中文 | English
项目主页 🌐 /
技术报告 📖 /
VoiceMem Utils 🤗 /
VoiceMem Model Families 🤗 /
ChatMem-400K 🤗
---
我们带来 **VoiceMem**，为语音模型增加最后一个组件：灵魂，让它真正越来越懂你。VoiceMem 建立在「流式双脑」架构之上，提供**精准、有情感、懂人格、低延迟且最便宜的记忆服务**。本仓库将「永久保持全部开源」。
快速理解 VoiceMem：
* **左脑：** 直接管理信息，在 Top-3 限制下维持 Mem0 的满载性能。
* **右脑：** 用长短期情绪归因管理「情商」，含交叉节点、与左脑信息联合维护。
* **低延迟：** 通过压缩信息、分层存储、流式查询（0–300 ms 投机预取），几乎不增加延迟。
* **简单实用：** 单轮查询约 300 token；架构全部解耦，全部组件（含底层记忆引擎）都可更换。
## 🔥 News
* 💬 **09/01/2026 · [v0.0.2](https://github.com/xzf-thu/VoiceMem/releases/tag/v0.0.2)** — 修复事件日期链路，移除右脑冗余记忆类别，开放可插拔语音合成层。
* 🎉 **08/27/2026 · [v0.0.1](https://github.com/xzf-thu/VoiceMem/releases/tag/v0.0.1)** — 发布初代 **VoiceMem** 和 **Technical Report**。
* 🤖 **08/21/2026** — 开源 **VoiceMem 模型系列**，可直接读取并理解 VoiceMem 提供的记忆。
* 🛠️ **08/21/2026** — 发布 **VoiceMem Utils**，开箱即用。
* 📦 **08/20/2026** — 开源 **ChatMem-400K** 数据集。
## 🎬 Demo
> **注意：** 播放前需要先取消静音。
https://github.com/user-attachments/assets/0d919f8c-e9ba-4fdb-8078-b049e4b99a28
## 📚 目录
* [🚀 快速开始](#-快速开始)
* [🧠 VoiceMem 双脑流式架构](#-voicemem基于流式双脑架构的记忆系统)
* [🤖 VoiceMem 官方记忆模型](#-voicemem-模型系列)
* [🔌 使用 VoiceMem 定制你的语音智能体](#-使用-voicemem-定制你的语音智能体)
* [🛠️ 模型微调](#️-模型微调)
* [📊 评测代码](#-评测)
* [📖 引用](#-引用)
* [致谢](#致谢)
* [许可证](#许可证)
## 🚀 快速开始
### 安装
```bash
git clone https://github.com/xzf-thu/VoiceMem.git
cd VoiceMem
# 安装记忆系统（含 ASR / 声纹 / 场景 / 情绪 / 本地 embedding 全套内置组件）
pip install voicemem
# 可选：用我们微调的 Qwen 回复模型
pip install "voicemem[slm]"
```
### 下载所需模型
```bash
pip install -U huggingface_hub
hf download zhifeixie/VoiceMem_Default_Models_Env --local-dir ./models
```
### 基础用法
#### 作为离线记忆引擎运行
```python
from voicemem import VoiceMem
vm = VoiceMem(
mode="normal",
openai_key="api_xxx",
top_k=5,
)
# 本地模型是懒加载的，先热起来，别让第一次调用去等加载
vm.warmup()
# 存：音频文件
# 内部跑 ASR / 声纹 / 场景 / 情绪感知 / Embedding 抽取
print("入库开始")
vm.ingest(audio="assets/input.wav") # 我是素食主义者，对坚果过敏。
print("入库结束")
# 查：写入慢是因为要抽事实、打标签、建图；查询走的是纯向量检索，跟写入无关
print("检索开始")
result = vm.search("我的饮食禁忌是什么？")
print("检索结束")
print(result.result_leftbrain, result.result_rightbrain)
# 存：左脑信息文本（无情感）
vm = VoiceMem(
mode="leftbrain_only",
openai_key="api_xxx",
top_k=5,
)
vm.ingest("我是素食主义者，对坚果过敏。")
result = vm.search("我的饮食禁忌是什么？")
