Kapre 0.3.4 (#101)

* working on example

* fix window function in backend

* add release note; bump version

Co-authored-by: Keunwoo Choi <[email protected]>

docs/quickstart.rst

@@ -1,46 +1,62 @@
-One-shot example
-^^^^^^^^^^^^^^^^
+Examples
+========
+
+How To Import
+-------------
+
+.. code-block:: python
+
+    import kapre  # to import the whole library
+    from kapre import (  # `time_frequency` layers can be directly imported from `kapre`
+        STFT,
+        InverseSTFT,
+        Magnitude,
+        Phase,
+        MagnitudeToDecibel,
+        ApplyFilterbank,
+        Delta,
+        ConcatenateFrequencyMap,
+    )
+    from kapre import (  # `signal` layers can be also directly imported from kapre
+        Frame,
+        Energy,
+        MuLawEncoding,
+        MuLawDecoding,
+        LogmelToMFCC,
+    )
+    # from kapre import backend  # we can do this, but `backend` might be a too general name
+    import kapre.backend  # for namespace sanity, you might prefer this
+    from kapre import backend as kapre_backend  # or maybe this
+    from kapre.composed import (  # function names in `composed` are purposefully verbose.
+        get_stft_magnitude_layer,
+        get_melspectrogram_layer,
+        get_log_frequency_spectrogram_layer,
+        get_perfectly_reconstructing_stft_istft,
+        get_stft_mag_phase,
+        get_frequency_aware_conv2d,
+    )
+
+Use STFT Magnitude
+------------------
 
 .. code-block:: python
 
-    from tensorflow.keras.models import Sequential
-    from tensorflow.keras.layers import Conv2D, BatchNormalization, ReLU, GlobalAveragePooling2D, Dense, Softmax
-    from kapre import STFT, Magnitude, MagnitudeToDecibel
-    from kapre.composed import get_melspectrogram_layer, get_log_frequency_spectrogram_layer, get_stft_magnitude_layer
-
-    # 6 channels (!), maybe 1-sec audio signal, for an example.
-    input_shape = (6, 44100)
-    sr = 44100
-    model = Sequential()
-    # A STFT layer
-    model.add(STFT(n_fft=2048, win_length=2018, hop_length=1024,
-                   window_fn=None, pad_end=False,
-                   input_data_format='channels_last', output_data_format='channels_last',
-                   input_shape=input_shape))
-    model.add(Magnitude())
-    model.add(MagnitudeToDecibel())  # these three layers can be replaced with get_stft_magnitude_layer()
-    # Alternatively, you may want to use a melspectrogram layer
-    # melgram_layer = get_melspectrogram_layer()
-    # or log-frequency layer
-    # log_stft_layer = get_log_frequency_spectrogram_layer() 
-
-    # add more layers as you want
-    model.add(Conv2D(32, (3, 3), strides=(2, 2)))
-    model.add(BatchNormalization())
-    model.add(ReLU())
-    model.add(GlobalAveragePooling2D())
-    model.add(Dense(10))
-    model.add(Softmax())
-
-    # Compile the model
-    model.compile('adam', 'categorical_crossentropy') # if single-label classification
-
-    # train it with raw audio sample inputs
-    # for example, you may have functions that load your data as below.
-    x = load_x() # e.g., x.shape = (10000, 6, 44100)
-    y = load_y() # e.g., y.shape = (10000, 10) if it's 10-class classification
-    # then..
-    model.fit(x, y)
-    # Done!
+from tensorflow.keras.models import Sequential
+from kapre import STFT, Magnitude, MagnitudeToDecibel
+from kapre.composed import get_stft_magnitude_layer
+
+sampling_rate = 16000  # sampling rate of your input audio
+duration = 20.0  # duration of the audio
+num_channel = 2  # number of channels of the audio
+input_shape = (num_channel, int(sampling_rate * duration))  # let's follow `channels_last` convention even for audio
+
+model = Sequential()
+model.add(STFT(n_fft=2048, win_length=2018, hop_length=1024,
+               window_name='hann_window', pad_end=False,
+               input_data_format='channels_last', output_data_format='channels_last',
+               input_shape=input_shape))
+model.add(Magnitude())
+model.add(MagnitudeToDecibel())  # these three layers can be replaced with get_stft_magnitude_layer()
+
 
