analyzeAudio

v0.2.0 suspicious
5.0
Medium Risk

Measure one or more aspects of one or more audio files.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows potential risks due to shell executions for FFmpeg and FFprobe operations, and the author's metadata is incomplete and possibly new or inactive, raising concerns about its legitimacy.

  • Shell risk due to FFmpeg and FFprobe operations
  • Incomplete and possibly new/inactive author metadata
Per-check LLM notes
  • Network: No network calls detected, which is normal for an audio processing package.
  • Shell: Shell executions are likely for FFmpeg and FFprobe operations, typical for audio analysis but should be reviewed to ensure commands do not inadvertently execute harmful actions.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent related to code obfuscation.
  • Credentials: No credential harvesting patterns detected, suggesting no immediate risk of secret or credential theft.
  • Metadata: The author's information is incomplete and the account seems new or inactive, raising some suspicion but not definitive evidence of malice.

📦 Package Quality Overall: Medium (6.6/10)

✦ High Test Suite 9.0

Test suite present — 6 test file(s) found

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

Some documentation present

  • Detailed PyPI description (32744 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • 158 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 3 unique contributor(s) across 100 commits in hunterhogan/analyzeAudio
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • '-' ] systemProcessFFmpeg = subprocess.run(commandLineFFmpeg, check=True, stderr=subprocess.PIPE) std
  • " ] systemProcessFFprobe = subprocess.Popen(commandLineFFprobe, stdin=subprocess.PIPE, stdout=subprocess
  • ocess.CompletedProcess[str] = subprocess.run( ['/usr/bin/dpkg-query', '--show', '--showformat=${Versi
  • ocess.CompletedProcess[str] = subprocess.run( ['ffprobe', '-hide_banner', '-show_entries', 'program_v
  • = 'ffmpeg-release.tar.xz' subprocess.run( [ '/usr/bin/wget' , '-qO' , filenameFFmp
  • ] , check=True ) subprocess.run(['/usr/bin/tar', '-xf', filenameFFmpegReleaseArchive], check
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

⚠ Registered Email Domain score 3.0

Suspicious email domain flags: Very short email domain: pm.me>

  • Very short email domain: pm.me>
✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository hunterhogan/analyzeAudio 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 analyzeAudio 
Develop a Python-based mini-application named 'AudioAnalyzer' that leverages the 'analyzeAudio' package to provide detailed analysis on various audio files. This tool will be particularly useful for music enthusiasts, sound engineers, and researchers who need to understand different characteristics of audio files such as frequency spectrum, duration, bitrate, and more.

### Project Overview:
- **Name**: AudioAnalyzer
- **Goal**: Create a user-friendly tool that can take input from multiple audio files and generate a comprehensive report detailing each file's unique attributes.
- **Target Audience**: Music producers, sound engineers, researchers, and general audio enthusiasts.
- **Features**:
  - Ability to upload multiple audio files at once.
  - Display basic information about each file including duration, format, and bitrate.
  - Perform spectral analysis to show the frequency spectrum of each audio file.
  - Calculate and display key acoustic metrics such as loudness, pitch, and tempo.
  - Save the analyzed data into a CSV file for further analysis.
  - Provide visual representations of the audio data using graphs or charts.

### Implementation Steps:
1. **Setup Environment**: Install necessary packages including 'analyzeAudio', 'pandas', 'matplotlib', and any other required dependencies.
2. **User Interface**: Design a simple command-line interface where users can specify the path to their audio files.
3. **File Processing**: Use 'analyzeAudio' to process the uploaded files and extract relevant metadata and analysis results.
4. **Data Presentation**: Present the analyzed data in a readable format, possibly using tables or plots generated with 'matplotlib'.
5. **Output Management**: Allow users to save the results into a CSV file for future reference or analysis.
6. **Testing & Validation**: Ensure the application works correctly with a variety of audio formats and file sizes.

### Utilizing 'analyzeAudio':
- Import 'analyzeAudio' to handle the technicalities of reading and analyzing audio files.
- Use its functions to get insights into the audio content, such as extracting the frequency spectrum, calculating the bitrate, and identifying key acoustic properties.
- Integrate these insights into the output of your application, ensuring they are presented clearly and concisely.

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