```
#### 以流式方式运行 VoiceMem
可以把 VoiceMem 的流式接口看作一个持续处理音频的 VAD 接口。
下面这段：先显式存一条事实，再喂一段**问句**音频，看记忆是怎么在人还没说完时就查好的；最后照例走一次入库判断。
```python
import asyncio
import os
from pprint import pprint
import numpy as np
import soundfile as sf
from voicemem import VoiceMem
# 沿用上面那个 vm；单独跑这段就自己建一个
vm = VoiceMem(mode="normal", openai_key=os.environ["OPENAI_API_KEY"], top_k=5)
# 本地模型是懒加载的，先热起来，别让第一块音频去等模型加载
vm.warmup()
# 先存一条事实，等下那个问句才有东西可查
vm.ingest("我是素食主义者，对坚果过敏。")
SPEC_MIN_CHARS = 6
searching = False
def on_partial(text):
"""边说边出字。够长了就说明后台这一刻已经开查了。"""
global searching
print(f"\r[partial] {text}", end="", flush=True)
if not searching and len(text) >= SPEC_MIN_CHARS:
searching = True
print("\n[检索开始] 人还没说完，后台已经在查了", flush=True)
async def main():
# 这段音频里是一个问句：「我的饮食禁忌是什么？」
audio, sr = sf.read("assets/question.wav", dtype="float32")
pcm = (np.clip(audio, -1, 1) * 32767).astype(np.int16)
stream = vm.stream(src_rate=sr, vad_threshold=0.5, on_partial=on_partial)
step = int(sr * .032)
for i in range(0, len(pcm), step):
st = await stream.feed(pcm[i:i + step].tobytes())
if st.state != "turn_over":
continue
# VAD 确认这一轮说完了。记忆早在说话过程中就查好了，这里直接取，不再等
print("[检索结束]")
print("转写 ", st.transcript)
print("左脑 ", st.result_leftbrain)
print("右脑 ", st.result_rightbrain)
pprint({k: getattr(st, k) for k in
["speaker_id", "speaker_voiceprint", "emotion",
"entity", "schema", "text_embedding"]})
# 每一轮都要走一次入库判断
print("[入库] LLM 正在判断这句话值不值得入库…", flush=True)
res = vm.ingest(st.transcript)
print(f"[入库] 抽出 {res['facts_count']} 条事实 -> {res['memory_ids']}")
asyncio.run(main())
```
### VoiceMem 交互式演示
演示代码在仓库里（pip 装的包只有库本身），先确认已经克隆并进入仓库目录。
```bash
python web/run.py
```
然后访问：
```text
http://localhost:8787
```
Demo 默认把终端输出（含 Python logging 和 Uvicorn 的日志）保存一份到
`results/logs/voicemem-时间-PID.log`，每行带时间戳和 stdout/stderr 标记。
启动时终端会打印实际路径。指定文件或临时关闭如下。
```bash
python web/run.py --log-file results/logs/debug.log
python web/run.py --no-file-log
```
回复模型的上下文由当前输入、本次会话尚未入库的对话和检索记忆组成。每轮对话先
进入内存 SessionBuffer；异步记忆写入完成并确认产生持久记忆后，对应 turn 从
SessionBuffer 移除。没有产生长期记忆的临时对话会保留到本次会话结束，不同
Memory Space 和不同 WebSocket 会话互相隔离。
播放期间的插话使用两阶段控制：VAD 首先暂停并保留音频队列；明确停止指令或稳定
