hpi-deeplearning / crnn-lid Goto Github PK
View Code? Open in Web Editor NEWCode for the paper Language Identification Using Deep Convolutional Recurrent Neural Networks
License: GNU General Public License v3.0
Code for the paper Language Identification Using Deep Convolutional Recurrent Neural Networks
License: GNU General Public License v3.0
Im new to AI and when i have try to run python wav_to_spectrogram.py --source audio_segment --target target_spectrogram it showing up an error "SpectrogramGenerator Exception: [Errno 2] No such file or directory: 'tmp_images/tmp_91484.png' audio_segment/malayalam" i have expecting your reply, Thanks in advance
While running the tsne.py code I'm getting the following error.
Traceback (most recent call last):
File "tsne.py", line 100, in
visualize_cluster(cli_args)
File "tsne.py", line 84, in visualize_cluster
plot_with_labels(lowD_weights, labels, config["label_names"], cli_args.plot_name)
File "tsne.py", line 17, in plot_with_labels
df = DataFrame({"x": lowD_Weights[:, 0], "y": lowD_Weights[:, 1], "label": labels})
File "/home/bini/.local/lib/python3.6/site-packages/pandas/core/frame.py", line 275, in init
mgr = self._init_dict(data, index, columns, dtype=dtype)
File "/home/bini/.local/lib/python3.6/site-packages/pandas/core/frame.py", line 411, in _init_dict
return _arrays_to_mgr(arrays, data_names, index, columns, dtype=dtype)
File "/home/bini/.local/lib/python3.6/site-packages/pandas/core/frame.py", line 5496, in _arrays_to_mgr
index = extract_index(arrays)
File "/home/bini/.local/lib/python3.6/site-packages/pandas/core/frame.py", line 5544, in extract_index
raise ValueError('arrays must all be same length')
ValueError: arrays must all be same length
I was trying to load model
model = load_model("../web-server/model/2017-01-31-14-29-14.CRNN_EN_DE_FR_ES_CN_RU.model")
, and I got this error, have you seen this before? I think it may be because of keras version. I think you are using Keras V1, but what specific version of keras are you using? Thanks!
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-14-89a8bb686762> in <module>()
15 print type(ckpt)
16
---> 17 model = load_model("../web-server/model/2017-01-31-14-29-14.CRNN_EN_DE_FR_ES_CN_RU.model")
18 print "get model"
19
/f/gfs1/yiyangli/.lang_env/local/lib/python2.7/site-packages/keras/models.pyc in load_model(filepath, custom_objects)
138 raise ValueError('No model found in config file.')
139 model_config = json.loads(model_config.decode('utf-8'))
--> 140 model = model_from_config(model_config, custom_objects=custom_objects)
141
142 # set weights
/f/gfs1/yiyangli/.lang_env/local/lib/python2.7/site-packages/keras/models.pyc in model_from_config(config, custom_objects)
187 raise Exception('`model_fom_config` expects a dictionary, not a list. '
188 'Maybe you meant to use `Sequential.from_config(config)`?')
--> 189 return layer_from_config(config, custom_objects=custom_objects)
190
191
/f/gfs1/yiyangli/.lang_env/local/lib/python2.7/site-packages/keras/utils/layer_utils.pyc in layer_from_config(config, custom_objects)
32 layer_class = get_from_module(class_name, globals(), 'layer',
33 instantiate=False)
---> 34 return layer_class.from_config(config['config'])
35
36
/f/gfs1/yiyangli/.lang_env/local/lib/python2.7/site-packages/keras/models.pyc in from_config(cls, config, layer_cache)
1059 conf = normalize_legacy_config(conf)
1060 layer = get_or_create_layer(conf)
-> 1061 model.add(layer)
1062 return model
/f/gfs1/yiyangli/.lang_env/local/lib/python2.7/site-packages/keras/models.pyc in add(self, layer)
322 output_shapes=[self.outputs[0]._keras_shape])
323 else:
--> 324 output_tensor = layer(self.outputs[0])
325 if type(output_tensor) is list:
326 raise Exception('All layers in a Sequential model '
/f/gfs1/yiyangli/.lang_env/local/lib/python2.7/site-packages/keras/engine/topology.pyc in __call__(self, x, mask)
