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i replace ".WAV" to wav, but it didn't work. When i run the train1.py, it stop in there,, what's more, when i use the command of "top" in ubuntu, i found the state of this program is "S", I din't know how to solve this problem.Can anyone help me ? Thanks a lot! #5

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AliceSky opened this issue Apr 7, 2019 · 1 comment
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i replace ".WAV" to wav, but it didn't work. When i run the train1.py, it stop in there,, what's more, when i use the command of "top" in ubuntu, i found the state of this program is "S", I din't know how to solve this problem.Can anyone help me ? Thanks a lot! #5

AliceSky opened this issue Apr 7, 2019 · 1 comment

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@AliceSky
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AliceSky commented Apr 7, 2019

xxt@alice:~/vc_program/cross_vc$ python train1.py
Training Graph loaded
2019-04-07 11:25:19.617084: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2019-04-07 11:25:19.617106: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2019-04-07 11:25:19.617112: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2019-04-07 11:25:19.617116: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2019-04-07 11:25:19.617120: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
2019-04-07 11:25:19.803318: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:893] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-04-07 11:25:19.803774: I tensorflow/core/common_runtime/gpu/gpu_device.cc:955] Found device 0 with properties:
name: GeForce GT 730
major: 3 minor: 5 memoryClockRate (GHz) 0.9015
pciBusID 0000:01:00.0
Total memory: 978.75MiB
Free memory: 772.50MiB
2019-04-07 11:25:19.803806: I tensorflow/core/common_runtime/gpu/gpu_device.cc:976] DMA: 0
2019-04-07 11:25:19.803818: I tensorflow/core/common_runtime/gpu/gpu_device.cc:986] 0: Y
2019-04-07 11:25:19.803835: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1045] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GT 730, pci bus id: 0000:01:00.0)
net1/conv1d_1/conv1d/bias (DT_FLOAT) [128]
net1/conv1d_1/conv1d/kernel (DT_FLOAT) [3,2048,128]
net1/conv1d_1/conv1d_1/beta (DT_FLOAT) [128]
net1/conv1d_1/conv1d_1/gamma (DT_FLOAT) [128]
net1/conv1d_2/conv1d/bias (DT_FLOAT) [128]
net1/conv1d_2/conv1d/kernel (DT_FLOAT) [3,128,128]
net1/conv1d_2/conv1d_2/beta (DT_FLOAT) [128]
net1/conv1d_2/conv1d_2/gamma (DT_FLOAT) [128]
net1/conv1d_banks/num_0/conv1d/bias (DT_FLOAT) [128]
net1/conv1d_banks/num_0/conv1d/kernel (DT_FLOAT) [1,128,128]
net1/conv1d_banks/num_0/num_0/beta (DT_FLOAT) [128]
net1/conv1d_banks/num_0/num_0/gamma (DT_FLOAT) [128]
net1/conv1d_banks/num_1/conv1d/bias (DT_FLOAT) [128]
net1/conv1d_banks/num_1/conv1d/kernel (DT_FLOAT) [2,128,128]
net1/conv1d_banks/num_1/num_1/beta (DT_FLOAT) [128]
net1/conv1d_banks/num_1/num_1/gamma (DT_FLOAT) [128]
net1/conv1d_banks/num_10/conv1d/bias (DT_FLOAT) [128]
net1/conv1d_banks/num_10/conv1d/kernel (DT_FLOAT) [11,128,128]
net1/conv1d_banks/num_10/num_10/beta (DT_FLOAT) [128]
net1/conv1d_banks/num_10/num_10/gamma (DT_FLOAT) [128]
net1/conv1d_banks/num_11/conv1d/bias (DT_FLOAT) [128]
net1/conv1d_banks/num_11/conv1d/kernel (DT_FLOAT) [12,128,128]
net1/conv1d_banks/num_11/num_11/beta (DT_FLOAT) [128]
net1/conv1d_banks/num_11/num_11/gamma (DT_FLOAT) [128]
net1/conv1d_banks/num_12/conv1d/bias (DT_FLOAT) [128]
net1/conv1d_banks/num_12/conv1d/kernel (DT_FLOAT) [13,128,128]
net1/conv1d_banks/num_12/num_12/beta (DT_FLOAT) [128]
net1/conv1d_banks/num_12/num_12/gamma (DT_FLOAT) [128]
net1/conv1d_banks/num_13/conv1d/bias (DT_FLOAT) [128]
net1/conv1d_banks/num_13/conv1d/kernel (DT_FLOAT) [14,128,128]
net1/conv1d_banks/num_13/num_13/beta (DT_FLOAT) [128]
net1/conv1d_banks/num_13/num_13/gamma (DT_FLOAT) [128]
net1/conv1d_banks/num_14/conv1d/bias (DT_FLOAT) [128]
net1/conv1d_banks/num_14/conv1d/kernel (DT_FLOAT) [15,128,128]
