1298 lines
45 KiB
Python
1298 lines
45 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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from __future__ import absolute_import, division, print_function
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import os
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import sys
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LOG_LEVEL_INDEX = sys.argv.index("--log_level") + 1 if "--log_level" in sys.argv else 0
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DESIRED_LOG_LEVEL = (
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sys.argv[LOG_LEVEL_INDEX] if 0 < LOG_LEVEL_INDEX < len(sys.argv) else "3"
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)
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os.environ["TF_CPP_MIN_LOG_LEVEL"] = DESIRED_LOG_LEVEL
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import json
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import shutil
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import time
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import numpy as np
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import progressbar
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import tensorflow.compat.v1 as tfv1
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import tensorflow as tf
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tfv1.logging.set_verbosity(
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{
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"0": tfv1.logging.DEBUG,
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"1": tfv1.logging.INFO,
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"2": tfv1.logging.WARN,
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"3": tfv1.logging.ERROR,
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}.get(DESIRED_LOG_LEVEL)
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)
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from datetime import datetime
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from coqui_stt_ctcdecoder import Scorer, ctc_beam_search_decoder
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from six.moves import range, zip
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from .evaluate import evaluate
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from .util.augmentations import NormalizeSampleRate
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from .util.checkpoints import (
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load_graph_for_evaluation,
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load_or_init_graph_for_training,
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reload_best_checkpoint,
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)
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from .util.config import (
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Config,
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create_progressbar,
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initialize_globals_from_cli,
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log_debug,
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log_error,
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log_info,
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log_progress,
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log_warn,
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)
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from .util.evaluate_tools import save_samples_json
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from .util.feeding import audio_to_features, audiofile_to_features, create_dataset
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from .util.helpers import ExceptionBox, check_ctcdecoder_version
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from .util.io import (
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is_remote_path,
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isdir_remote,
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listdir_remote,
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open_remote,
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remove_remote,
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)
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check_ctcdecoder_version()
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# Graph Creation
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# ==============
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def variable_on_cpu(name, shape, initializer):
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r"""
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Next we concern ourselves with graph creation.
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However, before we do so we must introduce a utility function ``variable_on_cpu()``
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used to create a variable in CPU memory.
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"""
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# Use the /cpu:0 device for scoped operations
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with tf.device(Config.cpu_device):
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# Create or get apropos variable
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var = tfv1.get_variable(name=name, shape=shape, initializer=initializer)
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return var
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def create_overlapping_windows(batch_x):
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batch_size = tf.shape(input=batch_x)[0]
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window_width = 2 * Config.n_context + 1
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num_channels = Config.n_input
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# Create a constant convolution filter using an identity matrix, so that the
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# convolution returns patches of the input tensor as is, and we can create
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# overlapping windows over the MFCCs.
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eye_filter = tf.constant(
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np.eye(window_width * num_channels).reshape(
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window_width, num_channels, window_width * num_channels
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),
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tf.float32,
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) # pylint: disable=bad-continuation
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# Create overlapping windows
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batch_x = tf.nn.conv1d(input=batch_x, filters=eye_filter, stride=1, padding="SAME")
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# Remove dummy depth dimension and reshape into [batch_size, n_windows, window_width, n_input]
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batch_x = tf.reshape(batch_x, [batch_size, -1, window_width, num_channels])
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return batch_x
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def dense(name, x, units, dropout_rate=None, relu=True, layer_norm=False):
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with tfv1.variable_scope(name):
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bias = variable_on_cpu("bias", [units], tfv1.zeros_initializer())
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weights = variable_on_cpu(
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"weights",
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[x.shape[-1], units],
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tfv1.keras.initializers.VarianceScaling(
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scale=1.0, mode="fan_avg", distribution="uniform"
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),
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)
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output = tf.nn.bias_add(tf.matmul(x, weights), bias)
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if relu:
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output = tf.minimum(tf.nn.relu(output), Config.relu_clip)
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if layer_norm:
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with tfv1.variable_scope(name):
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output = tf.contrib.layers.layer_norm(output)
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if dropout_rate is not None:
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output = tf.nn.dropout(output, rate=dropout_rate)
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return output
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def rnn_impl_lstmblockfusedcell(x, seq_length, previous_state, reuse):
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with tfv1.variable_scope("cudnn_lstm/rnn/multi_rnn_cell/cell_0"):
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fw_cell = tf.contrib.rnn.LSTMBlockFusedCell(
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Config.n_cell_dim,
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forget_bias=0,
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reuse=reuse,
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name="cudnn_compatible_lstm_cell",
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)
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output, output_state = fw_cell(
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inputs=x,
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dtype=tf.float32,
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sequence_length=seq_length,
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initial_state=previous_state,
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)
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return output, output_state
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def rnn_impl_cudnn_rnn(x, seq_length, previous_state, _):
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assert (
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previous_state is None
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) # 'Passing previous state not supported with CuDNN backend'
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# Hack: CudnnLSTM works similarly to Keras layers in that when you instantiate
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# the object it creates the variables, and then you just call it several times
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# to enable variable re-use. Because all of our code is structure in an old
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# school TensorFlow structure where you can just call tf.get_variable again with
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# reuse=True to reuse variables, we can't easily make use of the object oriented
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# way CudnnLSTM is implemented, so we save a singleton instance in the function,
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# emulating a static function variable.
