Merge pull request #305 from alreadytaikeune/fix_librosa_mono

Fix librosa mono

requirements.txt

@@ -2,7 +2,7 @@ importlib_resources; python_version<'3.7'
 requests
 setuptools>=41.0.0
 pandas==0.25.1
-tensorflow==1.15
+tensorflow==1.15.2
 ffmpeg-python
 norbert==0.2.1
 librosa==0.7.2

setup.py

@@ -16,7 +16,7 @@ __license__ = 'MIT License'
 project_name = 'spleeter'
 project_version = '1.5.0'
 tensorflow_dependency = 'tensorflow'
-tensorflow_version = '1.15'
+tensorflow_version = '1.15.2'
 here = path.abspath(path.dirname(__file__))
 readme_path = path.join(here, 'README.md')
 with open(readme_path, 'r') as stream:

spleeter/separator.py

@@ -123,12 +123,16 @@ class Separator(object):
         win = hann(N, sym=False)
         fstft = istft if inverse else stft
         win_len_arg = {"win_length": None, "length": length} if inverse else {"n_fft": N}
-        dl, dr = (data[:, :, 0].T, data[:, :, 1].T) if inverse else (data[:, 0], data[:, 1])
-        s1 = fstft(dl, hop_length=H, window=win, center=False, **win_len_arg)
-        s2 = fstft(dr, hop_length=H, window=win, center=False, **win_len_arg)
-        s1 = np.expand_dims(s1.T, 2-inverse)
-        s2 = np.expand_dims(s2.T, 2-inverse)
-        return np.concatenate([s1, s2], axis=2-inverse)
+        n_channels = data.shape[-1]
+        out = []
+        for c in range(n_channels):
+            d = data[:, :, c].T if inverse else data[:, c]
+            s = fstft(d, hop_length=H, window=win, center=False, **win_len_arg)
+            s = np.expand_dims(s.T, 2-inverse)
+            out.append(s)
+        if len(out) == 1:
+            return out[0]
+        return np.concatenate(out, axis=2-inverse)
 
     def separate_librosa(self, waveform, audio_id):
         out = {}
@@ -140,9 +144,13 @@ class Separator(object):
 
         # TODO: fix the logic, build sometimes return, sometimes set attribute
         outputs = builder.outputs
+        stft = self.stft(waveform)
+        if stft.shape[-1] == 1:
+            stft = np.concatenate([stft, stft], axis=-1)
+        elif stft.shape[-1] > 2:
+            stft = stft[:, :2]
 
         saver = tf.train.Saver()
-        stft = self.stft(waveform)
         with tf.Session() as sess:
             saver.restore(sess, latest_checkpoint)
             outputs = sess.run(outputs, feed_dict=input_provider.get_feed_dict(features, stft, audio_id))

tests/test_separator.py

@@ -8,83 +8,101 @@ __author__ = 'Deezer Research'
 __license__ = 'MIT License'
 
 import filecmp
-
+import itertools
 from os.path import splitext, basename, exists, join
 from tempfile import TemporaryDirectory
 
 import pytest
 import numpy as np
 
+import tensorflow as tf
+
 from spleeter import SpleeterError
 from spleeter.audio.adapter import get_default_audio_adapter
 from spleeter.separator import Separator
 
-TEST_AUDIO_DESCRIPTOR = 'audio_example.mp3'
-TEST_AUDIO_BASENAME = splitext(basename(TEST_AUDIO_DESCRIPTOR))[0]
-TEST_CONFIGURATIONS = [
-    ('spleeter:2stems', ('vocals', 'accompaniment'), 'tensorflow'),
-    ('spleeter:4stems', ('vocals', 'drums', 'bass', 'other'), 'tensorflow'),
-    ('spleeter:5stems', ('vocals', 'drums', 'bass', 'piano', 'other'), 'tensorflow'),
-    ('spleeter:2stems', ('vocals', 'accompaniment'), 'librosa'),
-    ('spleeter:4stems', ('vocals', 'drums', 'bass', 'other'), 'librosa'),
-    ('spleeter:5stems', ('vocals', 'drums', 'bass', 'piano', 'other'), 'librosa')
-]
+TEST_AUDIO_DESCRIPTORS = ['audio_example.mp3', 'audio_example_mono.mp3']
+BACKENDS = ["tensorflow", "librosa"]
+MODELS = ['spleeter:2stems', 'spleeter:4stems', 'spleeter:5stems']
+
+MODEL_TO_INST = {
+    'spleeter:2stems': ('vocals', 'accompaniment'),
+    'spleeter:4stems': ('vocals', 'drums', 'bass', 'other'),
+    'spleeter:5stems': ('vocals', 'drums', 'bass', 'piano', 'other'),
+}
 