ASR 文本确认后才清空队列并取消回复；附和、回声、无文字声音和单音节碎片会恢复
播放。候选静音回退和最长等待时间可分别通过 `BARGE_REJECT_SILENCE_MS`、
`BARGE_CANDIDATE_TIMEOUT_MS` 调整。
两种回复模式共用以 PCM 样本位置为基准的输出时间轴。浏览器 AudioWorklet 回报
实际渲染进度，打断时只把已经播放的回复写入 SessionBuffer。TTS 后端可选返回
`TimedAudioChunk` 提供文字对齐；普通 PCM 后端按分段音频长度和动态语速估算。
## 🧠 VoiceMem：基于流式双脑架构的记忆系统
**VoiceMem** 是一个面向实时语音智能体的记忆系统。
VoiceMem 不把所有记忆放进同一个检索数据库，而是将记忆拆分成两个互相配合的部分：
* **左脑**通过 Schema 和 Entity 组织事实记忆，用于更加准确地检索信息。
* **右脑**通过独立节点和跨实体节点管理人格、情绪和关系信息。
整个流程都是**流式**的。
在用户仍然说话时，VoiceMem 会持续完成音频分段、语音转写、记忆提取，并把结构化信息写入记忆图中。
查询时，VoiceMem 会**先路由，再排序，最后只把 Top-K 条记忆注入模型上下文**，从而在保留相关信息的同时控制上下文长度。
### 主要特性
* 🎯 **精准** — 在 **LoCoMo 上达到 91.2%**，Mem0 为 **61.68%**，并且只需要 **Top-5** 条记忆。
* ❤️ **有情感、懂人格** — 不只记住**用户说过什么**，还会记住**用户是谁、用户有什么感受**。在 **PersonaMem 上达到 69.44%**。
* 🎧 **多模态** — 可以从真实世界音频中记住**语音、说话人、声音事件、多人对话和音乐**。
* ⚡ **低延迟** — 响应时间为 **134 ms**，Mem0 为 **1,440 ms**，并支持在语音轮次内部进行流式检索。
* 💰 **低 Token 消耗** — 每次只使用 **430 个记忆 token**，Mem0 为 **6,956**，EverMemOS 为 **1,899**。
## 🤖 VoiceMem 模型系列
我们通过三阶段 OPD 训练流程构建 **ChatMem-400K**：
1. **Memory-world construction**
2. **SLM-validated online on-policy distillation（OPD）**
3. **Human refinement**
同一套流程在人工编辑后形成 **ChatMem-Bench**，评测语音模型是否能够在长期沉淀中形成对用户的理解。
VoiceMem 家族开源模型包括 **Qwen2.5-Omni、Qwen3-Omni 和 Step-Audio2-Mini**。这些模型可以在对话时接受并理解 VoiceMem 提供的记忆信息。
## 🔌 使用 VoiceMem 定制你的语音智能体
你可以将 VoiceMem 接入自己的语音模型，用于构建带有长期记忆能力的实时语音智能体。
整体流程如下：
**麦克风 → VoiceMem 监听语音并提前检索相关记忆 → 你的模型读取这些记忆并生成回答**
```bash
export OPENAI_API_KEY=sk-...
# 仅在写入记忆时用于事实信息提取。
# 记忆检索完全在本地运行。
python examples/03_simple_agent_with_voicemem_memory.py
```
换成你自己的模型：把生成那一步换掉就行，记忆那半边一行都不用动。
```python
def my_reply(text, memory_context): # 同步函数也可以，会自动丢线程
return my_model.generate(system=memory_context, user=text)
vm = VoiceMem(reply=my_reply)
```
## 🛠️ 模型微调
VoiceMem 提供完整的微调代码，可用于训练自己的 VoiceMem Model Family Adapter。
默认训练配置与发布的 `checkpoint-3318` 使用的配置一致。
使用默认参数运行下面的命令，可以复现相同的 Adapter：
```bash
pip install ms-swift==4.5.2 bitsandbytes
python finetune/train.py --data data/train.jsonl
```
训练数据格式、GPU 显存要求，以及如何更换基础模型，请参阅 **[finetune/README.md](finetune/README.md)**。
## 📊 评测
评测流程完全开源，并且可以复现。
### 运行评测
只需要一条命令即可运行 Benchmark：
```bash
export OPENAI_API_KEY=sk-...
