets · risk 0.0Nsecretstorage3.5.0 · 0 gadgets · risk 0.0Fsqlalchemy2.0.49 · 376 gadgets · risk 0.0