Large encoder-decoder models like Whisper achieve strong offline transcription but remain impractical for streaming applications due to high latency. However, due to the accessibility of pre-trained checkpoints, the open Thai ASR landscape remains dominated by these offline architectures, leaving a critical gap in efficient streaming solutions. We present Typhoon ASR Real-time, a 115M-parameter FastConformer-Transducer model for low-latency Thai speech recognition. We demonstrate that rigorous text normalization can match the impact of model scaling: our compact model achieves a 45x reduction in computational cost compared to Whisper Large-v3 while delivering comparable accuracy. Our normalization pipeline resolves systemic ambiguities in Thai transcription --including context-dependent number verbalization and repetition markers (mai yamok) --creating consistent training targets. We further introduce a two-stage curriculum learning approach for Isan (north-eastern) dialect adaptation that preserves Central Thai performance. To address reproducibility challenges in Thai ASR, we release the Typhoon ASR Benchmark, a gold-standard human-labeled datasets with transcriptions following established Thai linguistic conventions, providing standardized evaluation protocols for the research community.

翻译：尽管如Whisper等大型编码器-解码器模型在离线转录任务中表现优异，但其高延迟特性使其难以应用于实时流式场景。然而，由于预训练检查点的易获取性，当前开放的泰语自动语音识别领域仍由这些离线架构主导，导致高效流式解决方案存在显著空白。本文提出台风ASR实时系统——一个包含1.15亿参数的FastConformer-Transducer模型，专为低延迟泰语语音识别设计。我们通过实验证明，严格的文本规范化处理能达到与模型扩展相当的效果：相较于Whisper Large-v3，我们提出的紧凑模型在保持相当准确度的同时，实现了45倍的计算成本压缩。我们的规范化流程系统性地解决了泰语转录中的固有歧义问题，包括上下文相关的数字语音化表达及重复标记符号（ไม้ยมก），从而构建了统一的训练目标。此外，我们提出针对伊森（东北部）方言适配的两阶段课程学习方法，该方法在提升方言适应性的同时保持了中部泰语的处理性能。为应对泰语自动语音识别领域可复现性挑战，我们同步发布了台风ASR基准测试集——该数据集遵循标准泰语语言学规范进行人工标注，为研究社区提供包含标准化评估协议的高质量标注语料库。