repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
value |
|---|---|---|---|---|---|---|
poincare_glove | poincare_glove-master/util_scripts/compute_avg_delta_hyperbolicity.py | import argparse
from gensim.models.keyedvectors import VanillaWordEmbeddingsKeyedVectors, Vocab
from glove_code.src.glove_inner import read_all
from numpy import array, uint32, load, sort, log, sqrt, arccosh, exp
from timeit import default_timer
PRINT_EVERY = 1000000
graph_map = {}
max_coocc_count = 0.0
USE_PROBS = F... | 8,128 | 42.239362 | 184 | py |
poincare_glove | poincare_glove-master/util_scripts/split_similarity_set.py | import random
import sys
init_file = sys.argv[1]
validation_file = sys.argv[2]
test_file = sys.argv[3]
VALIDATION_RATIO = 0.5
with open(init_file, "r") as fin, open(validation_file, "w") as fv, open(test_file, "w") as ft:
lines = fin.readlines()
num_validation = int(VALIDATION_RATIO * len(lines))
valid_i... | 571 | 25 | 95 | py |
allosaurus | allosaurus-master/setup.py | from setuptools import setup,find_packages
setup(
name='allosaurus',
version='1.0.2',
description='a multilingual phone recognizer',
author='Xinjian Li',
author_email='xinjianl@cs.cmu.edu',
url="https://github.com/xinjli/allosaurus",
packages=find_packages(),
install_requires=[
'scipy',
... | 412 | 19.65 | 49 | py |
allosaurus | allosaurus-master/allosaurus/app.py | from allosaurus.am.utils import *
from pathlib import Path
from allosaurus.audio import read_audio
from allosaurus.pm.factory import read_pm
from allosaurus.am.factory import read_am
from allosaurus.lm.factory import read_lm
from allosaurus.bin.download_model import download_model
from allosaurus.model import resolve_m... | 3,316 | 35.450549 | 111 | py |
allosaurus | allosaurus-master/allosaurus/audio.py | import wave
import numpy as np
from pathlib import Path
import resampy
def read_audio(filename, header_only=False, channel=0):
"""
read_audio will read a raw wav and return an Audio object
:param header_only: only load header without samples
"""
if isinstance(filename, Path):
filename = ... | 3,766 | 24.281879 | 107 | py |
allosaurus | allosaurus-master/allosaurus/model.py | from pathlib import Path
import shutil
def get_all_models(alt_model_path=None):
"""
get all local models
:return:
"""
if alt_model_path:
model_dir = alt_model_path
else:
model_dir = Path(__file__).parent / 'pretrained'
models = list(sorted(model_dir.glob('*'), reverse=True)... | 2,331 | 25.5 | 173 | py |
allosaurus | allosaurus-master/allosaurus/run.py | from allosaurus.app import read_recognizer
from allosaurus.model import get_all_models, resolve_model_name
from allosaurus.bin.download_model import download_model
from pathlib import Path
import argparse
if __name__ == '__main__':
parser = argparse.ArgumentParser('Allosaurus phone recognizer')
parser.add_arg... | 3,809 | 47.227848 | 282 | py |
allosaurus | allosaurus-master/allosaurus/__init__.py | 0 | 0 | 0 | py | |
allosaurus | allosaurus-master/allosaurus/pm/utils.py | import numpy as np
def feature_cmvn(feature):
frame_cnt = feature.shape[0]
spk_sum = np.sum(feature, axis=0)
spk_mean = spk_sum / frame_cnt
spk_square_sum = np.sum(feature*feature, axis=0)
spk_std = (spk_square_sum / frame_cnt - spk_mean * spk_mean) ** 0.5
return (feature - spk_mean)/spk_std... | 588 | 24.608696 | 106 | py |
allosaurus | allosaurus-master/allosaurus/pm/mfcc.py | from allosaurus.pm.feature import mfcc
from allosaurus.pm.utils import *
from allosaurus.audio import resample_audio
import numpy as np
class MFCC:
def __init__(self, config):
self.model = config.model
# feature model config
self.config = config
# sample rate
self.sample... | 2,179 | 28.066667 | 161 | py |
allosaurus | allosaurus-master/allosaurus/pm/factory.py | from allosaurus.pm.mfcc import MFCC
import json
from argparse import Namespace
def read_pm(model_path, inference_config):
"""
read feature extraction model
:param pm_config:
:return:
"""
pm_config = Namespace(**json.load(open(str(model_path / 'pm_config.json'))))
assert pm_config.model =... | 525 | 26.684211 | 93 | py |
allosaurus | allosaurus-master/allosaurus/pm/__init__.py | 0 | 0 | 0 | py | |
allosaurus | allosaurus-master/allosaurus/pm/kdict.py | import numpy
import struct
import functools
import os.path
import gzip
import bz2
from pathlib import Path
class KaldiWriter:
def __init__(self, path=None, scp=True):
"""
writer of BOTH ark and scp
:param path:
:param scp:
"""
self.scp = scp
if path:
... | 10,350 | 32.716612 | 140 | py |
allosaurus | allosaurus-master/allosaurus/pm/preprocess.py | # This file includes routines for basic signal processing including framing and computing power spectra.
