Refactor train.py into separate scripts

Currently train.py is overloaded with many independent features.
Understanding the code and what will be the result of a training
call requires untangling the entire script. It's also an error
prone UX. This is a first step at separating independent parts
into their own scripts.

training/deepspeech_training/deepspeech_model.py

@@ -0,0 +1,287 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+import os
+import sys
+
+LOG_LEVEL_INDEX = sys.argv.index('--log_level') + 1 if '--log_level' in sys.argv else 0
+DESIRED_LOG_LEVEL = sys.argv[LOG_LEVEL_INDEX] if 0 < LOG_LEVEL_INDEX < len(sys.argv) else '3'
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = DESIRED_LOG_LEVEL
+
+import numpy as np
+import tensorflow as tf
+import tensorflow.compat.v1 as tfv1
+
+tfv1.logging.set_verbosity({
+    '0': tfv1.logging.DEBUG,
+    '1': tfv1.logging.INFO,
+    '2': tfv1.logging.WARN,
+    '3': tfv1.logging.ERROR
+}.get(DESIRED_LOG_LEVEL))
+
+from .util.config import Config
+from .util.feeding import audio_to_features
+from .util.flags import FLAGS
+
+
+def variable_on_cpu(name, shape, initializer):
+    r"""
+    Next we concern ourselves with graph creation.
+    However, before we do so we must introduce a utility function ``variable_on_cpu()``
+    used to create a variable in CPU memory.
+    """
+    # Use the /cpu:0 device for scoped operations
+    with tf.device(Config.cpu_device):
+        # Create or get apropos variable
+        var = tfv1.get_variable(name=name, shape=shape, initializer=initializer)
+    return var
+
+
+def create_overlapping_windows(batch_x):
+    batch_size = tf.shape(input=batch_x)[0]
+    window_width = 2 * Config.n_context + 1
+    num_channels = Config.n_input
+
+    # Create a constant convolution filter using an identity matrix, so that the
+    # convolution returns patches of the input tensor as is, and we can create
+    # overlapping windows over the MFCCs.
+    eye_filter = tf.constant(np.eye(window_width * num_channels)
+                               .reshape(window_width, num_channels, window_width * num_channels), tf.float32)
+
+    # Create overlapping windows
+    batch_x = tf.nn.conv1d(input=batch_x, filters=eye_filter, stride=1, padding='SAME')
+
+    # Remove dummy depth dimension and reshape into [batch_size, n_windows, window_width, n_input]
+    batch_x = tf.reshape(batch_x, [batch_size, -1, window_width, num_channels])
+
+    return batch_x
+
+
+def dense(name, x, units, dropout_rate=None, relu=True, layer_norm=False):
+    with tfv1.variable_scope(name):
+        bias = variable_on_cpu('bias', [units], tfv1.zeros_initializer())
+        weights = variable_on_cpu('weights', [x.shape[-1], units], tfv1.keras.initializers.VarianceScaling(scale=1.0, mode="fan_avg", distribution="uniform"))
+
+    output = tf.nn.bias_add(tf.matmul(x, weights), bias)
+
+    if relu:
+        output = tf.minimum(tf.nn.relu(output), FLAGS.relu_clip)
+
+    if layer_norm:
+        with tfv1.variable_scope(name):
+            output = tf.contrib.layers.layer_norm(output)
+
+    if dropout_rate is not None:
+        output = tf.nn.dropout(output, rate=dropout_rate)
+
+    return output
+
+
+def rnn_impl_lstmblockfusedcell(x, seq_length, previous_state, reuse):
+    with tfv1.variable_scope('cudnn_lstm/rnn/multi_rnn_cell/cell_0'):
+        fw_cell = tf.contrib.rnn.LSTMBlockFusedCell(Config.n_cell_dim,
+                                                    forget_bias=0,
+                                                    reuse=reuse,
+                                                    name='cudnn_compatible_lstm_cell')
+
+        output, output_state = fw_cell(inputs=x,
+                                       dtype=tf.float32,
+                                       sequence_length=seq_length,
+                                       initial_state=previous_state)
+
+    return output, output_state
+
+
+def rnn_impl_cudnn_rnn(x, seq_length, previous_state, _):
+    assert previous_state is None # 'Passing previous state not supported with CuDNN backend'
+
+    # Hack: CudnnLSTM works similarly to Keras layers in that when you instantiate
+    # the object it creates the variables, and then you just call it several times
+    # to enable variable re-use. Because all of our code is structure in an old
+    # school TensorFlow structure where you can just call tf.get_variable again with
+    # reuse=True to reuse variables, we can't easily make use of the object oriented
+    # way CudnnLSTM is implemented, so we save a singleton instance in the function,
+    # emulating a static function variable.
+    if not rnn_impl_cudnn_rnn.cell:
+        # Forward direction cell:
+        fw_cell = tf.contrib.cudnn_rnn.CudnnLSTM(num_layers=1,
+                                                 num_units=Config.n_cell_dim,
+                                                 input_mode='linear_input',
+                                                 direction='unidirectional',
+                                                 dtype=tf.float32)
+        rnn_impl_cudnn_rnn.cell = fw_cell
+
+    output, output_state = rnn_impl_cudnn_rnn.cell(inputs=x,
+                                                   sequence_lengths=seq_length)
+
+    return output, output_state
+
+rnn_impl_cudnn_rnn.cell = None
+
+
+def rnn_impl_static_rnn(x, seq_length, previous_state, reuse):
+    with tfv1.variable_scope('cudnn_lstm/rnn/multi_rnn_cell'):
+        # Forward direction cell:
+        fw_cell = tfv1.nn.rnn_cell.LSTMCell(Config.n_cell_dim,
+                                            forget_bias=0,
+                                            reuse=reuse,
+                                            name='cudnn_compatible_lstm_cell')
+
+        # Split rank N tensor into list of rank N-1 tensors
+        x = [x[l] for l in range(x.shape[0])]
+
+        output, output_state = tfv1.nn.static_rnn(cell=fw_cell,
+                                                  inputs=x,
+                                                  sequence_length=seq_length,
+                                                  initial_state=previous_state,
+                                                  dtype=tf.float32,
+                                                  scope='cell_0')
+
+        output = tf.concat(output, 0)
+
+    return output, output_state
+
+
+def create_model(batch_x, seq_length, dropout, reuse=False, batch_size=None, previous_state=None, overlap=True, rnn_impl=rnn_impl_lstmblockfusedcell):
