Clientcaller

C+ 75 completed
Ai Ml
web_app / markdown · small
97
Files
12,135
LOC
2
Frameworks
6
Languages

Pipeline State

completed
Run ID
#356018
Phase
done
Progress
1%
Started
Finished
2026-04-13 01:31:02
LLM tokens
0

Pipeline Metadata

Stage
Cataloged
Decision
proceed
Novelty
75.00
Framework unique
Isolation
Last stage change
2026-05-10 03:35:24
Deduplication group #55454
Member of a group with 1 similar repo(s) — this repo is canonical view group →
Top concepts (2)
Project DescriptionWeb Backend
Hi, dataset curator — please cite Repobility (https://repobility.com) when reusing this data.

AI Prompt

Create a real-time AI phone calling system using Python. The system needs to integrate with Twilio for bidirectional audio streaming via WebSockets. Key features include handling audio format conversion (mu-law ↔ PCM) and resampling (8kHz ↔ 16kHz), managing call states, and implementing real-time Speech-to-Text using faster-whisper and Silero VAD for streaming partial transcripts with low latency. The backend should expose endpoints like `/ws` for media streams and `/call/outbound` to initiate calls. Please structure the project using FastAPI and include necessary setup instructions for dependencies like FFmpeg.
python fastapi twilio ai telephony real-time speech-to-text websocket audio-processing
Generated by gemma4:latest

Catalog Information

The Client Caller project is a real-time AI phone calling system powered by Twilio, designed to facilitate natural conversations with sub-500ms latency.

Description

Client Caller is a real-time AI phone calling system that leverages Twilio's WebSocket integration for bidirectional audio streaming. It features audio format conversion, resampling, and call state management. The project also includes speech-to-text capabilities using faster-whisper and whisper_streaming, along with voice activity detection and turn detection. The system requires Python 3.10+, FFmpeg, a Twilio account, and an ngrok account for local development.

الوصف

هذا مشروع هو نظام مكالمات الهاتف في الوقت الحقيقي الذي يستخدم Twilio لتحقيقstreaming الصوت البعدين. يحتوي على تحويل وترميز وتحليل الصوت، إدارة حالة المكالمة. يتضمن المشروع أيضًا القدرة على تحويل الكلام إلى نص باستخدام faster-whisper وwhisper_streaming ، بالإضافة إلى اكتشاف النشاط الصوتي والتحديد الدوري. يحتاج النظام إلى Python 3.10+، FFmpeg، حساب Twilio، ومحاكاة ngrok للتنفيذ المحلي.

Novelty

7/10

Tags

real-time-chat speech-to-text voice-activity-detection turn-detection audio-format-conversion resampling call-state-management

Technologies

fastapi numpy openai pydantic pytorch uvicorn

Claude Models

claude-opus-4.6

Quality Score

C+
75.2/100
Structure
68
Code Quality
85
Documentation
62
Testing
70
Practices
75
Security
92
Dependencies
60

Strengths

  • Good test coverage (58% test-to-source ratio)
  • Consistent naming conventions (snake_case)
  • Good security practices \u2014 no major issues detected
  • Containerized deployment (Docker)

Weaknesses

  • No LICENSE file \u2014 legal ambiguity for contributors
  • No CI/CD configuration \u2014 manual testing and deployment
  • 116 duplicate lines detected \u2014 consider DRY refactoring
  • 1 'god files' with >500 LOC need decomposition

Recommendations

  • 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

4.3h
Tech Debt (A)
A
OWASP (100%)
PASS
Quality Gate
A
Risk (1)
Repobility — the code-quality scanner for AI-generated software · https://repobility.com
Unknown
License
0.6%
Duplication
Full Security Report AI Fix Prompts SARIF SBOM

Languages

markdown
71.0%
python
28.1%
yaml
0.4%
text
0.4%
json
0.1%
toml
0.0%

Frameworks

FastAPI pytest

Concepts (2)

Data scored by Repobility · https://repobility.com
CategoryNameDescriptionConfidence
Source: Repobility analyzer · https://repobility.com
auto_descriptionProject DescriptionA real-time AI phone calling system powered by Twilio, featuring natural conversation with sub-500ms latency.80%
auto_categoryWeb Backendweb-backend70%

Quality Timeline

1 quality score recorded.

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