ml-explore / ml-explore/mlx-examples

[Model Request] Porting Nvidia PersonaPlex-7B (Moshi architecture) & Mimi Codec

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Python
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Description

Hi team,

With the recent release of Nvidia PersonaPlex-7B-v1, which is based on the Moshi architecture (Kyutai), I am interested in working on a port for MLX to enable full-duplex speech-to-speech on Apple Silicon.

Since this is a complex architecture, I wanted to open this issue to track progress and gauge community interest.

The Roadmap:

  • Port the Mimi Codec: This is the neural audio codec used by Moshi/PersonaPlex. It requires converting the streaming convolutional/LSTM encoder-decoder to mlx.nn.
  • Port the LM Architecture: PersonaPlex uses a specific architecture (interleaved text/audio tokens) potentially based on Helium or Llama, adapted for streaming generation.
  • Streaming Loop: Implementing the real-time input/output loop.

I plan to start working on this in a separate repository. If anyone has already started looking into Moshi or Mimi specifically, please let me know to avoid duplicate work!

Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start by reviewing the proposed Mimi Codec, Moshi architecture, and PersonaPlex-7B components described in the issue. The payload names no repository files, tests, or entry points; done would require a working MLX port covering the codec, model architecture, and streaming loop, with work planned in a separate repository.

Written by the indexing model from the issue text.

Assessment

Tech stack
python
Domain
audio-video-rtc, machine-learning
Issue type
Feature
Difficulty
5/5
Estimated time
Over a week
Activity status
Stale
Clarity
Mostly clear
Newbie friendliness
25/100

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