---
license: mit
base_model: mlboydaisuke/DAC-16kHz-LiteRT
base_model_relation: quantized
library_name: openvino
pipeline_tag: audio-to-audio
tags:
- openvino
- onnx
- intel-npu
---

# DAC-16kHz-OpenVINO

DAC-16kHz for Intel NPUs, GPUs and CPUs: dac_16khz_encoder_fp16 and dac_16khz_deconly_zs_fp16 from [mlboydaisuke/DAC-16kHz-LiteRT](https://huggingface.co/mlboydaisuke/DAC-16kHz-LiteRT/tree/376f41e9b4e68ec1ad3088946d5d648fc77a9d45) as ONNX, OpenVINO IR with fp32 weights, and precompiled Intel NPU blobs. The encoder turns 1 s of 16 kHz audio into a latent [1,1024,50]. The RVQ on the CPU turns it into codes and back, with that repo's `dac_rvq.bin`. The decoder turns the latent into 15,992 samples. A Core Ultra 9 386H's NPU takes 23.91 ms (encoder) and 59.10 ms (decoder) per 1 s of audio.

Scripts, raw records, and the same table for all 52 graphs of LiteRT-Models: https://github.com/john-rocky/windows-zoo

## Files

| file | bytes | sha256 |
|---|---|---|
| `dac_16khz_deconly_zs_fp16.onnx` | 209,727,641 | `965d11e2e7a38094249e44926ca18cf5e3a0caff2eb093842cc35ebfae3ee2a4` |
| `openvino/dac_16khz_deconly_zs_fp16.xml` | 611,912 | `394fb94d5d5fbd4a52a7017a93ae53159a1a2a85b6ed34fc40c68cf161202e55` |
| `openvino/dac_16khz_deconly_zs_fp16.bin` | 209,451,688 | `0cc8cbf682061fad31f502eebcbfec2847c002913a0463b67c6e19d8bedab122` |
| `npu/dac_16khz_deconly_zs_fp16.blob` | 118,108,289 | `412c9345c54d8312345bed055435b3742c741086db627773366b01826c2e238d` |
| `dac_16khz_encoder_fp16.onnx` | 86,354,740 | `544231058e78b9228b00a6bfd3f451ea45e561acb46423d7f6c24ac229eac732` |
| `openvino/dac_16khz_encoder_fp16.xml` | 572,863 | `31922a9f01d1dd0350c56cd90d0a73758b5457440e454d349bea0d7561b7374b` |
| `openvino/dac_16khz_encoder_fp16.bin` | 86,090,404 | `0122fdb095750e1cb4d418b1ccab28680d1f7891bbacaa565565d7a1373b7a24` |
| `npu/dac_16khz_encoder_fp16.blob` | 47,616,129 | `8a2a98e7252ba521823188faff4b9d16a477ea998b8ea2b29420f83ea6a5dfa0` |

The NPU blobs were compiled with OpenVINO 2026.4.1, Intel NPU driver 32.0.100.4841 (OpenVINO reports NPU_DRIVER_VERSION 1004841, NPU_COMPILER_VERSION 524292). If `import_model` refuses a blob on another driver or OpenVINO version, compile the IR instead (the commented line in the snippet).

## Conversion

The ONNX files were converted from the `.tflite` files with tf2onnx 1.17.0 (opset 17).

## How it was measured

MSI Stealth 16 AI+ (Core Ultra 9 386H, RTX 5080 Laptop GPU at a 95 W power limit, Windows 11 build 26200.9711, on AC power), 2026-11-06 to 2026-11-07. On the laptop, each row is one process: load, one run, 5 warmup runs, then the median of 30 timed runs (2 warmup + 10 timed runs when the first run takes more than 3 s), numpy input to numpy output, every runtime at its default settings, one process at a time, each started after the CPU, the RTX and the Intel GPU had gone idle (the thresholds are in METHOD.md of the GitHub repo). Output check: each output against the CPU output in fp32 on the same input (ONNX Runtime's CPU EP); PASS = cosine ≥ 0.99, no NaN or Inf, and top-1 agreement ≥ 0.99 for class scores. The Snapdragon rows are Qualcomm AI Hub profile jobs of the same ONNX files (their own timer).

## dac_16khz_deconly_zs_fp16

| processor | runtime | file | ms | load s | output check |
|---|---|---|---|---|---|
| Intel AI Boost NPU | OpenVINO 2026.4.1 | `npu/` blob (`import_model`) | 59.10 | 0.07 | PASS (cos ≥ 0.9999) |
| Intel AI Boost NPU | ONNX Runtime 1.31.1 + OpenVINO EP 1.8.1 | `.onnx`, compiled when the session loads | 59.49 | 26.72 | PASS (cos ≥ 0.9999) |
| Intel Graphics (iGPU) | ONNX Runtime 1.31.1 + OpenVINO EP 1.8.1 | `.onnx` | 29.22 | 1.71 | FAIL: output_0_output: cos 0.5917 < 0.99 |
| RTX 5080 Laptop (95 W limit) | ONNX Runtime 1.31.1, CUDA EP | fp16 copy of the `.onnx` (not in this repo) | 6.20 | 1.31 | PASS (cos ≥ 0.9999) |
| RTX 5080 Laptop (95 W limit) | ONNX Runtime 1.31.1, CUDA EP | `.onnx` | 9.14 | 1.34 | PASS (cos ≥ 0.9999) |
| Core Ultra 9 386H CPU | ONNX Runtime 1.31.1, CPU EP | `.onnx` | 578.78 | 0.61 | reference |
| Snapdragon X2 Elite NPU | Qualcomm AI Hub, precompiled QNN ONNX | `.onnx` compiled by AI Hub | 155.58 | 0.52 | not compared (AI Hub profile job, 2026-10-30) |
| Snapdragon X2 Elite NPU | Qualcomm AI Hub, ONNX Runtime QNN EP | `.onnx`, compiled when it loads | 159.35 (4 layers on the CPU) | 88.34 | not compared (AI Hub profile job, 2026-10-30) |
| Galaxy S26 NPU | Qualcomm AI Hub, precompiled QNN ONNX | `.onnx` compiled by AI Hub | 150.21 | 0.42 | not compared (AI Hub profile job, 2026-10-30) |

The ONNX file against the source `.tflite` (LiteRT interpreter on the CPU, the same input): PASS, cosine ≥ 0.9999 for every output.

