ai.onnx.STFT

ai.onnx · standard ONNX operator · ONNX opset ≥ 17

Description

Computes the short-time Fourier transform (STFT) of a rank-3 real or complex signal by sliding a complete frame over the input and applying the DFT. The frameStep scalar represents the required positive frame_step input. At least one of window or frameLength is required; when both are supplied, their lengths must match. The output contains the real and imaginary components of every frequency bin for each complete frame.

See the ONNX STFT spec for the reference semantics.

Inputs

Name	Logical dtype	Rank	Shape	Description	Presence
`signal`	`T`	`3`	—	Real input signal with shape `(batch_size, signal_length, 1)`, or complex input with shape `(batch_size, signal_length, 2)`.	required
`window`	`T`	`1`	—	Optional 1-D window tensor with shape `(window_length)` to multiply by each frame before the DFT.	optional

Outputs

Name	Logical dtype	Rank	Shape	Description	Presence
`output`	`T`	`4`	derived	STFT result with shape `(batch_size, frames, dft_unique_bins, 2)` in one-sided mode or `(batch_size, frames, frame_length, 2)` in two-sided mode, with real and imaginary parts in the last dimension. `frames = floor((signal_length - frame_length) / frame_step) + 1`; partial trailing frames are not emitted.	required

Runtime arguments

Name	Kind	Upstream attribute	Description	Presence
`frameStep`	`i32`	`frame_step`	Positive number of signal positions between the starts of consecutive frames.	required
`frameLength`	`i32`	`frame_length`	Optional number of signal values in each frame; must equal the window length when both are supplied.	optional

Attributes

Default values (overridable per request):

Attribute	Default	Description
`onesided`	`1`	When 1 (default), returns `floor(n_fft / 2) + 1` non-redundant frequency bins with indices `[0, ..., floor(n_fft / 2)]` for a real-valued input (RFFT); set to 0 to return the full spectrum.

Type constraints

Variable	Allowed dtypes
`T`	`float32`

Files

metadata.json — kernel metadata (id, digests, per-variant templates, provenance)
manifest.json — the op contract (source of truth)
test.json — correctness cases
bench.json — benchmark + tuning cases
dft-tiled-real.wgsl.jinja
fft-radix2-dit-shared-inplace.wgsl.jinja
fft-radix2-dit-storage-pass.wgsl.jinja
fft-radix2-dit-storage.wgsl.jinja
fft-real-packed-stockham-shared.wgsl.jinja
fft-stockham-shared.wgsl.jinja
fft-stockham-twiddles.wgsl.jinja
stft-general.wgsl.jinja

Use with `@huggingface/kernels`

npm install --save-exact @huggingface/kernels@0.0.1-preview.2

Required output shapes and logical data types are inferred from the supplied inputs and attributes; result tensors are allocated automatically.

The version: 1 option selects the published kernel contract; it is independent of any operator opset, contrib since_version, or model version. It follows the v1 branch as fixes land. To pin exact artifact bytes, pass a 40-character commit revision instead of 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.STFT", { version: 1 });
const { output } = await kernel({
  signal: { data: signalData, shape: [1, 4, 1] },
  window: { data: windowData, shape: [4] },
  frameStep: 4,
});

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kernel

webgpu

wgsl

apache-2.0

WebGPU

Requires WebGPU support. See the compatibility table.