voiceclap-lco-7b-lora

voiceclap-lco-7b-lora

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

Single-tower: audio + text are both fed through the same Qwen2.5-Omni thinker; modality is determined by the chat-template placeholder (<|audio_bos|><|AUDIO|><|audio_eos|>). The last-non-pad-token hidden state at the final layer is the embedding (3,584-d, L2-normalized).

Training recipe

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)

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

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

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

1. Load LCO-Embedding/LCO-Embedding-Omni-7B, drop the unused talker submodule via disable_talker().
2. Wrap LLM linear modules with a peft LoRA (r=16, α=32, all-linear).
3. Contrastive train on voiceclap_10_safe WebDataset shards with finetune_omni_embed.py (open_clap_scaling repo).
4. 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.