(l-onnx-doc-STFT)=

# STFT


(l-onnx-op-stft-17)=

## STFT - 17

### Version

- **name**: [STFT (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#STFT)
- **domain**: `main`
- **since_version**: `17`
- **function**: `False`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `True`

This version of the operator has been available
**since version 17**.

### Summary

Computes the Short-time Fourier Transform of the signal.

### Attributes

* **onesided - INT** (default is `'1'`):

  If onesided is 1, only values for w in [0, 1, 2, ..., floor(n_fft/2) + 1] are returned because the real-to-complex Fourier transform satisfies the conjugate symmetry, i.e., X[m, w] = X[m,w]=X[m,n_fft-w]*. Note if the input or window tensors are complex, then onesided output is not possible. Enabling onesided with real inputs performs a Real-valued fast Fourier transform (RFFT).When invoked with real or complex valued input, the default value is 1. Values can be 0 or 1.

### Inputs

Between 2 and 4 inputs.

- **signal** (heterogeneous) - **T1**:

  Input tensor representing a real or complex valued signal. For real input, the following shape is expected: [batch_size][signal_length][1]. For complex input, the following shape is expected: [batch_size][signal_length][2], where [batch_size][signal_length][0] represents the real component and [batch_size][signal_length][1] represents the imaginary component of the signal.
- **frame_step** (heterogeneous) - **T2**:

  The number of samples to step between successive DFTs.
- **window** (optional, heterogeneous) - **T1**:

  A tensor representing the window that will be slid over the signal.The window must have rank 1 with shape: [window_shape]. It's an optional value.
- **frame_length** (optional, heterogeneous) - **T2**:

  A scalar representing the size of the DFT. It's an optional value.

### Outputs

- **output** (heterogeneous) - **T1**:

  The Short-time Fourier Transform of the signals.If onesided is 1, the output has the shape: [batch_size][frames][dft_unique_bins][2], where dft_unique_bins is frame_length // 2 + 1 (the unique components of the DFT) If onesided is 0, the output has the shape: [batch_size][frames][frame_length][2], where frame_length is the length of the DFT.

### Type Constraints

* **T1** in ( `tensor(bfloat16)`, `tensor(double)`, `tensor(float)`, `tensor(float16)` ):

  Constrain signal and output to float tensors.
* **T2** in ( `tensor(int32)`, `tensor(int64)` ):

  Constrain scalar length types to int64_t.