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rl/Lib/site-packages/tensorboard/plugins/audio/audio_plugin.py

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+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""The TensorBoard Audio plugin."""
+
+
+import urllib.parse
+
+from werkzeug import wrappers
+
+from tensorboard import errors
+from tensorboard import plugin_util
+from tensorboard.backend import http_util
+from tensorboard.data import provider
+from tensorboard.plugins import base_plugin
+from tensorboard.plugins.audio import metadata
+
+
+_DEFAULT_MIME_TYPE = "application/octet-stream"
+_DEFAULT_DOWNSAMPLING = 10  # audio clips per time series
+_MIME_TYPES = {
+    metadata.Encoding.Value("WAV"): "audio/wav",
+}
+_ALLOWED_MIME_TYPES = frozenset(
+    list(_MIME_TYPES.values()) + [_DEFAULT_MIME_TYPE]
+)
+
+
+class AudioPlugin(base_plugin.TBPlugin):
+    """Audio Plugin for TensorBoard."""
+
+    plugin_name = metadata.PLUGIN_NAME
+
+    def __init__(self, context):
+        """Instantiates AudioPlugin via TensorBoard core.
+
+        Args:
+          context: A base_plugin.TBContext instance.
+        """
+        self._data_provider = context.data_provider
+        self._downsample_to = (context.sampling_hints or {}).get(
+            self.plugin_name, _DEFAULT_DOWNSAMPLING
+        )
+        self._version_checker = plugin_util._MetadataVersionChecker(
+            data_kind="audio",
+            latest_known_version=0,
+        )
+
+    def get_plugin_apps(self):
+        return {
+            "/audio": self._serve_audio_metadata,
+            "/individualAudio": self._serve_individual_audio,
+            "/tags": self._serve_tags,
+        }
+
+    def is_active(self):
+        return False  # `list_plugins` as called by TB core suffices
+
+    def frontend_metadata(self):
+        return base_plugin.FrontendMetadata(element_name="tf-audio-dashboard")
+
+    def _index_impl(self, ctx, experiment):
+        """Return information about the tags in each run.
+
+        Result is a dictionary of the form
+
+            {
+              "runName1": {
+                "tagName1": {
+                  "displayName": "The first tag",
+                  "description": "<p>Long ago there was just one tag...</p>",
+                  "samples": 3
+                },
+                "tagName2": ...,
+                ...
+              },
+              "runName2": ...,
+              ...
+            }
+
+        For each tag, `samples` is the greatest number of audio clips that
+        appear at any particular step. (It's not related to "samples of a
+        waveform.") For example, if for tag `minibatch_input` there are
+        five audio clips at step 0 and ten audio clips at step 1, then the
+        dictionary for `"minibatch_input"` will contain `"samples": 10`.
+        """
+        mapping = self._data_provider.list_blob_sequences(
+            ctx,
+            experiment_id=experiment,
+            plugin_name=metadata.PLUGIN_NAME,
+        )
+        result = {run: {} for run in mapping}
+        for run, tag_to_time_series in mapping.items():
+            for tag, time_series in tag_to_time_series.items():
+                md = metadata.parse_plugin_metadata(time_series.plugin_content)
+                if not self._version_checker.ok(md.version, run, tag):
+                    continue
+                description = plugin_util.markdown_to_safe_html(
+                    time_series.description
+                )
+                result[run][tag] = {
+                    "displayName": time_series.display_name,
+                    "description": description,
+                    "samples": time_series.max_length,
+                }
+        return result
+
+    @wrappers.Request.application
+    def _serve_audio_metadata(self, request):
+        """Given a tag and list of runs, serve a list of metadata for audio.
+
+        Note that the actual audio data are not sent; instead, we respond
+        with URLs to the audio. The frontend should treat these URLs as
+        opaque and should not try to parse information about them or
+        generate them itself, as the format may change.
+
+        Args:
+          request: A werkzeug.wrappers.Request object.
