xcodec2-hf

模型介绍

The X-Codec2 model was proposed in Llasa: Scaling Train-Time and Inference-Time Compute for Llama-based Speech Synthesis. 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, EnCodec, X-Codec, Mimi), 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.

主要特点

特点摘要待依据来源补齐，不凭模型名称推断能力。

局限与注意事项

暂无足够来源支持具体缺点结论。请重点验证实际任务表现、时延、数据政策、许可与版本生命周期。

来源报价与费用分析

同一模型在不同渠道、地区和配置下可能采用不同价格。下表独立保留各渠道报价，不将渠道价格视为厂商官方统一价格。

公开评测与运行配置

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

常见问题

资料来源与可追溯记录

许可证：cc-by-nc-4.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。