 * See the Jupyter notebook at the `example folder <https://github.com/keunwoochoi/kapre/tree/master/examples>`_

docs/release_note.rst

@@ -1,6 +1,11 @@
 Release Note
 ^^^^^^^^^^^^
 
+* 29 Sep 2020
+  - 0.3.4
+    - Fix a bug in `kapre.backend.get_window_fn()`. Previously, it only correctly worked with `None` input and
+        an erorr was raised when non-default value was set for `window_name` in any layer.
+
 * 15 Sep 2020
   - 0.3.3
     - `kapre.augmentation` is added

kapre/__init__.py

@@ -1,4 +1,4 @@
-__version__ = '0.3.3'
+__version__ = '0.3.4'
 VERSION = __version__
 
 from . import composed

kapre/backend.py

@@ -53,7 +53,7 @@ def get_window_fn(window_name=None):
             )
         )
 
-    return window_name
+    return available_windows[window_name]
 
 
 def validate_data_format_str(data_format):

setup.py

@@ -2,7 +2,7 @@
 
 setup(
     name='kapre',
-    version='0.3.3',
+    version='0.3.4',
     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

@@ -106,6 +106,66 @@ def _get_stft_model(following_layer=None):
     allclose_phase(np.angle(S_complex), S)
 
 
+@pytest.mark.parametrize('data_format', ['channels_first', 'channels_last'])
+@pytest.mark.parametrize('window_name', [None, 'hann_window', 'hamming_window'])
+def test_spectrogram_correctness_more(data_format, window_name):
+    def _get_stft_model(following_layer=None):
+        # compute with kapre
+        stft_model = tensorflow.keras.models.Sequential()
+        stft_model.add(
+            STFT(
+                n_fft=n_fft,
+                win_length=win_length,
+                hop_length=hop_length,
+                window_name=window_name,
+                pad_end=False,
+                input_data_format=data_format,
+                output_data_format=data_format,
+                input_shape=input_shape,
+                name='stft',
+            )
+        )
+        if following_layer is not None:
+            stft_model.add(following_layer)
+        return stft_model
+
+    n_fft = 512
+    hop_length = 256
+    n_ch = 2
+
+    src_mono, batch_src, input_shape = get_audio(data_format=data_format, n_ch=n_ch)
+    win_length = n_fft  # test with x2
+    # compute with librosa
+    S_ref = librosa.core.stft(
+        src_mono,
+        n_fft=n_fft,
+        hop_length=hop_length,
+        win_length=win_length,
+        center=False,
+        window=window_name.replace('_window', '') if window_name else 'hann',
+    ).T  # (time, freq)
+
+    S_ref = np.expand_dims(S_ref, axis=2)  # time, freq, ch=1
+    S_ref = np.tile(S_ref, [1, 1, n_ch])  # time, freq, ch=n_ch
+    if data_format == 'channels_first':
+        S_ref = np.transpose(S_ref, (2, 0, 1))  # ch, time, freq
+
+    stft_model = _get_stft_model()
+
+    S_complex = stft_model.predict(batch_src)[0]  # 3d representation
+    allclose_complex_numbers(S_ref, S_complex)
+
+    # test Magnitude()
+    stft_mag_model = _get_stft_model(Magnitude())
+    S = stft_mag_model.predict(batch_src)[0]  # 3d representation
+    np.testing.assert_allclose(np.abs(S_ref), S, atol=2e-4)
+
+    # # test Phase()
+    stft_phase_model = _get_stft_model(Phase())
+    S = stft_phase_model.predict(batch_src)[0]  # 3d representation
+    allclose_phase(np.angle(S_complex), S)
+
+
 @pytest.mark.parametrize('n_fft', [512])
 @pytest.mark.parametrize('sr', [22050])
 @pytest.mark.parametrize('hop_length', [None, 256])