def testStreaming(self, input_samples):
# prepare non streaming model
stft_layer = stft.STFT(self.frame_size,
self.frame_step,
mode=modes.Modes.TRAINING,
inference_batch_size=1,
padding='causal')
input_tf = tf.keras.layers.Input(shape=(self.input_signal.shape[1], ),
batch_size=1)
net = stft_layer(input_tf)
model_non_stream = tf.keras.models.Model(input_tf, net)
params = test_utils.Params([1])
# shape of input data in the inference streaming mode (excluding batch size)
params.data_shape = (input_samples * stft_layer.frame_step, )
params.step = input_samples
# convert it to streaming model
model_stream = utils.to_streaming_inference(
model_non_stream, params,
modes.Modes.STREAM_INTERNAL_STATE_INFERENCE)
model_stream.summary()
# run streaming inference and compare it with default stft
stream_out = inference.run_stream_inference(params, model_stream,
self.input_signal)
stream_output_length = stream_out.shape[1]
self.assertAllClose(stream_out, self.stft_out[:,
0:stream_output_length])
def testNonStreaming(self):
# prepare non streaming model and compare it with default stft
stft_layer = stft.STFT(self.frame_size,
self.frame_step,
mode=modes.Modes.TRAINING,
inference_batch_size=1,
padding='causal')
input_tf = tf.keras.layers.Input(shape=(self.input_signal.shape[1], ),
batch_size=1)
net = stft_layer(input_tf)
model_non_stream = tf.keras.models.Model(input_tf, net)
self.non_stream_out = model_non_stream.predict(self.input_signal)
self.assertAllClose(self.non_stream_out, self.stft_out)
def setUp(self):
super(STFTTest, self).setUp()
test_utils.set_seed(123)
self.frame_size = 40
self.frame_step = 10
# layer definition
stft_layer = stft.STFT(self.frame_size,
self.frame_step,
mode=modes.Modes.TRAINING,
inference_batch_size=1,
padding='causal')
if stft_layer.window_type == 'hann_tf':
synthesis_window_fn = tf.signal.hann_window
else:
synthesis_window_fn = None
# prepare input data
self.input_signal = np.random.rand(1, 120)
# prepare default tf stft
padding_layer = temporal_padding.TemporalPadding(
padding_size=stft_layer.frame_size - 1, padding=stft_layer.padding)
# pylint: disable=g-long-lambda
stft_default_layer = tf.keras.layers.Lambda(
lambda x: tf.signal.stft(x,
stft_layer.frame_size,
stft_layer.frame_step,
fft_length=stft_layer.fft_size,
window_fn=synthesis_window_fn,
pad_end=False))
# pylint: enable=g-long-lambda
input_tf = tf.keras.layers.Input(shape=(self.input_signal.shape[1], ),
batch_size=1)
net = padding_layer(input_tf)
net = stft_default_layer(net)
model_stft = tf.keras.models.Model(input_tf, net)
self.stft_out = model_stft.predict(self.input_signal)