+    layers = {}
+
+    # Input shape: [batch_size, n_steps, n_input + 2*n_input*n_context]
+    if not batch_size:
+        batch_size = tf.shape(input=batch_x)[0]
+
+    # Create overlapping feature windows if needed
+    if overlap:
+        batch_x = create_overlapping_windows(batch_x)
+
+    # Reshaping `batch_x` to a tensor with shape `[n_steps*batch_size, n_input + 2*n_input*n_context]`.
+    # This is done to prepare the batch for input into the first layer which expects a tensor of rank `2`.
+
+    # Permute n_steps and batch_size
+    batch_x = tf.transpose(a=batch_x, perm=[1, 0, 2, 3])
+    # Reshape to prepare input for first layer
+    batch_x = tf.reshape(batch_x, [-1, Config.n_input + 2*Config.n_input*Config.n_context]) # (n_steps*batch_size, n_input + 2*n_input*n_context)
+    layers['input_reshaped'] = batch_x
+
+    # The next three blocks will pass `batch_x` through three hidden layers with
+    # clipped RELU activation and dropout.
+    layers['layer_1'] = layer_1 = dense('layer_1', batch_x, Config.n_hidden_1, dropout_rate=dropout[0], layer_norm=FLAGS.layer_norm)
+    layers['layer_2'] = layer_2 = dense('layer_2', layer_1, Config.n_hidden_2, dropout_rate=dropout[1], layer_norm=FLAGS.layer_norm)
+    layers['layer_3'] = layer_3 = dense('layer_3', layer_2, Config.n_hidden_3, dropout_rate=dropout[2], layer_norm=FLAGS.layer_norm)
+
+    # `layer_3` is now reshaped into `[n_steps, batch_size, 2*n_cell_dim]`,
+    # as the LSTM RNN expects its input to be of shape `[max_time, batch_size, input_size]`.
+    layer_3 = tf.reshape(layer_3, [-1, batch_size, Config.n_hidden_3])
+
+    # Run through parametrized RNN implementation, as we use different RNNs
+    # for training and inference
+    output, output_state = rnn_impl(layer_3, seq_length, previous_state, reuse)
+
+    # Reshape output from a tensor of shape [n_steps, batch_size, n_cell_dim]
+    # to a tensor of shape [n_steps*batch_size, n_cell_dim]
+    output = tf.reshape(output, [-1, Config.n_cell_dim])
+    layers['rnn_output'] = output
+    layers['rnn_output_state'] = output_state
+
+    # Now we feed `output` to the fifth hidden layer with clipped RELU activation
+    layers['layer_5'] = layer_5 = dense('layer_5', output, Config.n_hidden_5, dropout_rate=dropout[5], layer_norm=FLAGS.layer_norm)
+
+    # Now we apply a final linear layer creating `n_classes` dimensional vectors, the logits.
+    layers['layer_6'] = layer_6 = dense('layer_6', layer_5, Config.n_hidden_6, relu=False)
+
+    # Finally we reshape layer_6 from a tensor of shape [n_steps*batch_size, n_hidden_6]
+    # to the slightly more useful shape [n_steps, batch_size, n_hidden_6].
+    # Note, that this differs from the input in that it is time-major.
+    layer_6 = tf.reshape(layer_6, [-1, batch_size, Config.n_hidden_6], name='raw_logits')
+    layers['raw_logits'] = layer_6
+
+    # Output shape: [n_steps, batch_size, n_hidden_6]
+    return layer_6, layers
+
+
+def create_inference_graph(batch_size=1, n_steps=16, tflite=False):
+    batch_size = batch_size if batch_size > 0 else None
+
+    # Create feature computation graph
+    input_samples = tfv1.placeholder(tf.float32, [Config.audio_window_samples], 'input_samples')
+    samples = tf.expand_dims(input_samples, -1)
+    mfccs, _ = audio_to_features(samples, FLAGS.audio_sample_rate)
+    mfccs = tf.identity(mfccs, name='mfccs')
+
+    # Input tensor will be of shape [batch_size, n_steps, 2*n_context+1, n_input]
+    # This shape is read by the native_client in DS_CreateModel to know the
+    # value of n_steps, n_context and n_input. Make sure you update the code
+    # there if this shape is changed.
+    input_tensor = tfv1.placeholder(tf.float32, [batch_size, n_steps if n_steps > 0 else None, 2 * Config.n_context + 1, Config.n_input], name='input_node')
+    seq_length = tfv1.placeholder(tf.int32, [batch_size], name='input_lengths')
+
+    if batch_size <= 0:
+        # no state management since n_step is expected to be dynamic too (see below)
+        previous_state = None
+    else:
+        previous_state_c = tfv1.placeholder(tf.float32, [batch_size, Config.n_cell_dim], name='previous_state_c')
+        previous_state_h = tfv1.placeholder(tf.float32, [batch_size, Config.n_cell_dim], name='previous_state_h')
+
+        previous_state = tf.nn.rnn_cell.LSTMStateTuple(previous_state_c, previous_state_h)
+
+    # One rate per layer
+    no_dropout = [None] * 6
+
+    if tflite:
+        rnn_impl = rnn_impl_static_rnn
+    else:
+        rnn_impl = rnn_impl_lstmblockfusedcell
+
+    logits, layers = create_model(batch_x=input_tensor,
+                                  batch_size=batch_size,
+                                  seq_length=seq_length if not FLAGS.export_tflite else None,
+                                  dropout=no_dropout,
+                                  previous_state=previous_state,
+                                  overlap=False,
+                                  rnn_impl=rnn_impl)
+
+    # TF Lite runtime will check that input dimensions are 1, 2 or 4
+    # by default we get 3, the middle one being batch_size which is forced to
+    # one on inference graph, so remove that dimension
+    if tflite:
+        logits = tf.squeeze(logits, [1])
+
+    # Apply softmax for CTC decoder
+    probs = tf.nn.softmax(logits, name='logits')
+
+    if batch_size <= 0:
+        if tflite:
+            raise NotImplementedError('dynamic batch_size does not support tflite nor streaming')
+        if n_steps > 0:
+            raise NotImplementedError('dynamic batch_size expect n_steps to be dynamic too')
+        return (
+            {
+                'input': input_tensor,
+                'input_lengths': seq_length,
+            },
+            {
+                'outputs': probs,
+            },
+            layers
+        )
+
+    new_state_c, new_state_h = layers['rnn_output_state']
+    new_state_c = tf.identity(new_state_c, name='new_state_c')
+    new_state_h = tf.identity(new_state_h, name='new_state_h')
+
+    inputs = {
+        'input': input_tensor,
+        'previous_state_c': previous_state_c,
+        'previous_state_h': previous_state_h,
+        'input_samples': input_samples,
+    }
+
+    if not FLAGS.export_tflite:
+        inputs['input_lengths'] = seq_length
+
+    outputs = {
+        'outputs': probs,
+        'new_state_c': new_state_c,
+        'new_state_h': new_state_h,
+        'mfccs': mfccs,
+    }
+
+    return inputs, outputs, layers