# 建议先运行仓库中自带的小型示例，
# 确认环境和配置没有问题。
# 2 个对话，5 个问题。
python evaluation/run.py \
--dataset locomo \
--data evaluation/examples/locomo_sample.json
# 然后运行完整数据集。
python evaluation/run.py \
--dataset locomo \
--data data/locomo.json
```
示例结果：
```text
LoCoMo: 10 conversations · 152 questions
Score: 139/152 = 91.4%
multi_hop 88.2%
temporal 85.7%
single_hop 95.1%
Median retrieval latency: 12 ms
Median retrieved memory: 298 tokens
```
在运行完整评测之前，可以加入 `--inspect`，检查数据集是否被正确解析。
这个模式不会调用模型，因此也不会产生 API 费用：
```bash
python evaluation/run.py \
--dataset locomo \
--data data/locomo.json \
--inspect
```
评测过程中，回答模型**只会收到检索得到的记忆**，不会收到原始对话历史。
如果直接把完整对话交给模型，Benchmark 测试的就会变成模型的阅读理解能力，而不是记忆系统本身的能力。
完整评测流程，以及添加新 Benchmark 的方法，请参阅 **[evaluation/README.md](evaluation/README.md)**。添加一个新的 Benchmark 只需要增加一个文件并实现两个函数。
## 📖 引用
如果 VoiceMem 对你的研究有帮助，请引用我们的论文：
```bibtex
@misc{2608.26005,
author = {Zhifei Xie and Jiaqi Lang and Ze An and Yifan Zhao and Dongchao Yang and Kai Li and Ziyang Ma and Mingbao Lin and Chunyan Miao and Shuicheng Yan},
title = {{V}oice{M}em: {S}treaming {D}ual-{B}rain {M}emory for {R}eal-{T}ime {I}nteraction},
year = {2026},
eprint = {2608.26005},
note = {arXiv:2608.26005v1}
}
```
## 致谢
我们感谢以下优秀的开源项目：
* [mem0](https://github.com/mem0ai/mem0) — 向量记忆引擎
* [FunASR](https://github.com/modelscope/FunASR) — 基于 `paraformer-zh-streaming` 的流式 ASR
* [sherpa-onnx](https://github.com/k2-fsa/sherpa-onnx) — Silero VAD、3D-Speaker 说话人验证，以及备用流式 ASR
* [intfloat/multilingual-e5](https://huggingface.co/intfloat/multilingual-e5-small) — 本地 Embedding 和 Slot 分类
VoiceMem 同时使用 OpenAI API 提供 Chat、TTS 和 Realtime 功能。
## 许可证
VoiceMem 基于 **Apache License 2.0** 开源。
详细信息请参阅 [LICENSE](LICENSE)。
---
中文 | English
Project Page 🌐 /
Technical Report 📖 /
VoiceMem Utils 🤗 /
VoiceMem Model Families 🤗 /
ChatMem-400K 🤗
---
We introduce **VoiceMem**, adding the final component to voice models: a soul, so they truly come to understand you better over time. VoiceMem is built on a streaming dual-brain architecture and provides **accurate, emotional, personality-aware, low-latency, and lowest-cost memory services**. This repository will remain fully open source, permanently.
A quick overview of VoiceMem:
* **Left Brain:** Directly manages factual information and sustains Mem0's full performance under a Top-3 memory limit.
* **Right Brain:** Manages emotional intelligence through short-term and long-term emotional attribution, including cross-entity nodes and joint maintenance with Left Brain information.
* **Low Latency:** Uses information compression, hierarchical storage, and streaming retrieval with 0–300 ms speculative prefetching, adding almost no extra latency.
* **Simple and Practical:** Each query uses about 300 tokens. The architecture is fully decoupled, and every component, including the underlying memory engine, can be replaced.
## 🔥 News
* 💬 **09/01/2026 · [v0.0.2](https://github.com/xzf-thu/VoiceMem/releases/tag/v0.0.2)** — Fixed the memory event-date path, removed a redundant right-brain memory class, and opened up the speech synthesis layer.
* 🎉 **08/27/2026 · [v0.0.1](https://github.com/xzf-thu/VoiceMem/releases/tag/v0.0.1)** — Released the first version of **VoiceMem** and our **Technical Report**.
* 🤖 **08/21/2026** — Open-sourced the **VoiceMem model family** (Qwen2.5-Omni / Qwen3-Omni / Step-Audio2-Mini), able to read and use the memory VoiceMem provides.
