diff --git a/trains/binding/frameworks/tensorflow_bind.py b/trains/binding/frameworks/tensorflow_bind.py
index d95a497b..fbc3a6bd 100644
--- a/trains/binding/frameworks/tensorflow_bind.py
+++ b/trains/binding/frameworks/tensorflow_bind.py
@@ -15,7 +15,7 @@ from PIL import Image
 from ...debugging.log import LoggerRoot
 from ..frameworks import _patched_call, WeightsFileHandler, _Empty
 from ..import_bind import PostImportHookPatching
-from ...config import running_remotely, config
+from ...config import running_remotely
 from ...model import InputModel, OutputModel, Framework
 
 try:
@@ -189,6 +189,7 @@ class EventTrainsWriter(object):
         self._hist_x_granularity = 50
         self._max_step = 0
         self._graph_name_lookup = {}
+        self._generic_tensor_type_name_lookup = {}
 
     def _decode_image(self, img_str, width, height, color_channels):
         # noinspection PyBroadException
@@ -458,22 +459,28 @@ class EventTrainsWriter(object):
         except Exception:
             pass
 
-    def _add_audio(self, tag, step, values):
+    def _add_audio(self, tag, step, values, audio_data=None):
         # only report images every specific interval
         if step % self.image_report_freq != 0:
             return None
 
-        audio_str = values['encodedAudioString']
-        audio_data = base64.b64decode(audio_str)
+        if values:
+            audio_str = values['encodedAudioString']
+            audio_data = base64.b64decode(audio_str)
         if audio_data is None:
             return
 
-        title, series = self.tag_splitter(tag, num_split_parts=3, default_title='Images', logdir_header='title',
+        title, series = self.tag_splitter(tag, num_split_parts=3, default_title='Audio', logdir_header='title',
                                           auto_reduce_num_split=True)
         step = self._fix_step_counter(title, series, step)
 
         stream = BytesIO(audio_data)
-        file_extension = guess_extension(values['contentType']) or '.{}'.format(values['contentType'].split('/')[-1])
+        if values:
+            file_extension = guess_extension(values['contentType']) or \
+                             '.{}'.format(values['contentType'].split('/')[-1])
+        else:
+            # assume wav as default
+            file_extension = '.wav'
         self._logger.report_media(
             title=title,
             series=series,
@@ -550,8 +557,8 @@ class EventTrainsWriter(object):
                 tag = vdict.pop('tag', None)
                 if tag is None:
                     # we should not get here
-                    LoggerRoot.get_base_logger(TensorflowBinding).debug('No tag for \'value\' existing keys %s'
-                                                       % ', '.join(vdict.keys()))
+                    LoggerRoot.get_base_logger(TensorflowBinding).debug(
+                        'No tag for \'value\' existing keys %s' % ', '.join(vdict.keys()))
                     continue
                 metric, values = get_data(vdict, supported_metrics)
                 if metric == 'simpleValue':
@@ -563,9 +570,20 @@ class EventTrainsWriter(object):
                 elif metric == 'audio':
                     self._add_audio(tag, step, values)
                 elif metric == 'tensor' and values.get('dtype') == 'DT_STRING':
-                    # text, just print to console
-                    text = base64.b64decode('\n'.join(values['stringVal'])).decode('utf-8', errors='replace')
-                    self._logger.report_text(msg='SUMMARY LOG: {} {}'.format(tag, text), print_console=False)
+                    # generic tensor
+                    tensor_bytes = base64.b64decode('\n'.join(values['stringVal']))
+                    plugin_type = self._generic_tensor_type_name_lookup.get(tag) or \
+                        vdict.get('metadata', {}).get('pluginData', {}).get('pluginName', '').lower()
+                    if plugin_type == 'audio':
+                        self._generic_tensor_type_name_lookup[tag] = plugin_type
+                        self._add_audio(tag, step, None, tensor_bytes)
+                    elif plugin_type == 'text':
+                        # text, just print to console
+                        text = tensor_bytes.decode('utf-8', errors='replace')
+                        self._logger.report_text(msg='SUMMARY LOG: {} {}'.format(tag, text), print_console=False)
+                    else:
+                        # we do not support it
+                        pass
                 elif metric == 'tensor' and values.get('dtype') == 'DT_FLOAT':
                     self._add_plot(tag, step, values, vdict)
                 else:
@@ -1079,6 +1097,12 @@ class PatchTensorFlowEager(object):
                     PatchTensorFlowEager._add_histogram_event_helper(
                         event_writer, tag=str(tag), step=int(step.numpy()) if not isinstance(step, int) else step,
                         hist_data=tensor.numpy())
+                elif 'audio' in plugin_type:
+                    audio_bytes_list = [a for a in tensor.numpy().flatten() if a]
+                    for i, audio_bytes in enumerate(audio_bytes_list):
+                        event_writer._add_audio(tag=str(tag) + ('/{}'.format(i) if len(audio_bytes_list) > 1 else ''),
+                                                step=int(step.numpy()) if not isinstance(step, int) else step,
+                                                values=None, audio_data=audio_bytes)
                 else:
                     pass  # print('unsupported plugin_type', plugin_type)
             except Exception as ex: