Update README.md

README.md

@@ -1,112 +1,104 @@
----
-license: apache-2.0
-language:
-- zh
-- en
-- es
-- de
-- fr
-pipeline_tag: audio-to-audio
----
-
-# LLaSE-G1: Incentivizing Generalization Capability for LLaMA-based Speech Enhancement
-
-## Introduction
-
-LLaSE-G1 is a unified speech enhancement model capable of handling multiple tasks without extra task prompts, including:
-
-- **Noise Suppression (SE)**
-- **Target Speaker Extraction (TSE)**
-- **Packet Loss Concealment (PLC)**
-- **Acoustic Echo Cancellation (AEC)**
-- **Speech Separation (SS)**
-
-To mitigate acoustic inconsistency, LLaSE-G1 employs continuous representations from **WavLM** as input and predicts speech tokens using **X-Codec2**, maximizing acoustic preservation. The model surpasses prior task-specific discriminative and generative speech enhancement models, demonstrating scaling effects at test time and emerging capabilities for unseen speech enhancement tasks.
-
-For more details, refer to our paper: [LLaSE-G1 Paper](https://submission-papers.github.io/LLaSE-G1-demo-page/)
-
-## Demo
-
-You can listen to the enhancement results on our [Demo Page](https://submission-papers.github.io/LLaSE-G1-demo-page/).
-
-## Installation
-
-### 1. Clone the repository
-
-```bash
-git clone https://github.com/your-repo/LLaSE-G1.git
-cd LLaSE-G1
-```
-
-### 2. Create a Conda environment and install dependencies
-
-```bash
-conda create -n llase python=3.10
-conda activate llase
-pip install -r requirements.txt
-```
-
-### 3. Download Pretrained Models
-
-LLaSE-G1 requires three additional pre-trained models to function properly. You can download them using the provided shell script:
-
-```bash
-bash ./ckpt/download.sh
-```
-
-Alternatively, you can download them manually and place them in the `./ckpt/` directory.
-
-## Inference
-
-The main inference script is **`inference.py`**. The inference process consists of two stages:
-
-1. Extract the 6th-layer features from WavLM.
-2. Use the language model (LM) to predict speech tokens, and then decode them into audio using **X-Codec2**.
-
-### Running Inference
-
-To run inference, configure the parameters in `./config/test.yml`:
-
-| Parameter        | Description                                                                                                                                                            |
-| ---------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `infer_feat_too` | Whether to extract WavLM features during inference.                                                                                                                    |
-| `inference_time` | Number of inference iterations.                                                                                                                                        |
-| `feat_dir`       | Directory containing extracted features.                                                                                                                               |
-| `wav_dir`        | Directory of processed audio files.                                                                                                                                    |
-| `task`           | Task type: `SE` (Noise Suppression), `TSE` (Target Speaker Extraction), `PLC` (Packet Loss Concealment), `AEC` (Acoustic Echo Cancellation), `SS` (Speech Separation). |
-
-Command to run inference:
-
-```bash
-bash inference.sh
-```
-
-## Results
-
-Samples processed by LLaSE-G1 can be found on our [Demo Page](https://submission-papers.github.io/LLaSE-G1-demo-page/).
-
-## Model Checkpoints
-
-Our pretrained model is available on [Hugging Face](https://huggingface.co/ASLP-lab/LLaSE-G1).
-
-## Citation
-
-If you find this work useful, please cite our paper:
-
-```bibtex
-@article{yourpaper2025,
-  title={LLaSE-G1: Incentivizing Generalization Capability for LLaMA-based Speech Enhancement},
-  author={Your Name and Others},
-  journal={ACL},
-  year={2025}
-}
-```
-
-## License
-
-This project is released under the **Apache-2.0**.
-
-## Contact
-
-For any questions, please contact: `[email protected]`
-
+---
+license: apache-2.0
+language:
+- zh
+- en
+- es
+- de
+- fr
+pipeline_tag: audio-to-audio
+---
+
+# LLaSE-G1: Incentivizing Generalization Capability for LLaMA-based Speech Enhancement
+
+## Introduction
+
+LLaSE-G1 is a unified speech enhancement model capable of handling multiple tasks without extra task prompts, including:
+
+- **Noise Suppression (SE)**
+- **Target Speaker Extraction (TSE)**
+- **Packet Loss Concealment (PLC)**
+- **Acoustic Echo Cancellation (AEC)**
+- **Speech Separation (SS)**
+
+To mitigate acoustic inconsistency, LLaSE-G1 employs continuous representations from **WavLM** as input and predicts speech tokens using **X-Codec2**, maximizing acoustic preservation. The model surpasses prior task-specific discriminative and generative speech enhancement models, demonstrating scaling effects at test time and emerging capabilities for unseen speech enhancement tasks.
+
+For more details, refer to our paper: [LLaSE-G1 Paper](https://submission-papers.github.io/LLaSE-G1-demo-page/)
+
+## Demo
+
+You can listen to the enhancement results on our [Demo Page](https://submission-papers.github.io/LLaSE-G1-demo-page/).
+
+## Installation
+
+### 1. Clone the repository
+
+```bash
+git clone https://github.com/your-repo/LLaSE-G1.git
+cd LLaSE-G1
+```
+
+### 2. Create a Conda environment and install dependencies
+
+```bash
+conda create -n llase python=3.10
+conda activate llase
+pip install -r requirements.txt
+```
+
+### 3. Download Pretrained Models
+
+LLaSE-G1 requires three additional pre-trained models to function properly. You can download them using the provided shell script:
+
+```bash
+bash ./ckpt/download.sh
+```
+
+Alternatively, you can download them manually and place them in the `./ckpt/` directory.
+
+## Inference
+
+The main inference script is **`inference.py`**. The inference process consists of two stages:
+
+1. Extract the 6th-layer features from WavLM.
+2. Use the language model (LM) to predict speech tokens, and then decode them into audio using **X-Codec2**.
+
+### Running Inference
+
+To run inference, configure the parameters in `./config/test.yml`:
+
+| Parameter        | Description                                                                                                                                                            |
+| ---------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `infer_feat_too` | Whether to extract WavLM features during inference.                                                                                                                    |
+| `inference_time` | Number of inference iterations.                                                                                                                                        |
+| `feat_dir`       | Directory containing extracted features.                                                                                                                               |
+| `wav_dir`        | Directory of processed audio files.                                                                                                                                    |
+| `task`           | Task type: `SE` (Noise Suppression), `TSE` (Target Speaker Extraction), `PLC` (Packet Loss Concealment), `AEC` (Acoustic Echo Cancellation), `SS` (Speech Separation). |
+
+Command to run inference:
+
+```bash
+bash inference.sh
+```
+
+## Results
+
+Samples processed by LLaSE-G1 can be found on our [Demo Page](https://submission-papers.github.io/LLaSE-G1-demo-page/).
+
+## Model Checkpoints
+
+Our pretrained model is available on [Hugging Face](https://huggingface.co/ASLP-lab/LLaSE-G1).
+
+## Citation
+
+If you find this work useful, please cite our paper:
+
+
+## License
+
+This project is released under the **Apache-2.0**.
+
+## Contact
+
+For any questions, please contact: `[email protected]`
+