Kapre 0.3.6 (#132)

* Bugfix/pad end tflite (#131)

* update tensorflow requirements to have optional gpu version

* Update README.md

* Update README.md

* update CI

* remove extras requirement

* Add test for pad_end
Fix bug in tflite pad_end logic

* add missing arg

* comment

* better comment

* revert readme to master

* update travis

* Revert readme

* better readme

* test pad end

* Add tflite documentation

* with

* Add comment and formatting

Co-authored-by: Paul Kendrick <paul.kendrick@musictribe.com>

* bump version to 0.3.6

* reformat

* add release note

Co-authored-by: Paul Kendrick <kenders2000@gmail.com>
Co-authored-by: Paul Kendrick <paul.kendrick@musictribe.com>

README.md

@@ -86,6 +86,36 @@ model.fit(x, y)
 
 * See the Jupyter notebook at the [example folder](https://github.com/keunwoochoi/kapre/tree/master/examples)
 
+## Tflite compatbility
+
+The `STFT` layer is not tflite compatible (due to `tf.signal.stft`). To create a tflite
+compatible model, first train using the normal `kapre` layers then create a new
+model replacing `STFT` and `Magnitude` with `STFTTflite`, `MagnitudeTflite`.
+Tflite compatible layers are restricted to a batch size of 1 which prevents use
+of them during training.
+
+```python
+# assumes you have run the one-shot example above.
+from kapre import STFTTflite, MagnitudeTflite
+model_tflite = Sequential()
+
+model_tflite.add(STFTTflite(n_fft=2048, win_length=2018, hop_length=1024,
+               window_name=None, pad_end=False,
+               input_data_format='channels_last', output_data_format='channels_last',
+               input_shape=input_shape))
+model_tflite.add(MagnitudeTflite())
+model_tflite.add(MagnitudeToDecibel())  
+model_tflite.add(Conv2D(32, (3, 3), strides=(2, 2)))
+model_tflite.add(BatchNormalization())
+model_tflite.add(ReLU())
+model_tflite.add(GlobalAveragePooling2D())
+model_tflite.add(Dense(10))
+model_tflite.add(Softmax())
+
+# load the trained weights into the tflite compatible model.
+model_tflite.set_weights(model.get_weights())
+```
+
 # Citation
 
 Please cite this paper if you use Kapre for your work.

docs/release_note.rst

@@ -1,6 +1,10 @@
 Release Note
 ^^^^^^^^^^^^
 
+* 13 Nov 2021
+  - 0.3.6
+    - bugfix/pad end tflite #131
+
 * 18 March 2021
   - 0.3.5
     - Add `kapre.time_frequency_tflite` which uses tflite for a faster CPU inference.

kapre/__init__.py

@@ -1,4 +1,4 @@
-__version__ = '0.3.5'
+__version__ = '0.3.6'
 VERSION = __version__
 
 from . import composed

kapre/tflite_compatible_stft.py

@@ -175,9 +175,10 @@ def stft_tflite(signal, frame_length, frame_step, fft_length, window_fn, pad_end
     signal = tf.cast(signal, tf.float32)
     if pad_end:
         # the number of whole frames
+        # (NOTE: kenders2000), padding is pre-calculated and thus fixed in graph
         length_samples = signal.shape[-1]
-        num_steps_round_up = tf.math.ceil(length_samples / frame_step)
-        pad_amount = int((num_steps_round_up * frame_step) - length_samples)
+        num_steps_round_up = int(np.ceil(length_samples / frame_step))
+        pad_amount = (num_steps_round_up * frame_step + frame_length - frame_step) - length_samples
         signal = tf.pad(signal, tf.constant([[0, 0], [0, 0], [0, pad_amount]]))
     # Make the window be shape (1, frame_length) instead of just frame_length
     # in an effort to help the tflite broadcast logic.

kapre/time_frequency_tflite.py

@@ -31,7 +31,13 @@ class STFTTflite(STFT):
     Ues `stft_tflite` from tflite_compatible_stft.py, this contains a tflite
     compatible stft (using a rdft), and `fixed_frame()` to window the audio.
     Tflite does not cope with comple types so real and imaginary parts are stored in extra dim.
-    Ouput shape is now: (batch, channel, time, re/im) or (batch, time, channel, re/im)
+    Ouput shape is now: (batch, channel, time, re/im) or (batch, time, channel, re/im).
+    `MagnitudeTflite`, and `PhaseTflite` are versions of the `Magnitude` and `Phase`
+    layers that account for this extra dimensionality. Currently this layer is
+    restricted to a batch size of one, for training use the `STFT` layer, and
+    once complete transfer the weights to a new model, replacing the `STFT` layer
+    with the `STFTTflite` layer and `Magnitude` and `Phase` layers with
+    `MagnitudeTflite` and `PhaseTflite` layers.
 
     Additionally, it reshapes the output to be a proper 2D batch.
 

setup.py

@@ -2,7 +2,7 @@
 
 setup(
     name='kapre',
-    version='0.3.5',
+    version='0.3.6',
     description='Kapre: Keras Audio Preprocessors. Tensorflow.Keras layers for audio pre-processing in deep learning',
     author='Keunwoo Choi',
     url='http://github.com/keunwoochoi/kapre/',

tests/test_time_frequency.py

@@ -269,8 +269,9 @@ def _get_melgram_model(return_decibel, amin, dynamic_range, input_shape=None):
 @pytest.mark.parametrize('data_format', ['default', 'channels_first', 'channels_last'])
 @pytest.mark.parametrize('batch_size', [1, 2])
 @pytest.mark.parametrize('win_length', [1000, 512])
+@pytest.mark.parametrize('pad_end', [False, True])
 def test_spectrogram_tflite_correctness(
-    n_fft, hop_length, n_ch, data_format, batch_size, win_length
+    n_fft, hop_length, n_ch, data_format, batch_size, win_length, pad_end
 ):
     def _get_stft_model(following_layer=None, tflite_compatible=False):
         # compute with kapre
@@ -282,7 +283,7 @@ def _get_stft_model(following_layer=None, tflite_compatible=False):
                     win_length=win_length,
                     hop_length=hop_length,
                     window_name=None,
-                    pad_end=False,
+                    pad_end=pad_end,
                     input_data_format=data_format,
                     output_data_format=data_format,
                     input_shape=input_shape,
@@ -296,7 +297,7 @@ def _get_stft_model(following_layer=None, tflite_compatible=False):
                     win_length=win_length,
                     hop_length=hop_length,
                     window_name=None,
-                    pad_end=False,
+                    pad_end=pad_end,
                     input_data_format=data_format,
                     output_data_format=data_format,
                     input_shape=input_shape,