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*This model was published in HF papers on 2025-02-06 and contributed to Hugging Face Transformers on 2026-06-25.*

# X-Codec2

<div class="flex flex-wrap space-x-1">
<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
</div>

## Overview

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.

About its architecture:
- **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 Feature Scalar Quantization (FSQ)**: Unlike the multi-layer residual VQ in most approaches (e.g., [DAC](./dac), [EnCodec](./encodec), [X-Codec](./xcodec), [Mimi](./mimi.md)), X-Codec2 uses a single-layer of Feature Scalar Quantization (FSQ) for stability and compatibility with causal, autoregressive LLMs.
- **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.

A model checkpoint is available at [HKUSTAudio/xcodec2-hf](https://huggingface.co/HKUSTAudio/xcodec2-hf).

This model was contributed by [Eric Bezzam](https://huggingface.co/bezzam) and [Steven Zheng](https://huggingface.co/Steveeeeeeen).
The original modeling code can be found [here](https://huggingface.co/HKUSTAudio/xcodec2/blob/main/modeling_xcodec2.py), while their training code is [here](https://github.com/zhenye234/X-Codec-2.0).

## Usage example 

Here is a quick example of how to encode and decode an audio using this model:

```python 
from datasets import Audio, load_dataset
from transformers import AutoFeatureExtractor, AutoModel

model_id = "HKUSTAudio/xcodec2-hf"
model = AutoModel.from_pretrained(model_id, device_map="auto")
feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)

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"]
inputs = feature_extractor(audio=audio, sampling_rate=feature_extractor.sampling_rate, return_tensors="pt").to(
    model.device, model.dtype
)
print("Input waveform shape:", inputs["input_values"].shape)
# Input waveform shape: torch.Size([1, 1, 93760])

# encoder and decoder
audio_codes = model.encode(**inputs).audio_codes
print("Audio codes shape:", audio_codes.shape)
# Audio codes shape: torch.Size([1, 1, 293])
audio_values = model.decode(audio_codes).audio_values
print("Audio values shape:", audio_values.shape)
# Audio values shape: torch.Size([1, 1, 93760])

# Equivalently, you can do encoding and decoding in one step
model_output = model(**inputs)
audio_codes = model_output.audio_codes
audio_values = model_output.audio_values
```

### Batch processing

This implementation also supports batched input, unlike the original [release](https://huggingface.co/HKUSTAudio/xcodec2)!

```python
from datasets import Audio, load_dataset
from transformers import AutoFeatureExtractor, AutoModel

batch_size = 2
model_id = "HKUSTAudio/xcodec2-hf"
model = AutoModel.from_pretrained(model_id, device_map="auto")
feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)

dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
dataset = dataset.cast_column("audio", Audio(sampling_rate=feature_extractor.sampling_rate))
audios = [dataset[i]["audio"]["array"] for i in range(batch_size)]
inputs = feature_extractor(audio=audios, sampling_rate=feature_extractor.sampling_rate, return_tensors="pt").to(
    model.device, model.dtype
)
print("Input waveform shape:", inputs["input_values"].shape)
# Input waveform shape: torch.Size([2, 1, 93760])

# encoder and decoder
encoder_output = model.encode(**inputs)
audio_codes = encoder_output.audio_codes
print("Audio codes shape:", audio_codes.shape)
# Audio codes shape: torch.Size([2, 1, 293])
audio_values = model.decode(audio_codes).audio_values
print("Audio values shape:", audio_values.shape)
# Audio values shape: torch.Size([2, 1, 93760])

# Equivalently, you can do encoding and decoding in one step
model_output = model(**inputs)
audio_codes = model_output.audio_codes
audio_values = model_output.audio_values
```

### Speed-up with `torch.compile`

You can speed up inference with [`torch.compile`](https://pytorch.org/docs/stable/generated/torch.compile.html). The first few calls will be slower due to compilation overhead, but subsequent calls will be faster.

On an A100, we observed a speed-up of ~1.35 for a batch size of 4 ([script](https://gist.github.com/ebezzam/3b79481b5d48d8e35c4ecc582aee0cb3#file-benchmark_torch_compile-py)).

```python
import torch
from datasets import Audio, load_dataset
from transformers import AutoFeatureExtractor, AutoModel

batch_size = 4
model_id = "HKUSTAudio/xcodec2-hf"
model = AutoModel.from_pretrained(model_id, device_map="auto")
feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)

dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
dataset = dataset.cast_column("audio", Audio(sampling_rate=feature_extractor.sampling_rate))
audios = [dataset[i]["audio"]["array"] for i in range(batch_size)]
inputs = feature_extractor(
    audio=audios, sampling_rate=feature_extractor.sampling_rate, padding=True, return_tensors="pt"
).to(model.device, model.dtype)

compiled_model = torch.compile(model, fullgraph=True)

# Warmup (includes compilation on first call)
for _ in range(10):
    with torch.inference_mode():
        _ = compiled_model(**inputs)

with torch.inference_mode():
    output = compiled_model(**inputs)
print("Audio values shape:", output.audio_values.shape)
```

## Xcodec2Config

[[autodoc]] Xcodec2Config

## Xcodec2FeatureExtractor

[[autodoc]] Xcodec2FeatureExtractor
    - __call__

## Xcodec2Model

[[autodoc]] Xcodec2Model
    - decode
    - encode
    - forward
