audmodel

v1.4.2 suspicious
6.0
Medium Risk

Publish and load models

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package audmodel v1.4.2 is suspicious due to incomplete metadata and lack of a GitHub link, which raises concerns about the origin and maintainers of the package.

  • Metadata risk due to missing GitHub link and incomplete author information
  • No significant technical risks identified in terms of network, shell, or obfuscation
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires internet access for its functionality.
  • Shell: No shell execution patterns detected, indicating no immediate risk of unauthorized system command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, indicating low risk of malicious credential theft.
  • Metadata: The package has no GitHub link and the author information is incomplete, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Medium (5.6/10)

✦ High Test Suite 9.0

Test suite present — 11 test file(s) found

  • Test runner config found: conftest.py
  • Test runner config found: pyproject.toml
  • 11 test file(s) detected (e.g. conftest.py)
✦ High Documentation 9.0

Well-documented package

  • Documentation URL: "documentation" -> http://tools.pp.audeering.com/audmodel/
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (1205 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 56 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: audeering.com>

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://tools.pp.audeering.com/audmodel/
✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with audmodel 
Create a voice-based personal assistant application using Python and the 'audmodel' package. This application will allow users to interact with it through voice commands and receive responses via text or voice synthesis. The application will utilize the 'audmodel' package to publish and load speech recognition and text-to-speech models.

Step 1: Set up the environment
- Install necessary libraries including 'audmodel', 'speech_recognition', and 'gTTS' or similar text-to-speech packages.

Step 2: Design the user interface
- Develop a simple graphical user interface (GUI) using a library like Tkinter for input/output interactions.
- Implement a voice command activation feature that listens for a specific keyword to start the assistant.

Step 3: Implement speech recognition
- Use the 'audmodel' package to load a pre-trained speech recognition model.
- Integrate a function that converts spoken commands into text using the loaded model.

Step 4: Process user commands
- Create a command processing module that understands basic commands such as 'what's the weather?', 'set a reminder', etc.
- For each recognized command, perform the appropriate action or fetch information from external APIs if necessary.

Step 5: Text-to-speech response
- Utilize the 'audmodel' package to load a text-to-speech model.
- Convert the application's responses into spoken words using the text-to-speech functionality.

Suggested Features:
- Voice activation: Allow the assistant to be activated by a specific voice command.
- Command history: Maintain a log of previous commands and responses for reference.
- Customizable commands: Enable users to add their own custom commands and associated actions.
- Multi-language support: Extend the application to understand and respond in multiple languages by loading different models with 'audmodel'.
- Integration with external services: Fetch real-time data like news updates, weather forecasts, and more from web APIs.

How 'audmodel' is utilized:
- For speech recognition: Load a trained model using 'audmodel.load()' and pass audio data to predict text.
- For text-to-speech: Similarly, use 'audmodel.load()' to get a text-to-speech model and convert text to audio output.

💬 Discussion Feed

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