| --- |
| language: |
| - zh |
| size_categories: |
| - 10B<n<100B |
| tags: |
| - audio, |
| - dialog, |
| - long-term-memory, |
| - personalized-dialog, |
| - full-duplex |
| pretty_name: EgoMem Evaluation Data |
| license: cc-by-nc-4.0 |
| configs: |
| - config_name: default |
| data_files: |
| - split: original |
| path: data/original-* |
| - split: with_noise |
| path: data/with_noise-* |
| dataset_info: |
| features: |
| - name: meta |
| dtype: int64 |
| - name: user_wav |
| list: float32 |
| - name: bot_wav |
| list: float32 |
| - name: sampling_rate |
| dtype: int64 |
| - name: dialog_records |
| list: |
| - name: dialog_span |
| list: int64 |
| - name: dialog_span_in_seconds |
| list: float64 |
| - name: dialog_turn_contents |
| list: |
| - name: bot_stamp |
| dtype: int64 |
| - name: bot_stamp_end |
| dtype: int64 |
| - name: bot_stamp_end_audio |
| dtype: int64 |
| - name: bot_txt |
| dtype: string |
| - name: fact_kwd |
| list: string |
| - name: relation_kwd |
| list: string |
| - name: speaker |
| dtype: string |
| - name: support_dict |
| list: |
| - name: person |
| dtype: string |
| - name: person_fact |
| list: string |
| - name: person_relation |
| dtype: string |
| - name: user_stamp |
| dtype: int64 |
| - name: user_txt |
| dtype: string |
| - name: meta |
| dtype: string |
| - name: profile_summary |
| struct: |
| - name: person |
| dtype: string |
| - name: person_fact |
| dtype: string |
| - name: person_profile |
| dtype: string |
| - name: person_summary |
| dtype: string |
| - name: relation_graph |
| list: |
| - name: person |
| dtype: string |
| - name: person_fact |
| list: string |
| - name: person_relation |
| dtype: string |
| splits: |
| - name: original |
| num_bytes: 11406928704 |
| num_examples: 300 |
| - name: with_noise |
| num_bytes: 11406928704 |
| num_examples: 300 |
| download_size: 22870403263 |
| dataset_size: 22813857408 |
| --- |
| |
| # EgoMem Evaluation Data |
|
|
| ## Dataset Description |
|
|
| This repository contains the evaluation data for **EgoMem: Lifelong Memory Agent for Full-duplex Omnimodal Models**. |
|
|
| The dataset is divided into two splits: |
|
|
| - **`original`**: contains the original user audio without added noise. |
| - **`with_noise`**: contains noisy versions of the user audio. |
| |
| The two splits share the same data structure. The primary difference between them is the content of the `user_wav` field. |
| |
| ## Data Fields |
| |
| Each sample contains the following top-level fields: |
| |
| | Field | Description | |
| |---|---| |
| | `meta` | A unique identifier for the sample. | |
| | `user_wav` | A one-dimensional NumPy array containing the waveform of the user's input audio. | |
| | `bot_wav` | A one-dimensional NumPy array containing the ground-truth waveform of the model's response. | |
| | `sampling_rate` | The sampling rate of both `user_wav` and `bot_wav`. | |
| | `dialog_records` | A list containing multiple dialogue sessions that occur within `user_wav` and `bot_wav`. Each session is represented by a dictionary containing all annotations associated with that session. | |
| |
| ## Dialogue Session Structure |
| |
| Each element in `dialog_records` represents one dialogue session and contains the following fields: |
| |
| | Field | Description | |
| |---|---| |
| | `dialog_span` | The time span of the session within `user_wav` and `bot_wav`, expressed in audio sample points. | |
| | `dialog_span_in_seconds` | The same session span expressed in seconds. | |
| | `profile_summary` | A structured summary containing the user's name, relevant facts, personal profile, and a summary of previous conversations. | |
