wehub-resource-sync
115 lines
3.4 KiB
Python
115 lines
3.4 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
import unittest
|
|
|
|
import numpy as np
|
|
from numpy.lib.stride_tricks import as_strided
|
|
from op_test import OpTest
|
|
|
|
import paddle
|
|
|
|
|
|
def frame_from_librosa(x, frame_length, hop_length, axis=-1):
|
|
if axis == -1 and not x.flags["C_CONTIGUOUS"]:
|
|
x = np.ascontiguousarray(x)
|
|
elif axis == 0 and not x.flags["F_CONTIGUOUS"]:
|
|
x = np.asfortranarray(x)
|
|
|
|
n_frames = 1 + (x.shape[axis] - frame_length) // hop_length
|
|
strides = np.asarray(x.strides)
|
|
|
|
if axis == -1:
|
|
shape = [*list(x.shape)[:-1], frame_length, n_frames]
|
|
strides = [*strides, hop_length * x.itemsize]
|
|
|
|
elif axis == 0:
|
|
shape = [n_frames, frame_length, *list(x.shape)[1:]]
|
|
strides = [hop_length * x.itemsize, *strides]
|
|
|
|
else:
|
|
raise ValueError(f"Frame axis={axis} must be either 0 or -1")
|
|
|
|
return as_strided(x, shape=shape, strides=strides)
|
|
|
|
|
|
def stft_np(x, window, n_fft, hop_length, **kwargs):
|
|
frames = frame_from_librosa(x, n_fft, hop_length)
|
|
frames = np.multiply(frames.transpose([0, 2, 1]), window).transpose(
|
|
[0, 2, 1]
|
|
)
|
|
res = np.fft.rfft(frames, axis=1)
|
|
return res
|
|
|
|
|
|
class TestStftOp(OpTest):
|
|
def setUp(self):
|
|
self.op_type = "stft"
|
|
self.shape, self.type, self.attrs = self.initTestCase()
|
|
self.inputs = {
|
|
'X': np.random.random(size=self.shape).astype(self.type),
|
|
'Window': np.hamming(self.attrs['n_fft']).astype(self.type),
|
|
}
|
|
self.outputs = {
|
|
'Out': stft_np(
|
|
x=self.inputs['X'], window=self.inputs['Window'], **self.attrs
|
|
)
|
|
}
|
|
|
|
def initTestCase(self):
|
|
input_shape = (2, 100)
|
|
input_type = 'float64'
|
|
attrs = {
|
|
'n_fft': 50,
|
|
'hop_length': 15,
|
|
'normalized': False,
|
|
'onesided': True,
|
|
}
|
|
return input_shape, input_type, attrs
|
|
|
|
def test_check_output(self):
|
|
paddle.enable_static()
|
|
self.check_output(check_dygraph=False)
|
|
paddle.disable_static()
|
|
|
|
def test_check_grad_normal(self):
|
|
paddle.enable_static()
|
|
self.check_grad(['X'], 'Out', check_dygraph=False)
|
|
paddle.disable_static()
|
|
|
|
|
|
class TestStftOpReal(unittest.TestCase):
|
|
def test_as_real(self):
|
|
input = np.random.randn(4410)
|
|
x = paddle.to_tensor(data=input, dtype='float32')
|
|
n_fft = 400
|
|
res0 = paddle.signal.stft(n_fft=n_fft, x=x)
|
|
res_a = paddle.as_real(res0)
|
|
|
|
res_np = res0.numpy()
|
|
res_p = paddle.to_tensor(data=res_np)
|
|
res_b = paddle.as_real(res_p)
|
|
|
|
np.testing.assert_allclose(
|
|
res0.numpy(), res_p.numpy(), rtol=1e-5, atol=1e-5
|
|
)
|
|
|
|
np.testing.assert_allclose(
|
|
res_a.numpy(), res_b.numpy(), rtol=1e-5, atol=1e-5
|
|
)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|