azure-ai-voicelive

v1.2.0 safe
3.0
Low Risk

Microsoft Corporation Azure Ai Voicelive Client Library for Python

🤖 AI Analysis

Final verdict: SAFE

The package shows low risks across all categories except for network and obfuscation which have moderate concerns. However, these do not strongly indicate malicious activity.

  • Moderate network interaction risk
  • Potential obfuscation through base64 encoding
Per-check LLM notes
  • Network: The detection of network call patterns suggests legitimate API interactions, likely with Azure services, but requires further investigation to confirm.
  • Shell: No shell execution patterns detected, indicating low risk for direct system command execution.
  • Obfuscation: The use of base64 decoding indicates potential obfuscation but could also be legitimate for handling encoded data.
  • Credentials: No suspicious patterns detected that suggest credential harvesting.
  • Metadata: The maintainer has an incomplete profile and appears to be new or inactive, raising some suspicion but not conclusive evidence of malintent.

📦 Package Quality Overall: Medium (6.6/10)

✦ High Test Suite 9.0

Test suite present — 15 test file(s) found

  • Test runner config found: pyproject.toml
  • Test runner config found: conftest.py
  • 15 test file(s) detected (e.g. __init__.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (39700 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

  • 274 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 35 unique contributor(s) across 100 commits in Azure/azure-sdk-for-python
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • ders)} session = aiohttp.ClientSession() try: connection_obj = await se
⚠ Code Obfuscation score 8.0

Found 4 obfuscation pattern(s)

  • return attr return bytes(base64.b64decode(attr)) def _deserialize_bytes_base64(attr): if isinsta
  • ce("_", "/") return bytes(base64.b64decode(encoded)) def _deserialize_duration(attr): if isinstan
  • audio_bytes = len(base64.b64decode(audio_data)) except Exception: # pylint
  • audio_bytes = len(base64.b64decode(delta)) except Exception: # pyl
✓ 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: microsoft.com> license-expression: mit

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository Azure/azure-sdk-for-python appears legitimate

⚠ 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 azure-ai-voicelive 
Create a voice-driven personal assistant application using the 'azure-ai-voicelive' Python package. This application will allow users to interact with their computer through voice commands, making it easier to perform tasks such as setting reminders, sending emails, searching the web, and controlling smart home devices. The app should be designed with a user-friendly interface and provide immediate feedback on voice command recognition and execution status.

Step-by-Step Instructions:
1. Set up your development environment with Python and install the 'azure-ai-voicelive' package.
2. Initialize the voice live client from the 'azure-ai-voicelive' library to enable voice input/output functionalities.
3. Implement a voice command parser that can recognize specific keywords or phrases like 'set reminder', 'send email', 'search', etc.
4. Develop functions to handle different types of voice commands, such as scheduling events, sending emails, or controlling IoT devices.
5. Integrate the application with a graphical user interface (GUI) toolkit like Tkinter or PyQt to display visual feedback and command outputs.
6. Test the application thoroughly to ensure accurate voice command recognition and smooth operation of all integrated features.
7. Deploy the application on a local server or cloud platform for easy access and use.

Suggested Features:
- Voice Command Recognition: Recognize various voice commands and execute corresponding actions.
- Text-to-Speech Conversion: Use the 'azure-ai-voicelive' package to convert text responses into spoken words for better user interaction.
- Smart Home Integration: Control smart home devices such as lights, thermostats, and security systems using voice commands.
- Calendar Management: Allow users to set, view, and manage appointments and reminders via voice commands.
- Email Automation: Enable users to send emails directly from the voice assistant application.
- Web Search Functionality: Perform internet searches based on user voice queries.

How to Utilize 'azure-ai-voicelive':
- Use the 'azure-ai-voicelive' package to initialize a voice live client that handles audio input and output operations.
- Leverage the speech recognition capabilities provided by the package to process and understand user voice commands accurately.
- Employ text-to-speech conversion features to generate audible responses to user commands, enhancing the interactive experience.

💬 Discussion Feed

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