See the top-level README.md for more information on Moshi.
Moshi is a speech-text foundation model and full-duplex spoken dialogue framework. It uses Mimi, a state-of-the-art streaming neural audio codec. Mimi operates at a framerate of 12.5 Hz, and compresses 24 kHz audio down to 1.1 kbps, in a fully streaming manner (latency of 80ms, the frame size), yet performs better than existing, non-streaming, codec.
This is the MLX implementation for Moshi. For Mimi, this uses our Rust based implementation through the Python binding provided in rustymimi, available in the rust/ folder of our main repository.
You will need at least Python 3.10, we recommend Python 3.12.
pip install moshi_mlx # moshi MLX, from PyPI, best with Python 3.12.
# Or the bleeding edge versions for Moshi and Moshi-MLX.
pip install -e "git+https://[email protected]/kyutai-labs/moshi#egg=moshi_mlx&subdirectory=moshi_mlx"We have tested the MLX version with MacBook Pro M3.
If you are not using Python 3.12, you might get an error when installing
moshi_mlx or rustymimi (which moshi_mlx depends on). Then,you will need to install the Rust toolchain, or switch to Python 3.12.
Once you have installed moshi_mlx, you can run
python -m moshi_mlx.local -q 4 # weights quantized to 4 bits
python -m moshi_mlx.local -q 8 # weights quantized to 8 bits
# And using a different pretrained model:
python -m moshi_mlx.local -q 4 --hf-repo kyutai/moshika-mlx-q4
python -m moshi_mlx.local -q 8 --hf-repo kyutai/moshika-mlx-q8
# be careful to always match the `-q` and `--hf-repo` flag.This uses a command line interface, which is barebone. It does not perform any echo cancellation, nor does it try to compensate for a growing lag by skipping frames.
You can use --hf-repo to select a different pretrained model, by setting the proper Hugging Face repository.
See the model list for a reference of the available models.
Alternatively you can use python -m moshi_mlx.local_web to use
the web UI, the connection is via http, at localhost:8998.
The present code is provided under the MIT license.
If you use either Mimi or Moshi, please cite the following paper,
@techreport{kyutai2024moshi,
author = {Alexandre D\'efossez and Laurent Mazar\'e and Manu Orsini and Am\'elie Royer and
Patrick P\'erez and Herv\'e J\'egou and Edouard Grave and Neil Zeghidour},
title = {Moshi: a speech-text foundation model for real-time dialogue},
institution = {Kyutai},
year={2024},
month={September},
url={http://kyutai.org/Moshi.pdf},
}