audiobench

v0.2.0 suspicious
5.0
Medium Risk

Reproducible CLI benchmark for audio ML models.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risks due to its network activity and obfuscated code, which could be indicative of hidden malicious functionality. The low shell execution and credential risk scores slightly mitigate these concerns.

  • network risk (4/10)
  • obfuscation risk (7/10)
Per-check LLM notes
  • Network: The package makes network requests which could potentially be used for data exfiltration or C2 communication, but without more context, it's hard to determine if this is intended functionality.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: The code snippet shows signs of obfuscation which could be used to hide functionality or make reverse engineering more difficult.
  • Credentials: No clear evidence of credential harvesting patterns was found.
  • Metadata: The maintainer has a new or inactive account and lacks a proper author name, which could indicate potential issues but does not conclusively suggest malice.

📦 Package Quality Overall: Medium (6.6/10)

✦ High Test Suite 9.0

Test suite present — 9 test file(s) found

  • 9 test file(s) detected (e.g. test_asr_hallucination.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://thenirock.github.io/audiobench/
  • Detailed PyPI description (14443 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

  • 353 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 3 unique contributor(s) across 27 commits in THENIROCK/audiobench
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 6.0

Found 4 network call pattern(s)

  • .encode()) request = urllib.request.Request( self.endpoint, data=body.ge
  • POST", ) with urllib.request.urlopen(request, timeout=120) as response: # noqa: S310
  • dy).encode("utf-8") req = urllib.request.Request( url, data=data, headers={
  • , ) try: with urllib.request.urlopen(req, timeout=2.0) as resp: resp.read()
⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

  • ._device) self._model.eval() def answer(self, audio: np.ndarray, sample_rate: int,
  • e try: if getattr(__import__("sys").modules.get("__main__"), "navigator", None): pa
✓ 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: hmc.edu>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository THENIROCK/audiobench 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 audiobench 
Create a mini-application named 'AudioModelBenchmarker' using the Python package 'audiobench'. This tool will serve as a user-friendly interface for benchmarking various audio machine learning models based on performance metrics like latency, accuracy, and computational efficiency. Your application should include the following functionalities:

1. **User Interface**: Develop a simple command-line interface (CLI) that allows users to interact with the application. Users should be able to input commands to select the model they want to benchmark.
2. **Model Selection**: Implement a feature where users can choose from a predefined list of popular audio ML models (e.g., VGGish, YAMNet). Additionally, allow users to specify custom model paths if needed.
3. **Benchmark Execution**: Utilize the 'audiobench' package to execute benchmarks on selected models. Ensure that the benchmarks are reproducible and adhere to best practices for benchmarking ML models.
4. **Performance Metrics**: Collect and display key performance metrics such as inference time, accuracy scores, and memory usage for each benchmarked model. Provide a summary report at the end of each benchmark session.
5. **Data Handling**: Include functionality to load and process test datasets for benchmarking. The datasets should be preprocessed to ensure compatibility with the models being tested.
6. **Customization Options**: Allow users to customize certain aspects of the benchmarking process, such as the number of iterations, batch size, and whether to save the benchmark results to a file.
7. **Error Handling**: Implement robust error handling to manage common issues like invalid model paths, unsupported datasets, and unexpected errors during benchmark execution.
8. **Documentation**: Write clear documentation for your application, explaining how to install dependencies, run the application, and interpret the benchmark results.

Your goal is to create a comprehensive, yet easy-to-use tool that leverages the 'audiobench' package to provide valuable insights into the performance of different audio ML models. This application should not only serve as a practical utility but also as a showcase of how to effectively use the 'audiobench' package in real-world scenarios.

💬 Discussion Feed

Leave a comment

No discussion yet. Be the first to share your thoughts!