Update to TensorFlow 1.13

DeepSpeech.py

@@ -27,12 +27,6 @@
 from util.preprocess import preprocess
 from util.text import Alphabet
 
-#TODO: remove once fully switched to 1.13
-try:
-    import tensorflow.lite as lite # 1.13
-except ImportError:
-    import tensorflow.contrib.lite as lite # 1.12
-
 
 # Graph Creation
 # ==============
@@ -91,21 +85,21 @@ def BiRNN(batch_x, seq_length, dropout, reuse=False, batch_size=None, n_steps=-1
     b1 = variable_on_worker_level('b1', [Config.n_hidden_1], tf.zeros_initializer())
     h1 = variable_on_worker_level('h1', [Config.n_input + 2*Config.n_input*Config.n_context, Config.n_hidden_1], tf.contrib.layers.xavier_initializer())
     layer_1 = tf.minimum(tf.nn.relu(tf.add(tf.matmul(batch_x, h1), b1)), FLAGS.relu_clip)
-    layer_1 = tf.nn.dropout(layer_1, (1.0 - dropout[0]))
+    layer_1 = tf.nn.dropout(layer_1, rate=dropout[0])
     layers['layer_1'] = layer_1
 
     # 2nd layer
     b2 = variable_on_worker_level('b2', [Config.n_hidden_2], tf.zeros_initializer())
     h2 = variable_on_worker_level('h2', [Config.n_hidden_1, Config.n_hidden_2], tf.contrib.layers.xavier_initializer())
     layer_2 = tf.minimum(tf.nn.relu(tf.add(tf.matmul(layer_1, h2), b2)), FLAGS.relu_clip)
-    layer_2 = tf.nn.dropout(layer_2, (1.0 - dropout[1]))
+    layer_2 = tf.nn.dropout(layer_2, rate=dropout[1])
     layers['layer_2'] = layer_2
 
     # 3rd layer
     b3 = variable_on_worker_level('b3', [Config.n_hidden_3], tf.zeros_initializer())
     h3 = variable_on_worker_level('h3', [Config.n_hidden_2, Config.n_hidden_3], tf.contrib.layers.xavier_initializer())
     layer_3 = tf.minimum(tf.nn.relu(tf.add(tf.matmul(layer_2, h3), b3)), FLAGS.relu_clip)
-    layer_3 = tf.nn.dropout(layer_3, (1.0 - dropout[2]))
+    layer_3 = tf.nn.dropout(layer_3, rate=dropout[2])
     layers['layer_3'] = layer_3
 
     # Now we create the forward and backward LSTM units.
@@ -149,7 +143,7 @@ def BiRNN(batch_x, seq_length, dropout, reuse=False, batch_size=None, n_steps=-1
     b5 = variable_on_worker_level('b5', [Config.n_hidden_5], tf.zeros_initializer())
     h5 = variable_on_worker_level('h5', [Config.n_cell_dim, Config.n_hidden_5], tf.contrib.layers.xavier_initializer())
     layer_5 = tf.minimum(tf.nn.relu(tf.add(tf.matmul(output, h5), b5)), FLAGS.relu_clip)
-    layer_5 = tf.nn.dropout(layer_5, (1.0 - dropout[5]))
+    layer_5 = tf.nn.dropout(layer_5, rate=dropout[5])
     layers['layer_5'] = layer_5
 
     # Now we apply the weight matrix `h6` and bias `b6` to the output of `layer_5`
@@ -821,7 +815,7 @@ def do_graph_freeze(output_file=None, output_node_names=None, variables_blacklis
                 frozen_graph = do_graph_freeze(output_node_names=output_names, variables_blacklist='')
                 output_tflite_path = os.path.join(FLAGS.export_dir, output_filename.replace('.pb', '.tflite'))
 
-                converter = lite.TFLiteConverter(frozen_graph, input_tensors=inputs.values(), output_tensors=outputs.values())
+                converter = tf.lite.TFLiteConverter(frozen_graph, input_tensors=inputs.values(), output_tensors=outputs.values())
                 converter.post_training_quantize = True
                 tflite_model = converter.convert()
 

Dockerfile

@@ -1,6 +1,6 @@
 # Need devel version cause we need /usr/include/cudnn.h 
 # for compiling libctc_decoder_with_kenlm.so
-FROM nvidia/cuda:9.0-cudnn7-devel-ubuntu16.04
+FROM nvidia/cuda:10.0-cudnn7-devel-ubuntu18.04
 
 
 # >> START Install base software
@@ -31,21 +31,21 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
         liblzma-dev \
         locales \
         pkg-config \
-        libsox-dev
+        libsox-dev \
+        openjdk-8-jdk \
+        bash-completion \
+        g++ \
+        unzip
 
 # Install NCCL 2.2
-RUN apt-get install -qq -y --allow-downgrades --allow-change-held-packages libnccl2=2.2.13-1+cuda9.0 libnccl-dev=2.2.13-1+cuda9.0
-
+RUN apt-get install -qq -y --allow-downgrades --allow-change-held-packages libnccl2=2.3.7-1+cuda10.0 libnccl-dev=2.3.7-1+cuda10.0
 