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if not rnn_impl_cudnn_rnn.cell:
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# Forward direction cell:
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fw_cell = tf.contrib.cudnn_rnn.CudnnLSTM(
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num_layers=1,
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num_units=Config.n_cell_dim,
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input_mode="linear_input",
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direction="unidirectional",
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dtype=tf.float32,
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)
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rnn_impl_cudnn_rnn.cell = fw_cell
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output, output_state = rnn_impl_cudnn_rnn.cell(
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inputs=x, sequence_lengths=seq_length
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)
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return output, output_state
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rnn_impl_cudnn_rnn.cell = None
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def rnn_impl_static_rnn(x, seq_length, previous_state, reuse):
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with tfv1.variable_scope("cudnn_lstm/rnn/multi_rnn_cell"):
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# Forward direction cell:
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fw_cell = tfv1.nn.rnn_cell.LSTMCell(
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Config.n_cell_dim,
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forget_bias=0,
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reuse=reuse,
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name="cudnn_compatible_lstm_cell",
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)
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# Split rank N tensor into list of rank N-1 tensors
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x = [x[l] for l in range(x.shape[0])]
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output, output_state = tfv1.nn.static_rnn(
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cell=fw_cell,
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inputs=x,
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sequence_length=seq_length,
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initial_state=previous_state,
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dtype=tf.float32,
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scope="cell_0",
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)
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output = tf.concat(output, 0)
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return output, output_state
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def create_model(
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batch_x,
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seq_length,
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dropout,
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reuse=False,
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batch_size=None,
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previous_state=None,
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overlap=True,
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rnn_impl=rnn_impl_lstmblockfusedcell,
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):
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layers = {}
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# Input shape: [batch_size, n_steps, n_input + 2*n_input*n_context]
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if not batch_size:
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batch_size = tf.shape(input=batch_x)[0]
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# Create overlapping feature windows if needed
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if overlap:
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batch_x = create_overlapping_windows(batch_x)
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# Reshaping `batch_x` to a tensor with shape `[n_steps*batch_size, n_input + 2*n_input*n_context]`.
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# This is done to prepare the batch for input into the first layer which expects a tensor of rank `2`.
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# Permute n_steps and batch_size
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batch_x = tf.transpose(a=batch_x, perm=[1, 0, 2, 3])
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# Reshape to prepare input for first layer
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batch_x = tf.reshape(
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batch_x, [-1, Config.n_input + 2 * Config.n_input * Config.n_context]
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) # (n_steps*batch_size, n_input + 2*n_input*n_context)
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layers["input_reshaped"] = batch_x
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# The next three blocks will pass `batch_x` through three hidden layers with
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# clipped RELU activation and dropout.
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layers["layer_1"] = layer_1 = dense(
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"layer_1",
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batch_x,
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Config.n_hidden_1,
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dropout_rate=dropout[0],
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layer_norm=Config.layer_norm,
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)
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layers["layer_2"] = layer_2 = dense(
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"layer_2",
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layer_1,
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Config.n_hidden_2,
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dropout_rate=dropout[1],
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layer_norm=Config.layer_norm,
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)
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layers["layer_3"] = layer_3 = dense(
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"layer_3",
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layer_2,
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Config.n_hidden_3,
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dropout_rate=dropout[2],
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layer_norm=Config.layer_norm,
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)
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# `layer_3` is now reshaped into `[n_steps, batch_size, 2*n_cell_dim]`,
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# as the LSTM RNN expects its input to be of shape `[max_time, batch_size, input_size]`.
