I am using tensorflow 1.12 with CUDNN7.5 and CUDA 9.0 on an ubuntu 16.04. Upon running run-ldc93s1.sh, I get the following error. Which version combinations should I use for smooth operation?
(venv) root@asr:~/DeepSpeech# ./bin/run-ldc93s1.sh
+ [ ! -f DeepSpeech.py ]
+ [ ! -f data/ldc93s1/ldc93s1.csv ]
+ echo Downloading and preprocessing LDC93S1 example data, saving in ./data/ldc93s1.
Downloading and preprocessing LDC93S1 example data, saving in ./data/ldc93s1.
+ python -u bin/import_ldc93s1.py ./data/ldc93s1
No path "./data/ldc93s1" - creating ...
No archive "./data/ldc93s1/LDC93S1.wav" - downloading...
Progress | | N/A% completedNo archive "./data/ldc93s1/LDC93S1.txt" - downloading...
Progress |#################################################################################################################################################################################| 100% completed
Progress |#################################################################################################################################################################################| 100% completed
+ [ -d ]
+ python -c from xdg import BaseDirectory as xdg; print(xdg.save_data_path("deepspeech/ldc93s1"))
+ checkpoint_dir=/root/.local/share/deepspeech/ldc93s1
+ python -u DeepSpeech.py --noshow_progressbar --train_files data/ldc93s1/ldc93s1.csv --test_files data/ldc93s1/ldc93s1.csv --train_batch_size 1 --test_batch_size 1 --n_hidden 100 --epochs 200 --checkpoint_dir /root/.local/share/deepspeech/ldc93s1
Traceback (most recent call last):
File "DeepSpeech.py", line 833, in <module>
tf.app.run(main)
File "/root/venv/lib/python3.5/site-packages/tensorflow/python/platform/app.py", line 125, in run
_sys.exit(main(argv))
File "DeepSpeech.py", line 817, in main
train()
File "DeepSpeech.py", line 369, in train
cache_path=FLAGS.train_cached_features_path)
File "/root/DeepSpeech/util/feeding.py", line 92, in create_dataset
.map(entry_to_features, num_parallel_calls=tf.data.experimental.AUTOTUNE)
AttributeError: module 'tensorflow._api.v1.data.experimental' has no attribute 'AUTOTUNE'