module onnxrt.ops_cpu.op_stft
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Short summary#
module mlprodict.onnxrt.ops_cpu.op_stft
Runtime operator.
Classes#
class |
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STFT ==== Computes the Short-time Fourier Transform of the signal. Attributes |
Functions#
function |
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Reverses of stft. |
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Applies one dimensional FFT with window weights. torch defines the number of frames as: n_frames = 1 + (len - n_fft) / hop_length. … |
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Properties#
property |
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Returns the list of arguments as well as the list of parameters with the default values (close to the signature). … |
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Returns the list of modified parameters. |
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Returns the list of optional arguments. |
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Returns the list of optional arguments. |
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Returns all parameters in a dictionary. |
Methods#
method |
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Documentation#
Runtime operator.
- class mlprodict.onnxrt.ops_cpu.op_stft.STFT(onnx_node, desc=None, **options)#
Bases:
OpRun
Computes the Short-time Fourier Transform of the signal.
Attributes
onesided: 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. Default value is
nameonesidedi1typeINT
(INT)
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 tensor(float), tensor(float16), tensor(double), tensor(bfloat16): Constrain signal and output to float tensors.
T2 tensor(int32), tensor(int64): Constrain scalar length types to int64_t.
Version
Onnx name: STFT
This version of the operator has been available since version 17.
Runtime implementation:
STFT
- __init__(onnx_node, desc=None, **options)#
- _run(x, frame_step, window=None, frame_length=None, attributes=None, verbose=0, fLOG=None)#
Should be overwritten.
- mlprodict.onnxrt.ops_cpu.op_stft._concat(*args, axis=0)#
- mlprodict.onnxrt.ops_cpu.op_stft._istft(x, fft_length, hop_length, window, onesided=False)#
Reverses of stft.
- mlprodict.onnxrt.ops_cpu.op_stft._stft(x, fft_length, hop_length, n_frames, window, onesided=False)#
Applies one dimensional FFT with window weights. torch defines the number of frames as: n_frames = 1 + (len - n_fft) / hop_length.
- mlprodict.onnxrt.ops_cpu.op_stft._switch_axes(a, ax1, ax2)#
- mlprodict.onnxrt.ops_cpu.op_stft._unsqueeze(a, axis)#