
Over four months, Nikita Karpnev developed and documented multilingual audio data processing pipelines for the NVIDIA/NeMo-speech-data-processor and NVIDIA/NeMo-Curator repositories. He implemented end-to-end workflows in Python and YAML, including manifest creation, language identification, Voice Activity Detection, segmentation, and automated ASR inference using the NeMo framework. His work included restructuring repositories for multilingual support, expanding coverage to Portuguese and FLEURS datasets, and introducing Word Error Rate evaluation stages. By providing clear documentation, configuration management, and unit tests, Nikita improved onboarding, reproducibility, and evaluation reliability, enabling faster model development and more robust benchmarking for speech data processing teams.
August 2025 monthly summary for NVIDIA/NeMo-Curator focusing on delivering an end-to-end Audio Processing Pipeline for the FLEURS dataset with ASR and WER evaluation, plus supporting utilities and tests. This work enables automated ASR inference and transcription quality benchmarking, reducing manual setup and accelerating reproducible evaluation across models.
August 2025 monthly summary for NVIDIA/NeMo-Curator focusing on delivering an end-to-end Audio Processing Pipeline for the FLEURS dataset with ASR and WER evaluation, plus supporting utilities and tests. This work enables automated ASR inference and transcription quality benchmarking, reducing manual setup and accelerating reproducible evaluation across models.
July 2025: Delivered end-to-end Portuguese unlabeled audio data processing pipeline for NVIDIA/NeMo-speech-data-processor. Implemented manifest creation, duration extraction, language identification, language/duration filtering, Voice Activity Detection (VAD), segmentation, and manifest cleanup, with accompanying documentation updates. This work expands multilingual data coverage, enhances preprocessing quality, and accelerates data preparation for model training, reducing manual annotation effort.
July 2025: Delivered end-to-end Portuguese unlabeled audio data processing pipeline for NVIDIA/NeMo-speech-data-processor. Implemented manifest creation, duration extraction, language identification, language/duration filtering, Voice Activity Detection (VAD), segmentation, and manifest cleanup, with accompanying documentation updates. This work expands multilingual data coverage, enhances preprocessing quality, and accelerates data preparation for model training, reducing manual annotation effort.
Monthly summary for 2025-05: Implemented foundational multilingual dataset processing groundwork by restructuring the repository to enable multilingual support. The project structure was reorganized by renaming the dataset-processing directory to multilingual/granary, establishing a scalable path for future multilingual pipelines. This work aligns with our roadmap to broaden language coverage and improve data processing throughput, setting the stage for faster onboarding of multilingual data sources and more versatile dataset handling.
Monthly summary for 2025-05: Implemented foundational multilingual dataset processing groundwork by restructuring the repository to enable multilingual support. The project structure was reorganized by renaming the dataset-processing directory to multilingual/granary, establishing a scalable path for future multilingual pipelines. This work aligns with our roadmap to broaden language coverage and improve data processing throughput, setting the stage for faster onboarding of multilingual data sources and more versatile dataset handling.
April 2025 monthly summary for NVIDIA/NeMo-speech-data-processor: Delivered Granary dataset configs README documentation to clarify folder purpose, contents, and ongoing work, with explicit association to an upcoming paper. The work enhances reproducibility, onboarding, and collaboration for data processing pipelines.
April 2025 monthly summary for NVIDIA/NeMo-speech-data-processor: Delivered Granary dataset configs README documentation to clarify folder purpose, contents, and ongoing work, with explicit association to an upcoming paper. The work enhances reproducibility, onboarding, and collaboration for data processing pipelines.

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