+
+        Returns:
+          A werkzeug.Response application.
+        """
+        ctx = plugin_util.context(request.environ)
+        experiment = plugin_util.experiment_id(request.environ)
+        tag = request.args.get("tag")
+        run = request.args.get("run")
+        sample = int(request.args.get("sample", 0))
+
+        response = self._audio_response_for_run(
+            ctx, experiment, run, tag, sample
+        )
+        return http_util.Respond(request, response, "application/json")
+
+    def _audio_response_for_run(self, ctx, experiment, run, tag, sample):
+        """Builds a JSON-serializable object with information about audio.
+
+        Args:
+          run: The name of the run.
+          tag: The name of the tag the audio entries all belong to.
+          sample: The zero-indexed sample of the audio sample for which to
+          retrieve information. For instance, setting `sample` to `2` will
+            fetch information about only the third audio clip of each batch,
+            and steps with fewer than three audio clips will be omitted from
+            the results.
+
+        Returns:
+          A list of dictionaries containing the wall time, step, label,
+          content type, and query string for each audio entry.
+        """
+        all_audio = self._data_provider.read_blob_sequences(
+            ctx,
+            experiment_id=experiment,
+            plugin_name=metadata.PLUGIN_NAME,
+            downsample=self._downsample_to,
+            run_tag_filter=provider.RunTagFilter(runs=[run], tags=[tag]),
+        )
+        audio = all_audio.get(run, {}).get(tag, None)
+        if audio is None:
+            raise errors.NotFoundError(
+                "No audio data for run=%r, tag=%r" % (run, tag)
+            )
+        content_type = self._get_mime_type(ctx, experiment, run, tag)
+        response = []
+        for datum in audio:
+            if len(datum.values) < sample:
+                continue
+            query = urllib.parse.urlencode(
+                {
+                    "blob_key": datum.values[sample].blob_key,
+                    "content_type": content_type,
+                }
+            )
+            response.append(
+                {
+                    "wall_time": datum.wall_time,
+                    "label": "",
+                    "step": datum.step,
+                    "contentType": content_type,
+                    "query": query,
+                }
+            )
+        return response
+
+    def _get_mime_type(self, ctx, experiment, run, tag):
+        # TODO(@wchargin): Move this call from `/audio` (called many
+        # times) to `/tags` (called few times) to reduce data provider
+        # calls.
+        mapping = self._data_provider.list_blob_sequences(
+            ctx,
+            experiment_id=experiment,
+            plugin_name=metadata.PLUGIN_NAME,
+        )
+        time_series = mapping.get(run, {}).get(tag, None)
+        if time_series is None:
+            raise errors.NotFoundError(
+                "No audio data for run=%r, tag=%r" % (run, tag)
+            )
+        parsed = metadata.parse_plugin_metadata(time_series.plugin_content)
+        return _MIME_TYPES.get(parsed.encoding, _DEFAULT_MIME_TYPE)
+
+    @wrappers.Request.application
+    def _serve_individual_audio(self, request):
+        """Serve encoded audio data."""
+        ctx = plugin_util.context(request.environ)
+        experiment = plugin_util.experiment_id(request.environ)
+        mime_type = request.args["content_type"]
+        if mime_type not in _ALLOWED_MIME_TYPES:
+            raise errors.InvalidArgumentError(
+                "Illegal mime type %r" % mime_type
+            )
+        blob_key = request.args["blob_key"]
+        data = self._data_provider.read_blob(ctx, blob_key=blob_key)
+        return http_util.Respond(request, data, mime_type)
+
+    @wrappers.Request.application
+    def _serve_tags(self, request):
+        ctx = plugin_util.context(request.environ)
+        experiment = plugin_util.experiment_id(request.environ)
+        index = self._index_impl(ctx, experiment)
+        return http_util.Respond(request, index, "application/json")