 # Install Bazel
-RUN apt-get install -y openjdk-8-jdk
-# Use bazel 0.11.1 cause newer bazel fails to compile TensorFlow (https://github.com/tensorflow/tensorflow/issues/18450#issuecomment-381380000)
-RUN apt-get install -y --no-install-recommends bash-completion g++ zlib1g-dev
-RUN curl -LO "https://github.com/bazelbuild/bazel/releases/download/0.15.2/bazel_0.15.2-linux-x86_64.deb"
+RUN curl -LO "https://github.com/bazelbuild/bazel/releases/download/0.19.2/bazel_0.19.2-linux-x86_64.deb"
 RUN dpkg -i bazel_*.deb
 
 # Install CUDA CLI Tools
-RUN apt-get install -qq -y cuda-command-line-tools-9-0
+RUN apt-get install -qq -y cuda-command-line-tools-10-0
 
 # Install pip
 RUN wget https://bootstrap.pypa.io/get-pip.py && \
@@ -62,19 +62,17 @@ RUN wget https://bootstrap.pypa.io/get-pip.py && \
 # Clone TensoFlow from Mozilla repo
 RUN git clone https://github.com/mozilla/tensorflow/
 WORKDIR /tensorflow
-RUN git checkout r1.12
+RUN git checkout r1.13
 
 
 # GPU Environment Setup
 ENV TF_NEED_CUDA 1
 ENV CUDA_TOOLKIT_PATH /usr/local/cuda
-ENV CUDA_PKG_VERSION 9-0=9.0.176-1
-ENV CUDA_VERSION 9.0.176
-ENV TF_CUDA_VERSION 9.0
-ENV TF_CUDNN_VERSION 7.3.0
+ENV TF_CUDA_VERSION 10.0
+ENV TF_CUDNN_VERSION 7
 ENV CUDNN_INSTALL_PATH /usr/lib/x86_64-linux-gnu/
 ENV TF_CUDA_COMPUTE_CAPABILITIES 6.0
-ENV TF_NCCL_VERSION 2.2.13
+ENV TF_NCCL_VERSION 2.3
 # ENV NCCL_INSTALL_PATH /usr/lib/x86_64-linux-gnu/
 
 # Common Environment Setup
@@ -186,7 +184,7 @@ RUN cp /tensorflow/bazel-bin/native_client/generate_trie /DeepSpeech/native_clie
 
 # Install TensorFlow
 WORKDIR /DeepSpeech/
-RUN pip install tensorflow-gpu==1.12.0
+RUN pip install tensorflow-gpu==1.13.1
 
 
 # Make DeepSpeech and install Python bindings
@@ -212,7 +210,7 @@ ENV PYTHONIOENCODING UTF-8
 # Build KenLM in /DeepSpeech/native_client/kenlm folder
 WORKDIR /DeepSpeech/native_client
 RUN rm -rf kenlm \
-    && git clone https://github.com/kpu/kenlm && cd kenlm \    
+    && git clone --depth 1 https://github.com/kpu/kenlm && cd kenlm \
     && mkdir -p build \
     && cd build \
     && cmake .. \

README.md

@@ -238,7 +238,7 @@ If you have a capable (Nvidia, at least 8GB of VRAM) GPU, it is highly recommend
 
 ```bash
 pip3 uninstall tensorflow
-pip3 install 'tensorflow-gpu==1.12.0'
+pip3 install 'tensorflow-gpu==1.13.1'
 ```
 
 Please ensure you have the required [CUDA dependency](#cuda-dependency).

native_client/BUILD

@@ -3,7 +3,7 @@
 load("@org_tensorflow//tensorflow:tensorflow.bzl",
      "tf_cc_shared_object", "if_cuda")
 
-load("@org_tensorflow//tensorflow/contrib/lite:build_def.bzl",
+load("@org_tensorflow//tensorflow/lite:build_def.bzl",
      "tflite_copts", "tflite_linkopts")
 
 config_setting(
@@ -92,7 +92,7 @@ tf_cc_shared_object(
     }) + tflite_linkopts(),
     deps = select({
         "//native_client:tflite": [
-            "//tensorflow/contrib/lite/kernels:builtin_ops",
+            "//tensorflow/lite/kernels:builtin_ops",
         ],
         "//conditions:default": [
             "//tensorflow/core:core_cpu",

native_client/README.md

@@ -50,7 +50,7 @@ Check the [main README](../README.md) for more details.
 If you'd like to build the binaries yourself, you'll need the following pre-requisites downloaded/installed:
 
 * [TensorFlow requirements](https://www.tensorflow.org/install/install_sources)
-* [TensorFlow `r1.12` sources](https://github.com/mozilla/tensorflow/tree/r1.12)
+* [TensorFlow `r1.13` sources](https://github.com/mozilla/tensorflow/tree/r1.13)
 * [libsox](https://sourceforge.net/projects/sox/)
 
 It is required to use our fork of TensorFlow since it includes fixes for common problems encountered when building the native client files.

native_client/deepspeech.cc

@@ -19,8 +19,8 @@
   #include "tensorflow/core/platform/env.h"
   #include "tensorflow/core/util/memmapped_file_system.h"
 #else // USE_TFLITE
-  #include "tensorflow/contrib/lite/model.h"
-  #include "tensorflow/contrib/lite/kernels/register.h"
+  #include "tensorflow/lite/model.h"
+  #include "tensorflow/lite/kernels/register.h"
 #endif // USE_TFLITE
 
 #include "c_speech_features.h"

requirements.txt

@@ -1,7 +1,7 @@
 pandas
 progressbar2
 python-utils
-tensorflow == 1.12.0
+tensorflow == 1.13.1
 numpy == 1.15.4
 matplotlib
 scipy