| # 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 |
|
|
| ```bash |
| 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. |
|
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| To re-extract embeddings from the audio: |
|
|
| ```bash |
| bash reproduce.sh --extract # ~24 CPU-hours + ~1 GPU-hour |
| ``` |
|
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| 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 |
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|
| 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 |
| ``` |
|
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| `extraction_utils._resolve_root()` picks the root via: |
|
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| 1. `VIPBENCH_ROOT` environment variable (if set), else |
| 2. parent of the script's directory. |
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| 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 | |
|
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| 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`. |
|
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| ## Output format |
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| 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. |
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| 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`). |
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|
