#!/usr/bin/env python # -*- coding: utf-8 -*- from __future__ import absolute_import, division, print_function import os import sys LOG_LEVEL_INDEX = sys.argv.index('--log_level') + 1 if '--log_level' in sys.argv else 0 os.environ['TF_CPP_MIN_LOG_LEVEL'] = sys.argv[LOG_LEVEL_INDEX] if 0 < LOG_LEVEL_INDEX < len(sys.argv) else '3' import time import numpy as np import progressbar import shutil import tensorflow as tf from datetime import datetime from ds_ctcdecoder import ctc_beam_search_decoder, Scorer from evaluate import evaluate from six.moves import zip, range from tensorflow.python.tools import freeze_graph from util.config import Config, initialize_globals from util.feeding import create_dataset, samples_to_mfccs, audiofile_to_features from util.flags import create_flags, FLAGS from util.logging import log_info, log_error, log_debug, log_progress, create_progressbar # 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 = tf.get_variable(name=name, shape=shape, initializer=initializer) return var def create_overlapping_windows(batch_x): batch_size = tf.shape(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(batch_x, 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): with tf.variable_scope(name): bias = variable_on_cpu('bias', [units], tf.zeros_initializer()) weights = variable_on_cpu('weights', [x.shape[-1], units], tf.contrib.layers.xavier_initializer()) output = tf.nn.bias_add(tf.matmul(x, weights), bias) if relu: output = tf.minimum(tf.nn.relu(output), FLAGS.relu_clip) 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): # Forward direction cell: fw_cell = tf.contrib.rnn.LSTMBlockFusedCell(Config.n_cell_dim, reuse=reuse) 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_static_rnn(x, seq_length, previous_state, reuse): # Forward direction cell: fw_cell = tf.nn.rnn_cell.LSTMCell(Config.n_cell_dim, reuse=reuse) # Split rank N tensor into list of rank N-1 tensors x = [x[l] for l in range(x.shape[0])] # We parametrize the RNN implementation as the training and inference graph # need to do different things here. output, output_state = tf.nn.static_rnn(cell=fw_cell, inputs=x, initial_state=previous_state, dtype=tf.float32, sequence_length=seq_length) output = tf.concat(output, 0) return output, output_state def create_model(batch_x, seq_length, dropout, reuse=False, previous_state=None, overlap=True, rnn_impl=rnn_impl_lstmblockfusedcell): layers = {} # Input shape: [batch_size, n_steps, n_input + 2*n_input*n_context] batch_size = tf.shape(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(batch_x, [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]) layers['layer_2'] = layer_2 = dense('layer_2', layer_1, Config.n_hidden_2, dropout_rate=dropout[1]) layers['layer_3'] = layer_3 = dense('layer_3', layer_2, Config.n_hidden_3, dropout_rate=dropout[2]) # `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]) # 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 # ================= # In accord with 'Deep Speech: Scaling up end-to-end speech recognition' # (http://arxiv.org/abs/1412.5567), # the loss function used by our network should be the CTC loss function # (http://www.cs.toronto.edu/~graves/preprint.pdf). # Conveniently, this loss function is implemented in TensorFlow. # Thus, we can simply make use of this implementation to define our loss. def calculate_mean_edit_distance_and_loss(iterator, dropout, reuse): r''' This routine beam search decodes a mini-batch and calculates the loss and mean edit distance. Next to total and average loss it returns the mean edit distance, the decoded result and the batch's original Y. ''' # Obtain the next batch of data (batch_x, batch_seq_len), batch_y = iterator.get_next() # Calculate the logits of the batch logits, _ = create_model(batch_x, batch_seq_len, dropout, reuse=reuse) # Compute the CTC loss using TensorFlow's `ctc_loss` total_loss = tf.nn.ctc_loss(labels=batch_y, inputs=logits, sequence_length=batch_seq_len) # Calculate the average loss across the batch avg_loss = tf.reduce_mean(total_loss) # Finally we return the average loss return avg_loss # Adam Optimization # ================= # In contrast to 'Deep Speech: Scaling up end-to-end speech recognition' # (http://arxiv.org/abs/1412.5567), # in which 'Nesterov's Accelerated