pith:D2WMPN36
HAFM: Hierarchical Autoregressive Foundation Model for Music Accompaniment Generation
A hierarchical autoregressive model generates coherent instrumental accompaniments from isolated vocals using dual-rate tokenization.
arxiv:2604.09054 v3 · 2026-04-10 · cs.SD · cs.MM
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Claims
Experiments on MUSDB18 demonstrate that HAFM achieves a Fréchet Audio Distance (FAD) of 2.08 on isolated vocal inputs, outperforming retrieval baselines and matching prior state-of-the-art systems with fewer parameters.
The dual-rate codec tokenization scheme using HuBERT at 50 Hz for vocals and EnCodec at 75 Hz for instrumentals, combined with the three-stage hierarchical autoregressive architecture, produces time-aligned and coherent accompaniments without needing additional explicit alignment or synchronization steps.
HAFM uses a hierarchical autoregressive model with dual-rate HuBERT and EnCodec tokens to generate coherent instrumental music from vocals, achieving FAD 2.08 on MUSDB18 while matching prior systems with fewer parameters.
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| First computed | 2026-06-23T01:13:05.066943Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
1eacc7b77e4f7b3c0a138da6d580536ae8147e1ce568b073e40963c007d339ce
Aliases
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/D2WMPN36J55TYCQTRWTNLACTNL \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 1eacc7b77e4f7b3c0a138da6d580536ae8147e1ce568b073e40963c007d339ce
Canonical record JSON
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