# Author: James Lyons 2012
import decimal
import numpy as np
import math
import logging
def round_up_power_of_two(x):
return 1 if x == 0 else 2**(x - 1).bit_length()
def round_half_up(number):
return int(... | 6,984 | 41.078313 | 138 | py |
allosaurus | allosaurus-master/allosaurus/pm/feature.py | # calculate filterbank features. Provides e.g. fbank and mfcc features for use in ASR applications
# Author: James Lyons 2012
import numpy
from allosaurus.pm import preprocess
from scipy.fftpack import dct
def mfcc(signal,samplerate=16000,winlen=0.025,winstep=0.01,numcep=13,
nfilt=23,lowfreq=20,highfreq=None... | 9,424 | 53.796512 | 163 | py |
allosaurus | allosaurus-master/allosaurus/bin/update_phone.py | from pathlib import Path
from allosaurus.lm.inventory import Inventory
from allosaurus.model import get_model_path
import argparse
if __name__ == '__main__':
parser = argparse.ArgumentParser('Update language inventory')
parser.add_argument('-l', '--lang', type=str, required=True, help='specify which language... | 1,250 | 38.09375 | 169 | py |
allosaurus | allosaurus-master/allosaurus/bin/restore_phone.py | from pathlib import Path
from allosaurus.lm.inventory import Inventory
from allosaurus.model import get_model_path
import argparse
if __name__ == '__main__':
parser = argparse.ArgumentParser('Restore language inventory')
parser.add_argument('-l', '--lang', type=str, required=True, help='specify which languag... | 1,012 | 37.961538 | 169 | py |
allosaurus | allosaurus-master/allosaurus/bin/prep_feat.py | import argparse
from pathlib import Path
from allosaurus.model import resolve_model_name
from allosaurus.audio import read_audio
from allosaurus.pm.factory import read_pm
from allosaurus.pm.kdict import KaldiWriter
from tqdm import tqdm
def prepare_feature(data_path, model):
model_path = Path(__file__).parent.par... | 2,205 | 29.638889 | 121 | py |
allosaurus | allosaurus-master/allosaurus/bin/prep_token.py | import argparse
from pathlib import Path
from allosaurus.model import resolve_model_name
from allosaurus.lm.inventory import *
from tqdm import tqdm
def prepare_token(data_path, model, lang_id):
model_path = Path(__file__).parent.parent / 'pretrained' / model
#assert model_path.exists(), f"{model} is not a v... | 1,806 | 33.09434 | 121 | py |
allosaurus | allosaurus-master/allosaurus/bin/download_model.py | from pathlib import Path
import tarfile
from urllib.request import urlopen
import io
import argparse
import os
def download_model(model_name=None, alt_model_path=None):
if model_name is None:
model_name = 'latest'
if alt_model_path:
model_dir = alt_model_path
else:
model_dir = (Pat... | 1,350 | 31.166667 | 154 | py |
allosaurus | allosaurus-master/allosaurus/bin/list_lang.py | from pathlib import Path
from allosaurus.model import get_model_path
from allosaurus.lm.inventory import Inventory
import argparse
if __name__ == '__main__':
parser = argparse.ArgumentParser('List language phone inventory')
parser.add_argument('-l', '--lang', type=str, default='ipa', help='specify wh... | 926 | 43.142857 | 182 | py |
allosaurus | allosaurus-master/allosaurus/bin/list_model.py | from allosaurus.model import get_all_models
if __name__ == '__main__':
models = get_all_models()
if len(models) == 0:
print("No models are available, you can maually download a model with download command or just run inference to download the latest one automatically")
else:
print("Avail... | 508 | 30.8125 | 159 | py |
allosaurus | allosaurus-master/allosaurus/bin/list_phone.py | from pathlib import Path
from allosaurus.lm.inventory import Inventory
from allosaurus.model import get_model_path
import argparse
if __name__ == '__main__':
parser = argparse.ArgumentParser('List language phone inventory')
parser.add_argument('-l', '--lang', type=str, default='ipa', help='specify wh... | 1,468 | 46.387097 | 282 | py |
allosaurus | allosaurus-master/allosaurus/bin/__init__.py | 0 | 0 | 0 | py | |
allosaurus | allosaurus-master/allosaurus/bin/remove_model.py | from allosaurus.model import delete_model
import argparse
if __name__ == '__main__':
parser = argparse.ArgumentParser('an allosaurus util to delete model')
parser.add_argument('-m', '--model', required=True, help='model name to be deleted')
args = parser.parse_args()
delete_model(args.model) | 311 | 30.2 | 88 | py |
allosaurus | allosaurus-master/allosaurus/bin/write_phone.py | from pathlib import Path
from allosaurus.lm.inventory import Inventory
from allosaurus.lm.unit import write_unit
from allosaurus.model import get_model_path
import argparse
if __name__ == '__main__':
parser = argparse.ArgumentParser('Write out current phone file')
parser.add_argument('-l', '--lang', type=st... | 962 | 39.125 | 132 | py |
allosaurus | allosaurus-master/allosaurus/bin/adapt_model.py | import argparse
from pathlib import Path
from allosaurus.model import copy_model
from allosaurus.am.factory import transfer_am
from allosaurus.am.trainer import Trainer
from allosaurus.am.loader import read_loader
if __name__ == '__main__':
parser = argparse.ArgumentParser("fine-tune an existing model to your tar... | 2,912 | 56.117647 | 202 | py |
allosaurus | allosaurus-master/allosaurus/lm/inventory.py | import json
from allosaurus.lm.mask import *
class Inventory:
def __init__(self, model_path, inference_config=None):
self.model_path = model_path
self.lang_names = []
self.lang_ids = []
self.glotto_ids = []
