Remove ExceptionBox and remember_exception

TensorFlow already handles surfacing dataset exceptions internally.

training/coqui_stt_training/train.py

@@ -58,7 +58,7 @@ from .util.config import (
     log_warn,
 )
 from .util.feeding import create_dataset
-from .util.helpers import ExceptionBox, check_ctcdecoder_version
+from .util.helpers import check_ctcdecoder_version
 from .util.io import (
     is_remote_path,
     open_remote,
@@ -266,9 +266,11 @@ def early_training_checks():
         )
 
 
-def create_training_datasets(
-    exception_box,
-) -> (tf.data.Dataset, [tf.data.Dataset], [tf.data.Dataset],):
+def create_training_datasets() -> (
+    tf.data.Dataset,
+    [tf.data.Dataset],
+    [tf.data.Dataset],
+):
     """Creates training datasets from input flags.
 
     Returns a single training dataset and two lists of datasets for validation
@@ -282,7 +284,6 @@ def create_training_datasets(
         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,
@@ -297,7 +298,6 @@ def create_training_datasets(
                 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,
@@ -314,7 +314,6 @@ def create_training_datasets(
                 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,
@@ -332,9 +331,7 @@ def train():
     tfv1.reset_default_graph()
     tfv1.set_random_seed(Config.random_seed)
 
-    exception_box = ExceptionBox()
-
-    train_set, dev_sets, metrics_sets = create_training_datasets(exception_box)
+    train_set, dev_sets, metrics_sets = create_training_datasets()
 
     iterator = tfv1.data.Iterator.from_structure(
         tfv1.data.get_output_types(train_set),
@@ -512,9 +509,7 @@ def train():
                         ],
                         feed_dict=feed_dict,
                     )
-                    exception_box.raise_if_set()
                 except tf.errors.OutOfRangeError:
-                    exception_box.raise_if_set()
                     break
 
                 if problem_files.size > 0:

training/coqui_stt_training/util/feeding.py

@@ -12,7 +12,7 @@ import tensorflow as tf
 from .audio import DEFAULT_FORMAT, pcm_to_np, read_frames_from_file, vad_split
 from .augmentations import apply_graph_augmentations, apply_sample_augmentations
 from .config import Config
-from .helpers import MEGABYTE, remember_exception
+from .helpers import MEGABYTE
 from .sample_collections import samples_from_sources
 from .text import text_to_char_array
 
@@ -138,7 +138,6 @@ def create_dataset(
     train_phase=False,
     reverse=False,
     limit=0,
-    exception_box=None,
     process_ahead=None,
     buffering=1 * MEGABYTE,
 ):
@@ -197,7 +196,7 @@ def create_dataset(
     )
 
     dataset = tf.data.Dataset.from_generator(
-        remember_exception(generate_values, exception_box),
+        generate_values,
         output_types=(
             tf.string,
             tf.float32,
@@ -223,7 +222,6 @@ def split_audio_file(
     aggressiveness=3,
     outlier_duration_ms=10000,
     outlier_batch_size=1,
-    exception_box=None,
 ):
     def generate_values():
         frames = read_frames_from_file(audio_path)
@@ -240,7 +238,7 @@ def split_audio_file(
     def create_batch_set(bs, criteria):
         return (
             tf.data.Dataset.from_generator(
-                remember_exception(generate_values, exception_box),
+                generate_values,
                 output_types=(tf.int32, tf.int32, tf.float32),
             )
             .map(to_mfccs, num_parallel_calls=tf.data.experimental.AUTOTUNE)

training/coqui_stt_training/util/helpers.py

@@ -163,35 +163,6 @@ class LimitingPool:
         self.pool.close()
 
 
-class ExceptionBox:
-    """Helper class for passing-back and re-raising an exception from inside a TensorFlow dataset generator.
-    Used in conjunction with `remember_exception`."""
-
-    def __init__(self):
-        self.exception = None
-
-    def raise_if_set(self):
-        if self.exception is not None:
-            exception = self.exception
-            self.exception = None
-            raise exception  # pylint: disable = raising-bad-type
-
-
-def remember_exception(iterable, exception_box=None):
-    """Wraps a TensorFlow dataset generator for catching its actual exceptions
-    that would otherwise just interrupt iteration w/o bubbling up."""
-
-    def do_iterate():
-        try:
-            yield from iterable()
-        except StopIteration:
-            return
-        except Exception as ex:  # pylint: disable = broad-except
-            exception_box.exception = ex
-
-    return iterable if exception_box is None else do_iterate
-
-
 def get_value_range(value, target_type):
     """
     This function converts all possible supplied values for augmentation