489 '`layer.build(batch_input_shape)`')
490 if len(input_shapes) == 1:
--> 491 self.build(input_shapes[0])
492 else:
493 self.build(input_shapes)
/f/gfs1/yiyangli/.lang_env/local/lib/python2.7/site-packages/keras/layers/wrappers.pyc in build(self, input_shape)
216
217 def build(self, input_shape):
--> 218 self.forward_layer.build(input_shape)
219 self.backward_layer.build(input_shape)
220
/f/gfs1/yiyangli/.lang_env/local/lib/python2.7/site-packages/keras/layers/recurrent.pyc in build(self, input_shape)
731 self.W_o, self.U_o, self.b_o]
732
--> 733 self.W = K.concatenate([self.W_i, self.W_f, self.W_c, self.W_o])
734 self.U = K.concatenate([self.U_i, self.U_f, self.U_c, self.U_o])
735 self.b = K.concatenate([self.b_i, self.b_f, self.b_c, self.b_o])
/f/gfs1/yiyangli/.lang_env/local/lib/python2.7/site-packages/keras/backend/tensorflow_backend.pyc in concatenate(tensors, axis)
751 return tf.sparse_concat(axis, tensors)
752 else:
--> 753 return tf.concat(axis, [to_dense(x) for x in tensors])
754
755
/f/gfs1/yiyangli/.lang_env/local/lib/python2.7/site-packages/tensorflow/python/ops/array_ops.pyc in concat(values, axis, name)
1108 ops.convert_to_tensor(
1109 axis, name="concat_dim",
-> 1110 dtype=dtypes.int32).get_shape().assert_is_compatible_with(
1111 tensor_shape.scalar())
1112 return identity(values[0], name=scope)
/f/gfs1/yiyangli/.lang_env/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.pyc in convert_to_tensor(value, dtype, name, preferred_dtype)
1009 name=name,
1010 preferred_dtype=preferred_dtype,
-> 1011 as_ref=False)
1012
1013
/f/gfs1/yiyangli/.lang_env/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.pyc in internal_convert_to_tensor(value, dtype, name, as_ref, preferred_dtype, ctx)
1105
1106 if ret is None:
-> 1107 ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
1108
1109 if ret is NotImplemented:
/f/gfs1/yiyangli/.lang_env/local/lib/python2.7/site-packages/tensorflow/python/framework/constant_op.pyc in _constant_tensor_conversion_function(v, dtype, name, as_ref)
215 as_ref=False):
216 _ = as_ref
--> 217 return constant(v, dtype=dtype, name=name)
218
219
/f/gfs1/yiyangli/.lang_env/local/lib/python2.7/site-packages/tensorflow/python/framework/constant_op.pyc in constant(value, dtype, shape, name, verify_shape)
194 tensor_value.tensor.CopyFrom(
195 tensor_util.make_tensor_proto(
--> 196 value, dtype=dtype, shape=shape, verify_shape=verify_shape))
197 dtype_value = attr_value_pb2.AttrValue(type=tensor_value.tensor.dtype)
198 const_tensor = g.create_op(
/f/gfs1/yiyangli/.lang_env/local/lib/python2.7/site-packages/tensorflow/python/framework/tensor_util.pyc in make_tensor_proto(values, dtype, shape, verify_shape)
434 nparray = np.empty(shape, dtype=np_dt)
435 else:
--> 436 _AssertCompatible(values, dtype)
437 nparray = np.array(values, dtype=np_dt)
438 # check to them.
/f/gfs1/yiyangli/.lang_env/local/lib/python2.7/site-packages/tensorflow/python/framework/tensor_util.pyc in _AssertCompatible(values, dtype)
345 else:
346 raise TypeError("Expected %s, got %s of type '%s' instead." %
--> 347 (dtype.name, repr(mismatch), type(mismatch).__name__))
348
349
TypeError: Expected int32, got <tf.Variable 'forward_forward_lstm_1_W_i_5:0' shape=(256, 512) dtype=float32_ref> of type 'Variable' instead.
I am getting this error when trying to process train:
Logging to logs/2020-04-09-16-19-09
Traceback (most recent call last):
File "/home/varuzhan/Desktop/crnn-lid-master/keras/train.py", line 85, in
shutil.copytree("models", log_dir) # creates the log_dir
File "/usr/lib/python2.7/shutil.py", line 194, in copytree
names = os.listdir(src)
OSError: [Errno 2] No such file or directory: 'models'
hi i'm new to language identification and i would like to try your repo. but i encounter error when downloading the dataset. any tips how to solve it?