Gradient Descent' # (www.cs.toronto.edu/~fritz/absps/momentum.pdf) was used, # we will use the Adam method for optimization (http://arxiv.org/abs/1412.6980), # because, generally, it requires less fine-tuning. def create_optimizer(): optimizer = tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate, beta1=FLAGS.beta1, beta2=FLAGS.beta2, epsilon=FLAGS.epsilon) return optimizer # Towers # ====== # In order to properly make use of multiple GPU's, one must introduce new abstractions, # not present when using a single GPU, that facilitate the multi-GPU use case. # In particular, one must introduce a means to isolate the inference and gradient # calculations on the various GPU's. # The abstraction we intoduce for this purpose is called a 'tower'. # A tower is specified by two properties: # * **Scope** - A scope, as provided by `tf.name_scope()`, # is a means to isolate the operations within a tower. # For example, all operations within 'tower 0' could have their name prefixed with `tower_0/`. # * **Device** - A hardware device, as provided by `tf.device()`, # on which all operations within the tower execute. # For example, all operations of 'tower 0' could execute on the first GPU `tf.device('/gpu:0')`. def get_tower_results(iterator, optimizer, dropout_rates, drop_source_layers): r''' With this preliminary step out of the way, we can for each GPU introduce a tower for which's batch we calculate and return the optimization gradients and the average loss across towers. ''' # To calculate the mean of the losses tower_avg_losses = [] # Tower gradients to return tower_gradients = [] with tf.variable_scope(tf.get_variable_scope()): # Loop over available_devices for i in range(len(Config.available_devices)): # Execute operations of tower i on device i device = Config.available_devices[i] with tf.device(device): # Create a scope for all operations of tower i with tf.name_scope('tower_%d' % i): # Calculate the avg_loss and mean_edit_distance and retrieve the decoded # batch along with the original batch's labels (Y) of this tower avg_loss = calculate_mean_edit_distance_and_loss(iterator, dropout_rates, reuse=i > 0) # Allow for variables to be re-used by the next tower tf.get_variable_scope().reuse_variables() # Retain tower's avg losses tower_avg_losses.append(avg_loss) # # Compute gradients for model parameters using tower's mini-batch # gradients = optimizer.compute_gradients(avg_loss) if FLAGS.fine_tune: # train from source model and fine-tine # aka - update all layers gradients = optimizer.compute_gradients(avg_loss) else: # train from source model and freeze old layers # aka - only update new layers gradients = optimizer.compute_gradients( avg_loss, var_list = [ v for v in tf.trainable_variables() if any( layer in v.op.name for layer in drop_source_layers )] ) # Retain tower's gradients tower_gradients.append(gradients) avg_loss_across_towers = tf.reduce_mean(tower_avg_losses, 0) tf.summary.scalar(name='step_loss', tensor=avg_loss_across_towers, collections=['step_summaries']) # Return gradients and the average loss return tower_gradients, avg_loss_across_towers def average_gradients(tower_gradients): r''' A routine for computing each variable's average of the gradients obtained from the GPUs. Note also that this code acts as a synchronization point as it requires all GPUs to be finished with their mini-batch before it can run to completion. ''' # List of average gradients to return to the caller average_grads = [] # Run this on cpu_device to conserve GPU memory with tf.device(Config.cpu_device): # Loop over gradient/variable pairs from all towers for grad_and_vars in zip(*tower_gradients): # Introduce grads to store the gradients for the current variable grads = [] # Loop over the gradients for the current variable for g, _ in grad_and_vars: # Add 0 dimension to the gradients to represent the tower. expanded_g = tf.expand_dims(g, 0) # Append on a 'tower' dimension which we will average over below. grads.append(expanded_g) # Average over the 'tower' dimension grad = tf.concat(grads, 0) grad = tf.reduce_mean(grad, 0) # Create a gradient/variable tuple for the current variable with its average gradient grad_and_var = (grad, grad_and_vars[0][1]) # Add the current tuple to average_grads