从这一段说明中得出结论：能否使用windows运行？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/ directory.
The Python version using MLX for M series Macs is in the moshi_mlx/ directory.
The Rust version used in production is in the 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/ 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.
Moshiko for PyTorch (bf16): kyutai/moshiko-pytorch-bf16.
Moshika for MLX (int4, int8, bf16): kyutai/moshika-mlx-q4, kyutai/moshika-mlx-q8, kyutai/moshika-mlx-bf16.
Moshiko for MLX (int4, int8, bf16): kyutai/moshiko-mlx-q4, kyutai/moshiko-mlx-q8, kyutai/moshiko-mlx-bf16.
Moshika for Rust/Candle (int8, bf16): kyutai/moshika-candle-q8, kyutai/moshika-mlx-bf16.
Moshiko for Rust/Candle (int8, bf16): kyutai/moshiko-candle-q8, kyutai/moshiko-mlx-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:

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://git@github.com/kyutai-labs/moshi.git#egg=moshi&subdirectory=moshi"
pip install -e "git+https://git@github.com/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, 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. To compile GPU support, you will also need the CUDA 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:

python -m moshi.server [--gradio-tunnel] [--hf-repo kyutai/moshika-pytorch-bf16]
And then access the web UI on localhost:8998. If your GPU is on a distant machine with no direct access, --gradio-tunnel will create 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. Alternatively, you might want to use SSH to redirect your connection.

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

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.

Python (MLX) for local inference on macOS
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 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.

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

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.

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 command 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:

cargo run --bin moshi-cli -r -- tui --host localhost
Python with PyTorch
python -m moshi.client
WebUI
The web UI can be built from this repo via the following steps (these will require npm being installed).

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:

# 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):

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