---
library_name: transformers
tags:
  - audio
  - text-to-speech
  - vocoder
  - hifi-gan
  - bigvgan
  - neural-vocoder
  - speaker-conditioning
  - audio-watermarking
pipeline_tag: text-to-speech
license: cc-by-4.0
---

# VocBulwark HiFi-GAN — watermarking neural vocoder (inference export)

Speaker-conditioned **BigVGAN / HiFi-GAN** neural vocoder. It turns an input mel-spectrogram (*what* is said) into a 24 kHz waveform, conditioned on a **precomputed 768-d speaker embedding** (*whose* voice). Every clip it generates carries a **fixed 50-bit provenance watermark** that identifies this specific model instance, see [Watermark](#watermark).

This is the **lean, inference-only** vocoder: the frozen perceptual-loss base models (Whisper / WavLM / Wav2Vec2), the training discriminators, **and the speaker encoder** have all been stripped, you pass the speaker embedding in. Use the companion **speaker-encoder** repo to turn a reference clip into that embedding. The modeling code is bundled, so it loads with `trust_remote_code=True` **without** the training repo.

## Model summary

| | |
|---|---|
| Architecture | `HiFiGANArchitecture` (BigVGAN generator, snakebeta activation) |
| Inputs | log-mel spectrogram (**96 mel channels**) + speaker embedding (**768-d**) |
| Output | mono waveform, **24 kHz** |
| Speaker conditioning | precomputed embedding (from the companion speaker encoder) |
| Generator | initial channels 1536, upsample rates [4, 4, 2, 2, 2, 2] |
| Watermark | 50-bit fixed VocBulwark signature, always embedded |
| Framework | 🤗 Transformers, PyTorch, safetensors |


## Companion Models

This model is part of a set of 6 repositories:

| Repo | Role |
|------|------|
| `mlr2000/vocoder-large` | Large vocoder (this repo) |
| `mlr2000/vocoder-large-watermark-detector` | Watermark detector for the large model |
| `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

```python
import torch
from transformers import AutoModel

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

mel = torch.randn(1, model.config.hifigan_in_channels, 200)      # [B, mel, T]
emb = torch.randn(1, model.config.speaker_embedding_size)        # [B, 768] from the speaker encoder
with torch.no_grad():
    audio = model(mel_spectrogram=mel, speaker_embedding=emb).audio
# audio: [B, 1, samples] @ model.config.target_sample_rate
```

See **`example_roundtrip.ipynb`** in this repo for the full pipeline (reference
clip → speaker encoder → embedding → vocode → verify watermark).

## Training

| | |
|---|---|
| Training data | Multilingual LibriSpeech (8 languages, ~22,200h) and Common Voice (14 languages, ~3,000h) |
| Training steps | 1,000,000 |
| Hardware | 2 × NVIDIA H200 GPUs |
| Training objective | Discriminator-free: mel spectrogram + WavLM + wav2vec 2.0 + Whisper encoder losses |
| Effective batch size | 32 |
| Learning rate | 1e-4 |


## Watermark

Every clip this model generates carries a **fixed 50-bit provenance watermark** (`config.fixed_watermark`) that identifies this specific model instance. It is embedded automatically inside `forward` and **cannot be disabled or changed** through this interface — there is deliberately no watermark argument to override.

To verify whether a given audio clip was generated by this model, use the companion **detector** repo (`mlr2000/vocoder-large-watermark-detector`), which extracts the embedded bits and compares them to the known fixed code.


## Notes

- Inputs: log-mel spectrogram (`config.hifigan_in_channels` channels) and a
  `[B, config.speaker_embedding_size]` speaker embedding.
- Output: mono waveform at `config.target_sample_rate`.
- Use the embedding from the speaker encoder this vocoder was **trained with**, a
  mismatched encoder will not condition it correctly.
- Not intended for voice cloning of real individuals without consent, or any
  deceptive / impersonation use.

## Citation

If you use this model, please cite:

```bibtex
@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.