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layer_3 = tf.reshape(layer_3, [-1, batch_size, Config.n_hidden_3])
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# Run through parametrized RNN implementation, as we use different RNNs
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# for training and inference
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output, output_state = rnn_impl(layer_3, seq_length, previous_state, reuse)
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# Reshape output from a tensor of shape [n_steps, batch_size, n_cell_dim]
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# to a tensor of shape [n_steps*batch_size, n_cell_dim]
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output = tf.reshape(output, [-1, Config.n_cell_dim])
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layers["rnn_output"] = output
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layers["rnn_output_state"] = output_state
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# Now we feed `output` to the fifth hidden layer with clipped RELU activation
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layers["layer_5"] = layer_5 = dense(
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"layer_5",
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output,
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Config.n_hidden_5,
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dropout_rate=dropout[5],
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layer_norm=Config.layer_norm,
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)
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# Now we apply a final linear layer creating `n_classes` dimensional vectors, the logits.
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layers["layer_6"] = layer_6 = dense(
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"layer_6", layer_5, Config.n_hidden_6, relu=False
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)
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# Finally we reshape layer_6 from a tensor of shape [n_steps*batch_size, n_hidden_6]
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# to the slightly more useful shape [n_steps, batch_size, n_hidden_6].
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# Note, that this differs from the input in that it is time-major.
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layer_6 = tf.reshape(
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layer_6, [-1, batch_size, Config.n_hidden_6], name="raw_logits"
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)
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layers["raw_logits"] = layer_6
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# Output shape: [n_steps, batch_size, n_hidden_6]
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return layer_6, layers
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# Accuracy and Loss
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# =================
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# In accord with 'Deep Speech: Scaling up end-to-end speech recognition'
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# (http://arxiv.org/abs/1412.5567),
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# the loss function used by our network should be the CTC loss function
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# (http://www.cs.toronto.edu/~graves/preprint.pdf).
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# Conveniently, this loss function is implemented in TensorFlow.
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# Thus, we can simply make use of this implementation to define our loss.
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def calculate_mean_edit_distance_and_loss(iterator, dropout, reuse):
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r"""
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This routine beam search decodes a mini-batch and calculates the loss and mean edit distance.
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Next to total and average loss it returns the mean edit distance,
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the decoded result and the batch's original Y.
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"""
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# Obtain the next batch of data
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batch_filenames, (batch_x, batch_seq_len), batch_y = iterator.get_next()
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if Config.train_cudnn:
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rnn_impl = rnn_impl_cudnn_rnn
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else:
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rnn_impl = rnn_impl_lstmblockfusedcell
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# Calculate the logits of the batch
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logits, _ = create_model(
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batch_x, batch_seq_len, dropout, reuse=reuse, rnn_impl=rnn_impl
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)
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# Compute the CTC loss using TensorFlow's `ctc_loss`
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total_loss = tfv1.nn.ctc_loss(
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labels=batch_y, inputs=logits, sequence_length=batch_seq_len
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)
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# Check if any files lead to non finite loss
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non_finite_files = tf.gather(
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batch_filenames, tfv1.where(~tf.math.is_finite(total_loss))
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)
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# Calculate the average loss across the batch
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avg_loss = tf.reduce_mean(input_tensor=total_loss)
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# Finally we return the average loss
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return avg_loss, non_finite_files
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# Adam Optimization
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# =================
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# In contrast to 'Deep Speech: Scaling up end-to-end speech recognition'
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# (http://arxiv.org/abs/1412.5567),
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# in which 'Nesterov's Accelerated Gradient Descent'
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# (www.cs.toronto.edu/~fritz/absps/momentum.pdf) was used,
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# we will use the Adam method for optimization (http://arxiv.org/abs/1412.6980),
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# because, generally, it requires less fine-tuning.
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def create_optimizer(learning_rate_var):
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optimizer = tfv1.train.AdamOptimizer(
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learning_rate=learning_rate_var,
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beta1=Config.beta1,
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beta2=Config.beta2,
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epsilon=Config.epsilon,
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)
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return optimizer
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# Towers
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# ======
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# In order to properly make use of multiple GPU's, one must introduce new abstractions,
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# not present when using a single GPU, that facilitate the multi-GPU use case.
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# In particular, one must introduce a means to isolate the inference and gradient
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# calculations on the various GPU's.
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# The abstraction we intoduce for this purpose is called a 'tower'.
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# A tower is specified by two properties:
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# * **Scope** - A scope, as provided by `tf.name_scope()`,
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# is a means to isolate the operations within a tower.
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# For example, all operations within 'tower 0' could have their name prefixed with `tower_0/`.
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# * **Device** - A hardware device, as provided by `tf.device()`,
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# on which all operations within the tower execute.
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# For example, all operations of 'tower 0' could execute on the first GPU `tf.device('/gpu:0')`.
