README.md

metadata

library_name: transformers
tags:
  - audio
  - watermark
  - watermark-detection
  - provenance
  - vocbulwark
license: cc-by-4.0

VocBulwark watermark detector

Standalone detector for the fixed 50-bit provenance watermark embedded by the companion VocBulwark vocoder. Given an audio clip, it extracts the watermark bits with the Cage extractor and compares them to the known fixed code, reporting how many bits match.

Self-contained: loads with trust_remote_code=True, no training repo required.

Companion Models

This detector is part of a set of 6 repositories:

Repo	Role
mlr2000/vocoder-large	Large vocoder (generates the watermarked audio)
mlr2000/vocoder-large-watermark-detector	Watermark detector (this repo)
mlr2000/vocoder-large-speaker-encoder	Speaker encoder for the large model
mlr2000/vocoder-small	Small vocoder
mlr2000/vocoder-small-watermark-detector	Watermark detector for the small model
mlr2000/vocoder-small-speaker-encoder	Speaker encoder for the small model

Usage

import torchaudio
from transformers import AutoModel

det = AutoModel.from_pretrained("mlr2000/vocoder-large-watermark-detector", trust_remote_code=True).eval()

wav, sr = torchaudio.load("clip.wav")          # [C, T]
res = det.detect(wav, input_sample_rate=sr)    # resampled to 24 kHz internally
print(res)
# {'matches': 50, 'n_bits': 50, 'p_value': 8.9e-16}

See example_roundtrip.ipynb in this repo for an end-to-end example (generate with the vocoder → detect here).

How detection works

detect() returns a dict with:

• matches: how many of the 50 extracted bits equal the fixed code.
• n_bits: 50.
• p_value: the probability an unrelated clip matches at least this well under Binomial(50, 0.5).

A clip generated by our vocoder matches all 50 bits (p_value ~ 0). An unrelated clip matches about half of them (p_value ~ 1). There is no built-in yes/no threshold, read matches / p_value and pick whatever operating point you need. Requiring all 50 bits gives an astronomically small false-positive rate. Allowing a few mismatches trades that for robustness to lossy channels.

Robustness

The detector achieves near-perfect bit accuracy (≥ 0.999) against common signal processing attacks including:

• Gaussian noise (SNR ≥ 20 dB)
• High-pass and band-pass filtering
• Amplitude scaling
• Echo addition
• Resampling to 16 kHz
• MP3 compression
• Bit-depth quantization (16-bit and 8-bit)

Performance degrades under extreme transformations such as low-pass filtering at 2 kHz and time stretching, which also severely impact audio quality

Notes

• Detection runs on 24 kHz mono. Other inputs are resampled when you pass input_sample_rate, and multi-channel audio is downmixed to mono.
• A fixed public watermark is for attribution ("did our model make this"), not a secret mark.
• Robustness to compression / resampling should be validated empirically.
• This detector is paired with mlr2000/vocoder-large. It will not correctly verify audio generated by the small vocoder (mlr2000/vocoder-small), which uses a different fixed watermark.

Citation

If you use this model, please cite:

@misc{muletta2026,
  title  = {Training a Discriminator-Free Foundation Vocoder 
             with Integrated Audio Watermarking},
  author = {Muletta, Romolo and Deriu, Jan},
  year   = {2026},
  note   = {VT2 Project Report, ZHAW School of Engineering}
}

License

cc-by-4.0. Trained on MLS (CC-BY-4.0) and Common Voice (CC0). Builds on BigVGAN (MIT) and wav2vec 2.0 (Apache-2.0). Please retain attribution when redistributing or building on this model.