def calculate_scalar(args):
'''Calculate and write out scalar.
Args:
dataset_dir: string
workspace: string
data_type: 'train_weak'
mini_data: bool, set True for debugging on a small part of data
'''
# Arguments & parameters
workspace = args.workspace
mini_data = args.mini_data
data_type = args.data_type
assert data_type == 'train_weak', 'We only support using train_weak data ' \
'to calculate scalar. '
mel_bins = config.mel_bins
frames_per_second = config.frames_per_second
# Paths
if mini_data:
prefix = 'minidata_'
else:
prefix = ''
relative_name = get_relative_path_no_extension(data_type)
feature_path = os.path.join(
workspace, 'features',
'{}logmel_{}frames_{}melbins'.format(prefix, frames_per_second,
mel_bins),
'{}.h5'.format(relative_name))
scalar_path = os.path.join(
workspace, 'scalars',
'{}logmel_{}frames_{}melbins'.format(prefix, frames_per_second,
mel_bins),
'{}.h5'.format(relative_name))
create_folder(os.path.dirname(scalar_path))
# Load data
load_time = time.time()
with h5py.File(feature_path, 'r') as hf:
features = hf['feature'][:]
# Calculate scalar
features = np.concatenate(features, axis=0)
(mean, std) = calculate_scalar_of_tensor(features)
with h5py.File(scalar_path, 'w') as hf:
hf.create_dataset('mean', data=mean, dtype=np.float32)
hf.create_dataset('std', data=std, dtype=np.float32)
print('All features: {}'.format(features.shape))
print('mean: {}'.format(mean))
print('std: {}'.format(std))
print('Write out scalar to {}'.format(scalar_path))
def calculate_scalar(args):
'''Calculate and write out scalar of features.
Args:
workspace: string
subtask: 'a' | 'b' | 'c'
data_type: 'train'
mini_data: bool, set True for debugging on a small part of data
'''
# Arguments & parameters
workspace = args.workspace
subtask = args.subtask
data_type = args.data_type
mini_data = args.mini_data
mel_bins = config.mel_bins
frames_per_second = config.frames_per_second
# Paths
if mini_data:
prefix = 'minidata_'
else:
prefix = ''
sub_dir = get_subdir(subtask, data_type)
feature_path = os.path.join(
workspace, 'features_side',
'{}logmel_{}frames_{}melbins'.format(prefix, frames_per_second,
mel_bins),
'{}.h5'.format(sub_dir))
scalar_path = os.path.join(
workspace, 'scalars_side',
'{}logmel_{}frames_{}melbins'.format(prefix, frames_per_second,
mel_bins),
'{}.h5'.format(sub_dir))
create_folder(os.path.dirname(scalar_path))
# Load data
load_time = time.time()
with h5py.File(feature_path, 'r') as hf:
features = hf['feature_side'][:]
# Calculate scalar
features = np.concatenate(features, axis=0)
(mean, std) = calculate_scalar_of_tensor(features)
with h5py.File(scalar_path, 'w') as hf:
hf.create_dataset('mean', data=mean, dtype=np.float32)
hf.create_dataset('std', data=std, dtype=np.float32)
print('All features: {}'.format(features.shape))
print('mean: {}'.format(mean))
print('std: {}'.format(std))
print('Write out scalar to {}'.format(scalar_path))
def calculate_scalar(args):
'''Calculate and write out scalar of development data.