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def get_tower_results(iterator, optimizer, dropout_rates):
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r"""
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With this preliminary step out of the way, we can for each GPU introduce a
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tower for which's batch we calculate and return the optimization gradients
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and the average loss across towers.
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"""
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# To calculate the mean of the losses
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tower_avg_losses = []
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# Tower gradients to return
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tower_gradients = []
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# Aggregate any non finite files in the batches
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tower_non_finite_files = []
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with tfv1.variable_scope(tfv1.get_variable_scope()):
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# Loop over available_devices
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for i in range(len(Config.available_devices)):
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# Execute operations of tower i on device i
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device = Config.available_devices[i]
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with tf.device(device):
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# Create a scope for all operations of tower i
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with tf.name_scope("tower_%d" % i):
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# Calculate the avg_loss and mean_edit_distance and retrieve the decoded
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# batch along with the original batch's labels (Y) of this tower
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avg_loss, non_finite_files = calculate_mean_edit_distance_and_loss(
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iterator, dropout_rates, reuse=i > 0
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)
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# Allow for variables to be re-used by the next tower
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tfv1.get_variable_scope().reuse_variables()
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# Retain tower's avg losses
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tower_avg_losses.append(avg_loss)
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# Compute gradients for model parameters using tower's mini-batch
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gradients = optimizer.compute_gradients(avg_loss)
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# Retain tower's gradients
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tower_gradients.append(gradients)
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tower_non_finite_files.append(non_finite_files)
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avg_loss_across_towers = tf.reduce_mean(input_tensor=tower_avg_losses, axis=0)
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tfv1.summary.scalar(
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name="step_loss", tensor=avg_loss_across_towers, collections=["step_summaries"]
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)
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all_non_finite_files = tf.concat(tower_non_finite_files, axis=0)
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# Return gradients and the average loss
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return tower_gradients, avg_loss_across_towers, all_non_finite_files
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def average_gradients(tower_gradients):
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r"""
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A routine for computing each variable's average of the gradients obtained from the GPUs.
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Note also that this code acts as a synchronization point as it requires all
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GPUs to be finished with their mini-batch before it can run to completion.
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"""
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# List of average gradients to return to the caller
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average_grads = []
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# Run this on cpu_device to conserve GPU memory
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with tf.device(Config.cpu_device):
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# Loop over gradient/variable pairs from all towers
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for grad_and_vars in zip(*tower_gradients):
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# Introduce grads to store the gradients for the current variable
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grads = []
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# Loop over the gradients for the current variable
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for g, _ in grad_and_vars:
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# Add 0 dimension to the gradients to represent the tower.
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expanded_g = tf.expand_dims(g, 0)
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# Append on a 'tower' dimension which we will average over below.
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grads.append(expanded_g)
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# Average over the 'tower' dimension
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grad = tf.concat(grads, 0)
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grad = tf.reduce_mean(input_tensor=grad, axis=0)
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# Create a gradient/variable tuple for the current variable with its average gradient
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grad_and_var = (grad, grad_and_vars[0][1])
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# Add the current tuple to average_grads
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average_grads.append(grad_and_var)
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# Return result to caller
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return average_grads
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# Logging
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# =======
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|
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def log_variable(variable, gradient=None):
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r"""
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|
We introduce a function for logging a tensor variable's current state.
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It logs scalar values for the mean, standard deviation, minimum and maximum.
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Furthermore it logs a histogram of its state and (if given) of an optimization gradient.
