ai.onnx.BlackmanWindow
ai.onnx · standard ONNX operator · ONNX opset ≥ 17
Description
Generates a Blackman window of a given length using the cosine-sum formula 0.42 - 0.5*cos(2πn/N) + 0.08*cos(4πn/N). Supports periodic mode (divides by size) or symmetric mode (divides by size - 1).
See the ONNX BlackmanWindow spec for the reference semantics.
Inputs
| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
|---|---|---|---|---|---|---|
size |
size |
T1 |
0 |
— | Scalar length of the window to generate. | required |
Outputs
| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
|---|---|---|---|---|---|---|
output |
y |
T2 |
1 |
— | 1-D Blackman window of shape [size]. |
required |
Attributes
Default values (overridable per request):
| Attribute | Default | Description |
|---|---|---|
output_datatype |
1 |
Data type of the output tensor, specified as a TensorProto DataType enum integer; defaults to 1 (FLOAT). |
periodic |
1 |
If 1, generates a periodic window (length size + 1 trimmed to size); if 0, generates a symmetric window. |
Type constraints
| Variable | Allowed dtypes |
|---|---|
T1 |
int32 |
T2 |
float32, float16, uint32, int32, uint8, int8, int16 |
Files
metadata.json— kernel metadata (id, digests, provenance)manifest.json— the op contract (source of truth)test.json— correctness casesbench.json— benchmark + tuning caseswindow-cosine-sum.wgsl.jinja
Use with @huggingface/kernels
The loader automatically allocates outputs whose metadata it can derive from the manifest contract and this call.
The explicit outputs entries provide shape and logical dtype metadata for the results listed below:
y
Each entry either requests an optional result or supplies metadata that cannot be inferred from the inputs.
The version: 1 option selects the published kernel contract; it is independent of any operator opset, contrib since_version, or model version.
Replace each *Data placeholder with a typed array containing the corresponding input data.
import { getKernel } from "@huggingface/kernels";
const kernel = await getKernel("webgpu-kernels/ai.onnx.BlackmanWindow", { version: 1 });
// Explicit destinations request optional results or supply metadata that cannot be inferred.
const { y } = await kernel({ size: { data: sizeData, shape: [] } }, {
outputs: { y: { shape: [1], dtype: "float32" } },
});
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Requires WebGPU support. See the compatibility table.