rl/Lib/site-packages/tensorboard/plugins/audio/metadata.py

@@ -0,0 +1,70 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Internal information about the audio plugin."""
+
+
+from tensorboard.compat.proto import summary_pb2
+from tensorboard.plugins.audio import plugin_data_pb2
+
+PLUGIN_NAME = "audio"
+
+# The most recent value for the `version` field of the `AudioPluginData`
+# proto.
+PROTO_VERSION = 0
+
+# Expose the `Encoding` enum constants.
+Encoding = plugin_data_pb2.AudioPluginData.Encoding
+
+
+def create_summary_metadata(
+    display_name, description, encoding, *, converted_to_tensor=None
+):
+    """Create a `SummaryMetadata` proto for audio plugin data.
+
+    Returns:
+      A `SummaryMetadata` protobuf object.
+    """
+    content = plugin_data_pb2.AudioPluginData(
+        version=PROTO_VERSION,
+        encoding=encoding,
+        converted_to_tensor=converted_to_tensor,
+    )
+    metadata = summary_pb2.SummaryMetadata(
+        display_name=display_name,
+        summary_description=description,
+        plugin_data=summary_pb2.SummaryMetadata.PluginData(
+            plugin_name=PLUGIN_NAME, content=content.SerializeToString()
+        ),
+    )
+    return metadata
+
+
+def parse_plugin_metadata(content):
+    """Parse summary metadata to a Python object.
+
+    Arguments:
+      content: The `content` field of a `SummaryMetadata` proto
+        corresponding to the audio plugin.
+
+    Returns:
+      An `AudioPluginData` protobuf object.
+    """
+    if not isinstance(content, bytes):
+        raise TypeError("Content type must be bytes")
+    result = plugin_data_pb2.AudioPluginData.FromString(content)
+    if result.version == 0:
+        return result
+    # No other versions known at this time, so no migrations to do.
+    return result

rl/Lib/site-packages/tensorboard/plugins/audio/plugin_data_pb2.py

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+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorboard/plugins/audio/plugin_data.proto
+"""Generated protocol buffer code."""
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import message as _message
+from google.protobuf import reflection as _reflection
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n+tensorboard/plugins/audio/plugin_data.proto\x12\x0btensorboard\"\x9a\x01\n\x0f\x41udioPluginData\x12\x0f\n\x07version\x18\x01 \x01(\x05\x12\x37\n\x08\x65ncoding\x18\x02 \x01(\x0e\x32%.tensorboard.AudioPluginData.Encoding\x12\x1b\n\x13\x63onverted_to_tensor\x18\x03 \x01(\x08\" \n\x08\x45ncoding\x12\x0b\n\x07UNKNOWN\x10\x00\x12\x07\n\x03WAV\x10\x0b\x62\x06proto3')
+
+
+
+_AUDIOPLUGINDATA = DESCRIPTOR.message_types_by_name['AudioPluginData']
+_AUDIOPLUGINDATA_ENCODING = _AUDIOPLUGINDATA.enum_types_by_name['Encoding']
+AudioPluginData = _reflection.GeneratedProtocolMessageType('AudioPluginData', (_message.Message,), {
+  'DESCRIPTOR' : _AUDIOPLUGINDATA,
+  '__module__' : 'tensorboard.plugins.audio.plugin_data_pb2'
+  # @@protoc_insertion_point(class_scope:tensorboard.AudioPluginData)
+  })
+_sym_db.RegisterMessage(AudioPluginData)
+
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  _AUDIOPLUGINDATA._serialized_start=61
+  _AUDIOPLUGINDATA._serialized_end=215
+  _AUDIOPLUGINDATA_ENCODING._serialized_start=183
+  _AUDIOPLUGINDATA_ENCODING._serialized_end=215
+# @@protoc_insertion_point(module_scope)