* 🛠️ **08/21/2026** — Released **VoiceMem Utils**, all default local models packaged for out-of-the-box use.
* 📦 **08/20/2026** — Open-sourced **ChatMem-400K**, built with a three-stage OPD pipeline.
## 🎬 Demo Video
> **Note:** Please unmute the video before playback.
https://github.com/user-attachments/assets/0d919f8c-e9ba-4fdb-8078-b049e4b99a28
## 📚 Overview
* [🚀 Quick Start](#-quick-start)
* [🧠 VoiceMem Dual-Brain Streaming Architecture](#-voicemem-memory-with-a-streaming-dual-brain-architecture)
* [🤖 VoiceMem Model Families](#-voicemem-model-families)
* [🔌 Customize Your Voice Agent with VoiceMem](#-customize-your-voice-agent-with-voicemem)
* [🛠️ Finetuning](#️-finetuning)
* [📊 Evaluation](#-evaluation)
* [📖 Citation](#-citation)
* [Acknowledgements](#acknowledgements)
* [License](#license)
## 🚀 Quick Start
### Installation
**Prerequisite:** Python 3.10+
```bash
git clone https://github.com/xzf-thu/VoiceMem.git
cd VoiceMem
# Install the memory system (bundles ASR / speaker ID / scene / emotion / local embedding)
pip install voicemem
# Optional: run our fine-tuned Qwen reply model
pip install "voicemem[slm]"
```
### Required Model Download
```bash
pip install -U huggingface_hub
hf download zhifeixie/VoiceMem_Default_Models_Env --local-dir ./models
```
### Basic Usage
#### Run as an Offline Memory Engine
```python
from voicemem import VoiceMem
vm = VoiceMem(
mode="normal",
openai_key="api_xxx",
top_k=5,
)
# Local models load lazily -- warm them up so the first call doesn't pay for it.
vm.warmup()
# Store an audio file.
# VoiceMem internally runs ASR / speaker ID / scene / emotion / embedding extraction.
print("ingest start")
vm.ingest(audio="assets/input.wav") # I am vegetarian and allergic to nuts.
print("ingest done")
# Writing is slow because it extracts facts, tags them and builds the graph.
# Reading is a pure vector lookup -- independent of write cost.
print("search start")
result = vm.search("What are my dietary restrictions?")
print("search done")
print(result.result_leftbrain, result.result_rightbrain)
# Store Left Brain factual text directly (no emotional information).
vm = VoiceMem(
mode="leftbrain_only",
openai_key="api_xxx",
top_k=5,
)
vm.ingest("I am vegetarian and allergic to nuts.")
result = vm.search("What are my dietary restrictions?")
```
#### Run VoiceMem in Streaming Mode
Think of VoiceMem's streaming interface as a VAD interface that continuously processes audio.
The example below stores one fact explicitly, then feeds a **question** as audio to show how the memory is already retrieved before the speaker finishes. It ends, as always, with the ingest decision.
```python
import asyncio
import os
from pprint import pprint
import numpy as np
import soundfile as sf
from voicemem import VoiceMem
# Reuses the vm above; building one here so the block runs standalone
vm = VoiceMem(mode="normal", openai_key=os.environ["OPENAI_API_KEY"], top_k=5)
# Local models load lazily -- warm them up so the first audio chunk doesn't wait
vm.warmup()
# Store one fact first, so the question below has something to find
vm.ingest("I am vegetarian and allergic to nuts.")
SPEC_MIN_CHARS = 6
searching = False
def on_partial(text):
"""Partial transcripts as they arrive. Long enough = the search already started."""
