sarapapi/hearing2translate
Sep 2025 – Feb 2026
3 Months active
Languages Used
JSONMarkdownPythonJupyter Notebook
Technical Skills
Data EngineeringData ProcessingDataset ManagementDataset OrganizationDocumentationFile Management
Gerard I. Gállego
PROFILE
Gerard I. Gállego
Gerard Gallego developed robust data processing and analytics pipelines for the sarapapi/hearing2translate repository, focusing on dataset curation, management, and evaluation workflows. Over three months, he engineered end-to-end pipelines for CS-Dialogue and EmotionTalk datasets, implementing Python and Pandas for data extraction, transformation, and export. His work included restructuring dataset formats, enhancing export readability, and integrating model benchmarking with SpireLM, addressing both data quality and reproducibility. Gerard also improved documentation and usage guidelines, enabling efficient downstream machine learning evaluation. The depth of his contributions is reflected in the maintainable, extensible workflows and the clarity of analytics outputs supporting future experimentation.
Overall Statistics
Feature vs Bugs
86%Features
Repository Contributions
14Total
Bugs
1
Commits
14
Features
6
Lines of code
470,485
Activity Months3
Your Network
31 peopleSame Organization
@bsc.es16
Work History
February 2026
1 Commits • 1 Features
Feb 1, 2026February 2026 — sarapapi/hearing2translate: Focused delivery on expanding EmotionTalk export capabilities with enhanced data handling and clearer output formats. No critical bugs fixed this month; the emphasis was on feature delivery, quality improvements, and paving the way for richer analytics.
1 Commits • 1 Features
Feb 1, 2026February 2026 — sarapapi/hearing2translate: Focused delivery on expanding EmotionTalk export capabilities with enhanced data handling and clearer output formats. No critical bugs fixed this month; the emphasis was on feature delivery, quality improvements, and paving the way for richer analytics.
February 2026
October 2025
5 Commits • 3 Features
Oct 1, 2025For 2025-10, delivered a focused set of features in sarapapi/hearing2translate, established robust EmotionTalk tooling and dataset processing, added comprehensive performance analytics, and integrated SpireLM benchmarks to expand inference capabilities. The month yielded measurable business value through reproducible data pipelines, standardized outputs, and expanded benchmarking coverage. Key accomplishments spanned dataset tooling, evaluation analytics, and cross-model benchmarking, with targeted bug fixes to stabilize scripts and loading paths.
October 2025
5 Commits • 3 Features
Oct 1, 2025For 2025-10, delivered a focused set of features in sarapapi/hearing2translate, established robust EmotionTalk tooling and dataset processing, added comprehensive performance analytics, and integrated SpireLM benchmarks to expand inference capabilities. The month yielded measurable business value through reproducible data pipelines, standardized outputs, and expanded benchmarking coverage. Key accomplishments spanned dataset tooling, evaluation analytics, and cross-model benchmarking, with targeted bug fixes to stabilize scripts and loading paths.
September 2025
8 Commits • 2 Features
Sep 1, 2025Sep 2025 performance: Delivered a robust CS-Dialogue data pipeline and updated documentation for hearing2translate, focusing on data quality, reproducibility, and enabling efficient downstream ML workflows. Achievements include an end-to-end dataset processing pipeline with English-only target-language outputs, added dataset manifests, fixed structural issues for reliable data access, and comprehensive usage docs to support code-switching analysis.
8 Commits • 2 Features
Sep 1, 2025Sep 2025 performance: Delivered a robust CS-Dialogue data pipeline and updated documentation for hearing2translate, focusing on data quality, reproducibility, and enabling efficient downstream ML workflows. Achievements include an end-to-end dataset processing pipeline with English-only target-language outputs, added dataset manifests, fixed structural issues for reliable data access, and comprehensive usage docs to support code-switching analysis.
September 2025
Activity
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Quality Metrics
Correctness94.4%
Maintainability93.0%
Architecture91.4%
Performance91.4%
AI Usage24.2%
Skills & Technologies
Programming Languages
JSONJupyter NotebookMarkdownPython
Technical Skills
Data AnalysisData EngineeringData ProcessingDataset CurationDataset ManagementDataset OrganizationDocumentationFile I/OFile ManagementHugging Face HubJSONJupyter NotebookMachine Learning EvaluationPandasPython
Repositories Contributed To
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