import tensorflow as tf
# from data_provider2 import get_split
from tf_utils import start_interactive_session, set_gpu
from rnn_models import RNNplusModel
import numpy as np
set_gpu(5)
options = {
'data_root_dir': "/vol/atlas/homes/pt511/db/audio_to_3d/tf_records_lrs",
# "/home/michaeltrs/Projects/audio23d/data",
#"/vol/atlas/homes/pt511/db/audio_to_3d/tf_records_clean", #
'split_name': "devel", # 'devel',
'is_training' : False,
'data_in': 'melf', # mfcc, melf, melf_2d
'max_seq_len': -20,
'use_rmse': False,
'batch_size': 128, # number of examples in queue either for training or inference
#'reverse_time': False,
#'shuffle': False,
#'random_crop': False,
#'standardize_inputs_and_labels': True,
'mfcc_num_features': 20, # 20,
'raw_audio_num_features': 533, # 256,
'num_classes': 28, # number of output classes 29 = |a-z, " ", <sos>, <eos>|
'max_out_len_multiplier': 1.0, # max_out_len = max_out_len_multiplier * max_in_len
'mfcc_gaussian_noise_std': 0.0, # 0.05,
'label_gaussian_noise_std':0.0,
'has_encoder': True,
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from collections import defaultdict
from transformer_model import SelfAttentionEncoder
from tf_utils import start_interactive_session, set_gpu
set_gpu(7)
options = defaultdict(
lambda: None, # Set default value to None.
# Input params
#default_batch_size=2, # Maximum number of tokens per batch of examples.
max_length=50, # Maximum number of tokens per example.
# Model params
initializer_gain=1.0, # Used in trainable variable initialization.
vocab_size=28, # Number of tokens defined in the vocabulary file.
hidden_size=128, # Model dimension in the hidden layers.
num_hidden_layers=3, # Number of layers in the encoder and decoder stacks.
num_heads=4, # Number of heads to use in multi-headed attention.
filter_size=256, # Inner layer dimension in the feedforward network.
# Dropout values (only used when training)
layer_postprocess_dropout=0.1,
attention_dropout=0.1,
relu_dropout=0.1,
##################################################
data_root_dir=
"/vol/atlas/homes/pt511/db/audio_to_3d/tf_records_clean", # enhanced",
import tensorflow as tf
# from data_provider2 import get_split
from tf_utils import start_interactive_session, set_gpu
from mixed_seq_models import CNNRNNModel3
import numpy as np
set_gpu(0)
options = {
'data_root_dir':
"/vol/atlas/homes/pt511/db/audio_to_3d/tf_records_dtw_antonio",
# "/vol/atlas/homes/pt511/db/audio_to_3d/tf_records_dtwN",
# "/vol/atlas/homes/pt511/db/audio_to_3d/tf_records_lrs",
# "/vol/atlas/homes/pt511/db/audio_to_3d/tf_records_clean",
'is_training': True,
'data_in': 'melf', # mcc, melf, melf_2d
'split_name': 'train',
'batch_size':
20, # number of examples in queue either for training or inference
'random_crop': False,
'mfcc_num_features': 20, # 20,
'raw_audio_num_features': 533, # 256,
'num_classes':
28, # number of output classes 29 = |a-z, " ", <sos>, <eos>|
#'1dcnn_features_dims': [256, 256, 256],
'batch_norm': True,
'has_decoder': True,
'decoder_num_layers': 1, # number of hidden layers in decoder lstm
'residual_decoder': False, #
'decoder_num_hidden': 256, # number of hidden units in decoder lstm
import tensorflow as tf
# from data_provider2 import get_split
from tf_utils import start_interactive_session, set_gpu
from rnn_seq2seq_models import RNNSeq2SeqModel
import numpy as np
set_gpu(-1)
options = {
'data_root_dir': '/vol/atlas/homes/pt511/db/audio_to_3d/tf_records_dtwN',
# "/vol/atlas/homes/pt511/db/audio_to_3d/tf_records_lrs",
# "/vol/atlas/homes/pt511/db/audio_to_3d/tf_records_clean",
'is_training' : False,
'data_in': 'melf', # mfcc, melf, melf_2d
#'max_seq_len': -20,
'split_name': 'devel',
#'data_in': 'mfcc', # mfcc, melf, melf_2d
#'use_rmse': False,
'batch_size': 1, # number of examples in queue either for training or inference
#'reverse_time': False,
#'shuffle': True,
'random_crop': False,
#'standardize_inputs_and_labels': True,
'mfcc_num_features': 20, # 20,
'raw_audio_num_features': 533, # 256,
'num_classes': 28, # number of output classes 29 = |a-z, " ", <sos>, <eos>|
#'max_out_len_multiplier': 1.0, # max_out_len = max_out_len_multiplier * max_in_len
#'mfcc_gaussian_noise_std': 0.0, # 0.05,
