rwth-i6/i6_experiments
Jun 2025 – Oct 2025
5 Months active
Languages Used
Python
Technical Skills
ASRLLM IntegrationMachine LearningPython DevelopmentSpeech RecognitionASR Model Training
Haoran Zhang
PROFILE
Haoran Zhang
Haoran Sz worked on enhancing the rwth-i6/i6_experiments repository by developing and refining advanced ASR and language model evaluation pipelines. Over five months, Haoran integrated large language models such as HuggingFace Llama for rescoring, expanded multilingual ASR support, and improved experiment configuration and reporting. The work involved Python and PyTorch, leveraging deep learning and data engineering to enable dynamic dataset scoring, perplexity calculations, and robust evaluation workflows. Haoran addressed critical bugs, optimized data processing, and introduced modular, scalable experiment frameworks, resulting in more reliable transcription quality, streamlined experimentation, and maintainable code for end-to-end speech recognition and language modeling research.
Overall Statistics
Feature vs Bugs
63%Features
Repository Contributions
23Total
Bugs
3
Commits
23
Features
5
Lines of code
45,043
Activity Months5
Your Network
27 people
Work History
October 2025
6 Commits • 1 Features
Oct 1, 2025Monthly work summary for 2025-10 focusing on rwth-i6/i6_experiments. Delivered a major enhancement to the evaluation and experimentation workflow for LLM perplexity and ASR pipelines, including N-best and prior rescoring, BPE processing for word outputs, data handling refinements for N-best lists and corpus evaluation, flexible experiment configurations, and updated reporting. Perplexity calculations now use fixed context lengths with memory-efficient batching. Configuration enhancements improve LM experimentation workflows and reporting.
6 Commits • 1 Features
Oct 1, 2025Monthly work summary for 2025-10 focusing on rwth-i6/i6_experiments. Delivered a major enhancement to the evaluation and experimentation workflow for LLM perplexity and ASR pipelines, including N-best and prior rescoring, BPE processing for word outputs, data handling refinements for N-best lists and corpus evaluation, flexible experiment configurations, and updated reporting. Perplexity calculations now use fixed context lengths with memory-efficient batching. Configuration enhancements improve LM experimentation workflows and reporting.
October 2025
September 2025
6 Commits • 1 Features
Sep 1, 2025September 2025: Delivered substantial enhancements to the Language Model Experiments and Evaluation Framework in rwth-i6/i6_experiments, including a new CTC streaming fine-tuning job, enriched WER/PPL plotting and summaries, oracle WER checks, corpus processing support, LM dataset handling updates, and improved experiment configuration for LLMs and decoders. The work also fixed perplexity calculation for bf16 in batch processing, refining dtype handling and scoring in HuggingFaceLmPerplexityJobV2 to ensure consistent PPL across batch sizes and data types.
September 2025
6 Commits • 1 Features
Sep 1, 2025September 2025: Delivered substantial enhancements to the Language Model Experiments and Evaluation Framework in rwth-i6/i6_experiments, including a new CTC streaming fine-tuning job, enriched WER/PPL plotting and summaries, oracle WER checks, corpus processing support, LM dataset handling updates, and improved experiment configuration for LLMs and decoders. The work also fixed perplexity calculation for bf16 in batch processing, refining dtype handling and scoring in HuggingFaceLmPerplexityJobV2 to ensure consistent PPL across batch sizes and data types.
August 2025
2 Commits • 1 Features
Aug 1, 2025Month: 2025-08 — In rwth-i6/i6_experiments, delivered a Multilingual ASR Framework Expansion enabling Spanish and English experiments through new dataset configurations, model definitions, and multilingual utilities; updated AppTek CTC setup for smoother integration. Additionally, fixed a critical logits permutation bug in the ASR forward pass, contributing to pipeline stability and aligning with Librispeech processing updates, 16kHz data handling with SentencePiece tokenization, and LM training config refactor. These efforts broaden multilingual experimentation, improve training stability and data processing reliability, and accelerate end-to-end ASR development with stronger LM integration. Technologies demonstrated include Python-based model pipelines, dataset handling, SentencePiece tokenization, 16kHz audio processing, and LM training workflows.
