def test_melFilterbankLayer():
with make_scope() as session:
n_in, n_out = 257, 3
layer_name = "mel_filterbank_layer"
config = Config()
config.update({
"num_outputs": n_out,
"num_inputs": n_in,
"network": {
layer_name: {
"class": "mel_filterbank",
"fft_size": 512,
"nr_of_filters": n_out,
"n_out": n_out,
"is_output_layer": True
}
}
})
network = TFNetwork(config=config, train_flag=True)
network.construct_from_dict(config.typed_value("network"))
layer = network.layers[layer_name]
test_out = session.run(layer.output.placeholder,
feed_dict={
network.get_extern_data('data').placeholder:
np.ones((1, 1, 257))
})
assert np.sum(test_out - np.asarray(
[28.27923584, 53.10634232, 99.71585846], dtype=np.float32)) < 1e-5
def test_complexLinearProjectionLayer():
with make_scope() as session:
n_in, n_out = 514, 128
layer_name = "clp_layer"
config = Config()
config.update({
"num_outputs": n_out,
"num_inputs": n_in,
"network": {
layer_name: {
"class": "complex_linear_projection", "nr_of_filters": n_out, "n_out": n_out, "is_output_layer": True}
}})
network = TFNetwork(config=config, train_flag=True)
network.construct_from_dict(config.typed_value("network"))
layer = network.layers[layer_name]
assert isinstance(layer, ComplexLinearProjectionLayer)
i_r = np.ones((1, n_in // 2))
i_i = np.ones((1, n_in // 2)) * 0.5
test_input = np.expand_dims(np.reshape(np.transpose(
np.reshape(np.concatenate([i_r, i_i], axis=1), (1, 2, 257)), [0, 2, 1]), (1, 514)), 0)
test_clp_kernel = np.ones((2, n_in // 2, 128))
test_clp_output = session.run(
layer.output.placeholder,
feed_dict={network.get_extern_data('data').placeholder: test_input, layer._clp_kernel: test_clp_kernel})
assert test_clp_output[0, 0, 0] - 6.00722122 < 1e-5
def test_rfftStftConfig_01():
with make_scope() as session:
layer_name = "stft_layer"
fft_size = 400
frame_size = 400
frame_shift = 160
window = "hanning"
test_input = np.ones((1, 32000, 2), dtype=np.float32)
config = Config()
config.update({
"num_outputs": int(fft_size / 2) + 1 * test_input.shape[2],
"num_inputs": test_input.shape[2],
"network": {
layer_name: {
"class": "multichannel_stft_layer", "frame_shift": frame_shift, "frame_size": frame_size, "window": window, "fft_size": fft_size, "use_rfft": True, "nr_of_channels": 2, "is_output_layer": True}
}})
network = TFNetwork(config=config, train_flag=True)
network.construct_from_dict(config.typed_value("network"))
layer = network.layers[layer_name]
test_output = session.run(layer.output.placeholder, {network.get_extern_data('data').placeholder: test_input})
ref0 = _get_ref_output(test_input, fft_size, frame_size, frame_shift, window, 0, 0)
# np.fft.rfft and tensorflow.python.ops.rfft differ a little bit in their
# results, thus an error margin is allowed in the result
resultDiff = np.abs(test_output[0, 0, 0:(int(fft_size / 2) + 1)] - ref0)
assert np.mean(resultDiff) < 0.02
assert np.max(resultDiff) < 1
pass
def test_rfftStftConfig_01():
with make_scope() as session:
layer_name = "stft_layer"
fft_size = 400
frame_size = 400
frame_shift = 160
window = "hanning"
test_input = np.ones((1, 32000, 2), dtype=np.float32)
config = Config()
config.update({
"num_outputs": int(fft_size / 2) + 1 * test_input.shape[2],
"num_inputs": test_input.shape[2],
"network": {
layer_name: {
"class": "multichannel_stft_layer", "frame_shift": frame_shift, "frame_size": frame_size, "window": window, "fft_size": fft_size, "use_rfft": True, "nr_of_channels": 2, "is_output_layer": True}
}})
network = TFNetwork(config=config, train_flag=True)
network.construct_from_dict(config.typed_value("network"))
layer = network.layers[layer_name]
test_output = session.run(layer.output.placeholder, {network.get_extern_data('data').placeholder: test_input})
ref0 = _get_ref_output(test_input, fft_size, frame_size, frame_shift, window, 0, 0)
# np.fft.rfft and tensorflow.python.ops.rfft differ a little bit in their
# results, thus an error margin is allowed in the result
resultDiff = np.abs(test_output[0, 0, 0:(int(fft_size / 2) + 1)] - ref0)
assert np.mean(resultDiff) < 0.02
assert np.max(resultDiff) < 1
pass
def test_complexLinearProjectionLayer():
with make_scope() as session:
n_in, n_out = 514, 128
layer_name = "clp_layer"
config = Config()
config.update({
"num_outputs": n_out,
"num_inputs": n_in,
"network": {
layer_name: {
"class": "complex_linear_projection", "nr_of_filters": n_out, "n_out": n_out, "is_output_layer": True}
}})
network = TFNetwork(config=config, train_flag=True)
