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https://github.com/kyutai-labs/moshi


https://github.com/kyutai-labs/moshi

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# Moshi: a speech-text foundation model for real time dialogue



![precommit badge](https://github.com/kyutai-labs/moshi/workflows/precommit/badge.svg)

![rust ci badge](https://github.com/kyutai-labs/moshi/workflows/Rust%20CI/badge.svg)



[[Read the paper]][moshi] [[Demo]](https://moshi.chat) [[Hugging Face]](https://huggingface.co/collections/kyutai/moshi-v01-release-66eaeaf3302bef6bd9ad7acd)



 [Moshi][moshi] is a speech-text foundation model and **full-duplex** spoken dialogue framework.

 It uses [Mimi][moshi], a state-of-the-art streaming neural audio codec. Mimi processes 24 kHz audio, down to a 12.5 Hz representation

 with a bandwidth of 1.1 kbps, in a fully streaming manner (latency of 80ms, the frame size),

 yet performs better than existing, non-streaming, codecs like

 [SpeechTokenizer](https://github.com/ZhangXInFD/SpeechTokenizer) (50 Hz, 4kbps), or [SemantiCodec](https://github.com/haoheliu/SemantiCodec-inference) (50 Hz, 1.3kbps).



 Moshi models **two streams of audio**: one corresponds to Moshi, and the other one to the user.

 At inference, the stream from the user is taken from the audio input,

and the one for Moshi is sampled from the model's output. Along these two audio streams, Moshi predicts text tokens corresponding to its own speech, its **inner monologue**,

which greatly improves the quality of its generation. A small Depth Transformer models inter codebook dependencies for a given time step,

while a large, 7B parameter Temporal Transformer models the temporal dependencies. Moshi achieves a theoretical latency

of 160ms (80ms for the frame size of Mimi + 80ms of acoustic delay), with a practical overall latency as low as 200ms on an L4 GPU.



[Talk to Moshi](https://moshi.chat) now on our live demo.





Schema representing the structure of Moshi. Moshi models two streams of audio:
<br />    one corresponds to Moshi, and the other one to the user. At inference, the audio stream of the user is taken from the audio input, and the audio stream for Moshi is sampled from the model's output. Along that, Moshi predicts text tokens corresponding to its own speech for improved accuracy. A small Depth Transformer models inter codebook dependencies for a given step.



Mimi builds on previous neural audio codecs such as [SoundStream](https://arxiv.org/abs/2107.03312)

and [EnCodec](https://github.com/facebookresearch/encodec), adding a Transformer both in the encoder and decoder,

and adapting the strides to match an overall frame rate of 12.5 Hz. This allows Mimi to get closer to the

average frame rate of text tokens (~3-4 Hz), and limit the number of autoregressive steps in Moshi.

Similarly to SpeechTokenizer, Mimi uses a distillation loss so that the first codebook tokens match

a self-supervised representation from [WavLM](https://arxiv.org/abs/2110.13900), which allows modeling semantic and acoustic information with a single model. Interestingly, while Mimi is fully causal and streaming, it learns to match sufficiently well the non-causal

representation from WavLM, without introducing any delays. Finally, and similarly to [EBEN](https://arxiv.org/pdf/2210.14090),

Mimi uses **only an adversarial training loss**, along with feature matching, showing strong improvements in terms of

subjective quality despite its low bitrate.





Schema representing the structure of Mimi, our proposed neural codec. Mimi contains a Transformer
<br />in both its encoder and decoder, and achieves a frame rate closer to that of text tokens. This allows us to reduce
<br />the number of auto-regressive steps taken by Moshi, thus reducing the latency of the model.



## Organisation of the repository



There are three separate versions of the moshi inference stack in this repo.

- The Python version using PyTorch is in the [`moshi/`](moshi/) directory.

- The Python version using MLX for M series Macs is in the [`moshi_mlx/`](moshi_mlx/) directory.

- The Rust version used in production is in the [`rust/`](rust/) directory.

    This contains in particular a Mimi implementation in Rust, with Python bindings available

    as `rustymimi`.



Finally, the code for the live demo is provided in the [`client/`](client/) directory.



