Xcodec2

To use xcodec2, ensure you have it installed. You can install it using the following command:

conda create -n xcodec2 python=3.9
conda activate xcodec2
pip install xcodec2==0.1.3 (Fix the bug in the previous version to achieve better sound quality)

Then,

import torch
import soundfile as sf
from transformers import AutoConfig

 
from xcodec2.modeling_xcodec2 import XCodec2Model
 
model_path = "HKUST-Audio/xcodec2"  
 
model = XCodec2Model.from_pretrained(model_path)
model.eval().cuda()   

 
wav, sr = sf.read("test.wav")   
wav_tensor = torch.from_numpy(wav).float().unsqueeze(0)  # Shape: (1, T)

 
with torch.no_grad():
   # Only 16khz speech
   # Only supports single input. For batch inference, please refer to the link below.
    vq_code = model.encode_code(input_waveform=wav_tensor)
    print("Code:", vq_code )  

    recon_wav = model.decode_code(vq_code).cpu()       # Shape: (1, 1, T')

 
sf.write("reconstructed.wav", recon_wav[0, 0, :].numpy(), sr)
print("Done! Check reconstructed.wav")

If you want to train your own xcodec2, batch inference, or large-scale code extraction, the code is released here.

--- license: cc-by-4.0 tags: - audio-to-audio pipeline_tag: audio-to-audio --- ## Paper LLaSA: Scaling Train Time and Test Time Compute for LLaMA based Speech Synthesis (Comming soon) Codec Does Matter: Exploring the Semantic Shortcoming of Codec for Audio Language Model (AAAI 2025, xcodec 1.0) # Getting Started with XCodec2 on Hugging Face XCodec2 is a speech tokenizer that offers the following key features: 1. **Single Vector Quantization** 2. **50 Tokens per Second** 3. **Multilingual Speech Semantic Support and High-Quality Speech Reconstruction** To use `xcodec2`, ensure you have it installed. You can install it using the following command: ```bash conda create -n xcodec2 python=3.9 conda activate xcodec2 pip install xcodec2==0.1.3 (Fix the bug in the previous version to achieve better sound quality) ``` Then, ```python import torch import soundfile as sf from transformers import AutoConfig from xcodec2.modeling_xcodec2 import XCodec2Model model_path = "HKUST-Audio/xcodec2" model = XCodec2Model.from_pretrained(model_path) model.eval().cuda() wav, sr = sf.read("test.wav") wav_tensor = torch.from_numpy(wav).float().unsqueeze(0) # Shape: (1, T) with torch.no_grad(): # Only 16khz speech # Only supports single input. For batch inference, please refer to the link below. vq_code = model.encode_code(input_waveform=wav_tensor) print("Code:", vq_code ) recon_wav = model.decode_code(vq_code).cpu() # Shape: (1, 1, T') sf.write("reconstructed.wav", recon_wav[0, 0, :].numpy(), sr) print("Done! Check reconstructed.wav") ``` # If you want to train your own xcodec2, batch inference, or large-scale code extraction, the code is released [here](https://github.com/zhenye234/X-Codec-2.0).