network.construct_from_dict(config.typed_value("network"))
layer = network.layers[layer_name]
assert isinstance(layer, ComplexLinearProjectionLayer)
i_r = np.ones((1, n_in // 2))
i_i = np.ones((1, n_in // 2)) * 0.5
test_input = np.expand_dims(np.reshape(np.transpose(
np.reshape(np.concatenate([i_r, i_i], axis=1), (1, 2, 257)), [0, 2, 1]), (1, 514)), 0)
test_clp_kernel = np.ones((2, n_in // 2, 128))
test_clp_output = session.run(
layer.output.placeholder,
feed_dict={network.get_extern_data('data').placeholder: test_input, layer._clp_kernel: test_clp_kernel})
assert test_clp_output[0, 0, 0] - 6.00722122 < 1e-5
def test_stftConfig_multi_res_02():
with make_scope() as session:
layer_name = "stft_layer"
fft_sizes = [400, 200, 800]
frame_sizes = [400, 200, 800]
frame_shift = 160
window = "hanning"
test_input = np.random.normal(0, 0.6, (1, 3200, 2))
num_outputs = int(np.sum([(int(fft_size / 2) + 1) * test_input.shape[2] for fft_size in fft_sizes]))
config = Config()
config.update({
"num_outputs": num_outputs,
"num_inputs": test_input.shape[2],
"network": {
layer_name: {
"class": "multichannel_multiresolution_stft_layer", "frame_shift": frame_shift, "frame_sizes": frame_sizes, "window": window, "fft_sizes": fft_sizes, "use_rfft": True, "nr_of_channels": 2, "is_output_layer": True}
}})
network = TFNetwork(config=config, train_flag=True)
network.construct_from_dict(config.typed_value("network"))
layer = network.layers[layer_name]
test_output = session.run(layer.output.placeholder, {network.get_extern_data('data').placeholder: test_input})
assert test_output.shape[2] == num_outputs
comparison_frame = 6
ref00 = _get_ref_output_single_res(test_input, fft_sizes[0], frame_sizes[0], frame_shift, window, comparison_frame, 0)
ref01 = _get_ref_output_single_res(test_input, fft_sizes[0], frame_sizes[0], frame_shift, window, comparison_frame, 1)
ref10 = _get_ref_output_single_res(test_input, fft_sizes[1], frame_sizes[1], frame_shift, window, comparison_frame, 0)
ref11 = _get_ref_output_single_res(test_input, fft_sizes[1], frame_sizes[1], frame_shift, window, comparison_frame, 1)
ref20 = _get_ref_output_single_res(test_input, fft_sizes[2], frame_sizes[2], frame_shift, window, comparison_frame, 0)
ref21 = _get_ref_output_single_res(test_input, fft_sizes[2], frame_sizes[2], frame_shift, window, comparison_frame, 1)
ref = np.concatenate([ref00, ref01, ref10, ref11, ref20, ref21], axis=0)
resultDiff = np.abs(test_output[0, comparison_frame, :] - ref)
assert np.mean(resultDiff) < 0.06
assert np.max(resultDiff) < 1
def test_stftConfig_single_res_01():
with make_scope() as session:
layer_name = "stft_layer"
fft_sizes = [400]
frame_sizes = [400]
frame_shift = 160
window = "hanning"
test_input = np.ones((1, 32000, 2), dtype=np.float32)
num_outputs = (int(fft_sizes[0] / 2) + 1) * test_input.shape[2]
config = Config()
config.update({
"num_outputs": num_outputs,
"num_inputs": test_input.shape[2],
"network": {
layer_name: {
"class": "multichannel_multiresolution_stft_layer", "frame_shift": frame_shift, "frame_sizes": frame_sizes, "window": window, "fft_sizes": fft_sizes, "use_rfft": True, "nr_of_channels": 2, "is_output_layer": True}
}})
network = TFNetwork(config=config, train_flag=True)
network.construct_from_dict(config.typed_value("network"))
layer = network.layers[layer_name]
test_output = session.run(layer.output.placeholder, {network.get_extern_data('data').placeholder: test_input})
ref0 = _get_ref_output_single_res(test_input, fft_sizes[0], frame_sizes[0], frame_shift, window, 0, 0)
resultDiff = np.abs(test_output[0, 0, 0:(int(fft_sizes[0] / 2) + 1)] - ref0)
assert test_output.shape[2] == num_outputs
assert np.mean(resultDiff) < 0.02
assert np.max(resultDiff) < 1
def test_melFilterbankLayer():
with make_scope() as session:
n_in, n_out = 257, 3
layer_name = "mel_filterbank_layer"
config = Config()
config.update({
"num_outputs": n_out,
"num_inputs": n_in,
"network": {
layer_name: {
"class": "mel_filterbank", "fft_size": 512, "nr_of_filters": n_out, "n_out": n_out, "is_output_layer": True}
}})
network = TFNetwork(config=config, train_flag=True)
network.construct_from_dict(config.typed_value("network"))
layer = network.layers[layer_name]
test_out = session.run(
layer.output.placeholder,
feed_dict={network.get_extern_data('data').placeholder: np.ones((1, 1, 257))})
assert np.sum(test_out - np.asarray([28.27923584, 53.10634232, 99.71585846], dtype=np.float32)) < 1e-5