jaron/deep-listening

Deep Learning experiments for audio classification

deep-listening's Issues

Error in model.fit()

Hi Jaron,

I am trying to run the 4-us8k-cnn-salamon.ipynb notebook, however
in the section "Training on the full data set (intensive)"

I get the following error

model.fit(train_x, train_y, validation_data=(valid_x, valid_y), callbacks=[earlystop], batch_size=24, nb_epoch=2)

"TypeError: unsupported operand type(s) for -: 'NoneType' and 'int'"

Many thanks for the contribution!

Error in section "Saving Extracted Features (optional)" in Notebook 3

Hi,
I am trying to the 3-us8k-cnn-extract-train, but at the section "Saving Extracted Features (optional)", I got this results:

Saving fold1
Features of fold1 = (0, 60, 41, 2)
Labels of fold1 = (0, 0)
Saved data/us8k-np-cnn/fold1_x.npy
Saved data/us8k-np-cnn/fold1_y.npy

Saving fold2
Features of fold2 = (0, 60, 41, 2)
Labels of fold2 = (0, 0)
Saved data/us8k-np-cnn/fold2_x.npy
Saved data/us8k-np-cnn/fold2_y.npy

Saving fold3
Features of fold3 = (0, 60, 41, 2)
Labels of fold3 = (0, 0)
Saved data/us8k-np-cnn/fold3_x.npy
Saved data/us8k-np-cnn/fold3_y.npy

Saving fold4
Features of fold4 = (0, 60, 41, 2)
Labels of fold4 = (0, 0)
Saved data/us8k-np-cnn/fold4_x.npy
Saved data/us8k-np-cnn/fold4_y.npy

Saving fold5
Features of fold5 = (0, 60, 41, 2)
Labels of fold5 = (0, 0)
Saved data/us8k-np-cnn/fold5_x.npy
Saved data/us8k-np-cnn/fold5_y.npy

Saving fold6
Features of fold6 = (0, 60, 41, 2)
Labels of fold6 = (0, 0)
Saved data/us8k-np-cnn/fold6_x.npy
Saved data/us8k-np-cnn/fold6_y.npy

Saving fold7
Features of fold7 = (0, 60, 41, 2)
Labels of fold7 = (0, 0)
Saved data/us8k-np-cnn/fold7_x.npy
Saved data/us8k-np-cnn/fold7_y.npy

Saving fold8
Features of fold8 = (0, 60, 41, 2)
Labels of fold8 = (0, 0)
Saved data/us8k-np-cnn/fold8_x.npy
Saved data/us8k-np-cnn/fold8_y.npy

Saving fold9
Features of fold9 = (0, 60, 41, 2)
Labels of fold9 = (0, 0)
Saved data/us8k-np-cnn/fold9_x.npy
Saved data/us8k-np-cnn/fold9_y.npy

Saving fold10
Features of fold10 = (0, 60, 41, 2)
Labels of fold10 = (0, 0)
Saved data/us8k-np-cnn/fold10_x.npy
Saved data/us8k-np-cnn/fold10_y.npy

I think it's a problem causing a later error on "Training on a minimised data set" section.

---

IOError Traceback (most recent call last)
in ()
2
3 # load fold1 for testing
----> 4 train_x, train_y = load_folds([1])
5
6 # load fold2 for validation

in load_folds(folds)
42 feature_file = os.path.join(data_dir, fold_name + '_x.npy')
43 labels_file = os.path.join(data_dir, fold_name + '_y.npy')
---> 44 loaded_features = np.load(feature_file)
45 loaded_labels = np.load(labels_file)
46 print fold_name, "features: ", loaded_features.shape

/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/numpy/lib/npyio.pyc in load(file, mmap_mode, allow_pickle, fix_imports, encoding)
427 except:
428 raise IOError(
--> 429 "Failed to interpret file %s as a pickle" % repr(file))
430 finally:
431 if own_fid:

IOError: Failed to interpret file 'data/us8k-np-cnn-mini/fold1_x.npy' as a pickle

Many thanks for the helps!

How is number of frames in a window set during feature extraction(melspectogram)

@jaron Can you please explain how number of frames in each window is set in 3-us8k-cnn-extract-train.ipynb script. To be exact, I am referring to extract_features method. I understand that the script is creating overlapping windows from windows method, but I don't understand how librosa library is able to adjust frames with librosa.feature.melspectrogram function (script doesn't explicitly set number of frames in a window). I tried looking at librosa documentation, but no luck in getting the answer.

Thanks

Error in section Training on a minimised data set

Hi,
i am trying 3-us8k-cnn-extract-train but met with error at the section "Training on a minimised data set". hope you will help me to get out of this
this is what i got

fold1 features: (0, 60, 41, 2)
fold2 features: (0, 60, 41, 2)
fold3 features: (0, 60, 41, 2)
Building model...
Training model...

ValueError Traceback (most recent call last)
in ()
18 # now fit the model to the training data, evaluating loss against the validation data
19 print("Training model...")
---> 20 model.fit(train_x, train_y, validation_data=(valid_x, valid_y), callbacks=[earlystop], batch_size=20, epochs=3)
21
22 # now evaluate the trained model against the unseen test data
~\Anaconda3\lib\site-packages\keras\models.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)
1000 initial_epoch=initial_epoch,
1001 steps_per_epoch=steps_per_epoch,
-> 1002 validation_steps=validation_steps)
1003
1004 def evaluate(self, x=None, y=None,
~\Anaconda3\lib\site-packages\keras\engine\training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)
1628 sample_weight=sample_weight,
1629 class_weight=class_weight,
-> 1630 batch_size=batch_size)
1631 # Prepare validation data.
1632 do_validation = False
~\Anaconda3\lib\site-packages\keras\engine\training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, check_array_lengths, batch_size)
1478 output_shapes,
1479 check_batch_axis=False,
-> 1480 exception_prefix='target')
1481 sample_weights = _standardize_sample_weights(sample_weight,
1482 self._feed_output_names)
~\Anaconda3\lib\site-packages\keras\engine\training.py in _standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
121 ': expected ' + names[i] + ' to have shape ' +
122 str(shape) + ' but got array with shape ' +
--> 123 str(data_shape))
124 return data
125
ValueError: Error when checking target: expected activation_4 to have shape (10,) but got array with shape (0,)

and it is craeting problem in later sections