global searching
print(f"\r[partial] {text}", end="", flush=True)
if not searching and len(text) >= SPEC_MIN_CHARS:
searching = True
print("\n[search start] speaker isn't done yet, retrieval already running", flush=True)
async def main():
# This audio is a question: "What are my dietary restrictions?"
audio, sr = sf.read("assets/question.wav", dtype="float32")
pcm = (np.clip(audio, -1, 1) * 32767).astype(np.int16)
stream = vm.stream(src_rate=sr, vad_threshold=0.5, on_partial=on_partial)
step = int(sr * .032)
for i in range(0, len(pcm), step):
st = await stream.feed(pcm[i:i + step].tobytes())
if st.state != "turn_over":
continue
# VAD confirmed end of turn. Memory was fetched while the user spoke -- just read it
print("[search end]")
print("transcript ", st.transcript)
print("left brain ", st.result_leftbrain)
print("right brain ", st.result_rightbrain)
pprint({k: getattr(st, k) for k in
["speaker_id", "speaker_voiceprint", "emotion",
"entity", "schema", "text_embedding"]})
# Every turn runs the ingest decision
print("[ingest] LLM deciding whether this is worth storing...", flush=True)
res = vm.ingest(st.transcript)
print(f"[ingest] extracted {res['facts_count']} facts -> {res['memory_ids']}")
asyncio.run(main())
```
### Interactive Demo with VoiceMem
The demo lives in the repo (the pip package ships the library only) — make sure you have cloned it and are in the repo root.
```bash
python web/run.py
```
Then open:
```text
http://localhost:8787
```
By default, the demo mirrors terminal output — including Python logging and
Uvicorn's own logs — to `results/logs/voicemem-TIME-PID.log`, one timestamped
line per record, tagged stdout or stderr. The resolved path is printed at
startup. To choose a path or disable file logging:
```bash
python web/run.py --log-file results/logs/debug.log
python web/run.py --no-file-log
```
Reply context combines the current input, turns from this session that are not
yet represented by persistent memory, and retrieved memory. Each turn enters an
in-memory SessionBuffer first. The asynchronous ingest completion callback
removes it only after persistent memory is created. Buffers are isolated by
Memory Space and WebSocket session.
Barge-in uses two stages during playback. VAD first pauses playback while
preserving the audio queue. An explicit stop command or stable ASR updates
confirm cancellation; backchannels, echo, non-text sounds, and isolated
syllables resume playback. `BARGE_REJECT_SILENCE_MS` and
`BARGE_CANDIDATE_TIMEOUT_MS` configure rejection timing.
Both reply modes share a PCM-sample media timeline. The browser AudioWorklet
reports actual rendered progress, so interrupted context contains only the
heard prefix. TTS providers may return `TimedAudioChunk` alignment metadata;
plain PCM providers use segment duration and an adaptive speech-rate fallback.
## 🧠 VoiceMem: Memory with a Streaming Dual-Brain Architecture
**VoiceMem** is a memory system built for real-time voice agents.
Instead of storing every type of memory in a single retrieval database, VoiceMem separates memory into two complementary parts:
* **Left Brain** organizes factual memory using schemas and entities for more accurate retrieval.
* **Right Brain** manages personality, emotion, and relationships using independent and cross-entity memory nodes.
The entire pipeline is **streaming**.
While the user is still speaking, VoiceMem continuously segments audio, transcribes speech, extracts useful memories, and writes structured information into the memory graph.
At query time, VoiceMem **routes first, ranks second, and injects only the Top-K memories into the model context**. This keeps the context small while preserving the most relevant information.
### Key Features
* 🎯 **Accurate** — Reaches **91.2% on LoCoMo**, compared with **61.68% for Mem0**, using only **Top-5** memories.
* ❤️ **Emotional & Personal** — Remembers not only **what the user said**, but also **who the user is and how they feel**. Reaches **69.44% on PersonaMem**.
* 🎧 **Multimodal** — Remembers **speech, speakers, sound events, multi-speaker conversations, and music** from real-world audio.
* ⚡ **Fast** — Responds in **134 ms**, compared with **1,440 ms for Mem0**, with streaming retrieval inside the voice turn.
* 💰 **Low Token Usage** — Uses only **430 memory tokens**, compared with **6,956 for Mem0** and **1,899 for EverMemOS**.
---
## 🤖 VoiceMem Model Families
We built **ChatMem-400K** through a three-stage OPD training pipeline:
1. **Memory-world construction**
2. **SLM-validated online on-policy distillation (OPD)**
3. **Human refinement**
After human editing, the same pipeline produces **ChatMem-Bench**, which evaluates whether a voice model can build a long-term understanding of the user over time.
