dashengtokenizer

DashengTokenizer is a high-performance continious audio tokenizer designed for audio understanding and generation tasks. Compared to previous works, our framework trains a single linear layer to enable audio generation for semantically strong encoders.

Achievements:

• State-of-the-Art Audio Understanding: DashengTokenizer consistently outperforms most previous self-supervised and supervised audio encoders.
• High-Fidelity Signal Reconstruction: Maintains exceptional signal integrity, ensuring that audio remains crisp and accurate after processing.
• Accelerated Audio Generation Training: Achieves optimal performance significantly faster than standard VAE models, reducing training time and costs.
• Superior Speech Enhancement: Provides a more robust encoding foundation for isolating and clarifying speech in noisy environments.

Usage

Installation

uv pip install transformers torch torchaudio einops

Basic Usage

import torch
import torchaudio
from transformers import AutoModel

# Load the model
model = AutoModel.from_pretrained("mispeech/dashengtokenizer", trust_remote_code=True)
model.eval()

# Load audio file (only 16kHz supported!)
audio, sr = torchaudio.load("path/to/audio.wav")

# Optional: Create attention mask for variable-length inputs
# attention_mask = torch.ones(audio.shape[0], audio.shape[1])  # All ones for full audio
# attention_mask[0, 8000:] = 0  # Example: mask second half of first sample

# Method 1: End-to-end processing (encode + decode)
with torch.no_grad(), torch.autocast(device_type='cuda'):
    outputs = model(audio)  # Optionally pass attention_mask=attention_mask
    reconstructed_audio = outputs["audio"]
    embeddings = outputs['embeddings']

# Method 2: Separate encoding and decoding
with torch.no_grad(), torch.autocast(device_type='cuda'):
    # Encode audio to embeddings
    embeddings = model.encode(audio)  # Optionally pass attention_mask=attention_mask

    # Decode embeddings back to audio
    reconstructed_audio = model.decode(embeddings)

# Save reconstructed audio
torchaudio.save("reconstructed_audio.wav", reconstructed_audio, sr)

Use Cases

1. Audio Encoding

embeddings = model.encode(audio)
reconstructed = model.decode(embeddings)

2. Feature Extraction

# Extract rich audio features for downstream tasks
features = model.encode(audio)
# Use features for classification, clustering, etc.

Limitations

• Optimized for 16kHz mono audio

Results

Citation

If you use DashengTokenizer in your research, please cite:

@misc{dinkel_dashengtokenizer_2026,
  title={DashengTokenizer: One layer is enough for unified audio understanding and generation},
  author={MiLM Plus, Xiaomi},
  year={2026},
  url={https://huggingface.co/mispeech/dashengtokenizer}
}

License

Apache 2.0 License