## Models



We release three models:

- our speech codec Mimi,

- Moshi fine-tuned on a male synthetic voice (Moshiko),

- Moshi fine-tuned on a female synthetic voice (Moshika).



Depending on the backend, the file format and quantization available will vary. Here is the list

of the HuggingFace repo with each model. Mimi is bundled in each of those, and always use the same checkpoint format.



- Moshika for PyTorch (bf16): [kyutai/moshika-pytorch-bf16](https://huggingface.co/kyutai/moshika-pytorch-bf16).

- Moshiko for PyTorch (bf16): [kyutai/moshiko-pytorch-bf16](https://huggingface.co/kyutai/moshiko-pytorch-bf16).

- Moshika for MLX (int4, int8, bf16): [kyutai/moshika-mlx-q4](https://huggingface.co/kyutai/moshika-mlx-q4), [kyutai/moshika-mlx-q8](https://huggingface.co/kyutai/moshika-mlx-q8),  [kyutai/moshika-mlx-bf16](https://huggingface.co/kyutai/moshika-mlx-bf16).

- Moshiko for MLX (int4, int8, bf16): [kyutai/moshiko-mlx-q4](https://huggingface.co/kyutai/moshiko-mlx-q4), [kyutai/moshiko-mlx-q8](https://huggingface.co/kyutai/moshiko-mlx-q8),  [kyutai/moshiko-mlx-bf16](https://huggingface.co/kyutai/moshiko-mlx-bf16).

- Moshika for Rust/Candle (int8, bf16): [kyutai/moshika-candle-q8](https://huggingface.co/kyutai/moshika-candle-q8),  [kyutai/moshika-mlx-bf16](https://huggingface.co/kyutai/moshika-candle-bf16).

- Moshiko for Rust/Candle (int8, bf16): [kyutai/moshiko-candle-q8](https://huggingface.co/kyutai/moshiko-candle-q8),  [kyutai/moshiko-mlx-bf16](https://huggingface.co/kyutai/moshiko-candle-bf16).



All models are released under the CC-BY 4.0 license.



## Requirements



You will need at least Python 3.10, with 3.12 recommended. For specific requirements, please check the individual backends

directories. You can install the PyTorch and MLX clients with the following:



```bash

pip install moshi      # moshi PyTorch, from PyPI

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.git#egg=moshi&subdirectory=moshi"

pip install -e "git+https://[email protected]/kyutai-labs/moshi.git#egg=moshi_mlx&subdirectory=moshi_mlx"



pip install rustymimi  # mimi, rust implementation with Python bindings from PyPI

```



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](https://rustup.rs/), or switch to Python 3.12.



While we hope that the present codebase will work on Windows, we do not provide official support for it.

We have tested the MLX version on a MacBook Pro M3. At the moment, we do not support quantization

for the PyTorch version, so you will need a GPU with a significant amount of memory (24GB).



For using the Rust backend, you will need a recent version of the [Rust toolchain](https://rustup.rs/).

To compile GPU support, you will also need the [CUDA](https://developer.nvidia.com/cuda-toolkit) properly installed for your GPU, in particular with `nvcc`.



## Python (PyTorch)



The PyTorch based API can be found in the `moshi` directory. It provides a streaming

version of the audio tokenizer (mimi) and the language model (moshi).



In order to run in interactive mode, you need to start a server which will

run the model, you can then use either the web UI or a command line client.



Start the server with:

```bash

python -m moshi.server [--gradio-tunnel] [--hf-repo kyutai/moshika-pytorch-bf16]

```



And then access the web UI on [localhost:8998](http://localhost:8998).

If your GPU is on a distant machine this will not work as websites using http

are not allowed to use the audio worklet api. There are two ways to get around

this:

- Forward the remote 8998 port to your localhost using ssh `-L` flag. Then

  connects to [localhost:8998](http://localhost:8998) as mentionned previously.

- Use the `--gradio-tunnel` argument, this sets up a tunnel with a URL accessible from anywhere.

  Keep in mind that this tunnel goes through the US and can add significant

  latency (up to 500ms from Europe). You can use `--gradio-tunnel-token` to set a

  fixed secret token and reuse the same address over time.



