asr-consilium

v1.0.5 suspicious
4.0
Medium Risk

Ensemble of open-source ASR models

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows some suspicious activities, particularly with network and obfuscation risks, though there's no concrete evidence of malicious intent. Further scrutiny is required.

  • network risk due to URL downloads
  • obfuscation risk due to base64 decoding and editdistance usage
Per-check LLM notes
  • Network: The package appears to download content from URLs, which could be legitimate for downloading audio files, but requires further investigation into the legitimacy and purpose of the URLs.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: The use of base64 decoding and editdistance evaluation is somewhat suspicious but could be legitimate for specific functionalities like data processing or similarity checks.
  • Credentials: No clear patterns indicating credential harvesting were detected.
  • Metadata: Low risk due to lack of suspicious flags, but concerns about low effort metadata and new maintainer.

📦 Package Quality Overall: Low (3.0/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Brief PyPI description (227 chars)
○ 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

  • 142 type-annotated function signatures detected in source
○ Low Multiple Contributors 2.0

Single-author or unverifiable project

  • 1 unique contributor(s) across 12 commits in ZFTurbo/asr_consilium
  • Single author with few commits — possibly a personal or throwaway project

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • if is_url(x): with urllib.request.urlopen(x) as resp: audio_bytes = resp.read()
⚠ Code Obfuscation score 10.0

Found 5 obfuscation pattern(s)

  • 4.split(",", 1)[1] return base64.b64decode(b64) def load_audio_any(x: str) -> Tuple[np.ndarray, int]:
  • int: return editdistance.eval(text1.split(), text2.split()) def get_path_words_string
  • dist = editdistance.eval(tokenized_texts[i], tokenized_texts[j]) distanc
  • dist = editdistance.eval(all_candidates[i], tokenized_texts[j]) distance
  • model.bfloat16() model.eval() lines = open(jsonl_file, 'r', encoding="utf-8").rea
✓ 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 ZFTurbo/asr_consilium appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author "Roman Sol (ZFTurbo)" 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 asr-consilium 
Create a real-time speech-to-text transcription tool using the 'asr-consilium' package. This tool will allow users to input audio from their microphone or upload an audio file, and it will transcribe the spoken words into text in real-time. Here are the key steps and features to include in your project:

1. **Setup**: Install 'asr-consilium' and other necessary packages such as Flask for web framework, PyAudio for recording audio, and any additional libraries required for file handling and API requests.
2. **Web Interface**: Develop a simple web interface where users can either start a new recording or upload an existing audio file. Ensure the interface is user-friendly and responsive.
3. **Real-Time Transcription**: Integrate 'asr-consilium' to handle the real-time transcription of audio data. Utilize its ensemble capabilities to improve the accuracy of the transcription by combining outputs from multiple ASR models.
4. **Display Transcripts**: Display the transcribed text in real-time on the web interface as the user speaks or as the uploaded file plays.
5. **Save Transcripts**: Allow users to save the transcribed text to their local machine or provide options to export it as a text file.
6. **Error Handling**: Implement error handling to manage issues like incorrect audio formats, network errors, or model failures gracefully.
7. **Testing and Optimization**: Test the tool thoroughly with various types of audio inputs to ensure reliability and accuracy. Optimize performance and resource usage based on testing results.
8. **Documentation**: Provide clear documentation on how to install and use the tool, including setup instructions and troubleshooting tips.

By following these steps, you'll create a valuable tool that leverages the advanced capabilities of 'asr-consilium' to offer robust and accurate speech-to-text transcription services.

💬 Discussion Feed

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