AI Analysis
Final verdict: SUSPICIOUS
The package shows some signs of potential misuse due to shell execution and low metadata quality, though there's no concrete evidence of malicious intent.
- Shell execution detected requiring further investigation
- Low metadata quality suggesting new or poorly maintained package
Per-check LLM notes
- Network: No network calls detected, which is low risk.
- Shell: Shell execution detected may be legitimate for audio processing but requires further investigation to ensure it's not being exploited.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
- Credentials: No credential harvesting patterns detected, suggesting no risk of secret theft.
- Metadata: The package appears to be newly created with low metadata quality, raising suspicion but not definitive proof of malintent.
Package Quality Overall: Low (2.0/10)
○ Low
Test Suite
1.0
No test suite detected
No test files or test-runner configuration detected
○ Low
Documentation
1.0
No documentation detected
No documentation URL, doc files, or meaningful description found
○ Low
Contributing Guide
2.0
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium
Type Annotations
5.0
Partial type annotation coverage
4 type-annotated function signatures (partial)
○ 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
score 2.0
Found 1 shell execution pattern(s)
f"{audio_path.stem}.wav" subprocess.run( [ "ffmpeg", "-y",
Credential Harvesting
No credential harvesting patterns detected
Typosquatting
No typosquatting candidates detected
Registered Email Domain
No author email provided
Suspicious Page Links
All external links appear legitimate
Git Repository History
No GitHub repository linked
No GitHub repository link found
Maintainer History
score 8.0
4 maintainer concern(s) found
Only one version has ever been released — brand new packageAuthor name is missing or very shortAuthor "" appears to have only 1 package on PyPI (new or inactive account)Package has no PyPI classifiers (low effort / metadata quality)
Known CVE Vulnerabilities
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Use this prompt to build a project with audio-transc
Create a speech-to-text transcription tool using the 'audio-transc' package which leverages NVIDIA Parakeet ASR technology. This mini-application should allow users to upload an audio file from their local machine, transcribe it into text, and save the output transcription as a text file on their device. Additionally, implement the following features: 1. **Audio Format Support**: Ensure your application supports common audio formats such as .wav, .mp3, and .ogg. 2. **Real-time Progress Bar**: Display a progress bar during the transcription process to give users feedback on the status of their request. 3. **Error Handling**: Implement error handling to manage cases where the input audio file might be corrupted or unsupported. 4. **Customization Options**: Allow users to choose between different language models supported by 'audio-transc'. 5. **Output Text File Naming**: Automatically name the output text file based on the original audio file's name for easy identification. 6. **User Interface**: Develop a simple yet intuitive user interface using a web framework like Flask or Django. To utilize the 'audio-transc' package, first install it via pip. Then, use its core functionality to load the uploaded audio file, process it through the ASR model, and generate the textual transcript. Remember to include documentation on how to run the application and any prerequisites needed for setup.
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