rl/Lib/site-packages/tensorboard/plugins/audio/summary.py

@@ -0,0 +1,232 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Audio summaries and TensorFlow operations to create them.
+
+An audio summary stores a rank-2 string tensor of shape `[k, 2]`, where
+`k` is the number of audio clips recorded in the summary. Each row of
+the tensor is a pair `[encoded_audio, label]`, where `encoded_audio` is
+a binary string whose encoding is specified in the summary metadata, and
+`label` is a UTF-8 encoded Markdown string describing the audio clip.
+
+NOTE: This module is in beta, and its API is subject to change, but the
+data that it stores to disk will be supported forever.
+"""
+
+
+import functools
+import warnings
+
+import numpy as np
+
+from tensorboard.util import encoder as encoder_util
+from tensorboard.plugins.audio import metadata
+from tensorboard.plugins.audio import summary_v2
+
+
+# Export V2 versions.
+audio = summary_v2.audio
+
+
+_LABELS_WARNING = (
+    "Labels on audio summaries are deprecated and will be removed. "
+    "See <https://github.com/tensorflow/tensorboard/issues/3513>."
+)
+
+
+def op(
+    name,
+    audio,
+    sample_rate,
+    labels=None,
+    max_outputs=3,
+    encoding=None,
+    display_name=None,
+    description=None,
+    collections=None,
+):
+    """Create a legacy audio summary op for use in a TensorFlow graph.
+
+    Arguments:
+      name: A unique name for the generated summary node.
+      audio: A `Tensor` representing audio data with shape `[k, t, c]`,
+        where `k` is the number of audio clips, `t` is the number of
+        frames, and `c` is the number of channels. Elements should be
+        floating-point values in `[-1.0, 1.0]`. Any of the dimensions may
+        be statically unknown (i.e., `None`).
+      sample_rate: An `int` or rank-0 `int32` `Tensor` that represents the
+        sample rate, in Hz. Must be positive.
+      labels: Deprecated. Do not set.
+      max_outputs: Optional `int` or rank-0 integer `Tensor`. At most this
+        many audio clips will be emitted at each step. When more than
+        `max_outputs` many clips are provided, the first `max_outputs`
+        many clips will be used and the rest silently discarded.
+      encoding: A constant `str` (not string tensor) indicating the
+        desired encoding. You can choose any format you like, as long as
+        it's "wav". Please see the "API compatibility note" below.
+      display_name: Optional name for this summary in TensorBoard, as a
+        constant `str`. Defaults to `name`.
+      description: Optional long-form description for this summary, as a
+        constant `str`. Markdown is supported. Defaults to empty.
+      collections: Optional list of graph collections keys. The new
+        summary op is added to these collections. Defaults to
+        `[Graph Keys.SUMMARIES]`.
+
+    Returns:
+      A TensorFlow summary op.
+
+    API compatibility note: The default value of the `encoding`
+    argument is _not_ guaranteed to remain unchanged across TensorBoard
+    versions. In the future, we will by default encode as FLAC instead of
+    as WAV. If the specific format is important to you, please provide a
+    file format explicitly.
+    """
+    if labels is not None:
+        warnings.warn(_LABELS_WARNING)
+
+    # TODO(nickfelt): remove on-demand imports once dep situation is fixed.
+    import tensorflow.compat.v1 as tf
+
+    if display_name is None:
+        display_name = name
+    if encoding is None:
+        encoding = "wav"
+
+    if encoding == "wav":
+        encoding = metadata.Encoding.Value("WAV")
+        encoder = functools.partial(
+            tf.audio.encode_wav, sample_rate=sample_rate
+        )
+    else:
+        raise ValueError("Unknown encoding: %r" % encoding)
+
+    with tf.name_scope(name), tf.control_dependencies(
+        [tf.assert_rank(audio, 3)]
+    ):
+        limited_audio = audio[:max_outputs]
+        encoded_audio = tf.map_fn(
+            encoder, limited_audio, dtype=tf.string, name="encode_each_audio"
+        )
+        if labels is None:
+            limited_labels = tf.tile([""], tf.shape(input=limited_audio)[:1])
+        else:
+            limited_labels = labels[:max_outputs]
+        tensor = tf.transpose(a=tf.stack([encoded_audio, limited_labels]))
+        summary_metadata = metadata.create_summary_metadata(
+            display_name=display_name,
+            description=description,
+            encoding=encoding,
+        )
