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title: Stem Restoration + Generation
emoji: 🎛️
colorFrom: gray
colorTo: yellow
sdk: gradio
sdk_version: 6.19.0
python_version: "3.10"
app_file: app.py
pinned: false
---
# Stem Restoration + Generation
Upload a muffled or lo-fi song and get a cleaned-up version back. Under the hood we split it into instruments, then:
- **Restorer** — *cleans up* each muffled/damaged instrument so it sounds clear again (it fixes what's there; it doesn't add new parts). The glue model adds a small **residual-flow detail head** that samples back fine high-frequency detail; the plain deterministic backbone (*advramp*) is the selectable **baseline** and the default here.
- **Generator** — *invents* a missing instrument (mainly bass) that fits the song, like an AI session musician. Note: it fills a low end even for songs that may not have had a bass instrument.
- **Glue stage (training-time)** — restorer + generator are co-trained so their *summed* mix matches a real clean mix, judged by a multi-scale latent discriminator with feature-matching. It's a coherence objective; in practice it lands close to the deterministic baseline (both are selectable for A/B).
Everything runs in the compact **SAME-L** neural-audio latent space, on CPU.
Active models — restorers: ['remix_glue_v1', 'restorer_attn_advramp'] · generators: ['remix_glue_v1_gen', 'gen_advramp_v1']
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