training/deepspeech_training/evaluate.py

@@ -133,6 +133,13 @@ def evaluate(test_csvs, create_model):
         return samples
 
 
+def test():
+    from .train import create_model # pylint: disable=cyclic-import,import-outside-toplevel
+    samples = evaluate(FLAGS.test_files.split(','), create_model)
+    if FLAGS.test_output_file:
+        save_samples_json(samples, FLAGS.test_output_file)
+
+
 def main(_):
     initialize_globals()
 
@@ -141,16 +148,13 @@ def main(_):
                   'the --test_files flag.')
         sys.exit(1)
 
-    from .train import create_model # pylint: disable=cyclic-import,import-outside-toplevel
+    test()
-    samples = evaluate(FLAGS.test_files.split(','), create_model)
-
-    if FLAGS.test_output_file:
-        save_samples_json(samples, FLAGS.test_output_file)
 
 
 def run_script():
     create_flags()
     absl.app.run(main)
 
+
 if __name__ == '__main__':
     run_script()

training/deepspeech_training/export.py

@@ -0,0 +1,162 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+import os
+import sys
+
+LOG_LEVEL_INDEX = sys.argv.index('--log_level') + 1 if '--log_level' in sys.argv else 0
+DESIRED_LOG_LEVEL = sys.argv[LOG_LEVEL_INDEX] if 0 < LOG_LEVEL_INDEX < len(sys.argv) else '3'
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = DESIRED_LOG_LEVEL
+
+import absl.app
+import tensorflow as tf
+import tensorflow.compat.v1 as tfv1
+import shutil
+
+from .deepspeech_model import create_inference_graph
+from .util.checkpoints import load_graph_for_evaluation
+from .util.config import Config, initialize_globals
+from .util.flags import create_flags, FLAGS
+from .util.io import open_remote, rmtree_remote, listdir_remote, is_remote_path, isdir_remote
+from .util.logging import log_error, log_info
+
+
+def file_relative_read(fname):
+    return open(os.path.join(os.path.dirname(__file__), fname)).read()
+
+
+def export():
+    r'''
+    Restores the trained variables into a simpler graph that will be exported for serving.
+    '''
+    log_info('Exporting the model...')
+
+    inputs, outputs, _ = create_inference_graph(batch_size=FLAGS.export_batch_size, n_steps=FLAGS.n_steps, tflite=FLAGS.export_tflite)
+
+    graph_version = int(file_relative_read('GRAPH_VERSION').strip())
+    assert graph_version > 0
+
+    outputs['metadata_version'] = tf.constant([graph_version], name='metadata_version')
+    outputs['metadata_sample_rate'] = tf.constant([FLAGS.audio_sample_rate], name='metadata_sample_rate')
+    outputs['metadata_feature_win_len'] = tf.constant([FLAGS.feature_win_len], name='metadata_feature_win_len')
+    outputs['metadata_feature_win_step'] = tf.constant([FLAGS.feature_win_step], name='metadata_feature_win_step')
+    outputs['metadata_beam_width'] = tf.constant([FLAGS.export_beam_width], name='metadata_beam_width')
+    outputs['metadata_alphabet'] = tf.constant([Config.alphabet.Serialize()], name='metadata_alphabet')
+
+    if FLAGS.export_language:
+        outputs['metadata_language'] = tf.constant([FLAGS.export_language.encode('utf-8')], name='metadata_language')
+
+    # Prevent further graph changes
+    tfv1.get_default_graph().finalize()
+
+    output_names_tensors = [tensor.op.name for tensor in outputs.values() if isinstance(tensor, tf.Tensor)]
+    output_names_ops = [op.name for op in outputs.values() if isinstance(op, tf.Operation)]
+    output_names = output_names_tensors + output_names_ops
+
+    with tf.Session() as session:
+        # Restore variables from checkpoint
+        load_graph_for_evaluation(session)
+
+        output_filename = FLAGS.export_file_name + '.pb'
+        if FLAGS.remove_export:
+            if isdir_remote(FLAGS.export_dir):
+                log_info('Removing old export')
+                rmtree_remote(FLAGS.export_dir)
+
+        output_graph_path = os.path.join(FLAGS.export_dir, output_filename)
+
+        if not is_remote_path(FLAGS.export_dir) and not os.path.isdir(FLAGS.export_dir):
+            os.makedirs(FLAGS.export_dir)
+
+        frozen_graph = tfv1.graph_util.convert_variables_to_constants(
+            sess=session,
+            input_graph_def=tfv1.get_default_graph().as_graph_def(),
+            output_node_names=output_names)
+
+        frozen_graph = tfv1.graph_util.extract_sub_graph(
+            graph_def=frozen_graph,
+            dest_nodes=output_names)
+
+        if not FLAGS.export_tflite:
+            with open_remote(output_graph_path, 'wb') as fout:
+                fout.write(frozen_graph.SerializeToString())
+        else:
+            output_tflite_path = os.path.join(FLAGS.export_dir, output_filename.replace('.pb', '.tflite'))
+
+            converter = tf.lite.TFLiteConverter(frozen_graph, input_tensors=inputs.values(), output_tensors=outputs.values())
+            converter.optimizations = [tf.lite.Optimize.DEFAULT]
+            # AudioSpectrogram and Mfcc ops are custom but have built-in kernels in TFLite
+            converter.allow_custom_ops = True
+            tflite_model = converter.convert()
+
+            with open_remote(output_tflite_path, 'wb') as fout:
+                fout.write(tflite_model)
+
+        log_info('Models exported at %s' % (FLAGS.export_dir))
+
+    metadata_fname = os.path.join(FLAGS.export_dir, '{}_{}_{}.md'.format(
+        FLAGS.export_author_id,
+        FLAGS.export_model_name,
+        FLAGS.export_model_version))
+
+    model_runtime = 'tflite' if FLAGS.export_tflite else 'tensorflow'
+    with open_remote(metadata_fname, 'w') as f:
+        f.write('---\n')
+        f.write('author: {}\n'.format(FLAGS.export_author_id))
+        f.write('model_name: {}\n'.format(FLAGS.export_model_name))
+        f.write('model_version: {}\n'.format(FLAGS.export_model_version))
+        f.write('contact_info: {}\n'.format(FLAGS.export_contact_info))
+        f.write('license: {}\n'.format(FLAGS.export_license))
+        f.write('language: {}\n'.format(FLAGS.export_language))
+        f.write('runtime: {}\n'.format(model_runtime))
+        f.write('min_ds_version: {}\n'.format(FLAGS.export_min_ds_version))
+        f.write('max_ds_version: {}\n'.format(FLAGS.export_max_ds_version))
+        f.write('acoustic_model_url: <replace this with a publicly available URL of the acoustic model>\n')
+        f.write('scorer_url: <replace this with a publicly available URL of the scorer, if present>\n')
+        f.write('---\n')
+        f.write('{}\n'.format(FLAGS.export_description))
+
+    log_info('Model metadata file saved to {}. Before submitting the exported model for publishing make sure all information in the metadata file is correct, and complete the URL fields.'.format(metadata_fname))
+
+
+def package_zip():
+    # --export_dir path/to/export/LANG_CODE/ => path/to/export/LANG_CODE.zip
+    export_dir = os.path.join(os.path.abspath(FLAGS.export_dir), '') # Force ending '/'
+    if is_remote_path(export_dir):
+        log_error("Cannot package remote path zip %s. Please do this manually." % export_dir)
+        return
+
+    zip_filename = os.path.dirname(export_dir)
+
+    shutil.copy(FLAGS.scorer_path, export_dir)
+
+    archive = shutil.make_archive(zip_filename, 'zip', export_dir)
+    log_info('Exported packaged model {}'.format(archive))
+
+
+def main(_):
+    initialize_globals()
+
+    if FLAGS.export_dir:
+        tfv1.reset_default_graph()
+
+        if not FLAGS.export_zip:
+            # Export to folder
+            export()
+        else:
+            # Export and zip, TFLite only, creates package readable by Java example app
+            FLAGS.export_tflite = True
+
+            if listdir_remote(FLAGS.export_dir):
+                log_error('Directory {} is not empty, please fix this.'.format(FLAGS.export_dir))
+                sys.exit(1)
+
+            export()
+            package_zip()
+    else:
+        log_error('Calling export script directly but no --export_dir specified')
+        sys.exit(1)
+
+
+if __name__ == '__main__':
+    create_flags()
+    absl.app.run(main)