$ sh download-data.sh english
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
100 1004k 0 1004k 0 0 280k 0 --:--:-- 0:00:03 --:--:-- 280k
download-data.sh: line 24: wget: command not found
tar: tmp/1028-20100710-hne.tgz: Cannot open: No such file or directory
tar: Error is not recoverable: exiting now
ls: cannot access 'tmp/1028-20100710-hne/wav': No such file or directory
rm -f tmp/1028-20100710-hne.tgz
rm -rf tmp/1028-20100710-hne
download-data.sh: line 24: wget: command not found
tar: tmp/1337ad-20170321-ajg.tgz: Cannot open: No such file or directory
tar: Error is not recoverable: exiting now
ls: cannot access 'tmp/1337ad-20170321-ajg/wav': No such file or directory
this is the error i get about can't find the tmp folder. i already tried creating folder "tmp" but the error still persist.
Hi,
I'm working with four languages and for each I have downloaded only one video so that I can check that the scripts work as they should before running them on my VM on the cloud.
The issue I have is that the script wav_to_spectogram.py acts weird with one language.
The languages and the number of segmented .wav file for each are:
So, the expected result after running the script is that there will be 38 or 39 .png spectograms for each language since the language with the least number of .wav files is French. It does execute as it should when I run it for all the languages except English:
But running the script with English manages to count only 13 files in English, even though there are 42:
I still haven't come up with an explanation to why it's happening, so any clue would be of a great help!
Here's the sources.yml that I used to download the videos if someone prefers to check it himself.
croatian:
users:
-
playlists:
- https://www.youtube.com/playlist?list=PLv3j2_RROTdEh39boAuP-JPeDR7dy6wih
english:
users:
-
playlists:
- https://www.youtube.com/playlist?list=PLv3j2_RROTdHSp1oIY4L_t5xX0dFV3GMH
french:
users:
-
playlists:
- https://www.youtube.com/playlist?list=PLv3j2_RROTdEgT-oLhk11Xjbev7Q02F3-
spanish:
users:
-
playlists:
- https://www.youtube.com/playlist?list=PLv3j2_RROTdHpKmps4DaomrqXd8VmZV1g
I'd also note that I'm working with 3-seconds segments, so if someone will be recreating what I am doing, it is important to change the number of seconds by which the files will be splitted. It is on the line 66 in download_youtube.py from:
command = ["ffmpeg", "-y", "-i", f, "-map", "0", "-ac", "1", "-ar", "16000", "-f", "segment", "-segment_time", "10", output_filename]
to:
command = ["ffmpeg", "-y", "-i", f, "-map", "0", "-ac", "1", "-ar", "16000", "-f", "segment", "-segment_time", "3", output_filename]
For the same reason, it is necessary to change the size of the output spectogram on line 70 in wav_to_spectogram.py from:
parser.add_argument('--shape', dest='shape', default=[129, 500, 1], type=int, nargs=3)
to:
parser.add_argument('--shape', dest='shape', default=[129, 150, 1], type=int, nargs=3)
Thank you!
Why do we need to do this in fact: "Use ffmpeg to convert and split WAV files into 10 second parts"?
After downloading we have big wav files. We can then directly convert them to spectogram image files.
This will slice anyway the image into 10 seconds spectograms.
Dear Authors:
I want to know whether this code support GPU trainning? I tried install tensorflow-gpu with latest version and also 0.12.1 version. I will get errors tensor shapes doesn't match. I want to know whether you got the same error and how to fix it? Thanks
I am trying to run the train.py
script. I can see in the training section in line 59:
nb_val_samples=validation_data_generator.get_num_files()
validation_data_generator.get_num_files()
is returning 0.
Thats why I am getting the exception:
Exception: When using a generator for validation data, you must specify a value for "nb_val_samples".
Kindly tell me whats going wrong!
Thanks in advance!
HI. In /keras/models/topcoder_crnn_finetune.py file there is a line:
model.load_weights("logs/2017-04-08-13-03-44/weights.08.model", by_name=True)
But in project there is no direction or file like this.What file is this?How can I load it?
Hi,
I'm getting size issues wit convolutions for image size of 500x129. Max pooling is reducing too much the image after some convolutions
Hi. I am getting this error when I am trying to run prediction for a single mp3 file:
Using TensorFlow backend.
