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Code: extraction + analysis

This directory contains everything needed to (a) regenerate the pre-extracted embeddings from audio, and (b) reproduce the figures and tables in the accompanying NeurIPS 2026 paper.

Layout 

code/
  extraction_utils.py             # shared audio loading and save logic
  extract_*.py                    # 10 per-model extraction scripts
  extract_ssl_layers.py           # per-transformer-layer extraction (5 SSL models)
  run_all_extractions.sh          # master runner
  benchmark_analysis.ipynb        # main analysis notebook (80 cells)
  reproduce.sh                    # end-to-end reproduction (default: analysis only)
  README.md                       # this file

Quick start 

pip install -r ../requirements.txt
cd code
bash reproduce.sh

reproduce.sh defaults to analysis-only: it executes the notebook against the embeddings already shipped in ../data/embeddings/. This takes ~10 minutes on a laptop.

To re-extract embeddings from the audio:

bash reproduce.sh --extract        # ~24 CPU-hours + ~1 GPU-hour

The notebook auto-resolves paths via Path.cwd().parent, so just open it from code/ (jupyter notebook benchmark_analysis.ipynb) or run via the command above.

Path resolution

All scripts and the notebook expect the release directory layout:

<VIPBENCH_ROOT>/
  code/                <-- you are here
  data/audio/reference/*.wav
  data/audio/comparison/*.wav
  data/embeddings/<model>.npz             <-- output of extraction
  data/embeddings/layers/<model>.npz      <-- per-layer SSL output

extraction_utils._resolve_root() picks the root via:

  1. VIPBENCH_ROOT environment variable (if set), else
  2. parent of the script's directory.

Override with VIPBENCH_ROOT=/some/other/path bash reproduce.sh.

Models

Model Script HF checkpoint Dim Type
RawNet3 extract_rawnet3_embeddings.py espnet/voxcelebs12_rawnet3 192 Supervised
ECAPA-TDNN extract_ecapa_tdnn.py speechbrain/spkrec-ecapa-voxceleb 192 Supervised
TitaNet extract_titanet.py nvidia/speakerverification_en_titanet_large 192 Supervised
x-vector extract_xvector.py speechbrain/spkrec-xvect-voxceleb 512 Supervised
resemblyzer extract_resemblyzer.py (bundled with package) 256 Supervised
wav2vec 2.0 extract_wav2vec2.py facebook/wav2vec2-base 768 SSL
HuBERT extract_hubert.py facebook/hubert-base-ls960 768 SSL
WavLM extract_wavlm.py microsoft/wavlm-base-plus 768 SSL
XLS-R extract_xlsr.py facebook/wav2vec2-xls-r-300m 1024 SSL
Whisper extract_whisper.py openai/whisper-base (encoder) 512 Weakly supervised

Per-layer mean-pooled embeddings for the 5 SSL models are produced by extract_ssl_layers.py and saved to data/embeddings/layers/<model>.npz.

Output format

Each data/embeddings/<model>.npz is a key-value store keyed by audio basename without .wav (e.g. M01R, 1_F01). Values are 1-D np.float32 arrays of shape (embedding_dim,). The 9,900 keys cover 100 references plus 9,800 comparisons.

Per-layer bundles (layers/<model>.npz) use the same keys; values have shape (num_layers, embedding_dim).

Notes

  • TitaNet requires NVIDIA NeMo (nemo_toolkit[asr]); install is heavy (~5 GB). The line is commented out in requirements.txt.
  • The notebook caches expensive computations under code/cache/. Delete code/cache/ to force recompute.
  • Models are downloaded from Hugging Face on first run; subsequent runs use the local cache.

License

Code in this directory is MIT-licensed (see ../LICENSE-CODE). The dataset (audio, judgments, embeddings) is CC-BY-NC 4.0 (see ../LICENSE).