为锡克教赞歌实时字幕开发首个基准与系统，确保经文准确无误显示。
Live Gurbani Tracking: A Benchmark and Reference System for Captioning Sikh Kirtan
- 将字幕任务定义为精准匹配经文条目和行号，避免拼写错误。
- 在最困难的实时盲模式下达到57.9%帧准确率，10/12案例正确锁定经文。
- 专为宗教敏感性设计，适合宗教文本处理与低延迟语音系统研究者。
我们提出首个针对锡克教赞歌（Sikh Kirtan）实时字幕的基准与参考系统。赞歌是连续吟诵《古鲁·格兰特·萨希布》（SGGS）经文，其字幕必须为经文原文的精确逐字呈现，因任何拼写错误均被视为宗教不敬。我们将任务形式化为在每一时刻t预测（经文编号，行索引）或空值，并将问题划分为二维矩阵：实时/离线（因果/全音频访问）与盲模式/圣杯模式（经文身份未知/已知）。发布v1版本基准：4段人工标注赞歌录音 × 3种冷启动偏移 = 12个评估案例，约57分钟可评分音频；配套评分器以1秒分辨率计算帧准确率，采用1秒邻近窗口及边界间隙容忍机制。参考系统由微调的120M IndicConformer → 模糊匹配器 → 状态机构成，使用INT8 ONNX，在单个Apple Silicon核心上实现实时因子约0.05，整体帧准确率为57.9%（12例中10例正确锁定经文），在最严苛的实时盲模式下表现最佳。对比三种基线（空、滞后5秒、理想）后指出，标准ASR指标（WER/CER）衡量的是转录准确性，而非本任务所需的显示准确性。基准、参考系统及实时部署均已开源，采用宽松许可协议，促进后续改进。
原文摘要 · Abstract (English)
We present a benchmark and reference system for live captioning of Sikh Kirtan - the continuous, sung recitation of verses from the Sri Guru Granth Sahib Ji (SGGS). Unlike open-vocabulary lyrics transcription, Kirtan captioning is a closed-vocabulary problem: every displayed line must be an exact, word-for-word line from the canonical scripture, because displaying misspelled Gurmukhi is considered religiously inappropriate. We formalize the task as predicting, at every time t, a pair (shabad_id, line_idx) or null, and organize the problem space into a 2x2 matrix along two orthogonal axes: live vs. offline (causal vs. full-audio access) and blind vs. oracle (shabad identity discovered vs. given). We release v1 of the benchmark - 4 hand-annotated Kirtan recordings x 3 cold-start offsets = 12 evaluation cases, ~57 minutes of scored audio - together with a scorer that computes frame accuracy at 1s resolution over a scored region, with a 1s collar and gap-tolerant scoring at segment boundaries. We describe a reference system (fine-tuned 120M IndicConformer -> fuzzy matcher -> state machine; INT8 ONNX; RTF ~0.05 on one Apple Silicon core) that achieves 57.9% overall frame accuracy across all 12 cases (10/12 correct shabad locks) on the hardest variant (live x blind). We compare against three trivial baselines (empty, shifted-5s, perfect) and discuss why standard ASR metrics (WER/CER) measure transcription accuracy rather than the display accuracy this task requires. The benchmark, reference system, and a live deployment are released under permissive licenses to facilitate further improvements.
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