Language-Codec: Reducing the Gaps Between Discrete Codec Representation and Speech Language Models

Installation

To use Language-Codec, install it using:

conda create -n xxx python=3.8
conda activate xxx
pip install -r requirement.txt

Infer

Part1: Reconstruct audio from raw wav

from encodec.utils import convert_audio
import torchaudio
import torch
from vocos.pretrained import Vocos

device=torch.device('cpu')

config_path = "xxx/languagecodec/configs/languagecodec.yaml"
model_path = "xxx/xxx.ckpt"
audio_outpath = "xxx"
vocos = Vocos.from_pretrained0802(config_path, model_path)
vocos = vocos.to(device)

wav, sr = torchaudio.load(audio_path)
wav = convert_audio(wav, sr, 24000, 1) 
bandwidth_id = torch.tensor([0])
wav=wav.to(device)
features,discrete_code= vocos.encode(wav, bandwidth_id=bandwidth_id)
audio_out = vocos.decode(features, bandwidth_id=bandwidth_id) 
torchaudio.save(audio_outpath, audio_out, sample_rate=24000, encoding='PCM_S', bits_per_sample=16)

Part2: Generating discrete codecs

from encodec.utils import convert_audio
import torchaudio
import torch
from vocos.pretrained import Vocos

device=torch.device('cpu')

config_path = "xxx/languagecodec/configs/languagecodec.yaml"
model_path = "xxx/xxx.ckpt"
vocos = Vocos.from_pretrained0802(config_path, model_path)
vocos = vocos.to(device)

wav, sr = torchaudio.load(audio_path)
wav = convert_audio(wav, sr, 24000, 1) 
bandwidth_id = torch.tensor([0])
wav=wav.to(device)
_,discrete_code= vocos.encode(wav, bandwidth_id=bandwidth_id)
print(discrete_code)

Part3: Audio reconstruction through codecs

# audio_tokens [n_q,1,t]/[n_q,t]
features = vocos.codes_to_features(audio_tokens)
bandwidth_id = torch.tensor([0])  
audio_out = vocos.decode(features, bandwidth_id=bandwidth_id)

Pre-trained models

Currently, we have only released the results from our paper, and we plan to release additional checkpoints trained on a larger training dataset within the next two months.

Model Name	Dataset	Training Iterations
languagecodec_paper_8nq	3W Hours	2.0 M

Training

Step1: Prepare train dataset

# Process the data into a form similar to xxx/languagecodec/data/libritts_testother.txt

Step2: Modifying configuration files

# xxx/languagecodec/configs/languagecodec.yaml
# Modify the values of parameters such as batch_size, filelist_path, save_dir, device

Step3: Start training process

Refer to Pytorch Lightning documentation for details about customizing the training pipeline.

cd xxx/languagecodec
python train.py fit --config xxx/languagecodec/configs/languagecodec.yaml

Citation

If this code contributes to your research, please cite our work:

@misc{ji2024languagecodec,
      title={Language-Codec: Reducing the Gaps Between Discrete Codec Representation and Speech Language Models}, 
      author={Shengpeng Ji and Minghui Fang and Ziyue Jiang and Rongjie Huang and Jialung Zuo and Shulei Wang and Zhou Zhao},
      year={2024},
      eprint={2402.12208},
      archivePrefix={arXiv},
      primaryClass={eess.AS}
}

License

The code in this repository is released under the MIT license as found in the LICENSE file.

AmorJNYH / languagecodec_tmp