('SpectrogramGenerator Exception: ', IOError(2, 'No such file or directory'), 'aaaa.mp3')
File is located in crnn-lid-master folder. Terminal command is:
python keras/predict.py --model home/varuzhan/Desktop/crnn-lid-master/web-server/model/2017-01-31-14-29-14.CRNN_EN_DE_FR_ES_CN_RU.model --input aaaa.mp3
Thanks.
Hello!
I am trying to run train.py
module. Getting the following error:
IOError: Unable to open file (unable to open file: name = 'logs/2017-04-08-13-03-44/weights.08.model', errno = 2, error message = 'No such file or directory', flags = 0, o_flags = 0)
Error is coming form the topcoder_crnn_finetune.py
module line: 38
model.load_weights("logs/2017-04-08-13-03-44/weights.08.model", by_name=True)
Why there is a static 2017-04-08-13-03-44
dir here? As long as I can see inside the logs folder directory gets generated with current date-time. And where can I get weights.08.model ?
Please anyone kindly tell me whats going wrong here!
Thanks in advance!
Hi sir,
I am trying to using your crnn-lid. Firstly I have download the audio by using ./download-data.sh and convert audio files to spectrograms by running "python wav_to_spectrogram.py" .Then I set all the desired properties and hyperparameters in the config.yaml file. After that, I go into the keras
directory and run "python train.py". Unfortunately, I got something wrong as follows:
Can you give me some help? Waiting for your reply. Thank you!
I am trying to predict.
I have specified the audio path correctly but still I am getting error:
ValueError: need at least one array to stack
full error:
('SpectrogramGenerator Exception: ', IOError(2, 'No such file or directory'), 'audios/speech.mp3')
Traceback (most recent call last):
File "predict.py", line 42, in <module>
predict(cli_args)
File "predict.py", line 17, in predict
data = np.stack(data)
File "/usr/local/lib/python2.7/dist-packages/numpy/core/shape_base.py", line 335, in stack
raise ValueError('need at least one array to stack')
ValueError: need at least one array to stack
Does input audio needs to be of exact 10 secconds?
another question:
python predict.py --model <path/to/model> --input <path/to/speech.mp3>
Here, what should be the path to model ?
Dear Authors:
We trained your model actually (topcoderCRNN) which is good at training and even regression test(which means unseen data from training). But the live test is really bad. Live means we use microphone recorded data to test. Could you let us know what's the reason causing this? Is it the feature issue or the micro recorded data are much different from training data? Thanks.
I'm trying to train the crnn network using latest keras on 2 languages (English and French) on a "youtube spoken" dataset. But it seems the validation accuracy (and not only) blocks at 0.5.
Could you give me some advices about that? I'd like to share in fact some trained models using the latest keras version for the different models you've implemented.
Thanks again!
`
bidirectional_1 (Bidirection (None, 512) 1574912
Total params: 8,444,418
Trainable params: 8,439,938
Non-trainable params: 4,480
None
WARNING:tensorflow:Variable *= will be deprecated. Use variable.assign_mul if you want assignment to the variable value or 'x = x * y' if you want a new python Tensor object.
Epoch 1/50
16384/16384 [==============================] - 6176s 377ms/step - loss: 0.1435 - acc: 0.9892 - recall: 0.9999 - precision: 0.5000 - val_loss: 1.6510 - val_acc: 0.6196 - val_recall: 1.0000 - val_precision: 0.5000
Epoch 00001: val_acc improved from -inf to 0.61963, saving model to logs/2018-10-12-02-34-27/weights.01.model
Epoch 2/50
16384/16384 [==============================] - 6156s 376ms/step - loss: 0.0580 - acc: 0.9955 - recall: 1.0000 - precision: 0.5000 - val_loss: 4.5258 - val_acc: 0.5111 - val_recall: 1.0000 - val_precision: 0.5000
Epoch 00002: val_acc did not improve from 0.61963
Epoch 3/50
16384/16384 [==============================] - 6152s 375ms/step - loss: 0.0390 - acc: 0.9964 - recall: 1.0000 - precision: 0.5000 - val_loss: 4.0108 - val_acc: 0.5033 - val_recall: 1.0000 - val_precision: 0.5000
`
in voxforge/download-data.sh in Line 19: curl $VOXFORGE_DATA_URL | grep -o '<a .*href=.*>' | sed -e 's/<a /\n<a /g' | sed -e 's/<a .*href=['"'"'"]//' -e 's/["'"'"'].*$//' -e '/^$/ d' | grep tgz$ > $ZIPS
on my System (OSX Catalina): the "\n" inserts an "n" in front of the ZIPS urls and stops further processing.
the error is detected by ls in the extract_tgz.sh script...
after removing it i still get the message "tar: failed to set default locale"... however the script unpacks everything and moves the wavs to the right folder...
