README.md

---
license: mit
library_name: litert
pipeline_tag: audio-to-audio
tags:
- audio
- neural-audio-codec
- dac
- litert
- tflite
- on-device
- gpu
---

# DAC (Descript Audio Codec) 16 kHz — LiteRT (CompiledModel GPU)

[Descript Audio Codec](https://github.com/descriptinc/descript-audio-codec) running **on-device on
the LiteRT CompiledModel GPU** (ML Drift). The convolutional encoder/decoder run on the GPU; the RVQ
runs on CPU. **43:1 compression** (1 s → 12×50 codes), **RTF ≈ 0.82** (faster than real-time) on Pixel 8a.

## Files
- `dac_16khz_encoder_fp16.tflite` (43 MB) — `audio[1,1,16000]` → `latent[1,1024,50]`, GPU.
- `dac_16khz_deconly_zs_fp16.tflite` (105 MB) — `latent[1,1024,50]` → `audio`, GPU.
- `dac_rvq.bin` (1.2 MB) — RVQ weights (12 codebooks) for the CPU quantizer (float32 LE).

## Pipeline
```
audio -> encoder.tflite (GPU) -> z -> RVQ.encode (CPU) -> codes[12,50]
      -> RVQ.decode (CPU) -> z_q -> decoder.tflite (GPU) -> audio
```

## On-device (Pixel 8a, Tensor G3 — verified)
encoder **367/367** + decoder **398/398** nodes on the LiteRT GPU delegate (`LITERT_CL`, 1 partition,
no CPU fallback); warm RTF ~0.82; reconstruction **corr 1.0** vs PyTorch DAC.

## Why the split
The decoder's `ConvTranspose1d` are rewritten to a GPU-clean **zero-stuff** form (the real DAC's
odd stride-5 transposed conv fails converter legalization, and `TRANSPOSE_CONV` is rejected by Mali).
The RVQ uses `EMBEDDING_LOOKUP` + int64 indices (Mali-rejected) so it runs on CPU. So the float conv
graph stays fully on the GPU.

Android sample + conversion/validation scripts:
**https://github.com/john-rocky/LiteRT-Models/tree/main/dac**

License: MIT (Descript DAC).