average_grads.append(grad_and_var) # Return result to caller return average_grads # Logging # ======= def log_variable(variable, gradient=None): r''' We introduce a function for logging a tensor variable's current state. It logs scalar values for the mean, standard deviation, minimum and maximum. Furthermore it logs a histogram of its state and (if given) of an optimization gradient. ''' name = variable.name.replace(':', '_') mean = tf.reduce_mean(variable) tf.summary.scalar(name='%s/mean' % name, tensor=mean) tf.summary.scalar(name='%s/sttdev' % name, tensor=tf.sqrt(tf.reduce_mean(tf.square(variable - mean)))) tf.summary.scalar(name='%s/max' % name, tensor=tf.reduce_max(variable)) tf.summary.scalar(name='%s/min' % name, tensor=tf.reduce_min(variable)) tf.summary.histogram(name=name, values=variable) if gradient is not None: if isinstance(gradient, tf.IndexedSlices): grad_values = gradient.values else: grad_values = gradient if grad_values is not None: tf.summary.histogram(name='%s/gradients' % name, values=grad_values) def log_grads_and_vars(grads_and_vars): r''' Let's also introduce a helper function for logging collections of gradient/variable tuples. ''' for gradient, variable in grads_and_vars: log_variable(variable, gradient=gradient) def try_loading(session, saver, checkpoint_filename, caption): try: checkpoint = tf.train.get_checkpoint_state(FLAGS.checkpoint_dir, checkpoint_filename) if not checkpoint: return False checkpoint_path = checkpoint.model_checkpoint_path saver.restore(session, checkpoint_path) restored_step = session.run(tf.train.get_global_step()) log_info('Restored variables from %s checkpoint at %s, step %d' % (caption, checkpoint_path, restored_step)) return True except tf.errors.InvalidArgumentError as e: log_error(str(e)) log_error('The checkpoint in {0} does not match the shapes of the model.' ' Did you change alphabet.txt or the --n_hidden parameter' ' between train runs using the same checkpoint dir? Try moving' ' or removing the contents of {0}.'.format(checkpoint_path)) sys.exit(1) def train(): # Create training and validation datasets train_set = create_dataset(FLAGS.train_files.split(','), batch_size=FLAGS.train_batch_size, cache_path=FLAGS.feature_cache) iterator = tf.data.Iterator.from_structure(train_set.output_types, train_set.output_shapes, output_classes=train_set.output_classes) # Make initialization ops for switching between the two sets train_init_op = iterator.make_initializer(train_set) if FLAGS.dev_files: dev_csvs = FLAGS.dev_files.split(',') dev_sets = [create_dataset([csv], batch_size=FLAGS.dev_batch_size) for csv in dev_csvs] dev_init_ops = [iterator.make_initializer(dev_set) for dev_set in dev_sets] # The transfer learning approach here need us to supply the layers which we # want to exclude from the source model. # Say we want to exclude all layers except for the first one, we can use this: # # drop_source_layers=['2', '3', 'lstm', '5', '6'] # # If we want to use all layers from the source model except the last one, we use this: # # drop_source_layers=['6'] # drop_source_layers = ['2', '3', 'lstm', '5', '6'][-int(FLAGS.drop_source_layers):] # Dropout dropout_rates = [tf.placeholder(tf.float32, name='dropout_{}'.format(i)) for i in range(6)] dropout_feed_dict = { dropout_rates[0]: FLAGS.dropout_rate, dropout_rates[1]: FLAGS.dropout_rate2, dropout_rates[2]: FLAGS.dropout_rate3, dropout_rates[3]: FLAGS.dropout_rate4, dropout_rates[4]: FLAGS.dropout_rate5, dropout_rates[5]: FLAGS.dropout_rate6, } no_dropout_feed_dict = { rate: 0. for rate in dropout_rates } # Building the graph optimizer = create_optimizer() gradients, loss = get_tower_results(iterator, optimizer, dropout_rates, drop_source_layers) # Average tower gradients across GPUs avg_tower_gradients = average_gradients(gradients) log_grads_and_vars(avg_tower_gradients) # global_step is automagically incremented by the optimizer global_step = tf.train.get_or_create_global_step() apply_gradient_op = optimizer.apply_gradients(avg_tower_gradients, global_step=global_step) # Summaries step_summaries_op = tf.summary.merge_all('step_summaries') step_summary_writers = { 'train': tf.summary.FileWriter(os.path.join(FLAGS.summary_dir, 'train'), max_queue=120), 'dev': tf.summary.FileWriter(os.path.join(FLAGS.summary_dir, 