Bengali, despite being one of the most widely spoken languages globally, remains underrepresented in long form speech technology, particularly in systems addressing transcription and speaker attribution. We present frameworks for long form Bengali speech intelligence that address automatic speech recognition using a Whisper Medium based model and speaker diarization using a finetuned segmentation model. The ASR pipeline incorporates vocal separation, voice activity detection, and a gap aware windowing strategy to construct context preserving segments for stable decoding. For diarization, a pretrained speaker segmentation model is finetuned on the official competition dataset (provided as part of the DL Sprint 4.0 competition organized under BUET CSE Fest), to better capture Bengali conversational patterns. The resulting systems deliver both efficient transcription of long form audio and speaker aware transcription to provide scalable speech technology solutions for low resource languages.

翻译：尽管孟加拉语是全球使用最广泛的语言之一，但在长语音技术领域，特别是在涉及转写和说话人归属的系统中，其代表性仍然不足。我们提出了针对长格式孟加拉语语音智能处理的框架，分别使用基于 Whisper Medium 模型的自动语音识别和基于微调分割模型的说话人日志。该语音识别流程集成了人声分离、语音活动检测以及一种间隙感知的窗口化策略，以构建保留上下文的片段，实现稳定的解码。对于说话人日志，我们在官方竞赛数据集（由 BUET CSE Fest 组织的 DL Sprint 4.0 竞赛提供）上对预训练的说话人分割模型进行了微调，以更好地捕捉孟加拉语对话模式。最终的系统能够高效地对长音频进行转写，并提供说话人感知的转写，从而为低资源语言提供可扩展的语音技术解决方案。