a733-whisper-base

Whisper Base for Allwinner A733 NPU (VIPLite) & CPU ONNX

This repository provides optimized models and pre-compiled binaries for running Whisper Base in a hybrid inference architecture on Single Board Computers powered by the Allwinner A733 SoC (e.g., Radxa Cubie A7A and compatible boards):

• Whisper Encoder: Compiled into a static NBG binary (network_binary.nb) running directly on the Allwinner/VeriSilicon VIP9000 NPU via VIPLite runtime (ctypes, zero C-wrapper).
• Whisper Decoder: Executed via onnxruntime on the CPU (base-decoder.onnx).
• Wyoming Protocol: Ready for low-latency, private, on-device Speech-To-Text (ASR) integration with Home Assistant without PyTorch or OpenAI-Whisper dependencies.

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📦 Repository Files

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⚙️ Model Architecture & Specifications

To comply with the Allwinner A733 NPU engine requirements, the encoder has been exported with a fixed static tensor shape:

• Audio Duration: Fixed at 15 seconds (shorter audio must be zero-padded to 15s; longer audio must be truncated or split using VAD/external chunking).
• Audio Frontend: 16 kHz Mono $\to$ Log-Mel Spectrogram with 80 bins.
• Encoder Input Shape (NCHW): [1, 80, 1500] FP16 (mapped to [1500, 80, 1] in VIPLite's [W,H,C,N] ordering).
• Encoder Output Shape: 2 tensors (cross_k and cross_v) with shape [6, 1, 750, 512] FP16 (mapped to [512, 750, 1, 6] in VIPLite), converted to FP32 for CPU decoding.

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🛠️ System Prerequisites (Allwinner A733 SBC)

Ensure the board has the NPU kernel device and VIPLite dynamic libraries installed:

# Check device node
ls -l /dev/vipcore

# Verify VIPLite runtime shared libraries
/sbin/ldconfig -p | grep -E 'VIPhal|NBGlinker'

Required system paths:

/dev/vipcore
/lib/libVIPhal.so
/lib/libNBGlinker.so

If missing, obtain them from the official Radxa/Allwinner model zoo:

• Archive: allwinner-model-zoo.tar.gz
• Library path: allwinner-model-zoo/common/npuruntime/lib_linux_aarch64/A733/

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How network_binary.nb Was Built

The NBG model was generated from the k2-fsa/sherpa-onnx Whisper Base model using the Acuity/Pegasus toolchain inside a Docker/Podman container.

Step 1: Fix ONNX input shape to static (15s)

import onnx

model = onnx.load("base-encoder.onnx")

for inp in model.graph.input:
    if inp.name == "mel":
        dims = inp.type.tensor_type.shape.dim
        dims[0].ClearField("dim_param")
        dims[0].dim_value = 1       # Batch size 1
        dims[2].ClearField("dim_param")
        dims[2].dim_value = 1500    # 15 seconds (1500 frames)

onnx.checker.check_model(model)
onnx.save(model, "base-encoder-static-15s.onnx")

Step 2: Convert via Pegasus (Acuity Toolchain)

Inside container khalida5/ubuntu-npu:v2.0.10:

alias pegasus="python3 /usr/local/acuity_command_line_tools/pegasus.py"

# Import ONNX model
pegasus import onnx \
    --model base-encoder-static-15s.onnx \
    --output-data whisper_base_encoder.data \
    --output-model whisper_base_encoder.json

# Generate metadata files
pegasus generate inputmeta \
  --model whisper_base_encoder.json \
  --input-meta-output model_inputmeta.yml

pegasus generate postprocess-file \
  --model whisper_base_encoder.json \
  --postprocess-file-output model_postprocess.yml

mkdir -p output_nbg

# Export compiled NBG for Allwinner A733 (VIP9000)
pegasus export ovxlib \
  --model whisper_base_encoder.json \
  --model-data whisper_base_encoder.data \
  --dtype float \
  --with-input-meta model_inputmeta.yml \
  --postprocess-file model_postprocess.yml \
  --target-ide-project linux64 \
  --optimize VIP9000NANODI_PLUS_PID0X1000003B \
  --viv-sdk "${VIV_SDK}" \
  --pack-nbg-unify \
  --output-path ./output_nbg/whisper_base_encoder

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🏷️ Credits

• Base Whisper architecture by OpenAI.
• ONNX export & vocab tokens by k2-fsa/sherpa-onnx.
• Allwinner A733 NPU SDK and toolchain references provided by Radxa.