self.lang2phonefile = dict()
self.inference_config = i... | 3,764 | 28.186047 | 105 | py |
allosaurus | allosaurus-master/allosaurus/lm/articulatory.py | import panphon
import numpy as np
class Articulatory:
def __init__(self):
self.feature_table = panphon.FeatureTable()
def feature(self, phone):
try:
feats = self.feature_table.word_to_vector_list(phone, numeric=True)
except:
if len(phone) == 2:
... | 1,247 | 23 | 83 | py |
allosaurus | allosaurus-master/allosaurus/lm/factory.py | from allosaurus.lm.decoder import PhoneDecoder
import json
from argparse import Namespace
def read_lm(model_path, inference_config):
"""
read language model (phone inventory)
:param pm_config:
:return:
"""
lm_config = Namespace(**json.load(open(str(model_path / 'lm_config.json'))))
assert... | 892 | 36.208333 | 112 | py |
allosaurus | allosaurus-master/allosaurus/lm/decoder.py | from allosaurus.lm.inventory import *
from pathlib import Path
from itertools import groupby
import numpy as np
class PhoneDecoder:
def __init__(self, model_path, inference_config):
"""
This class is an util for decode both phones and words
:param model_path:
"""
# lm mod... | 2,616 | 27.445652 | 120 | py |
allosaurus | allosaurus-master/allosaurus/lm/__init__.py | 0 | 0 | 0 | py | |
allosaurus | allosaurus-master/allosaurus/lm/mask.py | from .articulatory import *
from .unit import *
from pathlib import Path
def read_prior(prior_path):
prior = {}
for i, line in open(str(prior_path), 'r', encoding='utf-8'):
unit, prob = line.split()
if i == 0:
assert unit == '<blk>', 'first element should be blank'
prior... | 4,991 | 27.363636 | 124 | py |
allosaurus | allosaurus-master/allosaurus/lm/unit.py | import numpy as np
def read_unit(unit_path):
# load unit from units.txt
# units.txt should start from index 1 (because ctc blank is taking the 0 index)
unit_to_id = dict()
unit_to_id['<blk>'] = 0
idx = 0
for line in open(str(unit_path), 'r', encoding='utf-8'):
fields = line.strip().... | 3,050 | 22.469231 | 97 | py |
allosaurus | allosaurus-master/allosaurus/am/reporter.py | from allosaurus.model import get_model_path
class Reporter:
def __init__(self, train_config):
self.train_config = train_config
self.model_path = get_model_path(train_config.new_model)
# whether write into std
self.verbose = train_config.verbose
# log file
self.lo... | 810 | 22.852941 | 84 | py |
allosaurus | allosaurus-master/allosaurus/am/utils.py | import torch
from collections import OrderedDict
import numpy as np
def torch_load(model, path, device_id, unit_mask=None):
"""Load torch model states.
Args:
path (str): Model path or snapshot file path to be loaded.
model (torch.nn.Module): Torch model.
device_id (int): gpu id (-1 ind... | 4,126 | 24.475309 | 95 | py |
allosaurus | allosaurus-master/allosaurus/am/dataset.py | from allosaurus.pm.kdict import read_matrix
from pathlib import Path
from torch.utils.data import Dataset
import numpy as np
class AllosaurusDataset(Dataset):
def __init__(self, data_path):
self.data_path = Path(data_path)
required_files = ['feat.scp', 'token', 'feat.ark', 'shape']
for r... | 3,451 | 26.616 | 140 | py |
allosaurus | allosaurus-master/allosaurus/am/factory.py | from allosaurus.am.allosaurus_torch import AllosaurusTorchModel
from allosaurus.am.utils import *
from allosaurus.lm.inventory import Inventory
from allosaurus.lm.unit import write_unit
import json
from argparse import Namespace
from allosaurus.model import get_model_path
def read_am(model_path, inference_config):
... | 2,194 | 29.486111 | 97 | py |
allosaurus | allosaurus-master/allosaurus/am/__init__.py | 0 | 0 | 0 | py | |
allosaurus | allosaurus-master/allosaurus/am/allosaurus_torch.py | import torch
import torch.nn as nn
class AllosaurusTorchModel(nn.Module):
def __init__(self, config):
super(AllosaurusTorchModel, self).__init__()
self.hidden_size = config.hidden_size
self.layer_size = config.layer_size
self.proj_size = config.proj_size
# decide input fe... | 4,359 | 40.52381 | 159 | py |
allosaurus | allosaurus-master/allosaurus/am/criterion.py | import torch
import torch.nn as nn
def read_criterion(train_config):
assert train_config.criterion == 'ctc', 'only ctc criterion is supported now'
return CTCCriterion(train_config)
class CTCCriterion(nn.Module):
def __init__(self, train_config):
super().__init__()
self.train_config = tr... | 841 | 29.071429 | 91 | py |
allosaurus | allosaurus-master/allosaurus/am/loader.py | from allosaurus.am.dataset import AllosaurusDataset
import numpy as np
def read_loader(data_path, train_config):
"""
create a dataloader for data_path
:param data_path:
:param train_config:
:return:
"""
return AllosaurusLoader(data_path, train_config)
class AllosaurusLoader:
def __... | 3,115 | 25.40678 | 93 | py |
allosaurus | allosaurus-master/allosaurus/am/trainer.py | from allosaurus.am.utils import move_to_tensor, torch_save
from allosaurus.am.criterion import read_criterion
from allosaurus.am.optimizer import read_optimizer
from allosaurus.am.reporter import Reporter
import editdistance
import numpy as np
import torch
from itertools import groupby
from allosaurus.model import get_... | 6,149 | 30.538462 | 172 | py |
allosaurus | allosaurus-master/allosaurus/am/optimizer.py | from torch.optim import SGD
def read_optimizer(model, train_config):
assert train_config.optimizer == 'sgd', 'only sgd is supported now, others optimizers would be easier to add though'
