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Deep learning for MIR
Thx for the awesome tutorial ๐
I have run it on my own, and found some typos and python compatibility issues.
12 output nodes
?432 = 36 x 12
, 36 x 12 + 12
next()
in iters as been renamed to __next__()
in python 3. If proceeded with this, keras model would give DataGen is not iterable
error.I have tested this with Keras==2.0.9
All datagen.next()
had to be modified to next(datagen)
in this cell.
When I run Example_5-1.py, error is like this:
the ExImportError: No module named 'models_MLP'
so how to import models_MLP?
thanks
On Example_5-1 there is:
import models_MLP
Not sure if there is a file missing from the repo.
Thanks in advance
Hi,
I downloaded and placed jamendo dataset in dataset_download/jamendo.
When I run
python main_preprocess.py jamendo
I am getting the following error
Traceback (most recent call last):
File "main_preprocess.py", line 95, in
main(sys.argv[1])
File "main_preprocess.py", line 79, in main
prep_jamendo()
File "main_preprocess.py", line 62, in prep_jamendo
srcs_sets, ys_sets = kapre.datasets.load_jamendo(save_path=DIR_JAMENDO_DOWNLOAD,
AttributeError: module 'kapre' has no attribute 'datasets'
While trying to visualize the predictions on this example, I am having difficulties understanding the data_gen() function and y_sample_to_frame().
1.1. On a test data, of one song.
n_hop = 256
nsp_y = 5637632
I end up receiving 20 chunks of len 22022, which is not equivalent to the entire song.
Shouldn't I need 256 (5637632// 256) of those?
1.2 Using predict_generator returns only 22022 predictions... which leads me back to question 1
n_hop = N_HOP
nsp_y = len(y)
ret = np.array([np.round(np.mean(y[max(0, (i - 1) * n_hop): min(nsp_y, (i + 1) * n_hop)])) \
for i in range(nsp_y // n_hop)], dtype=np.int)
Could you provide some comments on line 3?
In fact, I am trying to modify your example and see how it performs on SALAMI dataset. But it seems that the understanding of this two functions is fundamental. I have found relatively less information about the pre-processing of data for music structure analysis.
Sorry if my questions are not very clearly formulated, any extra information or source would be helpful.
Thanks in advance
When running python main_preprocess.py fma
, the following error occurs:
FileNotFoundError: File b'dataset_download/fma/fma_metadata/tracks.csv' does not exist
Following is the full error message.
Traceback (most recent call last):
File "main_preprocess.py", line 95, in <module>
main(sys.argv[1])
File "main_preprocess.py", line 77, in main
prep_fma_small()
File "main_preprocess.py", line 46, in prep_fma_small
tracks = pd.read_csv(os.path.join(DIR_FMA_CSV, 'tracks.csv'), index_col=0, header=[0, 1])
File "/home/kehops/dl4mir/venv/lib/python3.5/site-packages/pandas/io/parsers.py", line 705, in parser_f
return _read(filepath_or_buffer, kwds)
File "/home/kehops/dl4mir/venv/lib/python3.5/site-packages/pandas/io/parsers.py", line 445, in _read
parser = TextFileReader(filepath_or_buffer, **kwds)
File "/home/kehops/dl4mir/venv/lib/python3.5/site-packages/pandas/io/parsers.py", line 814, in __init__
self._make_engine(self.engine)
File "/home/kehops/dl4mir/venv/lib/python3.5/site-packages/pandas/io/parsers.py", line 1045, in _make_engine
self._engine = CParserWrapper(self.f, **self.options)
File "/home/kehops/dl4mir/venv/lib/python3.5/site-packages/pandas/io/parsers.py", line 1684, in __init__
self._reader = parsers.TextReader(src, **kwds)
File "pandas/_libs/parsers.pyx", line 391, in pandas._libs.parsers.TextReader.__cinit__
File "pandas/_libs/parsers.pyx", line 710, in pandas._libs.parsers.TextReader._setup_parser_source
FileNotFoundError: File b'dataset_download/fma/fma_metadata/tracks.csv' does not exist
Should I download tracks.csv
from separate source?
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