training/deepspeech_training/train.py

@@ -1,7 +1,5 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
-from __future__ import absolute_import, division, print_function
-
 import os
 import sys
 
@@ -10,13 +8,13 @@ DESIRED_LOG_LEVEL = sys.argv[LOG_LEVEL_INDEX] if 0 < LOG_LEVEL_INDEX < len(sys.a
 os.environ['TF_CPP_MIN_LOG_LEVEL'] = DESIRED_LOG_LEVEL
 
 import absl.app
-import numpy as np
 import progressbar
-import shutil
 import tensorflow as tf
 import tensorflow.compat.v1 as tfv1
 import time
 
+from datetime import datetime
+
 tfv1.logging.set_verbosity({
     '0': tfv1.logging.DEBUG,
     '1': tfv1.logging.INFO,
@@ -24,197 +22,23 @@ tfv1.logging.set_verbosity({
     '3': tfv1.logging.ERROR
 }.get(DESIRED_LOG_LEVEL))
 
-from datetime import datetime
+from ds_ctcdecoder import Scorer
-from ds_ctcdecoder import ctc_beam_search_decoder, Scorer
+
-from .evaluate import evaluate
+from . import export
-from six.moves import zip, range
+from . import evaluate
+from . import training_graph_inference
+from .deepspeech_model import create_model, rnn_impl_lstmblockfusedcell, rnn_impl_cudnn_rnn
 from .util.config import Config, initialize_globals
-from .util.checkpoints import load_or_init_graph_for_training, load_graph_for_evaluation, reload_best_checkpoint
+from .util.checkpoints import load_or_init_graph_for_training, reload_best_checkpoint
-from .util.evaluate_tools import save_samples_json
+from .util.feeding import create_dataset
-from .util.feeding import create_dataset, audio_to_features, audiofile_to_features
 from .util.flags import create_flags, FLAGS
 from .util.helpers import check_ctcdecoder_version, ExceptionBox
 from .util.logging import create_progressbar, log_debug, log_error, log_info, log_progress, log_warn
-from .util.io import open_remote, remove_remote, listdir_remote, is_remote_path, isdir_remote
+from .util.io import open_remote, remove_remote, listdir_remote, is_remote_path
+
 
 check_ctcdecoder_version()
 