return SGD(model.parameters(), lr=train_config.lr) | 247 | 34.428571 | 120 | py |
allosaurus | allosaurus-master/test/test_recognition.py | import unittest
from pathlib import Path
from allosaurus.app import read_recognizer
class TestRecognition(unittest.TestCase):
def test_latest_nonempty(self):
audio_file = Path(__file__).parent.parent / 'sample.wav'
model = read_recognizer('latest')
results = model.recognize(audio_file)
... | 970 | 34.962963 | 64 | py |
allosaurus | allosaurus-master/test/test_model.py | import unittest
from pathlib import Path
import requests
class TestModel(unittest.TestCase):
def test_latest_available(self):
model_name = "latest"
url = 'https://github.com/xinjli/allosaurus/releases/download/v1.0/' + model_name + '.tar.gz'
req = requests.head(url)
print(req.stat... | 452 | 24.166667 | 101 | py |
CRSTmodel | CRSTmodel-main/DCASE2021_baseline_platform/setup.py | from setuptools import setup
setup(
name="desed_task",
version="0.1.0",
description="Sound Event Detection and Separation in Domestic Environments.",
author="DCASE2021 Task 4 Organizers",
author_email="cornellsamuele@gmail.com",
license="MIT",
packages=["desed_task"],
python_requires=">... | 471 | 23.842105 | 81 | py |
CRSTmodel | CRSTmodel-main/DCASE2021_baseline_platform/evaluation/evaluation_measures.py | import os
import numpy as np
import pandas as pd
import psds_eval
import sed_eval
from psds_eval import PSDSEval, plot_psd_roc
def get_event_list_current_file(df, fname):
"""
Get list of events for a given filename
Args:
df: pd.DataFrame, the dataframe to search on
fname: the filename to ... | 8,788 | 33.876984 | 119 | py |
CRSTmodel | CRSTmodel-main/DCASE2021_baseline_platform/recipes/dcase2021_task4_baseline/train_sed_CRST.py | import argparse
from copy import deepcopy
import numpy as np
import os
import pandas as pd
import random
import torch
import yaml
import pytorch_lightning as pl
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.loggers import TensorBoardLogger
from desed_task.dataio import ... | 11,870 | 34.121302 | 118 | py |
CRSTmodel | CRSTmodel-main/DCASE2021_baseline_platform/recipes/dcase2021_task4_baseline/train_sed_SRST.py | import argparse
from copy import deepcopy
import numpy as np
import os
import pandas as pd
import random
import torch
import yaml
import pytorch_lightning as pl
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.loggers import TensorBoardLogger
from desed_task.dataio import ... | 10,734 | 34.429043 | 118 | py |
CRSTmodel | CRSTmodel-main/DCASE2021_baseline_platform/recipes/dcase2021_task4_baseline/train_sed.py | import argparse
from copy import deepcopy
import numpy as np
import os
import pandas as pd
import random
import torch
import yaml
import pytorch_lightning as pl
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.loggers import TensorBoardLogger
from desed_task.dataio import ... | 11,519 | 34.015198 | 118 | py |
CRSTmodel | CRSTmodel-main/DCASE2021_baseline_platform/recipes/dcase2021_task4_baseline/local/resample_folder.py | import argparse
import glob
import os
from pathlib import Path
import librosa
import torch
import torchaudio
import tqdm
parser = argparse.ArgumentParser("Resample a folder recursively")
parser.add_argument(
"--in_dir",
type=str,
default="/media/sam/bx500/DCASE_DATA/dataset/audio/validation/",
)
parser.ad... | 2,556 | 29.807229 | 102 | py |
CRSTmodel | CRSTmodel-main/DCASE2021_baseline_platform/recipes/dcase2021_task4_baseline/local/sed_trainer_SRST.py | import os
import random
from copy import deepcopy
from pathlib import Path
import local.config as cfg
import pandas as pd
import pytorch_lightning as pl
import torch
from torchaudio.transforms import AmplitudeToDB, MelSpectrogram
from desed_task.data_augm import mixup, add_noise
from desed_task.utils.scaler import To... | 29,014 | 37.077428 | 118 | py |
CRSTmodel | CRSTmodel-main/DCASE2021_baseline_platform/recipes/dcase2021_task4_baseline/local/utils.py | import os
from pathlib import Path
import pandas as pd
import scipy
from desed_task.evaluation.evaluation_measures import compute_sed_eval_metrics
from torch import nn
import soundfile
import glob
class JSD(nn.Module):
def __init__(self):
super(JSD, self).__init__()
self.kld = nn.KLDivLoss().cuda... | 6,982 | 35.369792 | 111 | py |
CRSTmodel | CRSTmodel-main/DCASE2021_baseline_platform/recipes/dcase2021_task4_baseline/local/utilities.py | import numpy as np
import scipy.signal as sp
import wave, struct
import torch
import torch.nn as nn
from scipy.io import wavfile, loadmat
from torchaudio.functional import lfilter
from torchaudio.transforms import Spectrogram
class LinearSpectrogram(nn.Module):
def __init__(self, nCh=128, n_fft=2048, hop_length=256,... | 10,213 | 28.865497 | 101 | py |
CRSTmodel | CRSTmodel-main/DCASE2021_baseline_platform/recipes/dcase2021_task4_baseline/local/config.py | import logging
import math
import os
import pandas as pd
import numpy as np
nClass = 10
# Make class label
tlab = np.diag(np.ones(nClass),-1)[:,:-1]
bag = [tlab]
for iter in range(1,nClass):
temp = np.diag(np.ones(nClass)) + np.diag(np.ones(nClass),iter)[:nClass,:nClass]
bag.append(temp[:nClass-iter,:])
for i... | 779 | 26.857143 | 143 | py |
CRSTmodel | CRSTmodel-main/DCASE2021_baseline_platform/recipes/dcase2021_task4_baseline/local/classes_dict.py | """
we store here a dict where we define the encodings for all classes in DESED task.
