Merge pull request #1868 from JRMeyer/data-augmentation-cleaning

Add logging and clean up some augmentation code

training/coqui_stt_training/util/augmentations.py

@@ -1,4 +1,3 @@
-
 import os
 import re
 import math
@@ -10,6 +9,7 @@ from multiprocessing import Queue, Process
 from .audio import gain_db_to_ratio, max_dbfs, normalize_audio, AUDIO_TYPE_NP, AUDIO_TYPE_PCM, AUDIO_TYPE_OPUS
 from .helpers import LimitingPool, int_range, float_range, pick_value_from_range, tf_pick_value_from_range, MEGABYTE
 from .sample_collections import samples_from_source, unpack_maybe
+from .logging import log_info
 
 BUFFER_SIZE = 1 * MEGABYTE
 SPEC_PARSER = re.compile(r'^(?P<cls>[a-z_]+)(\[(?P<params>.*)\])?$')
@@ -90,6 +90,7 @@ def parse_augmentation(augmentation_spec):
             kwargs[pair[0]] = pair[1]
         else:
             raise ValueError('Unable to parse augmentation value assignment')
+    log_info('Processed augmentation type: [{}] with parameter settings: {}'.format(augmentation_cls.__name__, kwargs))
     return augmentation_cls(*args, **kwargs)
 
 
@@ -106,7 +107,7 @@ def parse_augmentations(augmentation_specs):
     -------
     List of augmentation class instances from util.augmentations.*.
     """
-    return [] if augmentation_specs is None else list(map(parse_augmentation, augmentation_specs))
+    return list(map(parse_augmentation, augmentation_specs or []))
 
 
 def apply_graph_augmentations(domain, tensor, augmentations, transcript=None, clock=0.0):

training/coqui_stt_training/util/helpers.py

@@ -163,27 +163,41 @@ def remember_exception(iterable, exception_box=None):
 
 
 def get_value_range(value, target_type):
+    """
+    This function converts all possible supplied values for augmentation
+    into the [start,end,r] ValueRange type. The expected inputs are of the form:
+
+    <number>
+    <number>~<number>
+    <number>:<number>~<number>
+
+    Any "missing" values are filled so that ValueRange always includes [start,end,r].
+    """
     if isinstance(value, str):
-        r = target_type(0)
-        parts = value.split('~')
-        if len(parts) == 2:
+        if '~' in value:
+            parts = value.split('~')
+            if len(parts) != 2:
+                raise ValueError('Cannot parse value range')
             value = parts[0]
-            r = target_type(parts[1])
-        elif len(parts) > 2:
-            raise ValueError('Cannot parse value range')
+            r = parts[1]
+        else:
+            r = 0 # if no <r> supplied, use 0
         parts = value.split(':')
         if len(parts) == 1:
-            parts.append(parts[0])
-        elif len(parts) > 2:
+            parts.append(parts[0]) # only one <value> given, so double it
+        if len(parts) != 2:
             raise ValueError('Cannot parse value range')
-        return ValueRange(target_type(parts[0]), target_type(parts[1]), r)
+        return ValueRange(target_type(parts[0]), target_type(parts[1]), target_type(r))
     if isinstance(value, tuple):
         if len(value) == 2:
-            return ValueRange(target_type(value[0]), target_type(value[1]), 0)
+            return ValueRange(target_type(value[0]), target_type(value[1]), target_type(0))
         if len(value) == 3:
             return ValueRange(target_type(value[0]), target_type(value[1]), target_type(value[2]))
-        raise ValueError('Cannot convert to ValueRange: Wrong tuple size')
-    return ValueRange(target_type(value), target_type(value), 0)
+        else:
+            raise ValueError('Cannot convert to ValueRange: Wrong tuple size')
+    if isinstance(value, int) or isinstance(value, float):
+        return ValueRange(target_type(value), target_type(value), target_type(0))
+    raise ValueError('Cannot convert to ValueRange: Wrong tuple size')
 
 
 def int_range(value):
@@ -203,14 +217,20 @@ def pick_value_from_range(value_range, clock=None):
 
 def tf_pick_value_from_range(value_range, clock=None, double_precision=False):
     import tensorflow as tf  # pylint: disable=import-outside-toplevel
-    clock = (tf.random.stateless_uniform([], seed=(-1, 1), dtype=tf.float64) if clock is None
-             else tf.maximum(tf.constant(0.0, dtype=tf.float64), tf.minimum(tf.constant(1.0, dtype=tf.float64), clock)))
+    if clock is None:
+        clock = tf.random.stateless_uniform([], seed=(-1, 1), dtype=tf.float64)
+    else:
+        clock = tf.maximum(tf.constant(0.0, dtype=tf.float64),
+                           tf.minimum(tf.constant(1.0, dtype=tf.float64), clock))
     value = value_range.start + clock * (value_range.end - value_range.start)
-    value = tf.random.stateless_uniform([],
-                                        minval=value - value_range.r,
-                                        maxval=value + value_range.r,
-                                        seed=(clock * tf.int32.min, clock * tf.int32.max),
-                                        dtype=tf.float64)
+    if value_range.r:
+        # if the option <r> (<value>~<r>, randomization radius) is supplied,
+        # sample the value from a uniform distribution with "radius" <r>
+        value = tf.random.stateless_uniform([],
+                                            minval=value - value_range.r,
+                                            maxval=value + value_range.r,
+                                            seed=(clock * tf.int32.min, clock * tf.int32.max),
+                                            dtype=tf.float64)
     if isinstance(value_range.start, int):
         return tf.cast(tf.math.round(value), tf.int64 if double_precision else tf.int32)
     return tf.cast(value, tf.float64 if double_precision else tf.float32)