EOU
Datasets
All datasets matching “EOU”NeMo_RNNT_EOU
NVIDIA NeMo Speech
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Updates
2026-03: Nemotron 3 VoiceChat is now released in Early Access. Built on the Nemotron Nano v2 LLM backbone with Nemotron speech and TTS decoder, VoiceChat delivers full-duplex, natural, interruptible conversations with low latency. Try out the demo and apply for early access.
2026-03: Nemotron-Speech-Streaming v2603 has been
updated. It has… See the full description on the dataset page: https://huggingface.co/datasets/echodict/NeMo_RNNT_EOU.asr_rnnt_eou_from_scratch
NeMo ASR-EOU 训练脚本解读与论文出处梳理
目标文件：examples/asr/asr_eou/speech_to_text_rnnt_eou_train.py链接：https://github.com/NVIDIA-NeMo/NeMo/blob/main/examples/asr/asr_eou/speech_to_text_rnnt_eou_train.py
这份脚本本身是一个 训练入口脚本（Hydra + PyTorch Lightning），核心功能是：按配置创建 EncDecRNNTBPEEOUModel，并支持从已有 .nemo 初始化、添加/训练 adapter，以及在“词表扩展（新增 <EOU>/<EOB>）”时做权重迁移。
下面按“它用到的技术点 → 在代码/配置里怎么体现 → 原始论文出处”总结。
1) ASR-EOU：把“端点/话轮信息”并入 ASR（<EOU>, <EOB>）
它做什么：
除了输出转写文本外，还让模型在时间轴上预测：
EOU：End Of Utterance（一句话结束）… See the full description on the dataset page: https://huggingface.co/datasets/echodict/asr_rnnt_eou_from_scratch.2d_3d_seq_path_spatial_reasoning
Spatial Reasoning Dataset
A synthetic dataset of Hamiltonian path puzzles with rich chain-of-thought reasoning, designed for training and evaluating spatial reasoning in language models.
Overview
Each sample presents a grid-based puzzle where the solver must find a path visiting every cell exactly once, moving only up/down/left/right (plus above/below for 3D). Puzzles span 2D grids (3x3 to 8x8) and 3D cubes (3x3x3 to 4x4x4), covering solvable, impossible, and multi-turn… See the full description on the dataset page: https://huggingface.co/datasets/eousphoros/2d_3d_seq_path_spatial_reasoning.eou-synthetic-data-iteratedarabic-eou-livekitarabic_eou_sada_dataset
Arabic EOU SADA Dataset (Saudi Dialect)
414,053 conversational Arabic utterances annotated for End-of-Utterance (EOU) detectionStrong focus on natural Saudi dialect (خليجي / نجدي / حجازي)
Task
Binary classification:
label = 1 → End of speaker turn (EOU)
label = 0 → Speaker will continue
Columns
text: Arabic transcription
label: 0 or 1
silence_after_seconds: pause duration after this segment
split: train | validation | test (already included)… See the full description on the dataset page: https://huggingface.co/datasets/raghad23/arabic_eou_sada_dataset.