 
-@pytest.mark.parametrize('configuration, instruments, backend', TEST_CONFIGURATIONS)
-def test_separate(configuration, instruments, backend):
+MODELS_AND_TEST_FILES = list(itertools.product(TEST_AUDIO_DESCRIPTORS, MODELS))
+TEST_CONFIGURATIONS = list(itertools.product(TEST_AUDIO_DESCRIPTORS, MODELS, BACKENDS))
+
+
+print("RUNNING TESTS WITH TF VERSION {}".format(tf.__version__))
+
+
+@pytest.mark.parametrize('test_file, configuration, backend', TEST_CONFIGURATIONS)
+def test_separate(test_file, configuration, backend):
     """ Test separation from raw data. """
+    tf.reset_default_graph()
+    instruments = MODEL_TO_INST[configuration]
     adapter = get_default_audio_adapter()
-    waveform, _ = adapter.load(TEST_AUDIO_DESCRIPTOR)
+    waveform, _ = adapter.load(test_file)
     separator = Separator(configuration, stft_backend=backend)
-    prediction = separator.separate(waveform, TEST_AUDIO_DESCRIPTOR)
+    prediction = separator.separate(waveform, test_file)
     assert len(prediction) == len(instruments)
     for instrument in instruments:
         assert instrument in prediction
     for instrument in instruments:
         track = prediction[instrument]
-        assert waveform.shape == track.shape
+        assert waveform.shape[:-1] == track.shape[:-1]
         assert not np.allclose(waveform, track)
         for compared in instruments:
             if instrument != compared:
                 assert not np.allclose(track, prediction[compared])
 
 
-@pytest.mark.parametrize('configuration, instruments, backend', TEST_CONFIGURATIONS)
-def test_separate_to_file(configuration, instruments, backend):
+@pytest.mark.parametrize('test_file, configuration, backend', TEST_CONFIGURATIONS)
+def test_separate_to_file(test_file, configuration, backend):
     """ Test file based separation. """
+    tf.reset_default_graph()
+    instruments = MODEL_TO_INST[configuration]
     separator = Separator(configuration, stft_backend=backend)
+    name = splitext(basename(test_file))[0]
     with TemporaryDirectory() as directory:
         separator.separate_to_file(
-            TEST_AUDIO_DESCRIPTOR,
+            test_file,
             directory)
         for instrument in instruments:
             assert exists(join(
                 directory,
-                '{}/{}.wav'.format(TEST_AUDIO_BASENAME, instrument)))
+                '{}/{}.wav'.format(name, instrument)))
 
 
-@pytest.mark.parametrize('configuration, instruments, backend', TEST_CONFIGURATIONS)
-def test_filename_format(configuration, instruments, backend):
+@pytest.mark.parametrize('test_file, configuration, backend', TEST_CONFIGURATIONS)
+def test_filename_format(test_file, configuration, backend):
     """ Test custom filename format. """
+    tf.reset_default_graph()
+    instruments = MODEL_TO_INST[configuration]
     separator = Separator(configuration, stft_backend=backend)
+    name = splitext(basename(test_file))[0]
     with TemporaryDirectory() as directory:
         separator.separate_to_file(
-            TEST_AUDIO_DESCRIPTOR,
+            test_file,
             directory,
             filename_format='export/{filename}/{instrument}.{codec}')
         for instrument in instruments:
             assert exists(join(
                 directory,
-                'export/{}/{}.wav'.format(TEST_AUDIO_BASENAME, instrument)))
+                'export/{}/{}.wav'.format(name, instrument)))
 
 
-def test_filename_conflict():
+@pytest.mark.parametrize('test_file, configuration', MODELS_AND_TEST_FILES)
+def test_filename_conflict(test_file, configuration):
     """ Test error handling with static pattern. """
-    separator = Separator(TEST_CONFIGURATIONS[0][0])
+    tf.reset_default_graph()
+    separator = Separator(configuration)
     with TemporaryDirectory() as directory:
         with pytest.raises(SpleeterError):
             separator.separate_to_file(
-                TEST_AUDIO_DESCRIPTOR,
+                test_file,
                 directory,
                 filename_format='I wanna be your lover')