-# Graph Creation
-# ==============
-
-def variable_on_cpu(name, shape, initializer):
-    r"""
-    Next we concern ourselves with graph creation.
-    However, before we do so we must introduce a utility function ``variable_on_cpu()``
-    used to create a variable in CPU memory.
-    """
-    # Use the /cpu:0 device for scoped operations
-    with tf.device(Config.cpu_device):
-        # Create or get apropos variable
-        var = tfv1.get_variable(name=name, shape=shape, initializer=initializer)
-    return var
-
-
-def create_overlapping_windows(batch_x):
-    batch_size = tf.shape(input=batch_x)[0]
-    window_width = 2 * Config.n_context + 1
-    num_channels = Config.n_input
-
-    # Create a constant convolution filter using an identity matrix, so that the
-    # convolution returns patches of the input tensor as is, and we can create
-    # overlapping windows over the MFCCs.
-    eye_filter = tf.constant(np.eye(window_width * num_channels)
-                               .reshape(window_width, num_channels, window_width * num_channels), tf.float32) # pylint: disable=bad-continuation
-
-    # Create overlapping windows
-    batch_x = tf.nn.conv1d(input=batch_x, filters=eye_filter, stride=1, padding='SAME')
-
-    # Remove dummy depth dimension and reshape into [batch_size, n_windows, window_width, n_input]
-    batch_x = tf.reshape(batch_x, [batch_size, -1, window_width, num_channels])
-
-    return batch_x
-
-
-def dense(name, x, units, dropout_rate=None, relu=True, layer_norm=False):
-    with tfv1.variable_scope(name):
-        bias = variable_on_cpu('bias', [units], tfv1.zeros_initializer())
-        weights = variable_on_cpu('weights', [x.shape[-1], units], tfv1.keras.initializers.VarianceScaling(scale=1.0, mode="fan_avg", distribution="uniform"))
-
-    output = tf.nn.bias_add(tf.matmul(x, weights), bias)
-
-    if relu:
-        output = tf.minimum(tf.nn.relu(output), FLAGS.relu_clip)
-
-    if layer_norm:
-        with tfv1.variable_scope(name):
-            output = tf.contrib.layers.layer_norm(output)
-
-    if dropout_rate is not None:
-        output = tf.nn.dropout(output, rate=dropout_rate)
-
-    return output
-
-
-def rnn_impl_lstmblockfusedcell(x, seq_length, previous_state, reuse):
-    with tfv1.variable_scope('cudnn_lstm/rnn/multi_rnn_cell/cell_0'):
-        fw_cell = tf.contrib.rnn.LSTMBlockFusedCell(Config.n_cell_dim,
-                                                    forget_bias=0,
-                                                    reuse=reuse,
-                                                    name='cudnn_compatible_lstm_cell')
-
-        output, output_state = fw_cell(inputs=x,
-                                       dtype=tf.float32,
-                                       sequence_length=seq_length,
-                                       initial_state=previous_state)
-
-    return output, output_state
-
-
-def rnn_impl_cudnn_rnn(x, seq_length, previous_state, _):
-    assert previous_state is None # 'Passing previous state not supported with CuDNN backend'
-
-    # Hack: CudnnLSTM works similarly to Keras layers in that when you instantiate
-    # the object it creates the variables, and then you just call it several times
-    # to enable variable re-use. Because all of our code is structure in an old
-    # school TensorFlow structure where you can just call tf.get_variable again with
-    # reuse=True to reuse variables, we can't easily make use of the object oriented
-    # way CudnnLSTM is implemented, so we save a singleton instance in the function,
-    # emulating a static function variable.
-    if not rnn_impl_cudnn_rnn.cell:
-        # Forward direction cell:
-        fw_cell = tf.contrib.cudnn_rnn.CudnnLSTM(num_layers=1,
-                                                 num_units=Config.n_cell_dim,
-                                                 input_mode='linear_input',
-                                                 direction='unidirectional',
-                                                 dtype=tf.float32)
-        rnn_impl_cudnn_rnn.cell = fw_cell
-
-    output, output_state = rnn_impl_cudnn_rnn.cell(inputs=x,
-                                                   sequence_lengths=seq_length)
-
-    return output, output_state
-
-rnn_impl_cudnn_rnn.cell = None
-
-
-def rnn_impl_static_rnn(x, seq_length, previous_state, reuse):
-    with tfv1.variable_scope('cudnn_lstm/rnn/multi_rnn_cell'):
-        # Forward direction cell:
-        fw_cell = tfv1.nn.rnn_cell.LSTMCell(Config.n_cell_dim,
-                                            forget_bias=0,
-                                            reuse=reuse,
-                                            name='cudnn_compatible_lstm_cell')
-
-        # Split rank N tensor into list of rank N-1 tensors
-        x = [x[l] for l in range(x.shape[0])]
-
-        output, output_state = tfv1.nn.static_rnn(cell=fw_cell,
-                                                  inputs=x,
-                                                  sequence_length=seq_length,
-                                                  initial_state=previous_state,
-                                                  dtype=tf.float32,
-                                                  scope='cell_0')
-
-        output = tf.concat(output, 0)
-
-    return output, output_state
-
-
-def create_model(batch_x, seq_length, dropout, reuse=False, batch_size=None, previous_state=None, overlap=True, rnn_impl=rnn_impl_lstmblockfusedcell):
-    layers = {}
-
-    # Input shape: [batch_size, n_steps, n_input + 2*n_input*n_context]
-    if not batch_size:
-        batch_size = tf.shape(input=batch_x)[0]
-
-    # Create overlapping feature windows if needed
-    if overlap:
-        batch_x = create_overlapping_windows(batch_x)
-
-    # Reshaping `batch_x` to a tensor with shape `[n_steps*batch_size, n_input + 2*n_input*n_context]`.
-    # This is done to prepare the batch for input into the first layer which expects a tensor of rank `2`.
-
-    # Permute n_steps and batch_size
-    batch_x = tf.transpose(a=batch_x, perm=[1, 0, 2, 3])
-    # Reshape to prepare input for first layer
-    batch_x = tf.reshape(batch_x, [-1, Config.n_input + 2*Config.n_input*Config.n_context]) # (n_steps*batch_size, n_input + 2*n_input*n_context)
-    layers['input_reshaped'] = batch_x
-
-    # The next three blocks will pass `batch_x` through three hidden layers with
-    # clipped RELU activation and dropout.
-    layers['layer_1'] = layer_1 = dense('layer_1', batch_x, Config.n_hidden_1, dropout_rate=dropout[0], layer_norm=FLAGS.layer_norm)
-    layers['layer_2'] = layer_2 = dense('layer_2', layer_1, Config.n_hidden_2, dropout_rate=dropout[1], layer_norm=FLAGS.layer_norm)
-    layers['layer_3'] = layer_3 = dense('layer_3', layer_2, Config.n_hidden_3, dropout_rate=dropout[2], layer_norm=FLAGS.layer_norm)
-
-    # `layer_3` is now reshaped into `[n_steps, batch_size, 2*n_cell_dim]`,
-    # as the LSTM RNN expects its input to be of shape `[max_time, batch_size, input_size]`.
-    layer_3 = tf.reshape(layer_3, [-1, batch_size, Config.n_hidden_3])
-
-    # Run through parametrized RNN implementation, as we use different RNNs
-    # for training and inference
-    output, output_state = rnn_impl(layer_3, seq_length, previous_state, reuse)
-
-    # Reshape output from a tensor of shape [n_steps, batch_size, n_cell_dim]
-    # to a tensor of shape [n_steps*batch_size, n_cell_dim]
-    output = tf.reshape(output, [-1, Config.n_cell_dim])
-    layers['rnn_output'] = output
-    layers['rnn_output_state'] = output_state
-
-    # Now we feed `output` to the fifth hidden layer with clipped RELU activation
-    layers['layer_5'] = layer_5 = dense('layer_5', output, Config.n_hidden_5, dropout_rate=dropout[5], layer_norm=FLAGS.layer_norm)
-
-    # Now we apply a final linear layer creating `n_classes` dimensional vectors, the logits.
-    layers['layer_6'] = layer_6 = dense('layer_6', layer_5, Config.n_hidden_6, relu=False)
-
-    # Finally we reshape layer_6 from a tensor of shape [n_steps*batch_size, n_hidden_6]
-    # to the slightly more useful shape [n_steps, batch_size, n_hidden_6].
-    # Note, that this differs from the input in that it is time-major.
-    layer_6 = tf.reshape(layer_6, [-1, batch_size, Config.n_hidden_6], name='raw_logits')
-    layers['raw_logits'] = layer_6
-
-    # Output shape: [n_steps, batch_size, n_hidden_6]
-    return layer_6, layers
-
 
