| --- |
| license: mit |
| library_name: pytorch |
| tags: |
| - audio |
| - audio-codec |
| - neural-audio-codec |
| - henac |
| --- |
| |
| # HE-NAC 300k 🎧 |
|
|
| The 300k checkpoint for **HE-NAC: High-Efficiency Neural Audio Coding Via |
| Hierarchical and Residual Multi-Band Quantization**. HE-NAC is a 32 kHz neural |
| audio codec with core, mid-band, and high-band paths trained in sequence. Its |
| encoder and residual vector quantizers build on [DAC](https://github.com/descriptinc/descript-audio-codec). |
|
|
| - **Code and training recipes:** [darius522/henac on GitHub](https://github.com/darius522/henac) |
| - **Paper:** [IEEE Transactions on Audio, Speech and Language Processing](https://doi.org/10.1109/TASLPRO.2026.3739174) |
| - **Project page:** [minjekim.com/research-projects/he-nac](https://minjekim.com/research-projects/he-nac) |
| - **Checkpoint license:** MIT (see [LICENSE](LICENSE)) |
|
|
| ## Checkpoint |
|
|
| | File | Contents | SHA-256 | |
| | --- | --- | --- | |
| | `henac-300k.pth` | Generator weights and embedded model configuration | `978c7e9950b787b7a3673c198678c711cd5fab2fe766f4e41516125fc35a305e` | |
|
|
| This is a self-contained inference checkpoint made from the original 300k |
| high-band weights and their saved configuration. It does not include the |
| discriminator, optimizer, or training corpus. The recommended operating point |
| is **16 core, 4 mid, and 2 high codebooks**. |
|
|
| ## Use it 🚀 |
|
|
| From a checkout of the [code repository](https://github.com/darius522/henac): |
|
|
| ```bash |
| python -m pip install -e . |
| python -m pip install -U huggingface_hub |
| hf download darius522/henac henac-300k.pth --local-dir checkpoints |
| henac-infer --checkpoint checkpoints/henac-300k.pth \ |
| --input path/to/audio.wav --output outputs --codebooks 16 4 2 |
| ``` |
|
|
| The CLI accepts WAV or FLAC input, resamples to 32 kHz, and writes reconstructed |
| PCM WAVs. It averages multichannel input to mono for coding and copies the |
| reconstruction back to the input channel count. See the code repository for |
| staged training and evaluation commands. |
|
|
| ## Validation and scope |
|
|
| The consolidated implementation strictly loads this checkpoint. On 39 supplied |
| MUSHRA inputs, its `[16, 4, 2]` reconstructions were **byte identical** to |
| those regenerated by the original `entropy_ctrl_hb` implementation on the |
| checked A100 runtime. Against the supplied gold WAVs, the lowest exact PCM |
| fraction was 98.870% and the lowest signal-to-difference ratio was 47.96 dB; |
| the legacy implementation showed the same differences. See the |
| [parity record](https://github.com/darius522/henac/blob/master/docs/reference-parity.md) |
| for hashes and test conditions. |
|
|
| The current code reconstructs waveforms but does not produce a compressed |
| bitstream. Its `entropy.csv` values are empirical Shannon entropy estimates, |
| not actual Huffman-coded file sizes. The paper reports bitrates measured after |
| Huffman coding. The original training corpus is not redistributed here, so a |
| full 300k-step training trajectory has not been reproduced. |
|
|
| ## Citation 📄 |
|
|
| ```bibtex |
| @ARTICLE{11718947, |
| author={Pétermann, Darius and Lim, Wootaek and Beack, Seungkwon and Kim, Minje}, |
| journal={IEEE Transactions on Audio, Speech and Language Processing}, |
| title={HE-NAC: High-Efficiency Neural Audio Coding Via Hierarchical and Residual Multi-Band Quantization}, |
| year={2026}, |
| volume={}, |
| number={}, |
| pages={1-15}, |
| keywords={Modeling;Codes;Bit rate;Speech;Encoding;Audio coding;High frequency;Distance measurement;Frequency;Decoding;Neural audio coding;high-efficiency;multiband;residual}, |
| doi={10.1109/TASLPRO.2026.3739174}} |
| ``` |
|
|
