Sori Agent — sori (소리) is Korean for sound — is an audio understanding agent, and Sori-1B is its core model. You give it a clip and ask a question in plain language; it listens, writes down what it heard, and answers. Speech, music and environmental sound are one subject to it, and one call is one answer.
Setup
pip install "transformers>=5.13" "torch>=2.6" soundfile scipy numpy
huggingface-cli login # the repository is gated: accept the terms on this page first
Everything the model needs travels with the checkpoint (trust_remote_code=True loads the processor and the model
code). Audio is read with soundfile (wav, flac, ogg, and mp3 with libsndfile 1.1+) and resampled to 16 kHz; any
sample rate and any length are accepted, and clips are never cut. The model is fp32 end to
end, so the answer does not depend on the device or the batch size, and CPU works.
Usage
from transformers import AutoModel, AutoProcessor
proc = AutoProcessor.from_pretrained("snkii/Sori-1B", trust_remote_code=True)
model = AutoModel.from_pretrained("snkii/Sori-1B", trust_remote_code=True).cuda().eval()
sori = model.agent(proc)
sori.ask("clip.wav", "What is happening in this recording?")
sori.ask("clip.wav", "How many people are speaking?")
sori.ask("clip.wav", "What does the second speaker say?")
sori.ask("clip.wav", "When does the door slam?")
sori.ask("clip.wav", "Is a siren audible?", ["yes", "no"])
sori.ask("clip.wav", "What is heard right after the door?", ["drilling", "chainsaw", "jackhammer", "vacuum cleaner"])
sori.ask(["a.wav", "b.wav"], "Which clip is louder?", ["the first", "the second"])
sori.ask_many([{"audio": "a.wav", "question": "Describe the scene."},
{"audio": "b.wav", "question": "How many speakers are there?"}])
With choices, the answer is one of them, verbatim. Without them, it is a sentence, a number or a list — whatever the
question asks for. explain=True returns the answer together with the steps the agent took to reach it.
Two ways to answer, one preference. A question that can be computed is answered by code that a real interpreter
runs, every step shown and checkable; what cannot be computed — a description, what a speaker meant, a judgement — is
answered in language. The computed path needs the sorilm package (pip install git+https://github.com/snkii/sori);
with the checkpoint alone, the agent hears and answers in language.
For people. A terminal conversation about a file, where plain requests become interpreter lines and are shown:
python -m sorilm.core.chat clip.wav --ckpt snkii/Sori-1B
For agents. The tool surface is one tool. sori.tools() emits its JSON schema — ask(audio, question, choices?, explain?) — and a Model Context Protocol server serves it, so a planner can ask about a clip the way it calls any other
tool, without prompt engineering and without parsing prose:
python -m sorilm.core.mcp_server --ckpt snkii/Sori-1B # stdio transport
The model
Sori-1B is one architecture from the waveform to the words. Its audio encoder is built from Liquid AI's LFM2.5-Encoder-350M, initialised by distillation from the outputs of NVIDIA's Audio Flamingo Next encoder and then trained; its language model is LFM2.5-350M-Base, fully trained here. Together they are 933 million parameters. This is a research preview under active development, for academic use; how it is built and evaluated is described in the technical report that accompanies the project.
License
Sori-1B is released for non-commercial research use: use it, share it and build on it under the same terms, with attribution. Models, data and systems derived from it or from its outputs carry this license, a notice of what was changed, and a citation of this work; so do publications whose results depend on it. Commercial use is not licensed: the audio encoder was initialised by distillation from the outputs of NVIDIA's Audio Flamingo Next encoder (NVIDIA OneWay Noncommercial License), and part of the training data is licensed for non-commercial use. The Liquid AI models it is trained from remain under the LFM Open License v1.0, reproduced in full in LICENSE, where the complete terms are.
Citation
@software{kim_sori_agent_2026,
author = {Kim, Seonuk},
title = {{Sori Agent: Audio Understanding via Code Execution over Structured Perception}},
year = {2026},
month = aug,
institution = {Human Interface Laboratory, Seoul National University},
url = {https://huggingface.co/snkii/Sori-1B}
}