 # Accuracy and Loss
 # =================
@@ -678,255 +502,6 @@ def train():
     log_debug('Session closed.')
 
 
-def test():
-    samples = evaluate(FLAGS.test_files.split(','), create_model)
-    if FLAGS.test_output_file:
-        save_samples_json(samples, FLAGS.test_output_file)
-
-
-def create_inference_graph(batch_size=1, n_steps=16, tflite=False):
-    batch_size = batch_size if batch_size > 0 else None
-
-    # Create feature computation graph
-    input_samples = tfv1.placeholder(tf.float32, [Config.audio_window_samples], 'input_samples')
-    samples = tf.expand_dims(input_samples, -1)
-    mfccs, _ = audio_to_features(samples, FLAGS.audio_sample_rate)
-    mfccs = tf.identity(mfccs, name='mfccs')
-
-    # Input tensor will be of shape [batch_size, n_steps, 2*n_context+1, n_input]
-    # This shape is read by the native_client in DS_CreateModel to know the
-    # value of n_steps, n_context and n_input. Make sure you update the code
-    # there if this shape is changed.
-    input_tensor = tfv1.placeholder(tf.float32, [batch_size, n_steps if n_steps > 0 else None, 2 * Config.n_context + 1, Config.n_input], name='input_node')
-    seq_length = tfv1.placeholder(tf.int32, [batch_size], name='input_lengths')
-
-    if batch_size <= 0:
-        # no state management since n_step is expected to be dynamic too (see below)
-        previous_state = None
-    else:
-        previous_state_c = tfv1.placeholder(tf.float32, [batch_size, Config.n_cell_dim], name='previous_state_c')
-        previous_state_h = tfv1.placeholder(tf.float32, [batch_size, Config.n_cell_dim], name='previous_state_h')
-
-        previous_state = tf.nn.rnn_cell.LSTMStateTuple(previous_state_c, previous_state_h)
-
-    # One rate per layer
-    no_dropout = [None] * 6
-
-    if tflite:
-        rnn_impl = rnn_impl_static_rnn
-    else:
-        rnn_impl = rnn_impl_lstmblockfusedcell
-
-    logits, layers = create_model(batch_x=input_tensor,
-                                  batch_size=batch_size,
-                                  seq_length=seq_length if not FLAGS.export_tflite else None,
-                                  dropout=no_dropout,
-                                  previous_state=previous_state,
-                                  overlap=False,
-                                  rnn_impl=rnn_impl)
-
-    # TF Lite runtime will check that input dimensions are 1, 2 or 4
-    # by default we get 3, the middle one being batch_size which is forced to
-    # one on inference graph, so remove that dimension
-    if tflite:
-        logits = tf.squeeze(logits, [1])
-
-    # Apply softmax for CTC decoder
-    probs = tf.nn.softmax(logits, name='logits')
-
-    if batch_size <= 0:
-        if tflite:
-            raise NotImplementedError('dynamic batch_size does not support tflite nor streaming')
-        if n_steps > 0:
-            raise NotImplementedError('dynamic batch_size expect n_steps to be dynamic too')
-        return (
-            {
-                'input': input_tensor,
-                'input_lengths': seq_length,
-            },
-            {
-                'outputs': probs,
-            },
-            layers
-        )
-
-    new_state_c, new_state_h = layers['rnn_output_state']
-    new_state_c = tf.identity(new_state_c, name='new_state_c')
-    new_state_h = tf.identity(new_state_h, name='new_state_h')
-
-    inputs = {
-        'input': input_tensor,
-        'previous_state_c': previous_state_c,
-        'previous_state_h': previous_state_h,
-        'input_samples': input_samples,
-    }
-
-    if not FLAGS.export_tflite:
-        inputs['input_lengths'] = seq_length
-
-    outputs = {
-        'outputs': probs,
-        'new_state_c': new_state_c,
-        'new_state_h': new_state_h,
-        'mfccs': mfccs,
-    }
-
-    return inputs, outputs, layers
-
-
-def file_relative_read(fname):
-    return open(os.path.join(os.path.dirname(__file__), fname)).read()
-
-
-def export():
-    r'''
-    Restores the trained variables into a simpler graph that will be exported for serving.
-    '''
-    log_info('Exporting the model...')
-
-    inputs, outputs, _ = create_inference_graph(batch_size=FLAGS.export_batch_size, n_steps=FLAGS.n_steps, tflite=FLAGS.export_tflite)
-
-    graph_version = int(file_relative_read('GRAPH_VERSION').strip())
-    assert graph_version > 0
-
-    outputs['metadata_version'] = tf.constant([graph_version], name='metadata_version')
-    outputs['metadata_sample_rate'] = tf.constant([FLAGS.audio_sample_rate], name='metadata_sample_rate')
-    outputs['metadata_feature_win_len'] = tf.constant([FLAGS.feature_win_len], name='metadata_feature_win_len')
-    outputs['metadata_feature_win_step'] = tf.constant([FLAGS.feature_win_step], name='metadata_feature_win_step')
-    outputs['metadata_beam_width'] = tf.constant([FLAGS.export_beam_width], name='metadata_beam_width')
-    outputs['metadata_alphabet'] = tf.constant([Config.alphabet.Serialize()], name='metadata_alphabet')
-
-    if FLAGS.export_language:
-        outputs['metadata_language'] = tf.constant([FLAGS.export_language.encode('utf-8')], name='metadata_language')
-
-    # Prevent further graph changes
-    tfv1.get_default_graph().finalize()
-
-    output_names_tensors = [tensor.op.name for tensor in outputs.values() if isinstance(tensor, tf.Tensor)]
-    output_names_ops = [op.name for op in outputs.values() if isinstance(op, tf.Operation)]
-    output_names = output_names_tensors + output_names_ops
-
-    with tf.Session() as session:
-        # Restore variables from checkpoint
