Error code:   JWTInvalidSignature
Exception:    InvalidSignatureError
Message:      Signature verification failed
Traceback:    Traceback (most recent call last):
                File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt
                  decoded = jwt.decode(
                      jwt=token,
                  ...<2 lines>...
                      options=options,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode
                  decoded = self.decode_complete(
                      jwt,
                  ...<8 lines>...
                      leeway=leeway,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete
                  decoded = self._jws.decode_complete(
                      jwt,
                  ...<3 lines>...
                      detached_payload=detached_payload,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete
                  self._verify_signature(
                  ~~~~~~~~~~~~~~~~~~~~~~^
                      signing_input,
                      ^^^^^^^^^^^^^^
                  ...<4 lines>...
                      options=merged_options,
                      ^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature
                  raise InvalidSignatureError("Signature verification failed")
              jwt.exceptions.InvalidSignatureError: Signature verification failed

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VocSim — Avian Perception Alignment

A companion dataset for the VocSim benchmark that tests whether neural audio embeddings align with biological perceptual judgments. It packages zebra finch (Taeniopygia guttata) song-syllable recordings together with the behavioral probe and triplet results from Zandberg et al. (2024), so an embedding's pairwise distance matrix can be compared directly against the birds' perceptual decisions.

Basha, M., Zai, A. T., Stoll, S., & Hahnloser, R. H. R. VocSim: A Training-free Benchmark for Zero-shot Content Identity in Single-source Audio. ICML 2026. arXiv:2512.10120

How to use it

Extract features from each syllable with the audio model you want to evaluate.
Compute pairwise distances between embeddings.
Score the distances against the behavioral judgments in probes.csv (probe trials) and triplets.csv (triplet trials).

The reference implementation lives at github.com/vocsim/benchmark — see reproducibility/scripts/avian_perception.py and reproducibility/configs/avian_paper.yaml.

Bundled files:

Hugging Face Dataset with the audio + metadata.
probes.csv — probe-trial results (sound_id, left, right, decision, …), filtered to rows whose audio is present.
triplets.csv — triplet-trial results (Anchor, Positive, Negative, diff, …), filtered the same way.
missing_audio_files.txt (when applicable) — original IDs without matching audio.

Schema

{
  "audio": {"array": np.ndarray, "sampling_rate": 16000},
  "subset": "avian_perception",
  "index": 42,
  "speaker": "ZF_M_123",                     # bird ID
  "label": "ZF_M_123",                       # set to speaker for this dataset
  "original_name": "ZF_M_123_syllable_A.wav" # identifier used in the CSVs
}

Quick start

from datasets import load_dataset

ds = load_dataset("vocsim/avian-perception-benchmark", split="train")
print(ds[0])

Source data

The recordings and behavioral results are from Zandberg et al. (2024). Please cite both that work and the VocSim paper if you use this dataset.

Citation

@inproceedings{basha2026vocsim,
  title     = {VocSim: A Training-free Benchmark for Zero-shot Content Identity in Single-source Audio},
  author    = {Basha, Maris and Zai, Anja T. and Stoll, Sabine and Hahnloser, Richard H. R.},
  booktitle = {Proceedings of the 43rd International Conference on Machine Learning (ICML)},
  year      = {2026},
  doi       = {10.48550/arXiv.2512.10120}
}

@article{zandberg2024bird,
  author    = {Zandberg, Lies and Morfi, Veronica and George, Julia M. and Clayton, David F. and Stowell, Dan and Lachlan, Robert F.},
  title     = {Bird song comparison using deep learning trained from avian perceptual judgments},
  journal   = {PLoS Computational Biology},
  volume    = {20},
  number    = {8},
  pages     = {e1012329},
  year      = {2024},
  doi       = {10.1371/journal.pcbi.1012329},
  publisher = {Public Library of Science}
}

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Paper for vocsim/avian-perception-benchmark

VocSim: A Training-free Benchmark for Zero-shot Content Identity in Single-source Audio

Paper • 2512.10120 • Published Dec 10, 2025