"""
from collections import OrderedDict
classes_labels = OrderedDict(
{
"Alarm_bell_ringing": 0,
"Blender": 1,
"Cat": 2,
"Dishes": 3,
"Dog": 4,
"Electric_shaver_toothbrush": 5,
... | 425 | 18.363636 | 81 | py |
CRSTmodel | CRSTmodel-main/DCASE2021_baseline_platform/recipes/dcase2021_task4_baseline/local/sed_trainer.py | import os
import random
from copy import deepcopy
from pathlib import Path
import pandas as pd
import pytorch_lightning as pl
import torch
from torchaudio.transforms import AmplitudeToDB, MelSpectrogram
from desed_task.data_augm import mixup
from desed_task.utils.scaler import TorchScaler
import numpy as np
from .ut... | 29,083 | 37.675532 | 118 | py |
CRSTmodel | CRSTmodel-main/DCASE2021_baseline_platform/recipes/dcase2021_task4_baseline/local/sed_trainer_CRST.py | import os
import random
from copy import deepcopy
from pathlib import Path
import local.config as cfg
import pandas as pd
import pytorch_lightning as pl
import torch
from torchaudio.transforms import AmplitudeToDB, MelSpectrogram
from desed_task.data_augm import mixup, frame_shift, add_noise, temporal_reverse
from de... | 48,990 | 39.757903 | 118 | py |
CRSTmodel | CRSTmodel-main/DCASE2021_baseline_platform/nnet/CNN.py | import torch.nn as nn
import torch
import math
import torch.nn.functional as F
class GLU(nn.Module):
def __init__(self, input_num):
super(GLU, self).__init__()
self.sigmoid = nn.Sigmoid()
self.linear = nn.Linear(input_num, input_num)
def forward(self, x):
lin = self.linear(x.p... | 11,753 | 35.616822 | 154 | py |
CRSTmodel | CRSTmodel-main/DCASE2021_baseline_platform/nnet/CRNN.py | import warnings
import torch.nn as nn
import torch
from .RNN import BidirectionalGRU
from .CNN import CNN, ResidualCNN
class RCRNN(nn.Module):
def __init__(
self,
n_in_channel=1,
nclass=10,
attention=True,
activation="glu",
dropout=0.5,
train_cnn=True,
... | 9,567 | 31.767123 | 89 | py |
CRSTmodel | CRSTmodel-main/DCASE2021_baseline_platform/nnet/RNN.py | import warnings
import torch
from torch import nn as nn
class BidirectionalGRU(nn.Module):
def __init__(self, n_in, n_hidden, dropout=0, num_layers=1):
"""
Initialization of BidirectionalGRU instance
Args:
n_in: int, number of input
n_hidden: int, number of hi... | 1,488 | 26.072727 | 68 | py |
CRSTmodel | CRSTmodel-main/DCASE2021_baseline_platform/utils/scaler.py | import tqdm
import torch
class TorchScaler(torch.nn.Module):
"""
This torch module implements scaling for input tensors, both instance based
and dataset-wide statistic based.
Args:
statistic: str, (default='dataset'), represent how to compute the statistic for normalisation.
Choic... | 4,606 | 38.042373 | 119 | py |
CRSTmodel | CRSTmodel-main/DCASE2021_baseline_platform/utils/schedulers.py | from asteroid.engine.schedulers import *
import numpy as np
class ExponentialWarmup(BaseScheduler):
""" Scheduler to apply ramp-up during training to the learning rate.