Hello. I'm seeing some models who are described as "TOPCODER etc". Ex: topcoder_5s_finetune, Topcoder_CRNN, etc.
Are these models from the SpokenLanguage topcoder contest? If this is the case, can you share the weights?
Thanks in advance!
Your project is SUPERB by the way!
I have trained model on my custom dataset that only contains English and Japanese audios.
The model training stops early. And but no h5f file gets generated. I only get weight files like: weights.12.model
.
That's why receiving error:
IOError: Unable to open file (Unable to open file: name = 'models/logs/2020-07-29-05-05-31/weights.12.model', errno = 2, error message = 'no such file or directory', flags = 0, o_flags = 0)
full error:
Traceback (most recent call last):
File "predict.py", line 45, in <module>
predict(cli_args)
File "predict.py", line 23, in predict
model = load_model(cli_args.model_dir)
File "/usr/local/lib/python2.7/dist-packages/keras/models.py", line 133, in load_model
f = h5py.File(filepath, mode='r')
File "/usr/local/lib/python2.7/dist-packages/h5py/_hl/files.py", line 271, in __init__
fid = make_fid(name, mode, userblock_size, fapl, swmr=swmr)
File "/usr/local/lib/python2.7/dist-packages/h5py/_hl/files.py", line 101, in make_fid
fid = h5f.open(name, flags, fapl=fapl)
File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper (/tmp/pip-nCYoKW-build/h5py/_objects.c:2840)
File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper (/tmp/pip-nCYoKW-build/h5py/_objects.c:2798)
File "h5py/h5f.pyx", line 78, in h5py.h5f.open (/tmp/pip-nCYoKW-build/h5py/h5f.c:2117)
IOError: Unable to open file (Unable to open file: name = 'models/logs/2020-07-29-05-05-31/weights.12.model', errno = 2, error message = 'no such file or directory', flags = 0, o_flags = 0)
Hi,
Just a quick question - are the youtube audio samples evenly distributed between languages in some step? If they are, would you please tell me in which script is it happening? If they aren't, could you explain why? Sorry, I've just read in the paper that they are.
I'd also like to thank you for writing the paper in a really comprehensive way!
Dear authors:
I just want to know why you choose 10 seconds segmentation as your training and prediction. Choosing smaller one will bother the performance or not (i.e.500 ms) for the sake of latency? Thanks.
What do I need to change to train on shorter segments? I used data with minimum length of 3 seconds, but the wav_to_spec module still processes speech with only 10 seconds or more.
while predicting some audio file I am getting the following error
Using TensorFlow backend.
Traceback (most recent call last):
File "predict.py", line 41, in <module>
predict(cli_args)
File "predict.py", line 16, in predict
data = np.stack(data)
File "/home/gamut/anaconda2/envs/xyz/li/lib/python2.7/site-packages/numpy/core/shape_base.py", line 335, in stack
raise ValueError('need at least one array to stack')
ValueError: need at least one array to stack
I am getting this error during train(after downloading and spectograming downloads ).
Epoch 1/50
Process Process-1:
Traceback (most recent call last):
File "/usr/lib/python2.7/multiprocessing/process.py", line 267, in _bootstrap
self.run()
File "/usr/lib/python2.7/multiprocessing/process.py", line 114, in run
self._target(*self._args, **self._kwargs)
File "/home/varuzhan/.local/lib/python2.7/site-packages/keras/engine/training.py", line 404, in data_generator_task
generator_output = next(generator)
File "/home/varuzhan/Desktop/crnn-lid-master/keras/data_loaders/csv_loader.py", line 36, in get_data
label_batch[i, :] = to_categorical([label], nb_classes=self.config["num_classes"]) # one-hot encoding
File "/home/varuzhan/.local/lib/python2.7/site-packages/keras/utils/np_utils.py", line 23, in to_categorical
Y[i, y[i]] = 1.
