Add support for shuffling batches after N epochs

bin/run-ldc93s1.sh

@@ -20,7 +20,8 @@ fi
 # and when trying to run on multiple devices (like GPUs), this will break
 export CUDA_VISIBLE_DEVICES=0
 
-python -u train.py --alphabet_config_path "data/alphabet.txt" \
+python -m coqui_stt_training.train \
+  --alphabet_config_path "data/alphabet.txt" \
   --show_progressbar false \
   --train_files data/ldc93s1/ldc93s1.csv \
   --test_files data/ldc93s1/ldc93s1.csv \

training/coqui_stt_training/train.py

@@ -266,11 +266,9 @@ def early_training_checks():
         )
 
 
-def create_training_datasets() -> (
-    tf.data.Dataset,
-    [tf.data.Dataset],
-    [tf.data.Dataset],
-):
+def create_training_datasets(
+    epoch_ph: tf.Tensor = None,
+) -> (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
@@ -288,6 +286,7 @@ def create_training_datasets() -> (
         reverse=Config.reverse_train,
         limit=Config.limit_train,
         buffering=Config.read_buffer,
+        epoch_ph=epoch_ph,
     )
 
     dev_sets = []
@@ -331,7 +330,8 @@ def train():
     tfv1.reset_default_graph()
     tfv1.set_random_seed(Config.random_seed)
 
-    train_set, dev_sets, metrics_sets = create_training_datasets()
+    epoch_ph = tf.placeholder(tf.int64, name="epoch_ph")
+    train_set, dev_sets, metrics_sets = create_training_datasets(epoch_ph)
 
     iterator = tfv1.data.Iterator.from_structure(
         tfv1.data.get_output_types(train_set),
@@ -488,7 +488,7 @@ def train():
             ).start()
 
             # Initialize iterator to the appropriate dataset
-            session.run(init_op)
+            session.run(init_op, {epoch_ph: epoch})
 
             # Batch loop
             while True:
@@ -507,7 +507,7 @@ def train():
                             non_finite_files,
                             step_summaries_op,
                         ],
-                        feed_dict=feed_dict,
+                        feed_dict={**feed_dict, **{epoch_ph: epoch}},
                     )
                 except tf.errors.OutOfRangeError:
                     break

training/coqui_stt_training/util/config.py

@@ -340,6 +340,22 @@ class _SttConfig(Coqpit):
             help='after how many epochs the feature cache is invalidated again - 0 for "never"'
         ),
     )
+    shuffle_batches: bool = field(
+        default=False,
+        metadata=dict(
+            help="reshuffle batches every epoch, starting after N epochs, where N is set by the shuffle_start flag."
+        ),
+    )
+    shuffle_start: int = field(
+        default=1,
+        metadata=dict(help="epoch to start shuffling batches from (zero-based)."),
+    )
+    shuffle_buffer: int = field(
+        default=1000,
+        metadata=dict(
+            help="how many batches to keep in shuffle buffer when shuffling batches."
+        ),
+    )
 
     feature_win_len: int = field(
         default=32,

training/coqui_stt_training/util/feeding.py

@@ -140,6 +140,7 @@ def create_dataset(
     limit=0,
     process_ahead=None,
     buffering=1 * MEGABYTE,
+    epoch_ph=None,
 ):
     epoch_counter = Counter()  # survives restarts of the dataset and its generator
 
@@ -207,11 +208,18 @@ def create_dataset(
     ).map(process_fn, num_parallel_calls=tf.data.experimental.AUTOTUNE)
     if cache_path:
         dataset = dataset.cache(cache_path)
-    dataset = (
-        dataset.window(batch_size, drop_remainder=train_phase)
-        .flat_map(batch_fn)
-        .prefetch(len(Config.available_devices))
-    )
+    dataset = dataset.window(batch_size, drop_remainder=train_phase).flat_map(batch_fn)
+
+    if Config.shuffle_batches and epoch_ph is not None:
+        with tf.control_dependencies([tf.print("epoch:", epoch_ph)]):
+            epoch_buffer_size = tf.cond(
+                tf.less(epoch_ph, Config.shuffle_start),
+                lambda: tf.constant(1, tf.int64),
+                lambda: tf.constant(Config.shuffle_buffer, tf.int64),
+            )
+        dataset = dataset.shuffle(epoch_buffer_size, seed=epoch_ph)
+
+    dataset = dataset.prefetch(len(Config.available_devices))
     return dataset