-        load_graph_for_evaluation(session)
-
-        output_filename = FLAGS.export_file_name + '.pb'
-        if FLAGS.remove_export:
-            if isdir_remote(FLAGS.export_dir):
-                log_info('Removing old export')
-                remove_remote(FLAGS.export_dir)
-
-        output_graph_path = os.path.join(FLAGS.export_dir, output_filename)
-
-        if not is_remote_path(FLAGS.export_dir) and not os.path.isdir(FLAGS.export_dir):
-            os.makedirs(FLAGS.export_dir)
-
-        frozen_graph = tfv1.graph_util.convert_variables_to_constants(
-            sess=session,
-            input_graph_def=tfv1.get_default_graph().as_graph_def(),
-            output_node_names=output_names)
-
-        frozen_graph = tfv1.graph_util.extract_sub_graph(
-            graph_def=frozen_graph,
-            dest_nodes=output_names)
-
-        if not FLAGS.export_tflite:
-            with open_remote(output_graph_path, 'wb') as fout:
-                fout.write(frozen_graph.SerializeToString())
-        else:
-            output_tflite_path = os.path.join(FLAGS.export_dir, output_filename.replace('.pb', '.tflite'))
-
-            converter = tf.lite.TFLiteConverter(frozen_graph, input_tensors=inputs.values(), output_tensors=outputs.values())
-            converter.optimizations = [tf.lite.Optimize.DEFAULT]
-            # AudioSpectrogram and Mfcc ops are custom but have built-in kernels in TFLite
-            converter.allow_custom_ops = True
-            tflite_model = converter.convert()
-
-            with open_remote(output_tflite_path, 'wb') as fout:
-                fout.write(tflite_model)
-
-        log_info('Models exported at %s' % (FLAGS.export_dir))
-
-    metadata_fname = os.path.join(FLAGS.export_dir, '{}_{}_{}.md'.format(
-        FLAGS.export_author_id,
-        FLAGS.export_model_name,
-        FLAGS.export_model_version))
-
-    model_runtime = 'tflite' if FLAGS.export_tflite else 'tensorflow'
-    with open_remote(metadata_fname, 'w') as f:
-        f.write('---\n')
-        f.write('author: {}\n'.format(FLAGS.export_author_id))
-        f.write('model_name: {}\n'.format(FLAGS.export_model_name))
-        f.write('model_version: {}\n'.format(FLAGS.export_model_version))
-        f.write('contact_info: {}\n'.format(FLAGS.export_contact_info))
-        f.write('license: {}\n'.format(FLAGS.export_license))
-        f.write('language: {}\n'.format(FLAGS.export_language))
-        f.write('runtime: {}\n'.format(model_runtime))
-        f.write('min_ds_version: {}\n'.format(FLAGS.export_min_ds_version))
-        f.write('max_ds_version: {}\n'.format(FLAGS.export_max_ds_version))
-        f.write('acoustic_model_url: <replace this with a publicly available URL of the acoustic model>\n')
-        f.write('scorer_url: <replace this with a publicly available URL of the scorer, if present>\n')
-        f.write('---\n')
-        f.write('{}\n'.format(FLAGS.export_description))
-
-    log_info('Model metadata file saved to {}. Before submitting the exported model for publishing make sure all information in the metadata file is correct, and complete the URL fields.'.format(metadata_fname))
-
-
-def package_zip():
-    # --export_dir path/to/export/LANG_CODE/ => path/to/export/LANG_CODE.zip
-    export_dir = os.path.join(os.path.abspath(FLAGS.export_dir), '') # Force ending '/'
-    if is_remote_path(export_dir):
-        log_error("Cannot package remote path zip %s. Please do this manually." % export_dir)
-        return
-
-    zip_filename = os.path.dirname(export_dir)
-    
-    shutil.copy(FLAGS.scorer_path, export_dir)
-
-    archive = shutil.make_archive(zip_filename, 'zip', export_dir)
-    log_info('Exported packaged model {}'.format(archive))
-
-
-def do_single_file_inference(input_file_path):
-    with tfv1.Session(config=Config.session_config) as session:
-        inputs, outputs, _ = create_inference_graph(batch_size=1, n_steps=-1)
-
-        # Restore variables from training checkpoint
-        load_graph_for_evaluation(session)
-
-        features, features_len = audiofile_to_features(input_file_path)
-        previous_state_c = np.zeros([1, Config.n_cell_dim])
-        previous_state_h = np.zeros([1, Config.n_cell_dim])
-
-        # Add batch dimension
-        features = tf.expand_dims(features, 0)
-        features_len = tf.expand_dims(features_len, 0)
-
-        # Evaluate
-        features = create_overlapping_windows(features).eval(session=session)
-        features_len = features_len.eval(session=session)
-
-        probs = outputs['outputs'].eval(feed_dict={
-            inputs['input']: features,
-            inputs['input_lengths']: features_len,
-            inputs['previous_state_c']: previous_state_c,
-            inputs['previous_state_h']: previous_state_h,
-        }, session=session)
-
-        probs = np.squeeze(probs)
-
-        if FLAGS.scorer_path:
-            scorer = Scorer(FLAGS.lm_alpha, FLAGS.lm_beta,
-                            FLAGS.scorer_path, Config.alphabet)
-        else:
-            scorer = None
-        decoded = ctc_beam_search_decoder(probs, Config.alphabet, FLAGS.beam_width,
-                                          scorer=scorer, cutoff_prob=FLAGS.cutoff_prob,
-                                          cutoff_top_n=FLAGS.cutoff_top_n)
-        # Print highest probability result
-        print(decoded[0][1])
-
-
 def early_training_checks():
     # Check for proper scorer early
     if FLAGS.scorer_path:
@@ -948,38 +523,51 @@ def main(_):
     initialize_globals()
     early_training_checks()
 