Args:
optimizer: torch.optimizer.Optimizer, the optimizer from which to rampup the value from
max_lr: float, the maximum lear... | 1,094 | 32.181818 | 95 | py |
CRSTmodel | CRSTmodel-main/DCASE2021_baseline_platform/utils/encoder.py | import numpy as np
import pandas as pd
from dcase_util.data import DecisionEncoder
class ManyHotEncoder:
""""
Adapted after DecisionEncoder.find_contiguous_regions method in
https://github.com/DCASE-REPO/dcase_util/blob/master/dcase_util/data/decisions.py
Encode labels into numpy arrays w... | 8,185 | 37.252336 | 111 | py |
CRSTmodel | CRSTmodel-main/DCASE2021_baseline_platform/utils/torch_utils.py | import torch
import numpy as np
def nantensor(*args, **kwargs):
return torch.ones(*args, **kwargs) * np.nan
def nanmean(v, *args, inplace=False, **kwargs):
if not inplace:
v = v.clone()
is_nan = torch.isnan(v)
v[is_nan] = 0
return v.sum(*args, **kwargs) / (~is_nan).float().sum(*args, **k... | 327 | 20.866667 | 74 | py |
CRSTmodel | CRSTmodel-main/DCASE2021_baseline_platform/utils/__init__.py | from .encoder import ManyHotEncoder
from .schedulers import ExponentialWarmup
| 78 | 25.333333 | 41 | py |
CRSTmodel | CRSTmodel-main/DCASE2021_baseline_platform/desed_task/data_augm.py | import numpy as np
import torch
import random
def frame_shift(mels, labels, net_pooling=4):
bsz, n_bands, frames = mels.shape
shifted = []
new_labels = []
for bindx in range(bsz):
shift = int(random.gauss(0, 90))
shifted.append(torch.roll(mels[bindx], shift, dims=-1))
shift = ... | 3,931 | 35.073394 | 112 | py |
CRSTmodel | CRSTmodel-main/DCASE2021_baseline_platform/desed_task/dataio/sampler.py | from torch.utils.data import Sampler
import numpy as np
class ConcatDatasetBatchSampler(Sampler):
"""This sampler is built to work with a standard Pytorch ConcatDataset.
From SpeechBrain dataio see https://github.com/speechbrain/
It is used to retrieve elements from the different concatenated datasets pl... | 3,147 | 33.217391 | 107 | py |
CRSTmodel | CRSTmodel-main/DCASE2021_baseline_platform/desed_task/dataio/datasets.py | from torch.utils.data import Dataset
import pandas as pd
import os
import numpy as np
import torchaudio
import torch
import glob
def to_mono(mixture, random_ch=False):
if mixture.ndim > 1: # multi channel
if not random_ch:
mixture = torch.mean(mixture, 0)
else: # randomly select on... | 6,460 | 27.337719 | 83 | py |
CRSTmodel | CRSTmodel-main/DCASE2021_baseline_platform/desed_task/dataio/__init__.py | from .datasets import WeakSet, UnlabeledSet, StronglyAnnotatedSet
from .sampler import ConcatDatasetBatchSampler
| 113 | 37 | 65 | py |
CRSTmodel | CRSTmodel-main/DCASE2020_baseline_platform/main_MT_model.py | # -*- coding: utf-8 -*-
import argparse
import datetime
import inspect
import os
import time
from pprint import pprint
import pandas as pd
import numpy as np
import torch
from torch.utils.data import DataLoader
from torch import nn
from data_utils.Desed import DESED
from data_utils.DataLoad import DataLoadDf, Concat... | 22,648 | 47.189362 | 120 | py |
CRSTmodel | CRSTmodel-main/DCASE2020_baseline_platform/main_ICT_model.py | # -*- coding: utf-8 -*-
import argparse
import datetime
import inspect
import os
import time
from pprint import pprint
import pandas as pd
import numpy as np
import torch
from torch.utils.data import DataLoader
from torch import nn
from data_utils.Desed import DESED
from data_utils.DataLoad import DataLoadDf, Concat... | 24,772 | 47.57451 | 124 | py |
CRSTmodel | CRSTmodel-main/DCASE2020_baseline_platform/TestModel.py | # -*- coding: utf-8 -*-
import argparse
import os.path as osp
import torch
from torch.utils.data import DataLoader
import numpy as np
import pandas as pd
from data_utils.DataLoad import DataLoadDf
from data_utils.Desed import DESED
from evaluation_measures import psds_score, get_predictions_v2, \
compute_psds_fro... | 8,195 | 40.604061 | 115 | py |
CRSTmodel | CRSTmodel-main/DCASE2020_baseline_platform/TestModel_ss_late_integration.py | # -*- coding: utf-8 -*-
import argparse
import os
import os.path as osp
import scipy
import torch
from dcase_util.data import ProbabilityEncoder
import pandas as pd
import numpy as np
from data_utils.DataLoad import DataLoadDf
from data_utils.Desed import DESED
from TestModel import _load_scaler, _load_crnn
from eval... | 12,022 | 48.887967 | 118 | py |
CRSTmodel | CRSTmodel-main/DCASE2020_baseline_platform/evaluation_measures.py | # -*- coding: utf-8 -*-
import os
from os import path as osp
import psds_eval
import scipy
from dcase_util.data import ProbabilityEncoder
import sed_eval
import numpy as np
import pandas as pd
import torch
from psds_eval import plot_psd_roc, PSDSEval
import config as cfg
from utilities.Logger import create_logger
fro... | 22,296 | 42.044402 | 119 | py |
CRSTmodel | CRSTmodel-main/DCASE2020_baseline_platform/TestModel_dual.py | # -*- coding: utf-8 -*-
import argparse
import os.path as osp
import torch
from torch.utils.data import DataLoader
import numpy as np
import pandas as pd
from data_utils.DataLoad import DataLoadDf
from data_utils.Desed import DESED
from evaluation_measures import psds_score, get_predictions_v2, \
compute_psds_fro... | 8,284 | 40.633166 | 115 | py |
CRSTmodel | CRSTmodel-main/DCASE2020_baseline_platform/config.py | import logging
import math
import os
import pandas as pd
import numpy as np
dataspace = "/home/Databases/DESED/"
workspace = ".."
