docs/model_table.md

Baseline models

The 10 speech representations benchmarked in the accompanying paper. Each is loaded from a publicly available pretrained checkpoint; weights are downloaded by the corresponding extraction script in code/.

Model	Paradigm	Training data	Dim	HF checkpoint	Extraction script
x-vector	Supervised classification	VoxCeleb 1+2	512	speechbrain/spkrec-xvect-voxceleb	extract_xvector.py
ECAPA-TDNN	AAM-Softmax	VoxCeleb 1+2	192	speechbrain/spkrec-ecapa-voxceleb	extract_ecapa_tdnn.py
RawNet3	AAM-Softmax	VoxCeleb 1+2	192	espnet/voxcelebs12_rawnet3	extract_rawnet3_embeddings.py
TitaNet (large)	AAM-Softmax	VoxCeleb 1+2, Fisher, SWB, LibriSpeech, NIST SRE	192	nvidia/speakerverification_en_titanet_large	extract_titanet.py
resemblyzer	GE2E loss, 3-layer LSTM	LibriSpeech + VoxCeleb 1+2	256	bundled with resemblyzer package	extract_resemblyzer.py
wav2vec 2.0	Contrastive masked prediction	LibriSpeech 960 h	768	facebook/wav2vec2-base	extract_wav2vec2.py
HuBERT	Masked prediction	LibriSpeech 960 h	768	facebook/hubert-base-ls960	extract_hubert.py
WavLM	Masked prediction + denoising	94K h mixed	768	microsoft/wavlm-base-plus	extract_wavlm.py
XLS-R	Contrastive multilingual	436K h, 128 languages	1024	facebook/wav2vec2-xls-r-300m	extract_xlsr.py
Whisper (encoder, base)	Multitask weakly supervised ASR	680K h web audio	512	openai/whisper-base	extract_whisper.py

Output format

Each script saves <model>.npz to <VIPBENCH_ROOT>/data/embeddings/. The file is a key-value store with 9,900 keys (audio basenames without .wav) mapping to 1-D np.float32 arrays of shape (embedding_dim,).

For SSL models (wav2vec 2.0, HuBERT, WavLM, XLS-R, Whisper), extract_ssl_layers.py additionally produces a per-layer mean-pooled bundle saved to <VIPBENCH_ROOT>/data/embeddings/layers/<model>.npz. Values there have shape (num_layers, embedding_dim).

Pooling

Self-supervised models (wav2vec 2.0, HuBERT, WavLM, XLS-R) and Whisper produce frame-level outputs; we use mean pooling across time for the utterance-level embedding. Speaker-specialized models (x-vector, ECAPA-TDNN, RawNet3, TitaNet, resemblyzer) produce a single utterance-level vector by design and are passed through unchanged.

Best-layer protocol

For SSL models the final layer is known to underperform on speaker tasks (SUPERB, 2021). The notebook implements a SUPERB-style nested speaker-CV best-layer protocol: for each held-out speaker fold, the layer that maximizes Pearson r against P(same) on training speakers is selected and applied to the test speakers. No pair contributes to selecting its own layer. Per-layer Pearson r curves are reported in the appendix figure.

Licenses

Each baseline retains its original license. As of release time:

• speechbrain checkpoints: Apache 2.0
• ESPnet checkpoints: Apache 2.0
• NVIDIA NeMo: NVIDIA Source Code License (research permitted)
• Hugging Face hosted facebook/microsoft checkpoints: respective lab licenses (typically MIT / Apache 2.0)
• OpenAI Whisper: MIT
• resemblyzer: MIT

Verify the license at the linked checkpoint page before redistribution. The CC-BY-NC 4.0 license on VIPBench's audio + judgments + derived embeddings does not extend to these third-party model weights.