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[ICLR 2025] SOTA discrete acoustic codec models with 40/75 tokens per second for audio language modeling
| Date | Stars |
|---|---|
| 2026-07-24 | 1306 |
| 2026-07-25 | 1306 |
| 2026-07-28 | 1306 |
| 2026-07-30 | 1306 |
| 2026-07-31 | 1311 |
| 2026-08-06 | 1311 |
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# WavTokenizer
SOTA Discrete Codec Models With Forty Tokens Per Second for Audio Language Modeling
[](https://arxiv.org/abs/2408.16532)
[](https://wavtokenizer.github.io/)
[](https://huggingface.co/novateur/WavTokenizer)
### 🎉🎉 with WavTokenizer, you can represent speech, music, and audio with only 40 tokens per second!
### 🎉🎉 with WavTokenizer, You can get strong reconstruction results.
### 🎉🎉 WavTokenizer owns rich semantic information and is build for audio language models such as GPT-4o.
<!--
# Tips
We have noticed that several works (approximately exceed ten recent months) have incorrectly cited WavTokenizer. Below is the correct citation format. We sincerely appreciate the community's attention and interest.
```
@article{ji2024wavtokenizer,
title={Wavtokenizer: an efficient acoustic discrete codec tokenizer for audio language modeling},
author={Ji, Shengpeng and Jiang, Ziyue and Wang, Wen and Chen, Yifu and Fang, Minghui and Zuo, Jialong and Yang, Qian and Cheng, Xize and Wang, Zehan and Li, Ruiqi and others},
journal={arXiv preprint arXiv:2408.16532},
year={2024}
}
```
-->
# 🔥 News
- *2025.02.25*: We update WavTokenizer camera ready version for ICLR 2025 and update WavTokenizer-large-v2 checkpoint on [huggingface](https://huggingface.co/novateur/WavTokenizer-large-speech-75token).
- *2024.10.22*: We update WavTokenizer on arxiv and release WavTokenizer-Large checkpoint.
- *2024.09.09*: We release WavTokenizer-medium checkpoint on [huggingface](https://huggingface.co/collections/novateur/wavtokenizer-medium-large-66de94b6fd7d68a2933e4fc0).
- *2024.08.31*: We release WavTokenizer on arxiv.
## Installation
To use WavTokenizer, install it using:
```bash
conda create -n wavtokenizer python=3.9
conda activate wavtokenizer
pip install -r requirements.txt
```
## Infer
### Part1: Reconstruct audio from raw wav
```python
from encoder.utils import convert_audio
import torchaudio
import torch
from decoder.pretrained import WavTokenizer
device=torch.device('cpu')
config_path = "./configs/xxx.yaml"
model_path = "./xxx.ckpt"
audio_outpath = "xxx"
wavtokenizer = WavTokenizer.from_pretrained0802(config_path, model_path)
wavtokenizer = wavtokenizer.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= wavtokenizer.encode_infer(wav, bandwidth_id=bandwidth_id)
audio_out = wavtokenizer.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
```python
from encoder.utils import convert_audio
import torchaudio
import torch
from decoder.pretrained import WavTokenizer
device=torch.device('cpu')
config_path = "./configs/xxx.yaml"
model_path = "./xxx.ckpt"
wavtokenizer = WavTokenizer.from_pretrained0802(config_path, model_path)
wavtokenizer = wavtokenizer.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= wavtokenizer.encode_infer(wav, bandwidth_id=bandwidth_id)
print(discrete_code)
```
### Part3: Audio reconstruction through codecs
```python
# audio_tokens [n_q,1,t]/[n_q,t]
features = wavtokenizer.codes_to_features(audio_tokens)
bandwidth_id = torch.tensor([0])
audio_out = wavtokenizer.decode(features, bandwidth_id=bandwidth_id)
```
## Available models
🤗 links to the Huggingface model hub.
| Model name | HuggingFace Excerpt of 6,744 characters
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