MelWeightMatrix

MelWeightMatrix - 17

Version

• name: MelWeightMatrix (GitHub)

• domain: main

• since_version: 17

• function:

• support_level: SupportType.COMMON

• shape inference: True

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

Summary

Attributes

• output_datatype - INT : The data type of the output tensor. Strictly must be one of the values from DataType enum in TensorProto whose values correspond to T3. The default value is 1 = FLOAT.

Inputs

• num_mel_bins (heterogeneous) - T1:

• dft_length (heterogeneous) - T1:

• sample_rate (heterogeneous) - T1:

• lower_edge_hertz (heterogeneous) - T2:

• upper_edge_hertz (heterogeneous) - T2:

Outputs

• output (heterogeneous) - T3:

Type Constraints

• T1 in ( tensor(int32), tensor(int64) ): Constrain to integer tensors.

• T2 in ( tensor(bfloat16), tensor(double), tensor(float), tensor(float16) ): Constrain to float tensors

• T3 in ( tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8) ): Constrain to any numerical types.

Examples

default

import numpy as np
import onnx

node = onnx.helper.make_node(
    "MelWeightMatrix",
    inputs=[
        "num_mel_bins",
        "dft_length",
        "sample_rate",
        "lower_edge_hertz",
        "upper_edge_hertz",
    ],
    outputs=["output"],
)

num_mel_bins = np.int32(8)
dft_length = np.int32(16)
sample_rate = np.int32(8192)
lower_edge_hertz = np.float32(0)
upper_edge_hertz = np.float32(8192 / 2)

num_spectrogram_bins = dft_length // 2 + 1
frequency_bins = np.arange(0, num_mel_bins + 2)

low_frequency_mel = 2595 * np.log10(1 + lower_edge_hertz / 700)
high_frequency_mel = 2595 * np.log10(1 + upper_edge_hertz / 700)
mel_step = (high_frequency_mel - low_frequency_mel) / frequency_bins.shape[0]

frequency_bins = frequency_bins * mel_step + low_frequency_mel
frequency_bins = 700 * (np.power(10, (frequency_bins / 2595)) - 1)
frequency_bins = ((dft_length + 1) * frequency_bins) // sample_rate
frequency_bins = frequency_bins.astype(int)

output = np.zeros((num_spectrogram_bins, num_mel_bins))
output.flags.writeable = True

for i in range(num_mel_bins):
    lower_frequency_value = frequency_bins[i]  # left
    center_frequency_point = frequency_bins[i + 1]  # center
    higher_frequency_point = frequency_bins[i + 2]  # right
    low_to_center = center_frequency_point - lower_frequency_value
    if low_to_center == 0:
        output[center_frequency_point, i] = 1
    else:
        for j in range(lower_frequency_value, center_frequency_point + 1):
            output[j, i] = float(j - lower_frequency_value) / float(
                low_to_center
            )
    center_to_high = higher_frequency_point - center_frequency_point
    if center_to_high > 0:
        for j in range(center_frequency_point, higher_frequency_point):
            output[j, i] = float(higher_frequency_point - j) / float(
                center_to_high
            )

# Expected output
# 1.000000, 1.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
# 0.000000, 0.000000, 1.000000, 1.000000, 0.000000, 0.000000, 0.000000, 0.000000,
# 0.000000, 0.000000, 0.000000, 0.000000, 1.000000, 0.000000, 0.000000, 0.000000,
# 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 1.000000, 0.000000, 0.000000,
# 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 1.000000, 0.000000,
# 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 1.000000,
# 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
# 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
# 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
output = output.astype(np.float32)
expect(
    node,
    inputs=[
        num_mel_bins,
        dft_length,
        sample_rate,
        lower_edge_hertz,
        upper_edge_hertz,
    ],
    outputs=[output],
    name="test_melweightmatrix",
)