DialogueSidon

Separate a recording of two speakers into individual audio tracks on Apple Silicon. DialogueSidon also restores degraded speech and outputs two mono WAV files at 24 kHz, with the same duration as the input.

Follow the MLX Audio installation instructions before running the examples below.

Supported models

Precision	Repository ID	Weight size
FP32	mlx-community/DialogueSidon	1.78 GB
BF16	mlx-community/DialogueSidon-bf16	889 MB

Either repository ID works in the examples below. Choose BF16 for a smaller download and lower weight memory use. You can also load a local model directory.

Command line

python -m mlx_audio.sts.generate \
    --model mlx-community/DialogueSidon \
    --audio dialogue.wav --output-path separated.wav \
    --num-steps 30 --seed 0

This writes separated_speaker_1.wav and separated_speaker_2.wav.

Python

from mlx_audio.sts import load
from mlx_audio.audio_io import write

model = load("mlx-community/DialogueSidon")
result = model.separate("dialogue.wav", num_steps=30, seed=0)
for i, speaker in enumerate(result.speakers, 1):
    write(f"speaker_{i}.wav", speaker, result.sample_rate)

result.speakers contains two tracks with shape [2, samples], and result.sample_rate is 24000.

For a NumPy or MLX array, provide its sample rate:

from mlx_audio.audio_io import read

audio, sample_rate = read("dialogue.wav")
result = model.separate(audio, sample_rate=sample_rate, seed=0)

Arrays must use [samples] or [samples, channels] layout. Stereo and other multichannel inputs are averaged to mono before separation.

Options and long recordings

Python option	CLI option	Default	Purpose
num_steps	--num-steps	30	Fewer steps run faster but can change output quality.
seed	--seed	Random	Set a seed to repeat a run with the same input, model, and settings.
chunk_seconds	--chunk-seconds	20.0	Audio processed at once; smaller chunks reduce memory use.
overlap_seconds	--overlap-seconds	5.0	Overlap between chunks, in seconds. Must be less than the chunk duration.

Long recordings are processed automatically in overlapping chunks. To process an entire file at once in Python, use chunk_seconds=None; this uses more memory.

The model supports exactly two speakers and processes recorded audio offline. The tracks are numbered rather than labeled with speaker identities; a speaker may switch tracks after a long silence. Because the model also restores speech, adding the tracks together may not reproduce the input exactly.

License and attribution

Model weights use CC-BY-NC-4.0. Original model by Wataru Nakata, Yuki Saito, Kazuki Yamauchi, Emiru Tsunoo, and Hiroshi Saruwatari (SaruLab). See the original model card. The upstream Sidon code is MIT licensed.