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"""
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|
name = variable.name.replace(":", "_")
|
|
mean = tf.reduce_mean(input_tensor=variable)
|
|
tfv1.summary.scalar(name="%s/mean" % name, tensor=mean)
|
|
tfv1.summary.scalar(
|
|
name="%s/sttdev" % name,
|
|
tensor=tf.sqrt(tf.reduce_mean(input_tensor=tf.square(variable - mean))),
|
|
)
|
|
tfv1.summary.scalar(
|
|
name="%s/max" % name, tensor=tf.reduce_max(input_tensor=variable)
|
|
)
|
|
tfv1.summary.scalar(
|
|
name="%s/min" % name, tensor=tf.reduce_min(input_tensor=variable)
|
|
)
|
|
tfv1.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:
|
|
tfv1.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 train():
|
|
tfv1.reset_default_graph()
|
|
tfv1.set_random_seed(Config.random_seed)
|
|
|
|
exception_box = ExceptionBox()
|
|
|
|
# Create training and validation datasets
|
|
train_set = create_dataset(
|
|
Config.train_files,
|
|
batch_size=Config.train_batch_size,
|
|
epochs=Config.epochs,
|
|
augmentations=Config.augmentations,
|
|
cache_path=Config.feature_cache,
|
|
train_phase=True,
|
|
exception_box=exception_box,
|
|
process_ahead=len(Config.available_devices) * Config.train_batch_size * 2,
|
|
reverse=Config.reverse_train,
|
|
limit=Config.limit_train,
|
|
buffering=Config.read_buffer,
|
|
)
|
|
|
|
iterator = tfv1.data.Iterator.from_structure(
|
|
tfv1.data.get_output_types(train_set),
|
|
tfv1.data.get_output_shapes(train_set),
|
|
output_classes=tfv1.data.get_output_classes(train_set),
|
|
)
|
|
|
|
# Make initialization ops for switching between the two sets
|
|
train_init_op = iterator.make_initializer(train_set)
|
|
|
|
if Config.dev_files:
|
|
dev_sources = Config.dev_files
|
|
dev_sets = [
|
|
create_dataset(
|
|
[source],
|
|
batch_size=Config.dev_batch_size,
|
|
train_phase=False,
|
|
augmentations=[NormalizeSampleRate(Config.audio_sample_rate)],
|
|
exception_box=exception_box,
|
|
process_ahead=len(Config.available_devices) * Config.dev_batch_size * 2,
|
|
reverse=Config.reverse_dev,
|
|
limit=Config.limit_dev,
|
|
buffering=Config.read_buffer,
|
|
)
|
|
for source in dev_sources
|
|
]
|
|
dev_init_ops = [iterator.make_initializer(dev_set) for dev_set in dev_sets]
|
|
|
|
if Config.metrics_files:
|
|
metrics_sources = Config.metrics_files
|
|
metrics_sets = [
|
|
create_dataset(
|
|
[source],
|
|
batch_size=Config.dev_batch_size,
|
|
train_phase=False,
|
|
augmentations=[NormalizeSampleRate(Config.audio_sample_rate)],
|
|
exception_box=exception_box,
|
|
process_ahead=len(Config.available_devices) * Config.dev_batch_size * 2,
|
|
reverse=Config.reverse_dev,
|
|
limit=Config.limit_dev,
|
|
buffering=Config.read_buffer,
|
|
)
|
|
for source in metrics_sources
|
|
]
|
|
metrics_init_ops = [
|
|
iterator.make_initializer(metrics_set) for metrics_set in metrics_sets
|
|
]
|
|
|
|
# Dropout
|
|
dropout_rates = [
|
|
tfv1.placeholder(tf.float32, name="dropout_{}".format(i)) for i in range(6)
|
|
]
|
|
dropout_feed_dict = {
|
|
dropout_rates[0]: Config.dropout_rate,
|
|
dropout_rates[1]: Config.dropout_rate2,
|
|
dropout_rates[2]: Config.dropout_rate3,
|
|
dropout_rates[3]: Config.dropout_rate4,
|
|
dropout_rates[4]: Config.dropout_rate5,
|
|
dropout_rates[5]: Config.dropout_rate6,
|
|
}
|
|
no_dropout_feed_dict = {rate: 0.0 for rate in dropout_rates}
|
|
|
|
# Building the graph
|
|
learning_rate_var = tfv1.get_variable(
|
|
"learning_rate", initializer=Config.learning_rate, trainable=False
|
|
)
|
|
reduce_learning_rate_op = learning_rate_var.assign(
|
|
tf.multiply(learning_rate_var, Config.plateau_reduction)
|
|
)
|
|
optimizer = create_optimizer(learning_rate_var)
|
|
|
|
# Enable mixed precision training
|
|
if Config.automatic_mixed_precision:
|
|
log_info("Enabling automatic mixed precision training.")