from cnn_models import DenseNet1D
from tf_utils import start_interactive_session, set_gpu
import numpy as np
set_gpu(1)
options = {
'data_root_dir': "/vol/atlas/homes/pt511/db/audio_to_3d/tf_records_lrs",
# "/home/michaeltrs/Projects/audio23d/data",
# "/vol/atlas/homes/pt511/db/audio_to_3d/tf_records_lrs",
# , # "/vol/atlas/homes/pt511/db/audio_to_3d/tf_records_clean", # enhanced",
'is_training' : True,
'split_name': "train", # ""train", # 'devel',
'data_in': 'mfcc', # mfcc, melf, melf_2d
'max_seq_len': 10,
# 'use_rmse': False,
'batch_size': 64, # number of examples in queue either for training or inference
# 'reverse_time': False,
# 'shuffle': True,
'random_crop': False,
# 'standardize_inputs_and_labels': False,
'mfcc_num_features': 20, # 20,
'raw_audio_num_features': 533, # 256,
'num_classes': 28, # number of output classes 29 = |a-z, " ", <sos>, <eos>|
# 'max_out_len_multiplier': 1.0, # max_out_len = max_out_len_multiplier * max_in_len
'growth_rate': 20,
'num_layers' : 6,
'final_layer_dim': 64,
'loss_fun': "concordance_cc",
import tensorflow as tf
# from data_provider2 import get_split
from tf_utils import start_interactive_session, set_gpu
from mixed_seq2seq_models import CNNRNNSeq2SeqModel
import numpy as np
<<<<<<< HEAD
set_gpu(2)
options = {
'data_root_dir': "/vol/atlas/homes/pt511/db/audio_to_3d/tf_records_dtw_antonio",
# "/vol/atlas/homes/pt511/db/audio_to_3d/tf_records_dtwN",
=======
set_gpu(-1)
options = {
'data_root_dir': '/vol/atlas/homes/pt511/db/audio_to_3d/tf_records_dtwN',
>>>>>>> f798981d5c303deabd8107e5086cbc23a1985d2f
# "/vol/atlas/homes/pt511/db/audio_to_3d/tf_records_lrs",
# "/vol/atlas/homes/pt511/db/audio_to_3d/tf_records_clean",
'is_training' : False,
<<<<<<< HEAD
'data_in': 'melf', # mcc, melf, melf_2d
'split_name': 'devel',
'batch_size': 1, # number of examples in queue either for training or inference
'random_crop': False,
=======
'data_in': 'mfcc', # mcc, melf, melf_2d
#'max_seq_len': -20,
'split_name': 'train', # 'devel',
import tensorflow as tf
# from data_provider2 import get_split
from tf_utils import start_interactive_session, set_gpu
from mixed_seq_models import CNNRNNModel
import numpy as np
set_gpu(2)
options = {
'data_root_dir': "/vol/atlas/homes/pt511/db/audio_to_3d/tf_records_dtw_antonio",
# "/vol/atlas/homes/pt511/db/audio_to_3d/tf_records_dtwN",
# "/vol/atlas/homes/pt511/db/audio_to_3d/tf_records_lrs",
# "/vol/atlas/homes/pt511/db/audio_to_3d/tf_records_clean",
'is_training' : True,
'data_in': 'melf', # mcc, melf, melf_2d
'split_name': 'train',
'batch_size': 64, # number of examples in queue either for training or inference
'random_crop': True,
'mfcc_num_features': 20, # 20,
'raw_audio_num_features': 533, # 256,
'num_classes': 28, # number of output classes 29 = |a-z, " ", <sos>, <eos>|
'1dcnn_features_dims': [256, 256, 256],
'has_decoder': True,
'decoder_num_layers': 1, # number of hidden layers in decoder lstm
'residual_decoder': True, #
'decoder_num_hidden': 256, # number of hidden units in decoder lstm
'decoder_layer_norm': True,
'decoder_dropout_keep_prob': 1.0,
from cnn_models import CNNModel
from tf_utils import start_interactive_session, set_gpu
import numpy as np
import tensorflow as tf
set_gpu(3)
options = {
'data_root_dir': "/vol/atlas/homes/pt511/db/audio_to_3d/tf_records_dtw_antonio",
# "/vol/atlas/homes/pt511/db/audio_to_3d/tf_records_dtwN",
# "/home/michaeltrs/Projects/audio23d/data",
# "/vol/atlas/homes/pt511/db/audio_to_3d/tf_records_clean",
# "/vol/atlas/homes/pt511/db/audio_to_3d/tf_records_lrs",
'split_name': "devel", # 'devel',
'is_training' : False,
'data_in': 'melf', # mfcc, melf, melf_2d
'batch_size': 1, # number of examples in queue either for training or inference
'random_crop': False,
'mfcc_num_features': 20, # 20,
'raw_audio_num_features': 533, # 256,
'num_classes': 28, # number of output classes 29 = |a-z, " ", <sos>, <eos>|
'has_encoder': True,
'1dcnn_features_dims': [256, 256, 256],
'loss_fun': "concordance_cc",
'reg_constant': 0.000,
'max_grad_norm': 10.0,
'num_epochs': 100, # number of epochs over dataset for training
'start_epoch': 1, # epoch to start
'reset_global_step': True,