The open-source VoiceMem model family includes **Qwen2.5-Omni, Qwen3-Omni, and Step-Audio2-Mini**. These models can receive and understand memory information provided by VoiceMem during conversations.
## 🔌 Customize Your Voice Agent with VoiceMem
You can integrate VoiceMem with your own voice model to build a real-time voice agent with long-term memory.
The basic flow is:
**microphone → VoiceMem listens and prefetches relevant memories → your model reads those memories and generates a response**
```bash
export OPENAI_API_KEY=sk-...
# Only used for fact extraction when writing memories.
# Memory retrieval runs entirely locally.
python examples/03_simple_agent_with_voicemem_memory.py
```
To use your own model, replace the generation step — the memory half stays exactly as is:
```python
def my_reply(text, memory_context): # a sync function is fine, it runs off-thread
return my_model.generate(system=memory_context, user=text)
vm = VoiceMem(reply=my_reply)
```
## 🛠️ Finetuning
VoiceMem provides the complete finetuning pipeline for training your own VoiceMem Model Family adapter.
The default training configuration matches the one used for the released `checkpoint-3318`.
Running the following command with the default settings reproduces the same adapter:
```bash
pip install ms-swift==4.5.2 bitsandbytes
python finetune/train.py --data data/train.jsonl
```
See **[finetune/README.md](finetune/README.md)** for the training data format, GPU memory requirements, and instructions for using a different base model.
## 📊 Evaluation
The evaluation pipeline is fully open source and reproducible.
### Run Evaluation
A benchmark can be started with a single command:
```bash
export OPENAI_API_KEY=sk-...
# Start with the small example included in the repository
# to make sure everything is set up correctly.
# 2 conversations, 5 questions.
python evaluation/run.py \
--dataset locomo \
--data evaluation/examples/locomo_sample.json
# Then run the full dataset.
python evaluation/run.py \
--dataset locomo \
--data data/locomo.json
```
Example result:
```text
LoCoMo: 10 conversations · 152 questions
Score: 139/152 = 91.4%
multi_hop 88.2%
temporal 85.7%
single_hop 95.1%
Median retrieval latency: 12 ms
Median retrieved memory: 298 tokens
```
Before running a full evaluation, add `--inspect` to check how the dataset is parsed.
This mode does not call the model and does not incur API costs:
```bash
python evaluation/run.py \
--dataset locomo \
--data data/locomo.json \
--inspect
```
During evaluation, the answering model receives **only the retrieved memories**, not the original conversation history.
If the model receives the full conversation, the benchmark becomes a reading-comprehension test rather than an evaluation of the memory system itself.
See **[evaluation/README.md](evaluation/README.md)** for the complete evaluation protocol and instructions for adding a new benchmark. Adding a benchmark only requires one file and two functions.
## 📖 Citation
If VoiceMem is useful for your research, please cite our paper:
```bibtex
@misc{2608.26005,
author = {Zhifei Xie and Jiaqi Lang and Ze An and Yifan Zhao and Dongchao Yang and Kai Li and Ziyang Ma and Mingbao Lin and Chunyan Miao and Shuicheng Yan},
title = {{V}oice{M}em: {S}treaming {D}ual-{B}rain {M}emory for {R}eal-{T}ime {I}nteraction},
year = {2026},
eprint = {2608.26005},
note = {arXiv:2608.26005v1}
}
```
## Acknowledgements
We thank the following excellent open-source projects:
* [mem0](https://github.com/mem0ai/mem0) — vector memory engine
* [FunASR](https://github.com/modelscope/FunASR) — streaming ASR with `paraformer-zh-streaming`
* [sherpa-onnx](https://github.com/k2-fsa/sherpa-onnx) — Silero VAD, 3D-Speaker speaker verification, and fallback streaming ASR
* [intfloat/multilingual-e5](https://huggingface.co/intfloat/multilingual-e5-small) — local embeddings and slot classification
VoiceMem also uses OpenAI APIs for Chat, TTS, and Realtime functionality.
## License
VoiceMem is open source under the **Apache License 2.0**.
See [LICENSE](LICENSE) for details.
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