VocBulwark speaker encoder
Standalone speaker encoder: turns a raw waveform into a fixed 768-dimensional speaker embedding. It is the conditioning front-end for the companion VocBulwark vocoder — compute an embedding once from a reference clip of a speaker, then pass it to the vocoder to synthesize in that voice. The embedding is also usable on its own for speaker verification / similarity.
Wav2Vec2-based encoder with attentive statistics pooling, trained with a GE2E
objective. Self-contained: loads with trust_remote_code=True, no training repo
required.
Companion Models
This speaker encoder 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 for the large model |
mlr2000/vocoder-large-speaker-encoder | Speaker encoder (this repo) |
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, torchaudio.functional as AF
from transformers import AutoModel
enc = AutoModel.from_pretrained("mlr2000/vocoder-large-speaker-encoder", trust_remote_code=True).eval()
wav, sr = torchaudio.load("reference.wav") # [C, T]
wav = wav.mean(0, keepdim=True) # mono [1, T]
if sr != enc.config.raw_sample_rate: # encoder expects 22.05 kHz
wav = AF.resample(wav, sr, enc.config.raw_sample_rate)
emb = enc.embed(wav) # [1, 768] — feed as speaker_embedding to the vocoder
See example_roundtrip.ipynb in this repo for the full pipeline
(reference clip → embedding → vocode → detect watermark).
Notes
- Input: mono waveform at 22.05 kHz (
config.raw_sample_rate). Resample first if your audio differs. - Output:
[B, 768]L2-comparable speaker embeddings. - Pair with the VocBulwark vocoder trained jointly with this encoder — an embedding from a different encoder will not condition the vocoder correctly.
- This encoder is paired with
mlr2000/vocoder-large. For the small vocoder (mlr2000/vocoder-small) use the companion small speaker encoder (mlr2000/vocoder-small-speaker-encoder). - Not intended for speaker identification or surveillance of individuals without consent.
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.