def test_get_data_from_file():
# train=True
data_generator = DataGenerator(dataset,
feature_extractor,
folds=['all'],
batch_size=2,
shuffle=False)
for j in range(3):
X, Y = data_generator.get_data_from_file(j)
assert np.allclose(X, X_gt[j])
assert np.allclose(Y, Y_gt[j])
def generate_demo(n_clicks, list_of_contents, fold_ix, model_path,
list_of_names, list_of_dates, sr):
print('generate demo')
ctx = dash.callback_context
button_id = ctx.triggered[0]['prop_id'].split('.')[0]
print(button_id, n_clicks)
if (n_clicks is not None) & (button_id == 'btn_run_demo'):
fold_name = dataset.fold_list[fold_ix]
exp_folder_fold = conv_path(os.path.join(model_path, fold_name))
scaler_path = os.path.join(exp_folder_fold, 'scaler.pickle')
scaler = load_pickle(scaler_path)
data_generator_test = DataGenerator(
dataset,
feature_extractor,
folds=[fold_name],
batch_size=params['train']['batch_size'],
shuffle=True,
train=False,
scaler=scaler)
n_files = len(data_generator_test.audio_file_list)
ix = np.random.randint(n_files)
fold_name = dataset.fold_list[fold_ix]
exp_folder_fold = conv_path(os.path.join(model_path, fold_name))
X_features, Y_file = data_generator_test.get_data_from_file(ix)
with graph.as_default():
model_container.load_model_weights(exp_folder_fold)
Y_features = model_container.model.predict(X_features)
fig_demo = generate_figure_features(X_features, Y_features,
dataset.label_list)
audio_file = data_generator_test.audio_file_list[ix]
audio_data, sr = sf.read(audio_file['file_original'])
class_ix = np.argmax(Y_file[0])
file_label = dataset.label_list[class_ix]
return [
fig_demo, {
'autoPlay': False,
'src': encode_audio(audio_data, sr)
},
'ground-truth: %s' % file_label
]
if button_id == 'upload-data':
fold_name = dataset.fold_list[fold_ix]
exp_folder_fold = conv_path(os.path.join(model_path, fold_name))
scaler_path = os.path.join(exp_folder_fold, 'scaler.pickle')
scaler = load_pickle(scaler_path)
filename = conv_path('upload.wav')
data = list_of_contents.encode("utf8").split(b";base64,")[1]
with open(filename, "wb") as fp:
fp.write(base64.decodebytes(data))
X_feat = feature_extractor.calculate(filename)
X_feat = scaler.transform(X_feat)
with graph.as_default():
Y_t = model_container.model.predict(X_feat)
label_list = dataset.label_list
figure_features = generate_figure_features(X_feat, Y_t, label_list)
return [
figure_features, {
'autoPlay': False,
'src': list_of_contents
}, ""
]
X_feat = np.zeros((10, 128, 64))
Y_t = np.zeros((10, 10))
label_list = [] * 10
figure_features = generate_figure_features(X_feat, Y_t, label_list)
return [figure_features, {'autoPlay': False, 'src': ""}, ""]