# DESED Paths
weak = os.path.join(dataspace, 'dcase2019/dataset/metadata/train/weak.tsv')
unlabel = os.path.join(dataspace, 'dcase2019/dataset/metadata/train/unlabel_in_domain.tsv')
synthet... | 3,061 | 31.924731 | 143 | py |
CRSTmodel | CRSTmodel-main/DCASE2020_baseline_platform/main_CRST_model_v2.py | # -*- coding: utf-8 -*-
import argparse
import datetime
import inspect
import os
import time
from pprint import pprint
import pandas as pd
import numpy as np
import torch
from torch.utils.data import DataLoader
from torch import nn
from data_utils.Desed import DESED
from data_utils.DataLoad import DataLoadDf, Concat... | 28,622 | 48.35 | 158 | py |
CRSTmodel | CRSTmodel-main/DCASE2020_baseline_platform/main_CRST_model.py | # -*- coding: utf-8 -*-
import argparse
import datetime
import inspect
import os
import time
from pprint import pprint
import pandas as pd
import numpy as np
import torch
from torch.utils.data import DataLoader
from torch import nn
from data_utils.Desed import DESED
from data_utils.DataLoad import DataLoadDf, Concat... | 28,492 | 48.295848 | 158 | py |
CRSTmodel | CRSTmodel-main/DCASE2020_baseline_platform/EvaluatePredictions.py | import glob
import os.path as osp
import pandas as pd
from evaluation_measures import psds_score, compute_psds_from_operating_points, compute_metrics
from utilities.utils import generate_tsv_wav_durations
if __name__ == '__main__':
groundtruth_path = "../dataset/metadata/validation/validation.tsv"
durations_p... | 1,543 | 45.787879 | 99 | py |
CRSTmodel | CRSTmodel-main/DCASE2020_baseline_platform/main_SRST_model.py | # -*- coding: utf-8 -*-
import argparse
import datetime
import inspect
import os
import time
from pprint import pprint
import pandas as pd
import numpy as np
import torch
from torch.utils.data import DataLoader
from torch import nn
from data_utils.Desed import DESED
from data_utils.DataLoad import DataLoadDf, Concat... | 25,288 | 46.535714 | 120 | py |
CRSTmodel | CRSTmodel-main/DCASE2020_baseline_platform/models/CNN.py | import torch.nn as nn
import torch
class GLU(nn.Module):
def __init__(self, input_num):
super(GLU, self).__init__()
self.sigmoid = nn.Sigmoid()
self.linear = nn.Linear(input_num, input_num)
def forward(self, x):
lin = self.linear(x.permute(0, 2, 3, 1))
lin = lin.permut... | 4,002 | 37.12381 | 105 | py |
CRSTmodel | CRSTmodel-main/DCASE2020_baseline_platform/models/CRNN.py | import warnings
import torch.nn as nn
import torch
from models.RNN import BidirectionalGRU
from models.CNN import CNN
class CRNN(nn.Module):
def __init__(self, n_in_channel, nclass, attention=False, activation="Relu", dropout=0,
train_cnn=True, rnn_type='BGRU', n_RNN_cell=64, n_layers_RNN=1, d... | 4,037 | 38.588235 | 115 | py |
CRSTmodel | CRSTmodel-main/DCASE2020_baseline_platform/models/RNN.py | import warnings
import torch
from torch import nn as nn
class BidirectionalGRU(nn.Module):
def __init__(self, n_in, n_hidden, dropout=0, num_layers=1):
super(BidirectionalGRU, self).__init__()
self.rnn = nn.GRU(n_in, n_hidden, bidirectional=True, dropout=dropout, batch_first=True, num_layers=nu... | 1,498 | 31.586957 | 119 | py |
CRSTmodel | CRSTmodel-main/DCASE2020_baseline_platform/utilities/ManyHotEncoder.py | import numpy as np
import pandas as pd
from dcase_util.data import DecisionEncoder
class ManyHotEncoder:
""""
Adapted after DecisionEncoder.find_contiguous_regions method in
https://github.com/DCASE-REPO/dcase_util/blob/master/dcase_util/data/decisions.py
Encode labels into numpy arrays w... | 6,343 | 39.407643 | 117 | py |
CRSTmodel | CRSTmodel-main/DCASE2020_baseline_platform/utilities/utils.py | from __future__ import print_function
import glob
import warnings
import numpy as np
import pandas as pd
import soundfile
import os
import os.path as osp
import librosa
import torch
from desed.utils import create_folder
from torch import nn
import config as cfg
def median_smoothing(input_tensor, win_length):
n... | 11,860 | 33.988201 | 119 | py |
CRSTmodel | CRSTmodel-main/DCASE2020_baseline_platform/utilities/Logger.py | import logging
import sys
import logging.config
def create_logger(logger_name, terminal_level=logging.INFO):
""" Create a logger.
