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*This model was contributed to Hugging Face Transformers on 2026-02-19.*

# Higgs Audio V2 Tokenizer

## Overview

- Low Frame Rate: At 25 fps, our tokenizer halves the frame rate of many baselines when still maintaining high audio quality.
- Unified 24 kHz Training: We mix speech, music, and sound-event clips in one model, capturing both semantic and acoustic details, hugely facilitating the training of audio language models.
- Fast Inference: By avoiding diffusion steps, our encoder/decoder processes batches quickly, making it practical for real-time or large-scale tasks.

**Model Architecture:**
<div class="flex justify-center">
    <img src="https://huggingface.co/datasets/eustlb/documentation-images/resolve/main/higgs_audio_tokenizer_architecture.png"/>
</div>

## Usage

```python
from datasets import Audio, load_dataset

from transformers import AutoFeatureExtractor, HiggsAudioV2TokenizerModel


# load model and feature extractor
model_id = "eustlb/higgs-audio-v2-tokenizer"
feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)
model = HiggsAudioV2TokenizerModel.from_pretrained(model_id, device_map="auto")

# load audio sample
dummy_dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
dummy_dataset = dummy_dataset.cast_column("audio", Audio(sampling_rate=feature_extractor.sampling_rate))
audio_sample = dummy_dataset[-1]["audio"]["array"]
inputs = feature_extractor(raw_audio=audio_sample, sampling_rate=feature_extractor.sampling_rate, return_tensors="pt").to(model.device)

# encode and decode
encoder_outputs = model.encode(inputs["input_values"])
decoder_outputs = model.decode(encoder_outputs.audio_codes)
audio_values = decoder_outputs.audio_values

# or the equivalent with a forward pass
audio_values = model(inputs["input_values"]).audio_values
```

## HiggsAudioV2TokenizerConfig

[[autodoc]] HiggsAudioV2TokenizerConfig

## HiggsAudioV2TokenizerModel

[[autodoc]] HiggsAudioV2TokenizerModel
    - decode
    - encode
    - forward