audio-data-quality-toolkit

v0.2.0 suspicious
4.0
Medium Risk

Lint your audio datasets before training. 13 checks for TTS, ASR, and voice-cloning pipelines.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has minimal risks related to network calls, shell executions, obfuscations, and credential harvesting. However, the metadata issues raise concerns about its reliability and origin.

  • Metadata risk due to missing repository and author details
  • Potential unreliability due to incomplete package information
Per-check LLM notes
  • Network: No network calls detected, which is normal for a tool focused on local audio data quality analysis.
  • Shell: No shell execution patterns detected, aligning with the expected behavior of a benign utility.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows several red flags including a missing repository and author details, suggesting potential unreliability.

📦 Package Quality Overall: Low (4.2/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • 1 test file(s) detected (e.g. test_checks.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (9317 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

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

Could not retrieve contributor data from GitHub

  • GitHub API error: 404

🔬 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

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 3.0

Repository not found (deleted or private)

  • Repository not found (deleted or private)
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • 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 audio-data-quality-toolkit 
Develop a comprehensive audio dataset quality assurance tool using the 'audio-data-quality-toolkit' Python package. This tool will serve as a pre-training step to ensure the integrity and reliability of audio datasets used in Text-to-Speech (TTS), Automatic Speech Recognition (ASR), and voice-cloning applications. The tool should include the following functionalities:

1. **Dataset Importation**: Allow users to import their audio datasets in various formats (e.g., WAV, MP3).
2. **Quality Checks**: Utilize the 13 built-in checks provided by the 'audio-data-quality-toolkit' to assess the dataset's quality. These checks should cover aspects such as silence detection, noise levels, file format consistency, and more.
3. **Visualization Reports**: Generate visual reports summarizing the results of each check, highlighting any issues found in the dataset. Users should be able to download these reports in PDF or HTML format.
4. **Interactive Dashboard**: Create an interactive dashboard where users can view the status of their dataset's quality checks in real-time. Include options to filter and sort results based on specific criteria.
5. **Customizable Rules**: Enable users to define their own rules for additional quality checks if needed, allowing for greater flexibility and customization.
6. **Automatic Fixing Suggestions**: For common issues identified during the quality checks, provide automatic suggestions or scripts to help fix these problems.
7. **User Interface**: Design a user-friendly graphical interface using frameworks like PyQt or Tkinter to make the tool accessible to users without extensive programming knowledge.
8. **Documentation and Help**: Include detailed documentation and a help section within the tool to guide users through the process of importing datasets, interpreting results, and implementing fixes.

By completing this project, you will have created a powerful yet easy-to-use tool that significantly improves the quality and usability of audio datasets for machine learning applications.

💬 Discussion Feed

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