Unified Semantic and Acoustic Codec for Audio Language Model.
Title: Codec Does Matter: Exploring the Semantic Shortcoming of Codec for Audio Language Model
Authors: Zhen Ye, Peiwen Sun, Jiahe Lei, Hongzhan Lin, Xu Tan, Zheqi Dai, Qiuqiang Kong, Jianyi Chen, Jiahao Pan, Qifeng Liu, Yike Guo, Wei Xue
You can easily apply our approach to enhance any existing acoustic codec:
For example
class Codec():
def __init__(self):
# Acoustic codec components
self.encoder = Encoder(...) # Acoustic encoder
self.decoder = Decoder(...) # Acoustic decoder
self.quantizer = RVQ(...) # Residual Vector Quantizer (RVQ)
# Adding the semantic module
self.semantic_model = AutoModel.from_pretrained(...) # e.g., Hubert, WavLM
# Adding Projector
self.fc_prior = nn.Linear(...)
self.fc_post1 = nn.Linear(...)
self.fc_post2 = nn.Linear(...)
def forward(self, x, bw):
# Encode the input acoustically and semantically
e_acoustic = self.encoder(x)
e_semantic = self.semantic_model(x)
# Combine acoustic and semantic features
combined_features = torch.cat([e_acoustic, e_semantic])
# Apply prior transformation
transformed_features = self.fc_prior(combined_features)
# Quantize the unified semantic and acoustic features
quantized, codes, bandwidth, commit_loss = self.quantizer(transformed_features, bw)
# Post-process the quantized features
quantized_semantic = self.fc_post1(quantized)
quantized_acoustic = self.fc_post2(quantized)
# Decode the quantized acoustic features
output = self.decoder(quantized_acoustic)
def semantic_loss(self,semantic,quantized_semantic):
return F.mse_loss(semantic,quantized_semantic)
For more details, please refer to our code.
π€ links to the Huggingface model hub.
Model name | Hugging Face | Config | Semantic Model | Domain | Training Data |
---|---|---|---|---|---|
xcodec_hubert_librispeech | π€ | π€ | π€ Hubert-base | Speech | Librispeech |
xcodec_wavlm_mls (not mentioned in paper) | π€ | π€ | π€ Wavlm-base-plus | Speech | MLS English |
xcodec_wavlm_more_data (not mentioned in paper) | π€ | π€ | π€ Wavlm-base-plus | Speech | MLS English + Internal data |
xcodec_hubert_general_audio | π€ | π€ | π€Hubert-base-general-audio | General audio | 200k hours internal data |
xcodec_hubert_general_audio_more_data (not mentioned in paper) | π€ | π€ | π€Hubert-base-general-audio | General audio | More balanced data |
To run inference, first download the model and config from hugging face.
python inference.py
Prepare the training_file and validation_file in config. The file should list the paths to your audio files:
/path/to/your/xxx.wav
/path/to/your/yyy.wav
...
Then:
torchrun --nnodes=1 --nproc-per-node=8 main_launch_vqdp.py
I would like to extend a special thanks to authors of Uniaudio and DAC, since our code base is mainly borrowed from Uniaudio and DAC.
If you find this repo helpful, please consider citing in the following format:
@article{ye2024codecdoesmatterexploring,
title={Codec Does Matter: Exploring the Semantic Shortcoming of Codec for Audio Language Model},
author={Zhen Ye and Peiwen Sun and Jiahe Lei and Hongzhan Lin and Xu Tan and Zheqi Dai and Qiuqiang Kong and Jianyi Chen and Jiahao Pan and Qifeng Liu and Yike Guo and Wei Xue},
journal={arXiv preprint arXiv:2408.17175},
year={2024},
}