Lightning-AI/torchmetrics
Jan 2025 – Jun 2025
2 Months active
Languages Used
PythonJinja
Technical Skills
Machine LearningPyTorchSoftware DevelopmentAudio ProcessingDocumentationPython
Bas Krahmer
PROFILE
Bas Krahmer
Bas Krahmer contributed to the Lightning-AI/torchmetrics repository by developing a flexible classification metrics API and a PESQ-based TTS quality evaluation demo. He enhanced the MulticlassStatScores and multiclass_accuracy APIs in Python and PyTorch, allowing micro-averaging without requiring explicit num_classes, which improved usability and maintainability for machine learning workflows. In a separate feature, Bas built a text-to-speech evaluation demo using audio processing techniques, enabling side-by-side perceptual quality comparisons with an interactive audio playback UI. He also updated documentation to include a Text-to-Speech Gallery, increasing discoverability and supporting quality assurance for TTS systems. His work emphasized robust, user-friendly solutions.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
188
Activity Months2
Your Network
23 peopleWork History
June 2025
1 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary: Delivered a PESQ-based TTS Quality Evaluation Demo in Lightning-AI/torchmetrics, demonstrating generation with multiple speaker embeddings and PESQ-based quality comparison against a target voice, with an audio playback UI for side-by-side assessment. Documentation updates added a Text-to-Speech Gallery (#2801) to improve discoverability and usage. This work strengthens TTS evaluation workflows, provides a concrete perceptual-quality example for stakeholders, and aligns with quality assurance and product storytelling.
1 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary: Delivered a PESQ-based TTS Quality Evaluation Demo in Lightning-AI/torchmetrics, demonstrating generation with multiple speaker embeddings and PESQ-based quality comparison against a target voice, with an audio playback UI for side-by-side assessment. Documentation updates added a Text-to-Speech Gallery (#2801) to improve discoverability and usage. This work strengthens TTS evaluation workflows, provides a concrete perceptual-quality example for stakeholders, and aligns with quality assurance and product storytelling.
June 2025
January 2025
1 Commits • 1 Features
Jan 1, 2025January 2025 monthly summary for Lightning-AI/torchmetrics: Focused delivery of a robust, user-friendly classification metrics API with improvements to micro-averaging workflows. No major bugs fixed this month; emphasis on reliability, API ergonomics, and business value through cleaner usage and fewer configuration steps.
January 2025
1 Commits • 1 Features
Jan 1, 2025January 2025 monthly summary for Lightning-AI/torchmetrics: Focused delivery of a robust, user-friendly classification metrics API with improvements to micro-averaging workflows. No major bugs fixed this month; emphasis on reliability, API ergonomics, and business value through cleaner usage and fewer configuration steps.
Activity
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Quality Metrics
Correctness95.0%
Maintainability90.0%
Architecture90.0%
Performance90.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
JinjaPython
Technical Skills
Audio ProcessingDocumentationMachine LearningPyTorchPythonSoftware Development
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