Args:
logger_name: str, name of the logger
terminal_level: int, logging level in the terminal
"""
logging.config.dictConfig({
'version': 1,
... | 1,509 | 33.318182 | 80 | py |
CRSTmodel | CRSTmodel-main/DCASE2020_baseline_platform/utilities/ramps.py | import numpy as np
def exp_rampup(current, rampup_length):
"""Exponential rampup inspired by https://arxiv.org/abs/1610.02242
Args:
current: float, current step of the rampup
rampup_length: float: length of the rampup
"""
if rampup_length == 0:
return 1.0
else:... | 473 | 26.882353 | 70 | py |
CRSTmodel | CRSTmodel-main/DCASE2020_baseline_platform/utilities/Scaler.py | import time
import warnings
import numpy as np
import torch
import json
from utilities.Logger import create_logger
logger = create_logger(__name__)
class Scaler:
"""
operates on one or multiple existing datasets and applies operations
"""
def __init__(self):
self.mean_ = None
self.... | 6,478 | 31.888325 | 118 | py |
CRSTmodel | CRSTmodel-main/DCASE2020_baseline_platform/utilities/Transforms.py | import warnings
import librosa
import random
import numpy as np
import torch
class Transform:
def transform_data(self, data):
# Mandatory to be defined by subclasses
raise NotImplementedError("Abstract object")
def transform_label(self, label):
# Do nothing, to be changed in subclass... | 13,603 | 30.710956 | 155 | py |
CRSTmodel | CRSTmodel-main/DCASE2020_baseline_platform/data_utils/Desed.py | # -*- coding: utf-8 -*-
from __future__ import print_function
import functools
import glob
import multiprocessing
from contextlib import closing
import scipy.signal as sp
import numpy as np
import os
import os.path as osp
import librosa
import time
import pandas as pd
import desed
from tqdm import tqdm
import config... | 20,210 | 46.332553 | 122 | py |
CRSTmodel | CRSTmodel-main/DCASE2020_baseline_platform/data_utils/DataLoad.py | import bisect
import numpy as np
import pandas as pd
import torch
import random
import warnings
from torch.utils.data import Dataset
from torch.utils.data.sampler import Sampler
from utilities.Logger import create_logger
import config as cfg
from utilities.Transforms import Compose
torch.manual_seed(0)
random.seed(0)... | 10,066 | 35.607273 | 120 | py |
GalaxyDataset | GalaxyDataset-master/test.py | import numpy as np
# array = [[1, 2], 3]
#
# np.save("./test.npy", array)
#
# print(np.load("./test.npy", allow_pickle=True))
import yaml
import os
f = open("./config.yaml")
y = yaml.load(f)
print(y)
print(y["split_mode"])
# def readYaml(path, args):
# if not os.path.exists(path):
# return args
# f =... | 925 | 27.9375 | 71 | py |
GalaxyDataset | GalaxyDataset-master/GalaxyDataset.py | # -*- coding: utf-8 -*-
import torch
import torch.utils.data as Data
import numpy as np
import argparse
import os
import random
import yaml
import downloadData
import fdata
import preprocess
import mnist_bias
# 1. download dataset 2. split dataset
def make_dataset():
parser = argparse.ArgumentParser('parameters')... | 16,303 | 42.946092 | 239 | py |
GalaxyDataset | GalaxyDataset-master/downloadData.py | # -*- coding: utf-8 -*-
import argparse
import torch
from torchvision import datasets, transforms
# CIFAR-10,
# mean, [0.5, 0.5, 0.5]
# std, [0.5, 0.5, 0.5]
# CIFAR-100,
# mean, [0.5071, 0.4865, 0.4409]
# std, [0.2673, 0.2564, 0.2762]
def load_data(args):
args.batch_size = 1
train_loader = []
test_load... | 3,725 | 30.310924 | 98 | py |
GalaxyDataset | GalaxyDataset-master/fdata.py | from torch.utils.data import DataLoader, Dataset
from torchvision import datasets, transforms
import torch as t
import numpy as np
import random
from PIL import ImageFilter
from PIL import Image
class GaussianBlur(object):
def __init__(self, sigma=[.1, 2.]):
self.sigma = sigma
def __call__(self, x):
... | 3,878 | 33.945946 | 154 | py |
GalaxyDataset | GalaxyDataset-master/autoencoder.py | # Numpy
import numpy as np
# Torch
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
# Torchvision
import torchvision
import torchvision.transforms as transforms
# Matplotlib
# %matplotlib inline
import matplotlib.pyplot as plt
# OS
im... | 6,675 | 31.565854 | 87 | py |
GalaxyDataset | GalaxyDataset-master/NEI.py | # -*- coding: utf-8 -*-
import torch
import torch.nn as nn
import torch.utils.data as Data
from preprocess import load_npy
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
# Torchvision
import torchvision
import torchvision.transforms as transforms
class Autoencoder(nn.M... | 3,661 | 35.62 | 121 | py |
GalaxyDataset | GalaxyDataset-master/mnist_bias.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import datasets, transforms
import random, os, time, argparse, pickle
def mnist_image_raw2bias(image_raw, label, background, digit, id_1, id_2):
b = []
d = []
for i in range(8):
i_0 = i//4
... | 2,547 | 33.432432 | 139 | py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.