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What you can do with Speaker Mem R1

SpeakerMem-R1 is a Python research repo with a speaker-centered memory package, benchmark runners, writer-training recipes, and evaluation outputs for multi-party dialogue.

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2022hpsk/SpeakerMemR1 on GitHub

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What do you want to do?

4 use cases

  • Retrieve speaker-labeled evidence from a group chat locally

    Store a multi-person conversation and pull back who said what for a question, with no API key.

    View prompt
    Clone the SpeakerMem-R1 repo (2022hpsk/SpeakerMemR1), set up the virtual environment and install the package as its README describes, then run code/speakermem_pkg/examples/quickstart.py. Report the memory stats and the retrieved entries for the questions 'Who handles evaluation?' and 'What does Bob handle?'. Verify that each retrieved entry shows the speaker name.

  • Benchmark BM25 baseline retrieval on GroupMemBench

    Run the baseline runner on a benchmark split to get accuracy numbers to compare against SpeakerMem.

    View prompt
    Use the SpeakerMem-R1 repo (2022hpsk/SpeakerMemR1) from the code directory to run run_baseline.py with --baseline bm25 on the groupmem benchmark, writing to <results-dir>. Expected output is question-level results and a metrics summary in that folder. Verify that the run finished without missing-data errors and that the metrics include accuracy.

  • Evaluate SpeakerMem on SocialMemBench with a local Writer

    Run the main SpeakerMem evaluation with a locally served Writer model instead of an external LLM.

    View prompt
    In the SpeakerMem-R1 repo (2022hpsk/SpeakerMemR1), run speakermem_pkg/examples/socialmembench.py on SocialMemBench with --top-k 10 --s2-k 2 --s2-source-k 1 and a local Writer URL given as <writer-url>. Expected output is results written to the results directory you choose. Verify that the summary reports accuracy for the socialmem domains and that no ASK route was enabled, since the README says the principal comparison uses the no-ASK configuration.

  • Verify writer-training rewards and trajectories offline

    Run the offline test suite for writer training to confirm rewards and action handling before any RL run.

    View prompt
    In the SpeakerMem-R1 repo (2022hpsk/SpeakerMemR1), run the training test file code/speakermem_pkg/tests/test_training.py with the PYTHONPATH the README gives. Expected output is a pytest summary for the training tests. Verify that the test run passes before any change to the writer reward code is accepted.

How to use Speaker Mem R1

  1. Check you have what it needs: Python with the ability to create a virtual environment; A CPU environment is enough for the quickstart and offline checks; Model service credentials (kept outside version control) for full benchmark runs.
  2. Open 2022hpsk/SpeakerMemR1 on GitHub and read the README and license.
  3. Run python3 -m venv .venv and then source .venv/bin/activate at the repository root.
  4. Run python -m pip install --upgrade pip.
  5. Run python -m pip install -r code/requirements.txt.
  6. Run python -m pip install -e code/speakermem_pkg.

These steps come from our top use case, Retrieve speaker-labeled evidence from a group chat locally, which also has a copy-paste agent prompt.

Speaker Mem R1 alternatives · Best AI repos for Research

About this repo

  • Who it is forResearchers and developers who need memory retrieval that keeps track of who said what in group conversations, or who want to reproduce the paper's benchmark comparisons.
  • Why it is hot
  • LanguagePython
  • Sourcegithub.com/2022hpsk/SpeakerMemR1 · Back to Hot repos

Requirements

  • Python with the ability to create a virtual environment
  • A CPU environment is enough for the quickstart and offline checks
  • Model service credentials (kept outside version control) for full benchmark runs
  • Benchmark payloads placed as described in DATA_SOURCES.md for full evaluations
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Questions about Speaker Mem R1

What is Speaker Mem R1?

SpeakerMem-R1 is a Python research repo with a speaker-centered memory package, benchmark runners, writer-training recipes, and evaluation outputs for multi-party dialogue. It is an open-source project on GitHub: https://github.com/2022hpsk/SpeakerMemR1.

Who is Speaker Mem R1 for?

Researchers and developers who need memory retrieval that keeps track of who said what in group conversations, or who want to reproduce the paper's benchmark comparisons.

What can I do with Speaker Mem R1?

4 use cases so far, for example: Retrieve speaker-labeled evidence from a group chat locally; Benchmark BM25 baseline retrieval on GroupMemBench; Evaluate SpeakerMem on SocialMemBench with a local Writer. Each has steps and a copy-paste agent prompt.

What do I need to run Speaker Mem R1?

Python with the ability to create a virtual environment; A CPU environment is enough for the quickstart and offline checks; Model service credentials (kept outside version control) for full benchmark runs; Benchmark payloads placed as described in DATA_SOURCES.md for full evaluations.

What are alternatives to Speaker Mem R1?

Other hot AI repos in Research: Scientific Agent Skills, DSH Council, Evo Bot, AI Engineering Course, AI System Design.

Is Speaker Mem R1 safe to run?

Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.