## dac_16khz_encoder_fp16

| processor | runtime | file | ms | load s | output check |
|---|---|---|---|---|---|
| Intel AI Boost NPU | OpenVINO 2026.4.1 | `npu/` blob (`import_model`) | 23.91 | 0.03 | PASS (cos ≥ 0.9927) |
| Intel AI Boost NPU | ONNX Runtime 1.31.1 + OpenVINO EP 1.8.1 | `.onnx`, compiled when the session loads | 24.37 | 11.22 | PASS (cos ≥ 0.9927) |
| Intel Graphics (iGPU) | ONNX Runtime 1.31.1 + OpenVINO EP 1.8.1 | `.onnx` | 8.24 | 1.10 | PASS (cos ≥ 0.9930) |
| RTX 5080 Laptop (95 W limit) | ONNX Runtime 1.31.1, CUDA EP | fp16 copy of the `.onnx` (not in this repo) | 4.04 | 1.12 | PASS (cos ≥ 0.9929) |
| RTX 5080 Laptop (95 W limit) | ONNX Runtime 1.31.1, CUDA EP | `.onnx` | 5.62 | 1.16 | PASS (cos ≥ 0.9999) |
| Core Ultra 9 386H CPU | ONNX Runtime 1.31.1, CPU EP | `.onnx` | 79.90 | 0.17 | reference |
| Snapdragon X2 Elite NPU | Qualcomm AI Hub, precompiled QNN ONNX | `.onnx` compiled by AI Hub | 69.64 | 0.45 | not compared (AI Hub profile job, 2026-10-30) |
| Snapdragon X2 Elite NPU | Qualcomm AI Hub, ONNX Runtime QNN EP | `.onnx`, compiled when it loads | 74.74 | 24.80 | not compared (AI Hub profile job, 2026-10-30) |
| Galaxy S26 NPU | Qualcomm AI Hub, precompiled QNN ONNX | `.onnx` compiled by AI Hub | 70.31 | 0.30 | not compared (AI Hub profile job, 2026-10-30) |

The ONNX file against the source `.tflite` (LiteRT interpreter on the CPU, the same input): PASS, cosine ≥ 0.9999 for every output.

## Run it on the Intel NPU

Download this repo, then run the snippets from its folder:

```sh
pip install huggingface_hub
hf download mlboydaisuke/DAC-16kHz-OpenVINO --local-dir DAC-16kHz-OpenVINO
cd DAC-16kHz-OpenVINO
```

OpenVINO (`pip install openvino==2026.4.1`):

```python
import numpy as np
import openvino as ov

core = ov.Core()
if "NPU" not in core.available_devices:
    raise SystemExit(f"no Intel NPU visible to OpenVINO: {core.available_devices}")
with open("npu/dac_16khz_deconly_zs_fp16.blob", "rb") as f:
    compiled = core.import_model(f.read(), "NPU")  # precompiled: no compile at load
# or compile the IR on this machine: compiled = core.compile_model("openvino/dac_16khz_deconly_zs_fp16.xml", "NPU")
# replace the zeros with your preprocessed input (float32, the shapes printed by compiled.inputs)
feeds = {i.get_any_name(): np.zeros(list(i.shape), np.float32) for i in compiled.inputs}
outputs = compiled(feeds)
print({o.get_any_name(): outputs[o].shape for o in compiled.outputs})
```

ONNX Runtime with the OpenVINO EP (`pip install onnxruntime==1.31.1 onnxruntime-ep-openvino==1.8.1`):

```python
import numpy as np
import onnxruntime as ort
import onnxruntime_ep_openvino as ov_ep

ort.register_execution_provider_library(ov_ep.get_ep_name(), ov_ep.get_library_path())
npu = [d for d in ort.get_ep_devices()
       if d.ep_name == ov_ep.get_ep_name() and d.ep_metadata.get("ov_device") == "NPU"]
if not npu:
    raise SystemExit("no Intel NPU visible to the OpenVINO EP")
so = ort.SessionOptions()
so.add_provider_for_devices(npu, {})
sess = ort.InferenceSession("dac_16khz_deconly_zs_fp16.onnx", sess_options=so)  # compiles for the NPU here
# replace the zeros with your preprocessed input (float32)
feeds = {i.name: np.zeros(i.shape, np.float32) for i in sess.get_inputs()}
outputs = sess.run(None, feeds)
print([o.shape for o in outputs])
```

The same lines run dac_16khz_encoder_fp16 with its own file names.

The RVQ between the two graphs is `DacRVQ.kt` in [LiteRT-Models/dac](https://github.com/john-rocky/LiteRT-Models/tree/main/dac); it reads `dac_rvq.bin` from mlboydaisuke/DAC-16kHz-LiteRT.

## Licence

MIT, the licence of [mlboydaisuke/DAC-16kHz-LiteRT](https://huggingface.co/mlboydaisuke/DAC-16kHz-LiteRT).