'dev'), max_queue=120) } # Checkpointing checkpoint_saver = tf.train.Saver(max_to_keep=FLAGS.max_to_keep) checkpoint_path = os.path.join(FLAGS.checkpoint_dir, 'train') checkpoint_filename = 'checkpoint' best_dev_saver = tf.train.Saver(max_to_keep=1) best_dev_path = os.path.join(FLAGS.checkpoint_dir, 'best_dev') best_dev_filename = 'best_dev_checkpoint' initializer = tf.global_variables_initializer() with tf.Session(config=Config.session_config) as session: log_debug('Session opened.') ##################### # TRANSFER LEARNING # ##################### # Loading or initializing loaded = False if FLAGS.load in ['auto', 'last']: loaded = try_loading(session, checkpoint_saver, checkpoint_filename, 'most recent') if not loaded and FLAGS.load in ['auto', 'best']: loaded = try_loading(session, best_dev_saver, best_dev_filename, 'best validation') if not loaded: if FLAGS.source_model_checkpoint_dir: print('Initializing model from', FLAGS.source_model_checkpoint_dir) ckpt = tf.train.load_checkpoint(FLAGS.source_model_checkpoint_dir) variables = list(ckpt.get_variable_to_shape_map().keys()) # Load desired source variables for v in tf.global_variables(): if not any(layer in v.op.name for layer in drop_source_layers): print('Loading', v.op.name) v.load(ckpt.get_tensor(v.op.name), session=session) # Initialize all variables needed for DS, but not loaded from ckpt init_op = tf.variables_initializer( [v for v in tf.global_variables() if any(layer in v.op.name for layer in drop_source_layers) ]) session.run(init_op) elif FLAGS.load in ['auto', 'init']: log_info('Initializing variables...') session.run(initializer) else: log_error('Unable to load %s model from specified checkpoint dir' ' - consider using load option "auto" or "init".' % FLAGS.load) sys.exit(1) tf.get_default_graph().finalize() ##################### # TRANSFER LEARNING # ##################### def run_set(set_name, epoch, init_op, dataset=None): is_train = set_name == 'train' train_op = apply_gradient_op if is_train else [] feed_dict = dropout_feed_dict if is_train else no_dropout_feed_dict total_loss = 0.0 step_count = 0 step_summary_writer = step_summary_writers.get(set_name) checkpoint_time = time.time() # Setup progress bar class LossWidget(progressbar.widgets.FormatLabel): def __init__(self): progressbar.widgets.FormatLabel.__init__(self, format='Loss: %(mean_loss)f') def __call__(self, progress, data, **kwargs): data['mean_loss'] = total_loss / step_count if step_count else 0.0 return progressbar.widgets.FormatLabel.__call__(self, progress, data, **kwargs) prefix = 'Epoch {} | {:>10}'.format(epoch, 'Training' if is_train else 'Validation') widgets = [' | ', progressbar.widgets.Timer(), ' | Steps: ', progressbar.widgets.Counter(), ' | ', LossWidget()] suffix = ' | Dataset: {}'.format(dataset) if dataset else None pbar = create_progressbar(prefix=prefix, widgets=widgets, suffix=suffix).start() # Initialize iterator to the appropriate dataset session.run(init_op) # Batch loop while True: try: _, current_step, batch_loss, step_summary = \ session.run([train_op, global_step, loss, step_summaries_op], feed_dict=feed_dict) except tf.errors.OutOfRangeError: break total_loss += batch_loss step_count += 1 pbar.update(step_count) step_summary_writer.add_summary(step_summary, current_step) if is_train and FLAGS.checkpoint_secs > 0 and time.time() - checkpoint_time > FLAGS.checkpoint_secs: checkpoint_saver.save(session, checkpoint_path, global_step=current_step) checkpoint_time = time.time() pbar.finish() mean_loss = total_loss / step_count if step_count > 0 else 0.0 return mean_loss, step_count log_info('STARTING Optimization') train_start_time = datetime.utcnow() best_dev_loss = float('inf') dev_losses = [] try: for epoch in range(FLAGS.epochs): # Training log_progress('Training epoch %d...' % epoch) train_loss, _ = run_set('train', epoch, train_init_op) log_progress('Finished training epoch %d - loss: %f' % (epoch, train_loss)) checkpoint_saver.save(session, checkpoint_path, global_step=global_step) if FLAGS.dev_files: # Validation dev_loss = 0.0 total_steps = 0 for csv, init_op in zip(dev_csvs, dev_init_ops): log_progress('Validating epoch %d on %s...' % (epoch, csv)) set_loss, steps = run_set('dev', epoch, init_op, dataset=csv) dev_loss += set_loss * steps total_steps += steps log_progress('Finished validating epoch %d on %s - loss: %f' % (epoch, csv, set_loss)) dev_loss = dev_loss / total_steps dev_losses.append(dev_loss) if dev_loss < best_dev_loss: best_dev_loss = dev_loss save_path = best_dev_saver.save(session, best_dev_path, global_step=global_step, latest_filename=best_dev_filename) log_info("Saved new best validating model with loss %f to: %s" % (best_dev_loss, save_path)) # Early stopping if FLAGS.early_stop and len(dev_losses) >= FLAGS.es_steps: mean_loss = np.mean(dev_losses[-FLAGS.es_steps:-1]) std_loss = np.std(dev_losses[-FLAGS.es_steps:-1]) dev_losses = dev_losses[-FLAGS.es_steps:] log_debug('Checking for early stopping (last %d steps) validation loss: ' '%f, with standard deviation: %f and mean: %f' % (FLAGS.es_steps, dev_losses[-1], std_loss, mean_loss)) if dev_losses[-1] > np.max(dev_losses[:-1]) or \ (abs(dev_losses[-1] - mean_loss) < FLAGS.es_mean_th and std_loss < FLAGS.es_std_th): log_info('Early stop triggered as (for last %d steps) validation loss:' ' %f with standard deviation: %f and mean: %f' % (FLAGS.es_steps, dev_losses[-1], std_loss, mean_loss)) break except KeyboardInterrupt: pass log_info('FINISHED optimization in {}'.format(datetime.utcnow() - train_start_time)) log_debug('Session closed.') def test(): evaluate(FLAGS.test_files.split(','), create_model, try_loading) 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 = tf.placeholder(tf.float32, [Config.audio_window_samples], 'input_samples') samples = tf.expand_dims(input_samples, -1) mfccs, _ = samples_to_mfccs(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 = tf.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 = tf.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 = previous_state_c = previous_state_h = None else: if tflite: previous_state_c = tf.placeholder(tf.float32, [batch_size, Config.n_cell_dim], name='previous_state_c') previous_state_h = tf.placeholder(tf.float32, [batch_size, Config.n_cell_dim], name='previous_state_h') else: previous_state_c = variable_on_cpu('previous_state_c', [batch_size, Config.n_cell_dim], initializer=None) previous_state_h = variable_on_cpu('previous_state_h', [batch_size, Config.n_cell_dim], initializer=None) previous_state = tf.contrib.rnn.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, seq_length=seq_length if FLAGS.use_seq_length 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 logits = tf.nn.softmax(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': tf.identity(logits, name='logits'), }, layers ) new_state_c, new_state_h = layers['rnn_output_state'] if tflite: logits = tf.identity(logits, name='logits') 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 FLAGS.use_seq_length: inputs.update({'input_lengths': seq_length}) outputs = { 'outputs': logits, 'new_state_c': new_state_c, 'new_state_h': new_state_h, 'mfccs': mfccs, } else: zero_state = tf.zeros([batch_size, Config.n_cell_dim], tf.float32) initialize_c = tf.assign(previous_state_c, zero_state) initialize_h = tf.assign(previous_state_h, zero_state) initialize_state = tf.group(initialize_c, initialize_h, name='initialize_state') with tf.control_dependencies([tf.assign(previous_state_c, new_state_c), tf.assign(previous_state_h, new_state_h)]): logits = tf.identity(logits, name='logits') inputs = { 'input': input_tensor, 'input_lengths': seq_length, 'input_samples': input_samples, } outputs = { 'outputs': logits, 'initialize_state': initialize_state, '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...') from tensorflow.python.framework.ops import Tensor, Operation inputs, outputs, _ = create_inference_graph(batch_size=FLAGS.export_batch_size, n_steps=FLAGS.n_steps, tflite=FLAGS.export_tflite) output_names_tensors = [tensor.op.name for tensor in outputs.values() if isinstance(tensor, Tensor)] output_names_ops = [op.name for op in outputs.values() if isinstance(op, Operation)] output_names = ",".join(output_names_tensors + output_names_ops) if not FLAGS.export_tflite: mapping = {v.op.name: v for v in tf.global_variables() if not v.op.name.startswith('previous_state_')} else: # Create a saver using variables from the above newly created graph def fixup(name): if name.startswith('rnn/lstm_cell/'): return name.replace('rnn/lstm_cell/', 