|
|
optimizer = tfv1.train.experimental.enable_mixed_precision_graph_rewrite(
|
|
optimizer
|
|
)
|
|
|
|
gradients, loss, non_finite_files = get_tower_results(
|
|
iterator, optimizer, dropout_rates
|
|
)
|
|
|
|
# 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 = tfv1.train.get_or_create_global_step()
|
|
apply_gradient_op = optimizer.apply_gradients(
|
|
avg_tower_gradients, global_step=global_step
|
|
)
|
|
|
|
# Summaries
|
|
step_summaries_op = tfv1.summary.merge_all("step_summaries")
|
|
step_summary_writers = {
|
|
"train": tfv1.summary.FileWriter(
|
|
os.path.join(Config.summary_dir, "train"), max_queue=120
|
|
),
|
|
"dev": tfv1.summary.FileWriter(
|
|
os.path.join(Config.summary_dir, "dev"), max_queue=120
|
|
),
|
|
"metrics": tfv1.summary.FileWriter(
|
|
os.path.join(Config.summary_dir, "metrics"), max_queue=120
|
|
),
|
|
}
|
|
|
|
human_readable_set_names = {
|
|
"train": "Training",
|
|
"dev": "Validation",
|
|
"metrics": "Metrics",
|
|
}
|
|
|
|
# Checkpointing
|
|
checkpoint_saver = tfv1.train.Saver(max_to_keep=Config.max_to_keep)
|
|
checkpoint_path = os.path.join(Config.save_checkpoint_dir, "train")
|
|
|
|
best_dev_saver = tfv1.train.Saver(max_to_keep=1)
|
|
best_dev_path = os.path.join(Config.save_checkpoint_dir, "best_dev")
|
|
|
|
# Save flags next to checkpoints
|
|
if not is_remote_path(Config.save_checkpoint_dir):
|
|
os.makedirs(Config.save_checkpoint_dir, exist_ok=True)
|
|
flags_file = os.path.join(Config.save_checkpoint_dir, "flags.txt")
|
|
with open_remote(flags_file, "w") as fout:
|
|
json.dump(Config.serialize(), fout, indent=2)
|
|
|
|
with tfv1.Session(config=Config.session_config) as session:
|
|
log_debug("Session opened.")
|
|
|
|
# Prevent further graph changes
|
|
tfv1.get_default_graph().finalize()
|
|
|
|
# Load checkpoint or initialize variables
|
|
load_or_init_graph_for_training(session)
|
|
|
|
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()
|
|
|
|
if is_train and Config.cache_for_epochs > 0 and Config.feature_cache:
|
|
feature_cache_index = Config.feature_cache + ".index"
|
|
if epoch % Config.cache_for_epochs == 0 and os.path.isfile(
|
|
feature_cache_index
|
|
):
|
|
log_info("Invalidating feature cache")
|
|
remove_remote(
|
|
feature_cache_index
|
|
) # this will let TF also overwrite the related cache data files
|
|
|
|
# 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, human_readable_set_names[set_name]
|
|
)
|
|
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,
|
|
problem_files,
|
|
step_summary,
|
|
) = session.run(
|
|
[
|
|
train_op,
|
|
global_step,
|
|
loss,
|
|
non_finite_files,
|
|
step_summaries_op,
|
|
],
|
|
feed_dict=feed_dict,
|
|
)
|
|
exception_box.raise_if_set()
|
|
except tf.errors.OutOfRangeError:
|
|
exception_box.raise_if_set()
|
|
break
|
|
|
|
if problem_files.size > 0:
|
|
problem_files = [f.decode("utf8") for f in problem_files[..., 0]]
|
|
log_error(
|
|
"The following files caused an infinite (or NaN) "
|
|
"loss: {}".format(",".join(problem_files))
|
|
)
|
|
|
|
total_loss += batch_loss
|
|
step_count += 1
|
|
|
|
pbar.update(step_count)
|
|
|
|
step_summary_writer.add_summary(step_summary, current_step)
|
|
|
|
if (
|
|
is_train
|
|
and Config.checkpoint_secs > 0
|
|
and time.time() - checkpoint_time > Config.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 = []
|
|
epochs_without_improvement = 0
|
|
try:
|
|
for epoch in range(Config.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 Config.dev_files:
|
|
# Validation
|
|
dev_loss = 0.0
|
|
total_steps = 0
|
|
for source, init_op in zip(dev_sources, dev_init_ops):
|
|
log_progress("Validating epoch %d on %s..." % (epoch, source))
|
|
set_loss, steps = run_set("dev", epoch, init_op, dataset=source)
|
|
dev_loss += set_loss * steps
|
|
total_steps += steps
|
|
log_progress(
|
|
"Finished validating epoch %d on %s - loss: %f"
|
|
% (epoch, source, set_loss)
|
|
)
|
|
|
|
dev_loss = dev_loss / total_steps
|
|
dev_losses.append(dev_loss)
|
|
|
|
# Count epochs without an improvement for early stopping and reduction of learning rate on a plateau
|
|
# the improvement has to be greater than Config.es_min_delta
|
|
if dev_loss > best_dev_loss - Config.es_min_delta:
|
|
epochs_without_improvement += 1
|
|
else:
|
|
epochs_without_improvement = 0
|
|
|
|
# Save new best model
|
|
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_checkpoint",
|
|
)
|
|
log_info(
|
|
"Saved new best validating model with loss %f to: %s"
|
|
% (best_dev_loss, save_path)
|
|
)
|
|
|
|
# Early stopping
|
|
if (
|
|
Config.early_stop
|
|
and epochs_without_improvement == Config.es_epochs
|
|
):
|
|
log_info(
|
|
"Early stop triggered as the loss did not improve the last {} epochs".format(
|
|
epochs_without_improvement
|
|
)
|
|
)
|
|
break
|
|
|
|
# Reduce learning rate on plateau
|
|
# If the learning rate was reduced and there is still no improvement
|
|
# wait Config.plateau_epochs before the learning rate is reduced again
|
|
if (
|
|
Config.reduce_lr_on_plateau
|
|
and epochs_without_improvement > 0
|
|
and epochs_without_improvement % Config.plateau_epochs == 0
|
|
):
|
|
# Reload checkpoint that we use the best_dev weights again
|
|
reload_best_checkpoint(session)
|
|
|
|
# Reduce learning rate
|
|
session.run(reduce_learning_rate_op)
|
|
current_learning_rate = learning_rate_var.eval()
|
|
log_info(
|
|
"Encountered a plateau, reducing learning rate to {}".format(
|
|
current_learning_rate
|
|
)
|
|
)
|
|
|
|
# Overwrite best checkpoint with new learning rate value
|
|
save_path = best_dev_saver.save(
|
|
session,
|
|
best_dev_path,
|
|
global_step=global_step,
|
|
latest_filename="best_dev_checkpoint",
|
|
)
|
|
log_info(
|
|
"Saved best validating model with reduced learning rate to: %s"
|
|
% (save_path)
|
|
)
|
|
|
|
if Config.metrics_files:
|
|
# Read only metrics, not affecting best validation loss tracking
|
|
for source, init_op in zip(metrics_sources, metrics_init_ops):
|
|
log_progress("Metrics for epoch %d on %s..." % (epoch, source))
|
|
set_loss, _ = run_set("metrics", epoch, init_op, dataset=source)
|
|
log_progress(
|
|
"Metrics for epoch %d on %s - loss: %f"
|
|
% (epoch, source, set_loss)
|
|
)
|
|
|
|
print("-" * 80)
|
|
|
|
except KeyboardInterrupt:
|
|
pass
|
|
log_info(
|
|
"FINISHED optimization in {}".format(datetime.utcnow() - train_start_time)
|
|
)
|
|
log_debug("Session closed.")
|
|
|
|
|
|
def test():
|
|
tfv1.reset_default_graph()
|
|
|
|
samples = evaluate(Config.test_files, create_model)
|
|
if Config.test_output_file:
|
|
save_samples_json(samples, Config.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, Config.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 STT_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 Config.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 Config.export_tflite:
|
|
inputs["input_lengths"] = seq_length
|
|
|
|
outputs = {
|
|
"outputs": probs,
|
|
"new_state_c": new_state_c,
|
|
"new_state_h": new_state_h,
|
|
"mfccs": mfccs,
|
|
# Expose internal layers for downstream applications
|
|
"layer_3": layers["layer_3"],
|
|
"layer_5": layers["layer_5"],
|
|
}
|
|
|
|
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...")
|
|
|
|
tfv1.reset_default_graph()
|
|
|
|
inputs, outputs, _ = create_inference_graph(
|
|
batch_size=Config.export_batch_size,
|
|
n_steps=Config.n_steps,
|
|
tflite=Config.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(
|
|
[Config.audio_sample_rate], name="metadata_sample_rate"
|
|
)
|
|
outputs["metadata_feature_win_len"] = tf.constant(
|
|
[Config.feature_win_len], name="metadata_feature_win_len"
|
|
)
|
|
outputs["metadata_feature_win_step"] = tf.constant(
|
|
[Config.feature_win_step], name="metadata_feature_win_step"
|
|
)
|
|
outputs["metadata_beam_width"] = tf.constant(
|
|
[Config.export_beam_width], name="metadata_beam_width"
|
|
)
|
|
outputs["metadata_alphabet"] = tf.constant(
|
|
[Config.alphabet.Serialize()], name="metadata_alphabet"
|
|
)
|
|
|
|
if Config.export_language:
|
|
outputs["metadata_language"] = tf.constant(
|
|
[Config.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 = Config.export_file_name + ".pb"
|
|
if Config.remove_export:
|
|
if isdir_remote(Config.export_dir):
|
|