| | `relation_graph` | The user's relation graph. It is represented as a list in which each element describes another person related to the current user, including the person's name, relationship to the user, and known facts. | |
| | `dialog_turn_contents` | A list containing the user's questions and the bot's ground-truth responses, together with their timestamps, text, retrieval annotations, and supporting facts. | |
| |
| ### `profile_summary` |
| |
| The `profile_summary` dictionary contains the following fields: |
| |
| | Field | Description | |
| |---|---| |
| | `person` | The name of the current user. | |
| | `person_fact` | Facts associated with the current user. | |
| | `person_profile` | A general profile of the current user. | |
| | `person_summary` | A summary of the user's previous dialogue history. | |
| |
| ### `relation_graph` |
| |
| Each element in `relation_graph` contains the following fields: |
| |
| | Field | Description | |
| |---|---| |
| | `person` | The name of a person related to the current user. | |
| | `person_relation` | The relationship between this person and the current user. | |
| | `person_fact` | A list of known facts about this person. | |
| |
| ### `dialog_turn_contents` |
| |
| Each element in `dialog_turn_contents` describes one dialogue turn and contains information about the user's question and the bot's response. |
| |
| | Field | Description | |
| |---|---| |
| | `user_stamp` | The start timestamp of the user's question. | |
| | `user_txt` | The transcription of the user's question. | |
| | `bot_stamp` | The start timestamp of the bot's response. | |
| | `bot_stamp_end` | The end timestamp of the bot's response. | |
| | `bot_stamp_end_audio` | The end timestamp of the bot's audio response. | |
| | `bot_txt` | The ground-truth text of the bot's response. | |
| | `speaker` | The speaker associated with the dialogue turn. | |
| | `fact_kwd` | Ground-truth keywords used for fact retrieval. | |
| | `relation_kwd` | Ground-truth keywords used for relation retrieval. | |
| | `support_dict` | The supporting facts required to answer the user's question correctly. It is generally a subset of `relation_graph`. | |
| |
| Each element in `support_dict` follows the same structure as an element in `relation_graph`, containing `person`, `person_relation`, and `person_fact`. |
| |
| ## Scoring Prompt |
| ``` |
| prompt = "以下是一个语音对话系统的个性化对话能力评分任务。首先，我解释一下每个字段的含义：\n person: 用户的名字，unknown_speaker代表目前名字未知。\n person_summary: 用户的对话历史 \n 每一轮对话中，question是用户问题，模型实际听到的是question的语音；ground_truth是标准答案，而\ |
| model_response是模型给出的回复。对于每一轮对话，你需要给出一个个性化分fact_score，以及一个答案质量分answer_score。\nfact_score的评分标准是：如果模型的回复model_response中提到用户的名字、对话历史或用户画像，且与person、person_summary中所存储的事实相符，则得1分，否则得0分。如果模型的回复不涉及\ |
| 用户名字、对话历史、用户画像，标记为-1。\nanswer_score的评分标准是：只评价与问题本身相关的部分，不考虑用户的名字、对话历史或用户画像有关的部分。满分10分。参考ground_truth，如果模型对问题本身的回答合理，则应获得8分以上，否则不应该超过8分。\n\n\ |
| 你需要先进行思考，最后在回答的最后用一段json代码组织你的评分，每一轮的轮数号作为key, [fact_score, answer_score] 作为value。结果中需要包含所有轮次的评分。\n 例子：```json\n%s```。\n\n待评分数据：%s" |
| ``` |
| |
| ## Citation |
| |
| Please cite the following paper when using this dataset: |
| |
| ```bibtex |
| @article{yao2025egomem, |
| title = {EgoMem: Lifelong Memory Agent for Full-duplex Omnimodal Models}, |
| author = {Yao, Yiqun and Yu, Naitong and Li, Xiang and Jiang, Xin and |
| Fang, Xuezhi and Ma, Wenjia and Meng, Xuying and Li, Jing and |
| Sun, Aixin and Wang, Yequan}, |
| journal = {arXiv preprint arXiv:2509.11914}, |
| year = {2025}, |
| doi = {10.48550/arXiv.2509.11914}, |
| url = {https://arxiv.org/abs/2509.11914} |
| } |
| ``` |