You can use `--hf-repo` to select a different pretrained model, by setting the proper Hugging Face repository.



Accessing a server that is not localhost via http may cause issues with using

the microphone in the web UI (in some browsers this is only allowed using

https).



A local client is also available, as

```bash

python -m moshi.client [--url URL_TO_GRADIO]

```

However note that, unlike the web browser, this client is barebone: it does not perform any echo cancellation,

nor does it try to compensate for a growing lag by skipping frames.



For more information, in particular on how to use the API directly, please

checkout [moshi/README.md](moshi/README.md).



## Python (MLX) for local inference on macOS



Once you have installed `moshi_mlx`, you can run

```bash

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 command line interface is also barebone. It does not perform any echo cancellation,

nor does it try to compensate for a growing lag by skipping frames.



Alternatively you can run `python -m moshi_mlx.local_web` to use

the web UI, the connection is via http and will be at [localhost:8998](http://localhost:8998).



## Rust



In order to run the Rust inference server, use the following command from within

the `rust` directory:



```bash

cargo run --features cuda --bin moshi-backend -r -- --config moshi-backend/config.json standalone

```



When using macOS, you can replace `--features cuda` with `--features metal`.



Alternatively you can use `config-q8.json` rather than `config.json` to use the

quantized q8 model. You can select a different pretrained model, e.g. Moshika,

by changing the `"hf_repo"` key in either file.



Once the server has printed 'standalone worker listening', you can use the web

UI. By default the Rust server uses https so it will be at

[localhost:8998](https://localhost:8998).



You will get warnings about the site being unsafe. When using chrome you

can bypass these by selecting "Details" or "Advanced", then "Visit this unsafe

site" or "Proceed to localhost (unsafe)".



## Clients



We recommend using the web UI as it provides additional echo cancellation that helps

the overall model quality. Note that most commands will directly serve this UI

in the provided URL, and there is in general nothing more to do.



Alternatively, we provide command line interfaces

for the Rust and Python versions, the protocol is the same as with the web UI so

there is nothing to change on the server side.



For reference, here is the list of clients for Moshi.



### Rust Command Line



From within the `rust` directory, run the following:

```bash

cargo run --bin moshi-cli -r -- tui --host localhost

```



### Python with PyTorch



```bash

python -m moshi.client

```



### Gradio Demo



You can launch a Gradio demo locally with the following command:



```bash

python -m moshi.client_gradio --url 

```



Prior to running the Gradio demo, please install `gradio-webrtc>=0.0.18`.



### Docker Compose (CUDA only)



```bash

docker compose up

```



* Requires [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html)



### WebUI



The web UI can be built from this repo via the

following steps (these will require `npm` being installed).

```bash

cd client

npm install

npm run build

```



The web UI can then be found in the `client/dist` directory.



## Development



If you wish to install from a clone of this repository, maybe to further develop Moshi, you can do the following:

```bash

# From the root of the clone of the repo

pip install -e 'moshi[dev]'

pip install -e 'moshi_mlx[dev]'

pre-commit install

```



If you wish to build locally `rustymimi` (assuming you have Rust properly installed):

```bash

pip install maturin

maturin dev -r -m rust/mimi-pyo3/Cargo.toml

```



## FAQ



Checkout the [Frequently Asked Questions](FAQ.md) section before opening an issue.



## License



The present code is provided under the MIT license for the Python parts, and Apache license for the Rust backend.

The web client code is provided under the MIT license.

Note that parts of this code is based on [AudioCraft](https://github.com/facebookresearch/audiocraft), released under

the MIT license.



The weights for the models are released under the CC-BY 4.0 license.



## Citation



If you use either Mimi or Moshi, please cite the following paper,



```

@techreport{kyutai2024moshi,

      title={Moshi: a speech-text foundation model for real-time dialogue},

      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},

      year={2024},

      eprint={2410.00037},

      archivePrefix={arXiv},

      primaryClass={eess.AS},

      url={https://arxiv.org/abs/2410.00037},

}

```



[moshi]: https://arxiv.org/abs/2410.00037