Update README.md

README.md

@@ -1,88 +1 @@
-[![arXiv](https://img.shields.io/badge/arXiv-Paper-<COLOR>.svg)](https://arxiv.org/abs/2502.04128)
- 
-# X-Codec-2.0
-Paper: LLaSA: Scaling Train-time and Inference-time Compute for LLaMA-based Speech Synthesis  
-
-**Update (2025-02-13):** Add [Llasa finetune instruction](https://github.com/zhenye234/LLaSA_training/tree/main/finetune).
-
-**Update (2025-02-07):** Our paper has been released!
-
-
-## Directly used on Hugging Face
-
-**Codec**: [xcodec2](https://huggingface.co/HKUST-Audio/xcodec2) (Use `xcodec2==0.1.5` for codec inference and llasa fine-tuning. I’ve removed unnecessary dependencies, and it works fine in my testing. However,  I’m not sure if other problems may arise. If you prefer more stability, I recommend using `xcodec2==0.1.3` which accurately aligns during my codec training.)
- 
-
-**Llasa-collections**: [Llasa-collections](https://huggingface.co/collections/HKUSTAudio/llasa-679b87dbd06ac556cc0e0f44)
-
-## Features
-
-- **Single Vector Quantization**
-  - 65536 Codebook Size using Finite Scalar Quantization achieving 99% codebook usage. ( comparable to text tokenizers, LLaMA3 128256)
-  - 50x1 Tokens per Second
-
-- **Multilingual Speech Semantic Support**
-  - Uses Wav2Vec2-BERT, a semantic encoder pre-trained on 4.5M hours of unlabeled audio data covering more than 143 languages.
-  - Codec trained on 150k hours of multilingual speech data, including Emilia (En/Zh/De/Fr/Ja/Ko) and MLS (En/Fr/De/Nl/Es/It/Pt/Pl).
-
-- **High-Quality Speech Reconstruction**
-  - Transformer + Vocos Decoder
-  - BigCodec encoder
-  - Spec discriminator with FFT sizes {78, 126, 206, 334, 542, 876, 1418, 2296} tailored for transformer decoder. [Details here](https://openreview.net/pdf?id=4YpMrGfldX)
-  - Achieving UTMOS 4.13 WER 2.47 (hubert-large-ls960-ft)  sim 0.82 (wavlm_large_finetune) stoi 0.92  pesq-nb 3.05  pesq-wb 2.44 on librispeech-test-clean reconstruction (gt: WER 1.96 UTMOS 4.09)
-  - Only for 16kHz speech
-
-
-##  Commandline Usage
-## Setup
-Code is tested on `python3.9`
-
-Please follow the following steps to setup your environment
-1. Clone this repo
-2. conda create --name xcodec2 python=3.9 
-3. conda activate xcodec2  
-2. `pip install -r requirements.txt`
-3. [Download the pretrained checkpoint here](https://huggingface.co/HKUST-Audio/xcodec2/blob/main/ckpt/epoch%3D4-step%3D1400000.ckpt)
-
-
-## Inference
-```bash
-python inference.py  
-```
- 
-## Train
-To train a XCodec2, firstly you have to prepare your data 
-
-1. Make a file list by:
-```bash
-python get_tsv.py
-```
-
-2. Train a X-Codec-2.0 with the default setting by:
-
-```bash
-python train.py log_dir=/path/to/log_dir
-```
-
-## Large-scale training, Batch inference and large-scale code extracting:
-
-Batch inference
-```bash
-python inference_save_code.py
-```
-Training
-```bash
-Sbatch train_slurm.sh
-```
-
-Code extracting
-```bash
-Sbatch large_scale_save_code.sh
-```
-
-Code will save in output folder with the same subfolder structure for audio file.
-
-
- 
-## Acknowledgement
-I would like to extend a special thanks to authors of BigCodec, since our code base is mainly borrowed from  [BigCodec](https://github.com/Aria-K-Alethia/BigCodec).
+it is not fully tested. I just know the training starts.