IndexError: index 4 is out of bounds for axis 1 with size 4
Traceback (most recent call last):
File "/home/varuzhan/Desktop/crnn-lid-master/keras/train.py", line 88, in <module>
model_file_name = train(cli_args, log_dir)
File "/home/varuzhan/Desktop/crnn-lid-master/keras/train.py", line 62, in train
pickle_safe=True
File "/home/varuzhan/.local/lib/python2.7/site-packages/keras/models.py", line 907, in fit_generator
pickle_safe=pickle_safe)
File "/home/varuzhan/.local/lib/python2.7/site-packages/keras/engine/training.py", line 1425, in fit_generator
'or (x, y). Found: ' + str(generator_output))
Exception: output of generator should be a tuple (x, y, sample_weight) or (x, y). Found: None
Can anyone explaint me training process step by step.I have tried every method and way but no results only errors:).
I am try 4 different datasets. The biggest one contains 4 languages with 20600 pngs with 10 second spectrogramm for each language.
No luck. Train accuracy is 0.97 Validation and Test accuracy is 0.2 - 0.4. What dataset size I am must use?
P.S. I am use you default config. I am changed code (a little) to use Keras2 and Tensorflow 1.14.
command:
python3 predict.py --model 2017-01-31-14-29-14.CRNN_EN_DE_FR_ES_CN_RU.model --input aaa.wav
error:
Traceback (most recent call last):
File "predict.py", line 7, in
from data_loaders.SpectrogramGenerator import SpectrogramGenerator
File "/home/varuzhan/Desktop/crnn-lid-master/keras/data_loaders/init.py", line 2, in
from .image_loader import ImageLoader
File "/home/varuzhan/Desktop/crnn-lid-master/keras/data_loaders/image_loader.py", line 2, in
from scipy.misc import imread
ImportError: cannot import name 'imread'
I am trying to train model on my dataset. However at epoch-3 training doesn't go further. It remains still.
Besides the code doesn't also utilize gpu.
Can anyone tell how to make it utilize GPU and accelerate training?
Thanks in advance!
Dear authors,
have you ever considered to include a label for an "unknown" class filled with data from languages that we don't want to identify?
Furthermore, what about a label that represents silence?
I'll let you know when I completed my experiments :)
I am trying to train model. But While training, all on a sudden Early Stopping occurs. The model is supposed to be trained upto 50 epochs. But at 15-16 epochs it stops.
Can anyone tell why this early stopping occurs?
Inside keras/models
package there is an __init__.py
file in which author has imported all the self created modules.
I am getting error: ModuleNotFoundError: No module named 'topcoder'
. Self-created modules are failing to import.
Why am I getting this? What should I do?
Thanks in advance!
If someone is having sox error while installing pysox. Do follow the second answer in this stakoverflow.
https://stackoverflow.com/questions/14756346/installing-pysox-on-ubuntu-via-pip-install-cannot-resolve-sox-h
Hi there, first, thanks for the toolkit.
I am interested in applying this on short audios. I did a simple test by chopping the web-server/audio/samples audios into 10 seconds segments and ran predict.py separately on these segments with the existing model from web-server folder (assuming this model would be the best;)). When predicting them separately, the accuracy seemed quite low, about 60%. More similar tests with our own dataset received worse results... I understand short audio would be much tougher, but I still wonder if you'd have any insights if we can improve this. Thanks in advance.
Ben
While running the evaluate.py code I'm getting the following error. I'm using ubuntu (18.04) terminal
Traceback (most recent call last):
File "evaluate.py", line 66, in
evaluate(cli_args)
File "evaluate.py", line 53, in evaluate
y_pred = np.argmax(probabilities, axis=1)
File "<array_function internals>", line 6, in argmax
File "/home/bini/.local/lib/python3.6/site-packages/numpy/core/fromnumeric.py", line 1153, in argmax
return _wrapfunc(a, 'argmax', axis=axis, out=out)
File "/home/bini/.local/lib/python3.6/site-packages/numpy/core/fromnumeric.py", line 58, in _wrapfunc
return _wrapit(obj, method, *args, **kwds)
File "/home/bini/.local/lib/python3.6/site-packages/numpy/core/fromnumeric.py", line 47, in _wrapit
result = getattr(asarray(obj), method)(*args, **kwds)
numpy.AxisError: axis 1 is out of bounds for array of dimension 1
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