Downloads · 30 days
84
100% of all-time downloads
DialogueSidon
mlx-community/DialogueSidon
DialogueSidon is a machine learning model from mlx-community. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for mlx-audio. The card lists the license as cc-by-nc-4.0.
Two-speaker dialogue separation and restoration, with 24 kHz mono stems. Converted from sarulab-speech/DialogueSidon for use with mlx-audio.
All-time downloads
84
Public
Parameters
444M
1.8 GB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors1.8 GB · 100%
At a glance
- Library
- mlx-audio
- License
- cc-by-nc-4.0
- Model type
- dialogue_sidon
- Access
- Public
- Created
- Sep 6, 2026
- Updated
- Sep 6, 2026
- SHA
- bd2b2c93
Base models
Model card
From the Hugging Face model README
DialogueSidon
Two-speaker dialogue separation and restoration, with 24 kHz mono stems. Converted from sarulab-speech/DialogueSidon for use with mlx-audio.
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)
The two channels are anonymous speaker slots. Long recordings use overlapping
chunks and waveform correlation to maintain channel assignment. Consistency
across silence is not guaranteed. This is an offline generative model, so the
stems need not sum to the mixture. The default chunk and overlap are 20 and 5
seconds; use chunk_seconds=None for whole-file inference when memory permits.
The checkpoint retains 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 and Sidon implementation.
- Library
- mlx-audio
- Type
- dialogue_sidon
- License
- cc-by-nc-4.0
- Created
- Sep 6, 2026
- Updated
- Sep 6, 2026