Moshi - PyTorch

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 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 PyTorch implementation for Moshi and Mimi.

Requirements

You will need at least Python 3.10. We kept a minimal set of dependencies for the current project. It was tested with PyTorch 2.2 or 2.4. If you need a specific CUDA version, please make sure to have PyTorch properly installed before installing Moshi.

pip install moshi      # moshi PyTorch, from PyPI
# Or the bleeding edge versions for Moshi
pip install -e "git+https://[email protected]/kyutai-labs/moshi#egg=moshi&subdirectory=moshi"

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

Usage

This package provides a streaming version of the audio tokenizer (Mimi) and the lm 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]

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. See the model list for a reference of the available models.

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.

API

You can use programmatically the Mimi/Moshi as follows:

from huggingface_hub import hf_hub_download
import torch

from moshi.models import loaders, LMGen

mimi_weight = hf_hub_download(loaders.DEFAULT_REPO, loaders.MIMI_NAME)
mimi = loaders.get_mimi(mimi_weight, device='cpu')
mimi.set_num_codebooks(8)  # up to 32 for mimi, but limited to 8 for moshi.

wav = torch.randn(1, 1, 24000 * 10)  # should be [B, C=1, T]
with torch.no_grad():
    codes = mimi.encode(wav)  # [B, K = 8, T]
    decoded = mimi.decode(codes)

    # Supports streaming too.
    frame_size = int(mimi.sample_rate / mimi.frame_rate)
    all_codes = []
    with mimi.streaming(batch_size=1):
        for offset in range(0, wav.shape[-1], frame_size):
            frame = wav[:, :, offset: offset + frame_size]
            codes = mimi.encode(frame)
            assert codes.shape[-1] == 1, codes.shape
            all_codes.append(codes)

## WARNING: When streaming, make sure to always feed a total amount of audio that is a multiple
#           of the frame size (1920), otherwise the last frame will not be complete, and thus
#           will not be encoded. For simplicity, we recommend feeding in audio always in multiple
#           of the frame size, so that you always know how many time steps you get back in `codes`.

# Now if you have a GPU around.
mimi.cuda()
moshi_weight = hf_hub_download(loaders.DEFAULT_REPO, loaders.MOSHI_NAME)
moshi = loaders.get_moshi_lm(moshi_weight, device='cuda')
lm_gen = LMGen(moshi, temp=0.8, temp_text=0.7)  # this handles sampling params etc.
out_wav_chunks = []
# Now we will stream over both Moshi I/O, and decode on the fly with Mimi.
with torch.no_grad(), lm_gen.streaming(1), mimi.streaming(1):
    for idx, code in enumerate(all_codes):
        tokens_out = lm_gen.step(code.cuda())
        # tokens_out is [B, 1 + 8, 1], with tokens_out[:, 1] representing the text token.
        if tokens_out is not None:
            wav_chunk = mimi.decode(tokens_out[:, 1:])
            out_wav_chunks.append(wav_chunk)
        print(idx, end='\r')
out_wav = torch.cat(out_wav_chunks, dim=-1)

Development

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

# From the current folder (e.g. `moshi/`)
pip install -e '.[dev]'
pre-commit install

Once locally installed, Mimi can be tested with the following command, from the root of the repository,

wget https://github.com/metavoiceio/metavoice-src/raw/main/assets/bria.mp3
python scripts/mimi_streaming_test.py

Similary, Moshi can be tested (with a GPU) with

python scripts/moshi_benchmark.py

License

The present code is provided under the MIT license. Note that parts of this code is based on AudioCraft, released under the MIT license.

Citation

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},
}