2 Commits • 1 Features
Aug 1, 2025Month: 2025-08 — In rwth-i6/i6_experiments, delivered a Multilingual ASR Framework Expansion enabling Spanish and English experiments through new dataset configurations, model definitions, and multilingual utilities; updated AppTek CTC setup for smoother integration. Additionally, fixed a critical logits permutation bug in the ASR forward pass, contributing to pipeline stability and aligning with Librispeech processing updates, 16kHz data handling with SentencePiece tokenization, and LM training config refactor. These efforts broaden multilingual experimentation, improve training stability and data processing reliability, and accelerate end-to-end ASR development with stronger LM integration. Technologies demonstrated include Python-based model pipelines, dataset handling, SentencePiece tokenization, 16kHz audio processing, and LM training workflows.
August 2025
July 2025
8 Commits • 1 Features
Jul 1, 2025Summary for 2025-07 — rwth-i6/i6_experiments: Delivered a Unified Rescoring & Language Model Evaluation Upgrade (CTC/LM) with SPM/BPE integration, enabling dynamic dataset scoring, perplexity calculations, plotting, and improved experiment configuration for deeper analysis and faster business decisions. Fixed a GnuPlot plotting syntax error to ensure reliable visualizations. Refactored core pipeline and added jobs to improve maintainability and deployment readiness. Expanded evaluation coverage with SentencePiece and BPE LMs, enabling broader language support and better model comparisons. Overall impact: data-driven decision-making accelerated, higher-confidence ASR model evaluation, and a scalable, maintainable evaluation workflow. Technologies demonstrated: Python, pipeline design, ASR evaluation, language modeling with SPM/BPE, plotting (GnuPlot), and code refactoring.
July 2025
8 Commits • 1 Features
Jul 1, 2025Summary for 2025-07 — rwth-i6/i6_experiments: Delivered a Unified Rescoring & Language Model Evaluation Upgrade (CTC/LM) with SPM/BPE integration, enabling dynamic dataset scoring, perplexity calculations, plotting, and improved experiment configuration for deeper analysis and faster business decisions. Fixed a GnuPlot plotting syntax error to ensure reliable visualizations. Refactored core pipeline and added jobs to improve maintainability and deployment readiness. Expanded evaluation coverage with SentencePiece and BPE LMs, enabling broader language support and better model comparisons. Overall impact: data-driven decision-making accelerated, higher-confidence ASR model evaluation, and a scalable, maintainable evaluation workflow. Technologies demonstrated: Python, pipeline design, ASR evaluation, language modeling with SPM/BPE, plotting (GnuPlot), and code refactoring.
June 2025
1 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for rwth-i6/i6_experiments: Delivered an ML-assisted enhancement to the ASR pipeline by integrating HuggingFace Llama for LLM-based rescoring. Implemented perplexity calculation and rescoring of n-best hypotheses, enabling higher-quality transcriptions and more reliable downstream analytics. The work is anchored by the llmrescoring job and a dedicated code path in rwth-i6/i6_experiments. No critical defects were recorded this month; focus was on delivering a measurable improvement in transcription quality and establishing a reusable ML-driven decoding workflow. Technologies demonstrated include HuggingFace Transformers (Llama), perplexity-based scoring, Python-based pipelines, and modular experiment design to support scalable AL/ML experiments.
1 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for rwth-i6/i6_experiments: Delivered an ML-assisted enhancement to the ASR pipeline by integrating HuggingFace Llama for LLM-based rescoring. Implemented perplexity calculation and rescoring of n-best hypotheses, enabling higher-quality transcriptions and more reliable downstream analytics. The work is anchored by the llmrescoring job and a dedicated code path in rwth-i6/i6_experiments. No critical defects were recorded this month; focus was on delivering a measurable improvement in transcription quality and establishing a reusable ML-driven decoding workflow. Technologies demonstrated include HuggingFace Transformers (Llama), perplexity-based scoring, Python-based pipelines, and modular experiment design to support scalable AL/ML experiments.
June 2025
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Quality Metrics
Correctness82.2%
Maintainability81.4%
Architecture80.8%
Performance66.6%
AI Usage27.8%
Skills & Technologies
Programming Languages
Python
Technical Skills
ASRASR Model TrainingAudio ProcessingBackend DevelopmentCode RefactoringConfiguration ManagementData AnalysisData EngineeringData ParsingData ProcessingData VisualizationDeep LearningDocumentationExperiment ManagementExperimentation
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