hf-audio
/

xcodec2

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 ---
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 ---
 library_name: transformers
+license: cc-by-nc-4.0
+tags:
+- audio-to-audio
+pipeline_tag: audio-to-audio
 ---
+# [Xcodec2](https://huggingface.co/HKUSTAudio/xcodec2) (Transformers-compatible version)
+The X-Codec2 model was proposed in [Llasa: Scaling Train-Time and Inference-Time Compute for Llama-based Speech Synthesis](https://huggingface.co/papers/2502.04128).
+X-Codec2 is a neural audio codec designed to improve speech synthesis and general audio generation for large language model (LLM) pipelines. It extends the original X-Codec by refining how semantic and acoustic information is integrated and tokenized, enabling efficient and high-fidelity audio representation.
+Its architecture is based on [X-Codec](./xcodec) with several major differences:
+- **Unified Semantic-Acoustic Tokenization**: X-Codec2 fuses outputs from a semantic encoder (e.g., Wav2Vec2-BERT) and an acoustic encoder into a single embedding, capturing both high-level meaning (e.g., text content, emotion) and low-level audio details (e.g., timbre).
+- **Single-Stage Vector Quantization (VQ)**: Unlike the multi-layer residual VQ in most approaches (e.g., [X-Codec](./xcodec), [DAC](./dac), [EnCodec](./encodec)), X-Codec2 uses a single-layer Feature-Space Quantization (FSQ) for stability and compatibility with causal, autoregressive LLMs.
+- **Semantic Supervision During Training**: It adds a semantic reconstruction loss, ensuring that the discrete tokens preserve meaningful linguistic and emotional information — crucial for TTS tasks.
+- **Transformer-Friendly Design**: The 1D token structure of X-Codec2 naturally aligns with the autoregressive modeling in LLMs like LLaMA, improving training efficiency and downstream compatibility.
+## Usage example
+Here is a quick example of how to encode and decode an audio using this model:
+```python
+>>> import torch
+>>> from datasets import Audio, load_dataset
+>>> from transformers import AutoFeatureExtractor, Xcodec2Model
+>>> torch_device = "cuda" if torch.cuda.is_available() else "cpu"
+>>> # load model and feature extractor
+>>> model_id = "bezzam/xcodec2"
+>>> model = Xcodec2Model.from_pretrained(model_id).to(torch_device).eval()
+>>> feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)
+>>> # load data
+>>> dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+>>> dataset = dataset.cast_column("audio", Audio(sampling_rate=feature_extractor.sampling_rate))
+>>> audio = dataset[0]["audio"]["array"]
+>>> # prepare data
+>>> inputs = feature_extractor(raw_audio=audio, sampling_rate=feature_extractor.sampling_rate, return_tensors="pt").to(torch_device)
+>>> # encoder and decode
+>>> audio_codes = model.encode(inputs["input_values"]).audio_codes
+>>> audio_values = model.decode(audio_codes).audio_values
+>>> # or the equivalent with a forward pass
+>>> model_output = model(inputs["input_values"])
+>>> audio_codes = model_output.audio_codes
+>>> audio_values = model_output.audio_values
+```
+This model was contributed by [Steven Zheng](https://huggingface.co/Steveeeeeeen) and [Eric Bezzam](https://huggingface.co/bezzam).
+The original code can be found [here](https://github.com/zhenye234/X-Codec-2.0).