Update README.md

README.md

@@ -16,7 +16,7 @@ X-Codec2 is a neural audio codec designed to improve speech synthesis and genera
 Its architecture is based on X-Codec 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.
+- **Single-Stage Vector Quantization (VQ)**: Unlike the multi-layer residual VQ in most approaches (e.g., X-Codec, DAC, 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.