Lets try one more time @lissyx @kdavis shall we. One more question. Like I said, when I have edited evaluate.py code I get results good enough below is sample and same the wav I am using everywhere, but this time getting results I expect:
python3 mnz_evaluate.py
2019-05-30 17:49:50.974290: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-05-30 17:49:51.109638: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:964] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-05-30 17:49:51.109996: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Found device 0 with properties:
name: GeForce RTX 2080 Ti major: 7 minor: 5 memoryClockRate(GHz): 1.635
pciBusID: 0000:01:00.0
totalMemory: 10.73GiB freeMemory: 10.32GiB
2019-05-30 17:49:51.110008: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1511] Adding visible gpu devices: 0
2019-05-30 17:49:51.313417: I tensorflow/core/common_runtime/gpu/gpu_device.cc:982] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-05-30 17:49:51.313443: I tensorflow/core/common_runtime/gpu/gpu_device.cc:988] 0
2019-05-30 17:49:51.313447: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 0: N
2019-05-30 17:49:51.313575: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/device:GPU:0 with 9959 MB memory) -> physical GPU (device: 0, name: GeForce RTX 2080 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5)
Preprocessing [‘test_m.csv’]
Preprocessing done
2019-05-30 17:49:53.046422: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1511] Adding visible gpu devices: 0
2019-05-30 17:49:53.046480: I tensorflow/core/common_runtime/gpu/gpu_device.cc:982] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-05-30 17:49:53.046485: I tensorflow/core/common_runtime/gpu/gpu_device.cc:988] 0
2019-05-30 17:49:53.046488: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 0: N
2019-05-30 17:49:53.046625: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9959 MB memory) -> physical GPU (device: 0, name: GeForce RTX 2080 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5)
Computing acoustic model predictions…
100% (1 of 1) |########################################################################################################################################################################| Elapsed Time: 0:00:00 Time: 0:00:00
Decoding predictions…
100% (1 of 1) |########################################################################################################################################################################| Elapsed Time: 0:00:00 Time: 0:00:00
Test - WER: 0.846154, CER: 90.000000, loss: 287.961700
WER: 0.846154, CER: 90.000000, loss: 287.961700
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src: “no niin tarviis viela perua nii tana iltana kymmeneen mennessa ooksa muuten missa vaiheessa kuullut tost meidan autotarkastus kampanjasta joka on nyt meneillaan satanelkytyhdeksan euroa tarkastus”
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res: "niin jos kavis sielta taa hintaan peruutusmaksu mutta missa lasku tai autotarkastus kampanja elanyt menee janne yhdeksan euron tarkastus "
So, I can run that code independently and changing test_m.csv content I can do that to any wav and get speech to text … It gives me results good enough even validation loss is high.
Questions goes: What on earth that evaluate.py does different than client.py ? Evaluate.py uses checkpoints, not exported model (right?) … Language model is same, Trie is same, alphabet is same.
One last shot