---
license: apache-2.0
---
# Auden-Voice

**Auden-Voice** is a general-purpose voice encoder trained to learn robust speaker representations.

The model is trained using **multi-task learning**, where jointly optimizing speaker identification, emotion, gender, and age classification objectives leads to more general and transferable voice representations.

---

## Model Details

- **Model type**: Voice encoder
- **Architecture**: Zipformer
- **Embedding dimension**: 768
- **Number of parameters**: ~156M
- **Framework**: PyTorch
- **Output**: Frame-level embeddings `[B, T, D]`
- **Pooling**: User-defined (e.g., mean pooling for utterance-level embeddings)

---

## Training

### Training Strategy

Multi-task learning was found to work best. The model is jointly trained on the following tasks:

- Speaker identification
- Emotion classification
- Gender classification
- Age classification

This setup encourages the encoder to learn robust and general-purpose voice representations.

### Training Data

The model is trained on publicly available academic speech datasets, totaling approximately **2050 hours** of audio.

| Task | Dataset(s) | #Samples | Hours |
|-----|-----------|----------|-------|
| Speaker Identification | VoxCeleb2 | 974k | 2026 |
| Paralinguistic Tasks | CREMA-D, RAVDESS, IEMOCAP, TESS | 18.3k | 20 |

### Training Code

Full training scripts and configurations are available at:  
https://github.com/AudenAI/Auden/tree/main/examples/voice

---

## Intended Use

This model is intended to be used as a **general-purpose voice encoder** for:

- Speaker identification and verification
- Speaker diarization
- Emotion, gender, and age classification
- Audio–text and text–audio retrieval
- Speech-related downstream tasks that benefit from pretrained voice embeddings

---

## How to Use

### Load the Encoder

```python
from auden.auto.auto_model import AutoModel
import torch

encoder = AutoModel.from_pretrained("AudenAI/auden-encoder-voice")
encoder = encoder.to("cuda" if torch.cuda.is_available() else "cpu")

# Extract Voice Embeddings
import torch.nn.functional as F

audio_files = ["/path/to/audio1.wav", "/path/to/audio2.wav"]
embeddings_list = []

for audio_file in audio_files:
    x, x_lens = encoder.extract_feature([audio_file])
    x, x_lens = x.to(device), x_lens.to(device)

    with torch.no_grad():
        encoder_output = encoder(x, x_lens)
        frame_embeddings = encoder_output["encoder_out"]  # [B, T, D]

        # Global average pooling (example for speaker verification)
        T = frame_embeddings.size(1)
        mask = (torch.arange(T, device=device).unsqueeze(0) < x_lens.unsqueeze(1)).unsqueeze(-1).float()
        utterance_embedding = (frame_embeddings * mask).sum(dim=1) / mask.sum(dim=1)

        embeddings_list.append(utterance_embedding)

embeddings = torch.cat(embeddings_list, dim=0)  # [N, D]
embeddings = F.normalize(embeddings, p=2, dim=-1)

similarity = torch.matmul(embeddings[0], embeddings[1])
print(f"Cosine similarity: {similarity:.4f}")


# Expected Output
🎵 Audio 1:
   Frame embeddings shape: torch.Size([1, 97, 768])
   Utterance embedding shape: torch.Size([1, 768])

🎵 Audio 2: 
   Frame embeddings shape: torch.Size([1, 138, 768])
   Utterance embedding shape: torch.Size([1, 768])

Cosine similarity: 0.7234
Same speaker: YES
```

## Performance

| Task - Dataset | Metric |
|----------------|--------|
| Speaker Identification - VoxCeleb2 | Accuracy 95.25% |
| Speaker Verification - VoxCeleb1-O | EER 3% |
| Speaker Diarization - VoxConverse | DER 17% |
| Age Classification - CREMA-D | Accuracy 93.91% |
| Gender Classification - CREMA-D | Accuracy 99.72% |
| Gender Classification - RAVDESS | Accuracy 100% |
| Emotion Classification - CREMA-D | Accuracy 83.99% |
| Emotion Classification - RAVDESS | Accuracy 89.71% |
| Audio → Text Retrieval - ParaspeechCaps | R@1 63.31 |
| Text → Audio Retrieval - ParaspeechCaps | R@1 61.69 |
| LLM-QA Emotion - AirBench-MELD | Accuracy 27.23% |
| LLM-QA Emotion - AirBench-IEMOCAP | Accuracy 84.70% |
| LLM-QA Gender - AirBench-MELD | Accuracy 81.58% |
| LLM-QA Gender - AirBench-CommonVoice | Accuracy 93.15% |
| LLM-QA Age - AirBench-CommonVoice | Accuracy 58.27% |


## Limitations

- The model is trained primarily on English speech data and may not generalize well to other languages.
- The model is not evaluated on generative tasks such as speech synthesis or voice conversion.
- Utterance-level representations depend on the pooling strategy selected by the user.

## Citation

If you use this model in your research, please cite:

```bibtex
@article{huo2025auden,
  title={Auden-Voice: General-Purpose Voice Encoder for Speech and Language Understanding},
  author={Huo, Mingyue and Tseng, Wei-Cheng and Shao, Yiwen and Zhang, Hao and Yu, Dong},
  journal={arXiv preprint arXiv:2511.15145},
  year={2025}
}