net1/conv1d_banks/num_14/num_14/beta (DT_FLOAT) [128]
net1/conv1d_banks/num_14/num_14/gamma (DT_FLOAT) [128]
net1/conv1d_banks/num_15/conv1d/bias (DT_FLOAT) [128]
net1/conv1d_banks/num_15/conv1d/kernel (DT_FLOAT) [16,128,128]
net1/conv1d_banks/num_15/num_15/beta (DT_FLOAT) [128]
net1/conv1d_banks/num_15/num_15/gamma (DT_FLOAT) [128]
net1/conv1d_banks/num_2/conv1d/bias (DT_FLOAT) [128]
net1/conv1d_banks/num_2/conv1d/kernel (DT_FLOAT) [3,128,128]
net1/conv1d_banks/num_2/num_2/beta (DT_FLOAT) [128]
net1/conv1d_banks/num_2/num_2/gamma (DT_FLOAT) [128]
net1/conv1d_banks/num_3/conv1d/bias (DT_FLOAT) [128]
net1/conv1d_banks/num_3/conv1d/kernel (DT_FLOAT) [4,128,128]
net1/conv1d_banks/num_3/num_3/beta (DT_FLOAT) [128]
net1/conv1d_banks/num_3/num_3/gamma (DT_FLOAT) [128]
net1/conv1d_banks/num_4/conv1d/bias (DT_FLOAT) [128]
net1/conv1d_banks/num_4/conv1d/kernel (DT_FLOAT) [5,128,128]
net1/conv1d_banks/num_4/num_4/beta (DT_FLOAT) [128]
net1/conv1d_banks/num_4/num_4/gamma (DT_FLOAT) [128]
net1/conv1d_banks/num_5/conv1d/bias (DT_FLOAT) [128]
net1/conv1d_banks/num_5/conv1d/kernel (DT_FLOAT) [6,128,128]
net1/conv1d_banks/num_5/num_5/beta (DT_FLOAT) [128]
net1/conv1d_banks/num_5/num_5/gamma (DT_FLOAT) [128]
net1/conv1d_banks/num_6/conv1d/bias (DT_FLOAT) [128]
net1/conv1d_banks/num_6/conv1d/kernel (DT_FLOAT) [7,128,128]
net1/conv1d_banks/num_6/num_6/beta (DT_FLOAT) [128]
net1/conv1d_banks/num_6/num_6/gamma (DT_FLOAT) [128]
net1/conv1d_banks/num_7/conv1d/bias (DT_FLOAT) [128]
net1/conv1d_banks/num_7/conv1d/kernel (DT_FLOAT) [8,128,128]
net1/conv1d_banks/num_7/num_7/beta (DT_FLOAT) [128]
net1/conv1d_banks/num_7/num_7/gamma (DT_FLOAT) [128]
net1/conv1d_banks/num_8/conv1d/bias (DT_FLOAT) [128]
net1/conv1d_banks/num_8/conv1d/kernel (DT_FLOAT) [9,128,128]
net1/conv1d_banks/num_8/num_8/beta (DT_FLOAT) [128]
net1/conv1d_banks/num_8/num_8/gamma (DT_FLOAT) [128]
net1/conv1d_banks/num_9/conv1d/bias (DT_FLOAT) [128]
net1/conv1d_banks/num_9/conv1d/kernel (DT_FLOAT) [10,128,128]
net1/conv1d_banks/num_9/num_9/beta (DT_FLOAT) [128]
net1/conv1d_banks/num_9/num_9/gamma (DT_FLOAT) [128]
net1/dense/bias (DT_FLOAT) [128]
net1/dense/kernel (DT_FLOAT) [128,128]
net1/dense_1/bias (DT_FLOAT) [161]
net1/dense_1/kernel (DT_FLOAT) [256,161]
net1/gru/bidirectional_rnn/bw/gru_cell/candidate/bias (DT_FLOAT) [128]
net1/gru/bidirectional_rnn/bw/gru_cell/candidate/kernel (DT_FLOAT) [256,128]
net1/gru/bidirectional_rnn/bw/gru_cell/gates/bias (DT_FLOAT) [256]
net1/gru/bidirectional_rnn/bw/gru_cell/gates/kernel (DT_FLOAT) [256,256]
net1/gru/bidirectional_rnn/fw/gru_cell/candidate/bias (DT_FLOAT) [128]
net1/gru/bidirectional_rnn/fw/gru_cell/candidate/kernel (DT_FLOAT) [256,128]
net1/gru/bidirectional_rnn/fw/gru_cell/gates/bias (DT_FLOAT) [256]
net1/gru/bidirectional_rnn/fw/gru_cell/gates/kernel (DT_FLOAT) [256,256]
net1/highwaynet_0/dense1/bias (DT_FLOAT) [128]
net1/highwaynet_0/dense1/kernel (DT_FLOAT) [128,128]
net1/highwaynet_0/dense2/bias (DT_FLOAT) [128]
net1/highwaynet_0/dense2/kernel (DT_FLOAT) [128,128]
net1/highwaynet_1/dense1/bias (DT_FLOAT) [128]
net1/highwaynet_1/dense1/kernel (DT_FLOAT) [128,128]
net1/highwaynet_1/dense2/bias (DT_FLOAT) [128]
net1/highwaynet_1/dense2/kernel (DT_FLOAT) [128,128]
net1/highwaynet_2/dense1/bias (DT_FLOAT) [128]
net1/highwaynet_2/dense1/kernel (DT_FLOAT) [128,128]
net1/highwaynet_2/dense2/bias (DT_FLOAT) [128]
net1/highwaynet_2/dense2/kernel (DT_FLOAT) [128,128]
net1/highwaynet_3/dense1/bias (DT_FLOAT) [128]
net1/highwaynet_3/dense1/kernel (DT_FLOAT) [128,128]
net1/highwaynet_3/dense2/bias (DT_FLOAT) [128]
net1/highwaynet_3/dense2/kernel (DT_FLOAT) [128,128]
net1/prenet/dense1/bias (DT_FLOAT) [256]
net1/prenet/dense1/kernel (DT_FLOAT) [40,256]
net1/prenet/dense2/bias (DT_FLOAT) [128]
net1/prenet/dense2/kernel (DT_FLOAT) [256,128]
training/global_step (DT_INT32) []

0%| | 0/195 [00:00<?, ?b/s]
@zhaoxy0303
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Hello,did you solve this problem?

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@AliceSky @zhaoxy0303