log_info("Removing old export")
|
|
remove_remote(Config.export_dir)
|
|
|
|
output_graph_path = os.path.join(Config.export_dir, output_filename)
|
|
|
|
if not is_remote_path(Config.export_dir) and not os.path.isdir(
|
|
Config.export_dir
|
|
):
|
|
os.makedirs(Config.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 Config.export_tflite:
|
|
with open_remote(output_graph_path, "wb") as fout:
|
|
fout.write(frozen_graph.SerializeToString())
|
|
else:
|
|
output_tflite_path = os.path.join(
|
|
Config.export_dir, output_filename.replace(".pb", ".tflite")
|
|
)
|
|
|
|
converter = tf.lite.TFLiteConverter(
|
|
frozen_graph,
|
|
input_tensors=inputs.values(),
|
|
output_tensors=outputs.values(),
|
|
)
|
|
|
|
if Config.export_quantize:
|
|
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" % (Config.export_dir))
|
|
|
|
metadata_fname = os.path.join(
|
|
Config.export_dir,
|
|
"{}_{}_{}.md".format(
|
|
Config.export_author_id,
|
|
Config.export_model_name,
|
|
Config.export_model_version,
|
|
),
|
|
)
|
|
|
|
model_runtime = "tflite" if Config.export_tflite else "tensorflow"
|
|
with open_remote(metadata_fname, "w") as f:
|
|
f.write("---\n")
|
|
f.write("author: {}\n".format(Config.export_author_id))
|
|
f.write("model_name: {}\n".format(Config.export_model_name))
|
|
f.write("model_version: {}\n".format(Config.export_model_version))
|
|
f.write("contact_info: {}\n".format(Config.export_contact_info))
|
|
f.write("license: {}\n".format(Config.export_license))
|
|
f.write("language: {}\n".format(Config.export_language))
|
|
f.write("runtime: {}\n".format(model_runtime))
|
|
f.write("min_stt_version: {}\n".format(Config.export_min_stt_version))
|
|
f.write("max_stt_version: {}\n".format(Config.export_max_stt_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(Config.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(Config.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(Config.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):
|
|
tfv1.reset_default_graph()
|
|
|
|
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 Config.scorer_path:
|
|
scorer = Scorer(
|
|
Config.lm_alpha, Config.lm_beta, Config.scorer_path, Config.alphabet
|
|
)
|
|
else:
|
|
scorer = None
|
|
decoded = ctc_beam_search_decoder(
|
|
probs,
|
|
Config.alphabet,
|
|
Config.beam_width,
|
|
scorer=scorer,
|
|
cutoff_prob=Config.cutoff_prob,
|
|
cutoff_top_n=Config.cutoff_top_n,
|
|
)
|
|
# Print highest probability result
|
|
print(decoded[0][1])
|
|
|
|
|
|
def early_training_checks():
|
|
# Check for proper scorer early
|
|
if Config.scorer_path:
|
|
scorer = Scorer(
|
|
Config.lm_alpha, Config.lm_beta, Config.scorer_path, Config.alphabet
|
|
)
|
|
del scorer
|
|
|
|
if (
|
|
Config.train_files
|
|
and Config.test_files
|
|
and Config.load_checkpoint_dir != Config.save_checkpoint_dir
|
|
):
|
|
log_warn(
|
|
"WARNING: You specified different values for --load_checkpoint_dir "
|
|
"and --save_checkpoint_dir, but you are running training and testing "
|
|
"in a single invocation. The testing step will respect --load_checkpoint_dir, "
|
|
"and thus WILL NOT TEST THE CHECKPOINT CREATED BY THE TRAINING STEP. "
|
|
"Train and test in two separate invocations, specifying the correct "
|
|
"--load_checkpoint_dir in both cases, or use the same location "
|
|
"for loading and saving."
|
|
)
|
|
|
|
if not Config.alphabet_config_path and not Config.bytes_output_mode:
|
|
log_error("Missing --alphabet_config_path flag, can't continue")
|
|
sys.exit(1)
|
|
|
|
|
|
def main():
|
|
initialize_globals_from_cli()
|
|
early_training_checks()
|
|
|
|
if Config.train_files:
|
|
train()
|
|
|
|
if Config.test_files:
|
|
test()
|
|
|
|
if Config.export_dir and not Config.export_zip:
|
|
export()
|
|
|
|
if Config.export_zip:
|
|
Config.export_tflite = True
|
|
|
|
if listdir_remote(Config.export_dir):
|
|
log_error(
|
|
"Directory {} is not empty, please fix this.".format(Config.export_dir)
|
|
)
|
|
sys.exit(1)
|
|
|
|
export()
|
|
package_zip()
|
|
|
|
if Config.one_shot_infer:
|
|
do_single_file_inference(Config.one_shot_infer)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|