A rank-16 LoRA finetune of LCO-Embedding-Omni-7B (Qwen2.5-Omni thinker) trained contrastively on the voiceclap_10_safe mix (got-talent, emolia, majestrino, vocal bursts, ears, expresso, vox1, vox2 — 9 datasets, ~2,909 WebDataset shards) for voice-emotion audio↔text retrieval.

This is the best-performing single model on emolia per-emo balanced accuracy (0.7044) from the Track I LoRA fine-tune sweep — matches a 6-way ensemble baseline at single-model cost. The LoRA delta was re-merged into the base safetensors via the salvage_lora_snapshot.py tool (manual ΔW = (α/r) · B @ A merge) to work around a save-path bug in the original fine-tune script.

Architecture


Base model	`LCO-Embedding/LCO-Embedding-Omni-7B`
Embedding dim	3,584 (L2-normalized)
Audio input	16 kHz mono FLAC, max 15s at train (20s eval)
Total parameters	~7 B
Loss	symmetric InfoNCE on (audio, text) batches with gather-with-grad

Training recipe


Split	`voiceclap_10_safe.txt` (~2,909 shards · ~14 M unique samples)
Samples seen	76,000 × 6 epochs = ~456k (≈ 3% of one full pass)
LoRA	r = 16, α = 32, dropout = 0.05, target = `all-linear`
lr / wd	1e-4 / 0.01 (cosine, warmup = 200 steps)
Batch	2 × accum 16 × 4 GH200 GPUs = effective 128
Precision	bf16
Best epoch	1 (selected on emolia per-emo bal_acc)

Evaluation

Reported numbers are for epoch 1 (the saved checkpoint) on the two voice-emotion benchmarks the project is built around.

emolia-bench (7,984 audio · 40-emotion binary present/absent queries)

Metric	Value
Balanced accuracy (per-emotion threshold)	0.7044
Balanced accuracy (optimal global threshold)	0.6731
Spearman ρ (within-emotion vote correlation)	0.1964

emonet-voice (12,600 voice clips · 40 emotions)

Metric	Value
top-1 accuracy	0.1553
top-3 accuracy	0.3225
Spearman ρ	0.3506

Context

voiceclap-lco-7b-lora improves on the released laion/voiceclap-large on emolia per-emo bal_acc (+0.007) and emonet top-1 (+0.009) for the same model class, at the cost of weaker emonet Spearman (-0.034). It is a member of the E9 ensemble that reaches emolia per-emo 0.7157.

Quick start

python

import torch
from sentence_transformers import SentenceTransformer

model = SentenceTransformer(
    "gijs/voiceclap-lco-7b-lora",
    trust_remote_code=True,
    model_kwargs={"torch_dtype": torch.bfloat16},
)

# audio
audio_emb = model.encode("voice_clip.flac")

# text
text_emb = model.encode("A person speaking with anger in their voice")

# cosine similarity
score = (audio_emb @ text_emb.T).item()

How it was built

Load LCO-Embedding/LCO-Embedding-Omni-7B, drop the unused talker submodule via disable_talker().
Wrap LLM linear modules with a peft LoRA (r=16, α=32, all-linear).
Contrastive train on voiceclap_10_safe WebDataset shards with finetune_omni_embed.py (open_clap_scaling repo).
After training, manually apply ΔW = (α/r) · B @ A to the base safetensors via salvage_lora_snapshot.py so the merged model loads cleanly through sentence_transformers.

Caveats

emolia per-emo thresholds are tuned on the eval set, so the 0.7044 number contains mild leakage (~+0.005-0.017 vs. a clean held-out threshold). Use the optimal-global-threshold number (0.6731) for production claims.
This LoRA was trained on a tiny fraction of the corpus (~3% of one full pass). Single-epoch / full-pass training was not attempted at this scale; the contrastive plateau appears to be in the model class, not the data budget.
emonet top-1 is reported on the 40-class taxonomy (Arousal + Authenticity excluded — they are dimensional attributes, not emotions). Chance baseline is 1/40 = 2.5%.

License

Apache-2.0 (inherits from the base model). See laion/voiceclap-large for the LAION-trained sibling model on a newer 9-corpus mix with MOSS-Audio captions.