'lstm_fused_cell/') return name mapping = {fixup(v.op.name): v for v in tf.global_variables()} saver = tf.train.Saver(mapping) # Restore variables from training checkpoint checkpoint = tf.train.get_checkpoint_state(FLAGS.checkpoint_dir) checkpoint_path = checkpoint.model_checkpoint_path output_filename = 'output_graph.pb' if FLAGS.remove_export: if os.path.isdir(FLAGS.export_dir): log_info('Removing old export') shutil.rmtree(FLAGS.export_dir) try: output_graph_path = os.path.join(FLAGS.export_dir, output_filename) if not os.path.isdir(FLAGS.export_dir): os.makedirs(FLAGS.export_dir) def do_graph_freeze(output_file=None, output_node_names=None, variables_blacklist=None): return freeze_graph.freeze_graph_with_def_protos( input_graph_def=tf.get_default_graph().as_graph_def(), input_saver_def=saver.as_saver_def(), input_checkpoint=checkpoint_path, output_node_names=output_node_names, restore_op_name=None, filename_tensor_name=None, output_graph=output_file, clear_devices=False, variable_names_blacklist=variables_blacklist, initializer_nodes='') if not FLAGS.export_tflite: frozen_graph = do_graph_freeze(output_node_names=output_names, variables_blacklist='previous_state_c,previous_state_h') frozen_graph.version = int(file_relative_read('GRAPH_VERSION').strip()) # Add a no-op node to the graph with metadata information to be loaded by the native client metadata = frozen_graph.node.add() metadata.name = 'model_metadata' metadata.op = 'NoOp' metadata.attr['sample_rate'].i = FLAGS.audio_sample_rate metadata.attr['feature_win_len'].i = FLAGS.feature_win_len metadata.attr['feature_win_step'].i = FLAGS.feature_win_step if FLAGS.export_language: metadata.attr['language'].s = FLAGS.export_language.encode('ascii') with open(output_graph_path, 'wb') as fout: fout.write(frozen_graph.SerializeToString()) else: 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 = tf.lite.TFLiteConverter(frozen_graph, input_tensors=inputs.values(), output_tensors=outputs.values()) converter.post_training_quantize = True # AudioSpectrogram and Mfcc ops are custom but have built-in kernels in TFLite converter.allow_custom_ops = True tflite_model = converter.convert() with open(output_tflite_path, 'wb') as fout: fout.write(tflite_model) log_info('Exported model for TF Lite engine as {}'.format(os.path.basename(output_tflite_path))) log_info('Models exported at %s' % (FLAGS.export_dir)) except RuntimeError as e: log_error(str(e)) def do_single_file_inference(input_file_path): with tf.Session(config=Config.session_config) as session: inputs, outputs, _ = create_inference_graph(batch_size=1, n_steps=-1) # Create a saver using variables from the above newly created graph mapping = {v.op.name: v for v in tf.global_variables() if not v.op.name.startswith('previous_state_')} saver = tf.train.Saver(mapping) # Restore variables from training checkpoint # TODO: This restores the most recent checkpoint, but if we use validation to counteract # over-fitting, we may want to restore an earlier checkpoint. checkpoint = tf.train.get_checkpoint_state(FLAGS.checkpoint_dir) if not checkpoint: log_error('Checkpoint directory ({}) does not contain a valid checkpoint state.'.format(FLAGS.checkpoint_dir)) exit(1) checkpoint_path = checkpoint.model_checkpoint_path saver.restore(session, checkpoint_path) session.run(outputs['initialize_state']) features, features_len = audiofile_to_features(input_file_path) # 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) logits = outputs['outputs'].eval(feed_dict={ inputs['input']: features, inputs['input_lengths']: features_len, }, session=session) logits = np.squeeze(logits) scorer = Scorer(FLAGS.lm_alpha, FLAGS.lm_beta, FLAGS.lm_binary_path, FLAGS.lm_trie_path, Config.alphabet) decoded = ctc_beam_search_decoder(logits, Config.alphabet, FLAGS.beam_width, scorer=scorer) # Print highest probability result print(decoded[0][1]) def main(_): initialize_globals() if FLAGS.train_files: tf.reset_default_graph() tf.set_random_seed(FLAGS.random_seed) train() if FLAGS.test_files: tf.reset_default_graph() test() if FLAGS.export_dir: tf.reset_default_graph() export() if FLAGS.one_shot_infer: tf.reset_default_graph() do_single_file_inference(FLAGS.one_shot_infer) if __name__ == '__main__': create_flags() tf.app.run(main)