AI Analysis
Final verdict: SUSPICIOUS
The package shows minimal risks across all categories except for metadata, where the maintainer's single package raises a flag. This could suggest a less established developer profile, warranting closer scrutiny.
- Metadata risk due to a single package from the maintainer
- Otherwise low risk indicators across other categories
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires external services.
- Shell: No shell execution patterns detected, indicating no immediate signs of malicious activities.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The maintainer has only one package, which might indicate a new or less active account, raising some suspicion but not conclusive evidence of malice.
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
Repository pe-trik/OmniSTEval appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Peter Polák" 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 OmniSTEval
Create a mini-application named 'SpeechTextEvaluator' using Python and the 'OmniSTEval' package. This application will serve as a tool for evaluating the performance of simultaneous speech-to-text and text-to-text translation systems. Users should be able to input their translated texts or audio files, along with corresponding reference texts, and receive a comprehensive evaluation report including various quality metrics (BLEU, chrF, COMET) and latency metrics (YAAL). Additionally, for longer-form content, the application should automatically re-segment the output to align with the reference text segments before performing evaluations. Step-by-Step Guide: 1. **Setup Environment**: Ensure Python and 'OmniSTEval' are installed. If not, provide instructions on how to install them via pip. 2. **Input Handling**: Design a user-friendly interface where users can upload their translated texts or audio files and corresponding reference texts. Include error handling for incorrect file types or missing inputs. 3. **Preprocessing**: Implement preprocessing steps for both shortform and longform content. For audio files, convert them into text using a speech-to-text service (e.g., Google Speech-to-Text API). 4. **Evaluation Metrics**: Utilize the 'OmniSTEval' package to calculate BLEU, chrF, COMET scores for assessing translation quality, and YAAL for measuring system latency. 5. **Re-Segmentation for Longform Content**: For longer texts, use 'OmniSTEval' functionalities to automatically re-segment the output to match the reference text segmentation before applying the quality and latency metrics. 6. **Report Generation**: Develop a feature to generate a detailed report summarizing the evaluation results, including visual representations such as graphs or charts. 7. **User Feedback**: Allow users to save their reports and view past evaluations. Suggested Features: - Integration with popular speech-to-text APIs for seamless audio processing. - Option to evaluate multiple translations against the same reference. - Real-time feedback during the evaluation process. - Customizable settings for different evaluation parameters. - Support for multiple languages. How 'OmniSTEval' is Utilized: - Import necessary modules from 'OmniSTEval' at the beginning of your script. - Use 'OmniSTEval' functions to compute BLEU, chrF, and COMET scores for text quality assessment. - Apply YAAL metric from 'OmniSTEval' to measure the latency of translation systems. - Leverage 'OmniSTEval' tools for automatic re-segmentation of longform texts to ensure accurate evaluation.