+    def deprecated_msg(prefix):
+        return ('{} Using the training script as a generic driver for all training '
+                'related functionality is deprecated and will be removed soon. Use '
+                'the specific scripts: train/evaluate/export/training_graph_inference.'.format(prefix))
+
     if FLAGS.train_files:
         tfv1.reset_default_graph()
         tfv1.set_random_seed(FLAGS.random_seed)
         train()
+    else:
+        log_warn(deprecated_msg('Calling training script without --train_files.'))
 
     if FLAGS.test_files:
+        log_warn(deprecated_msg('Specifying --test_files when calling train script.'))
         tfv1.reset_default_graph()
-        test()
+        evaluate.test()
 
-    if FLAGS.export_dir and not FLAGS.export_zip:
+    if FLAGS.export_dir:
+        log_warn(deprecated_msg('Specifying --export_dir when calling train script.'))
         tfv1.reset_default_graph()
-        export()
 
-    if FLAGS.export_zip:
+        if not FLAGS.export_zip:
-        tfv1.reset_default_graph()
+            # Export to folder
-        FLAGS.export_tflite = True
+            export.export()
+        else:
+            # Export and zip, TFLite only, creates package readable by Java example app
+            FLAGS.export_tflite = True
 
-        if listdir_remote(FLAGS.export_dir):
+            if listdir_remote(FLAGS.export_dir):
-            log_error('Directory {} is not empty, please fix this.'.format(FLAGS.export_dir))
+                log_error('Directory {} is not empty, please fix this.'.format(FLAGS.export_dir))
-            sys.exit(1)
+                sys.exit(1)
 
-        export()
+            export.export()
-        package_zip()
+            export.package_zip()
 
     if FLAGS.one_shot_infer:
+        log_warn(deprecated_msg('Specifying --one_shot_infer when calling train script.'))
         tfv1.reset_default_graph()
-        do_single_file_inference(FLAGS.one_shot_infer)
+        training_graph_inference.do_single_file_inference(FLAGS.one_shot_infer)
 
 
 def run_script():
     create_flags()
     absl.app.run(main)
 
+
 if __name__ == '__main__':
     run_script()

training/deepspeech_training/training_graph_inference.py

@@ -0,0 +1,77 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+import os
+import sys
+
+LOG_LEVEL_INDEX = sys.argv.index('--log_level') + 1 if '--log_level' in sys.argv else 0
+DESIRED_LOG_LEVEL = sys.argv[LOG_LEVEL_INDEX] if 0 < LOG_LEVEL_INDEX < len(sys.argv) else '3'
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = DESIRED_LOG_LEVEL
+
+import absl.app
+import numpy as np
+import tensorflow as tf
+import tensorflow.compat.v1 as tfv1
+
+from ds_ctcdecoder import ctc_beam_search_decoder, Scorer
+from .deepspeech_model import create_inference_graph, create_overlapping_windows
+from .util.checkpoints import load_graph_for_evaluation
+from .util.config import Config, initialize_globals
+from .util.feeding import audiofile_to_features
+from .util.flags import create_flags, FLAGS
+from .util.logging import log_error
+
+
+def do_single_file_inference(input_file_path):
+    with tfv1.Session(config=Config.session_config) as session:
+        inputs, outputs, _ = create_inference_graph(batch_size=1, n_steps=-1)
+
+        # Restore variables from training checkpoint
+        load_graph_for_evaluation(session)
+
+        features, features_len = audiofile_to_features(input_file_path)
+        previous_state_c = np.zeros([1, Config.n_cell_dim])
+        previous_state_h = np.zeros([1, Config.n_cell_dim])
+
+        # Add batch dimension
+        features = tf.expand_dims(features, 0)
+        features_len = tf.expand_dims(features_len, 0)
+
+        # Evaluate
+        features = create_overlapping_windows(features).eval(session=session)
+        features_len = features_len.eval(session=session)
+
+        probs = outputs['outputs'].eval(feed_dict={
+            inputs['input']: features,
+            inputs['input_lengths']: features_len,
+            inputs['previous_state_c']: previous_state_c,
+            inputs['previous_state_h']: previous_state_h,
+        }, session=session)
+
+        probs = np.squeeze(probs)
+
+        if FLAGS.scorer_path:
+            scorer = Scorer(FLAGS.lm_alpha, FLAGS.lm_beta,
+                            FLAGS.scorer_path, Config.alphabet)
+        else:
+            scorer = None
+        decoded = ctc_beam_search_decoder(probs, Config.alphabet, FLAGS.beam_width,
+                                          scorer=scorer, cutoff_prob=FLAGS.cutoff_prob,
+                                          cutoff_top_n=FLAGS.cutoff_top_n)
+        # Print highest probability result
+        print(decoded[0][1])
+
+
+def main(_):
+    initialize_globals()
+
+    if FLAGS.one_shot_infer:
+        tfv1.reset_default_graph()
+        do_single_file_inference(FLAGS.one_shot_infer)
+    else:
+        log_error('Calling training_graph_inference script directly but no --one_shot_infer input audio file specified')
+        sys.exit(1)
+
+
+if __name__ == '__main__':
+    create_flags()
+    absl.app.run(main)

training/deepspeech_training/util/config.py

@@ -15,6 +15,7 @@ from .helpers import parse_file_size
 from .augmentations import parse_augmentations
 from .io import path_exists_remote
 
+
 class ConfigSingleton:
     _config = None
 

training/deepspeech_training/util/io.py

@@ -77,5 +77,11 @@ def remove_remote(filename):
     """
     Wrapper that can remove local and remote files like `gs://...`
     """
-    # Conditional import
+    return gfile.remove(filename)
-    return gfile.remove_remote(filename)
+
+
+def rmtree_remote(foldername):
+    """
+    Wrapper that can remove local and remote directories like `gs://...`
+    """
+    return gfile.rmtree(foldername)