Args:
dataset_dir: string
workspace: string
audio_type: 'foa' | 'mic'
mini_data: bool, set True for debugging on a small part of data
'''
# Arguments & parameters
workspace = args.workspace
audio_type = args.audio_type
mini_data = args.mini_data
data_type = 'dev'
mel_bins = config.mel_bins
frames_per_second = config.frames_per_second
# Paths
if mini_data:
prefix = 'minidata_'
else:
prefix = ''
features_dir = os.path.join(
workspace, 'features',
'{}{}_{}_logmel_{}frames_{}melbins'.format(prefix, audio_type,
data_type,
frames_per_second,
mel_bins))
scalar_path = os.path.join(
workspace, 'scalars',
'{}{}_{}_logmel_{}frames_{}melbins'.format(prefix, audio_type,
data_type,
frames_per_second,
mel_bins), 'scalar.h5')
create_folder(os.path.dirname(scalar_path))
# Load data
load_time = time.time()
feature_names = os.listdir(features_dir)
all_features = []
for feature_name in feature_names:
feature_path = os.path.join(features_dir, feature_name)
with h5py.File(feature_path, 'r') as hf:
feature = hf['feature'][:]
all_features.append(feature)
print('Load feature time: {:.3f} s'.format(time.time() - load_time))
# Calculate scalar
all_features = np.concatenate(all_features, axis=1)
(mean, std) = calculate_scalar_of_tensor(all_features)
with h5py.File(scalar_path, 'w') as hf:
hf.create_dataset('mean', data=mean, dtype=np.float32)
hf.create_dataset('std', data=std, dtype=np.float32)
print('All features: {}'.format(all_features.shape))
print('mean: {}'.format(mean))
print('std: {}'.format(std))
print('Write out scalar to {}'.format(scalar_path))
def calculate_scalar(args):
'''Calculate and write out scalar of features.
Args:
workspace: string
subtask: 'a' | 'b' | 'c'
data_type: 'train'
mini_data: bool, set True for debugging on a small part of data
'''
# Arguments & parameters
# workspace = args.workspace
# subtask = args.subtask
# data_type = args.data_type
# mini_data = args.mini_data
workspace = 'D:/Project/DCASE_test'
subtask = 'a'
data_type = 'development'
mini_data = False
mel_bins = config.mel_bins
frames_per_second = config.frames_per_second
# Paths
if mini_data:
prefix = 'minidata_'
else:
prefix = ''
sub_dir = get_subdir(subtask, data_type)
feature_path = os.path.join(workspace, 'features',
'{}logmel_{}frames_{}melbins'.format(prefix, frames_per_second, mel_bins),
'{}.h5'.format(sub_dir))
scalar_path = os.path.join(workspace, 'scalars',
'{}logmel_{}frames_{}melbins'.format(prefix, frames_per_second, mel_bins),
'{}.h5'.format(sub_dir))
create_folder(os.path.dirname(scalar_path))
# Load data
load_time = time.time()
with h5py.File(feature_path, 'r') as hf:
features = hf['feature'][:]
features_gamm = hf['feature_gamm'][:]
features_mfcc = hf['feature_mfcc'][:]
features_panns = hf['feature_panns'][:]
# Calculate scalar
features = np.concatenate(features[None,:], axis=0)
(mean, std) = calculate_scalar_of_tensor(features)
features_gamm = np.concatenate(features_gamm, axis=0)
(mean_gamm, std_gamm) = calculate_scalar_of_tensor(features_gamm)
features_mfcc = np.concatenate(features_mfcc, axis=0)
(mean_mfcc, std_mfcc) = calculate_scalar_of_tensor(features_mfcc)
features_panns = np.concatenate(features_panns[None,:], axis=0)
(mean_panns, std_panns) = calculate_scalar_of_tensor(features_panns)
with h5py.File(scalar_path, 'w') as hf:
hf.create_dataset('mean', data=mean, dtype=np.float32)
hf.create_dataset('std', data=std, dtype=np.float32)
hf.create_dataset('mean_gamm', data=mean_gamm, dtype=np.float32)
hf.create_dataset('std_gamm', data=std_gamm, dtype=np.float32)
hf.create_dataset('mean_mfcc', data=mean_mfcc, dtype=np.float32)
hf.create_dataset('std_mfcc', data=std_mfcc, dtype=np.float32)
hf.create_dataset('mean_panns', data=mean_panns, dtype=np.float32)
hf.create_dataset('std_panns', data=std_panns, dtype=np.float32)
print('Write out scalar to {}'.format(scalar_path))