sarapapi/hearing2translate
Sep 2025 – Feb 2026
3 Months active
Languages Used
JSONPython
Technical Skills
AI Model EvaluationAI Model IntegrationAPI developmentAudio ProcessingData AugmentationData Curation
Sara Papi
PROFILE
Sara Papi
Sara Papi developed and expanded cross-dataset, multilingual output pipelines for the sarapapi/hearing2translate repository, focusing on scalable integration of AI models for translation and audio processing. Over three months, she engineered robust data orchestration across datasets such as FLEURS, CoVoST2, EuroParlST, and LibriStutter, consolidating outputs from models like Qwen3-Omni, Voxtral, and Phi4Multimodal. Using Python and JSON, Sara implemented API-driven workflows and rigorous data curation, enabling consistent benchmarking and production-ready evaluation. Her work addressed data quality issues, streamlined multi-modal output consolidation, and established maintainable patterns for future expansion, demonstrating depth in machine learning, backend development, and natural language processing.
Overall Statistics
Feature vs Bugs
95%Features
Repository Contributions
109Total
Bugs
2
Commits
109
Features
37
Lines of code
1,514,059
Activity Months3
Your Network
15 peopleWork History
February 2026
16 Commits • 1 Features
Feb 1, 2026February 2026: Expanded Qwen3-Omni outputs across models, datasets, and modalities in sarapapi/hearing2translate to deliver richer, more versatile translation/output capabilities. Implemented broad data-path coverage across Libristutter, FLEURS, cs-dialogue, cs_fleurs, emotion detection, ACL6060, MCIF, EuroParlST, CoVoST2, WinoST, WMT, mExpresso, ambient/babble, and Mandi variants, establishing a scalable, end-to-end output layer for diverse tasks and audiences.
16 Commits • 1 Features
Feb 1, 2026February 2026: Expanded Qwen3-Omni outputs across models, datasets, and modalities in sarapapi/hearing2translate to deliver richer, more versatile translation/output capabilities. Implemented broad data-path coverage across Libristutter, FLEURS, cs-dialogue, cs_fleurs, emotion detection, ACL6060, MCIF, EuroParlST, CoVoST2, WinoST, WMT, mExpresso, ambient/babble, and Mandi variants, establishing a scalable, end-to-end output layer for diverse tasks and audiences.
February 2026
October 2025
40 Commits • 16 Features
Oct 1, 2025October 2025 monthly summary for sarapapi/hearing2translate: Delivered broad, dataset-spanning outputs across Voxtral, Desta, Phi4M, and Qwen families for the hearing2translate project. Implemented multi-dataset outputs for Voxtral, Desta, and Phi4M across mexpresso, CS-Dialogue, EuroParlST, and EmotionTalk. Expanded Qwen coverage to CS-Dialogue and EmotionTalk, with Qwen2Audio outputs for EuroParlST. Achieved LibriStutter integration across Voxtral, DeSTA2, Qwen2Audio, and Phi4Multimodal. Consolidated NoisyFLEURS ambient and babble outputs across DeSTA2, Phi4Multimodal, Gemma, and Qwen2Audio. Also fixed a quality issue by redoing Gemma+Whisper FLEURS outputs for IT/EN and ZH/EN. These efforts increase model-output coverage, consistency, and benchmarking capabilities across datasets, languages, and use cases, enabling better evaluation pipelines and end-user experiences; demonstrated strong cross-model integration, data orchestration, and commit-level traceability.
October 2025
40 Commits • 16 Features
Oct 1, 2025October 2025 monthly summary for sarapapi/hearing2translate: Delivered broad, dataset-spanning outputs across Voxtral, Desta, Phi4M, and Qwen families for the hearing2translate project. Implemented multi-dataset outputs for Voxtral, Desta, and Phi4M across mexpresso, CS-Dialogue, EuroParlST, and EmotionTalk. Expanded Qwen coverage to CS-Dialogue and EmotionTalk, with Qwen2Audio outputs for EuroParlST. Achieved LibriStutter integration across Voxtral, DeSTA2, Qwen2Audio, and Phi4Multimodal. Consolidated NoisyFLEURS ambient and babble outputs across DeSTA2, Phi4Multimodal, Gemma, and Qwen2Audio. Also fixed a quality issue by redoing Gemma+Whisper FLEURS outputs for IT/EN and ZH/EN. These efforts increase model-output coverage, consistency, and benchmarking capabilities across datasets, languages, and use cases, enabling better evaluation pipelines and end-user experiences; demonstrated strong cross-model integration, data orchestration, and commit-level traceability.
September 2025
53 Commits • 20 Features
Sep 1, 2025September 2025 monthly summary for sarapapi/hearing2translate. Focused on delivering cross-dataset outputs, consolidating multimodal pipelines, and stabilizing outputs across languages and platforms. Key features delivered expanded the coverage of FLEURS, Qwen2Audio, Voxtral, and DeSTA2/Desta outputs across multiple datasets; these enhancements enable consistent benchmarking, faster validation, and production readiness. Critical bug fix removed NL-EN FLEURS outputs from Desta2 outputs to ensure clean, consistent data pipelines. Overall impact includes broader interoperability across CoVoST2, ACL6060, MANDI, MCIF, WMT, and mexpresso-style datasets, reducing manual curation and accelerating feature validation. Demonstrated technologies include FLEURS, Qwen2Audio, Voxtral, Phi4Multimodal pipelines, and cross-dataset output consolidation, underpinned by commit-driven changes across the repository.
53 Commits • 20 Features
Sep 1, 2025September 2025 monthly summary for sarapapi/hearing2translate. Focused on delivering cross-dataset outputs, consolidating multimodal pipelines, and stabilizing outputs across languages and platforms. Key features delivered expanded the coverage of FLEURS, Qwen2Audio, Voxtral, and DeSTA2/Desta outputs across multiple datasets; these enhancements enable consistent benchmarking, faster validation, and production readiness. Critical bug fix removed NL-EN FLEURS outputs from Desta2 outputs to ensure clean, consistent data pipelines. Overall impact includes broader interoperability across CoVoST2, ACL6060, MANDI, MCIF, WMT, and mexpresso-style datasets, reducing manual curation and accelerating feature validation. Demonstrated technologies include FLEURS, Qwen2Audio, Voxtral, Phi4Multimodal pipelines, and cross-dataset output consolidation, underpinned by commit-driven changes across the repository.
September 2025
Activity
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Quality Metrics
Correctness95.4%
Maintainability96.0%
Architecture96.2%
Performance95.8%
AI Usage48.0%
Skills & Technologies
Programming Languages
JSONPython
Technical Skills
AI DevelopmentAI IntegrationAI Model EvaluationAI Model IntegrationAI integrationAI model integrationAPI developmentAPI integrationAudio ProcessingData AugmentationData CleaningData CurationData EngineeringData ProcessingData processing
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