+        return tf.summary.tensor_summary(
+            name="audio_summary",
+            tensor=tensor,
+            collections=collections,
+            summary_metadata=summary_metadata,
+        )
+
+
+def pb(
+    name,
+    audio,
+    sample_rate,
+    labels=None,
+    max_outputs=3,
+    encoding=None,
+    display_name=None,
+    description=None,
+):
+    """Create a legacy audio summary protobuf.
+
+    This behaves as if you were to create an `op` with the same arguments
+    (wrapped with constant tensors where appropriate) and then execute
+    that summary op in a TensorFlow session.
+
+    Arguments:
+      name: A unique name for the generated summary node.
+      audio: An `np.array` representing audio data with shape `[k, t, c]`,
+        where `k` is the number of audio clips, `t` is the number of
+        frames, and `c` is the number of channels. Elements should be
+        floating-point values in `[-1.0, 1.0]`.
+      sample_rate: An `int` that represents the sample rate, in Hz.
+        Must be positive.
+      labels: Deprecated. Do not set.
+      max_outputs: Optional `int`. At most this many audio clips will be
+        emitted. When more than `max_outputs` many clips are provided, the
+        first `max_outputs` many clips will be used and the rest silently
+        discarded.
+      encoding: A constant `str` indicating the desired encoding. You
+        can choose any format you like, as long as it's "wav". Please see
+        the "API compatibility note" below.
+      display_name: Optional name for this summary in TensorBoard, as a
+        `str`. Defaults to `name`.
+      description: Optional long-form description for this summary, as a
+        `str`. Markdown is supported. Defaults to empty.
+
+    Returns:
+      A `tf.Summary` protobuf object.
+
+    API compatibility note: The default value of the `encoding`
+    argument is _not_ guaranteed to remain unchanged across TensorBoard
+    versions. In the future, we will by default encode as FLAC instead of
+    as WAV. If the specific format is important to you, please provide a
+    file format explicitly.
+    """
+    if labels is not None:
+        warnings.warn(_LABELS_WARNING)
+
+    # TODO(nickfelt): remove on-demand imports once dep situation is fixed.
+    import tensorflow.compat.v1 as tf
+
+    audio = np.array(audio)
+    if audio.ndim != 3:
+        raise ValueError("Shape %r must have rank 3" % (audio.shape,))
+    if encoding is None:
+        encoding = "wav"
+
+    if encoding == "wav":
+        encoding = metadata.Encoding.Value("WAV")
+        encoder = functools.partial(
+            encoder_util.encode_wav, samples_per_second=sample_rate
+        )
+    else:
+        raise ValueError("Unknown encoding: %r" % encoding)
+
+    limited_audio = audio[:max_outputs]
+    if labels is None:
+        limited_labels = [b""] * len(limited_audio)
+    else:
+        limited_labels = [
+            tf.compat.as_bytes(label) for label in labels[:max_outputs]
+        ]
+
+    encoded_audio = [encoder(a) for a in limited_audio]
+    content = np.array([encoded_audio, limited_labels]).transpose()
+    tensor = tf.make_tensor_proto(content, dtype=tf.string)
+
+    if display_name is None:
+        display_name = name
+    summary_metadata = metadata.create_summary_metadata(
+        display_name=display_name, description=description, encoding=encoding
+    )
+    tf_summary_metadata = tf.SummaryMetadata.FromString(
+        summary_metadata.SerializeToString()
+    )
+
+    summary = tf.Summary()
+    summary.value.add(
+        tag="%s/audio_summary" % name,
+        metadata=tf_summary_metadata,
+        tensor=tensor,
+    )
+    return summary

rl/Lib/site-packages/tensorboard/plugins/audio/summary_v2.py

@@ -0,0 +1,125 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Audio summaries and TensorFlow operations to create them, V2 versions.
+
+An audio summary stores a rank-2 string tensor of shape `[k, 2]`, where
+`k` is the number of audio clips recorded in the summary. Each row of
+the tensor is a pair `[encoded_audio, label]`, where `encoded_audio` is
+a binary string whose encoding is specified in the summary metadata, and
+`label` is a UTF-8 encoded Markdown string describing the audio clip.
+"""
+
+
+import functools
+
+from tensorboard.compat import tf2 as tf
+from tensorboard.plugins.audio import metadata
+from tensorboard.util import lazy_tensor_creator
+
+
+def audio(
+    name,
+    data,
+    sample_rate,
+    step=None,
+    max_outputs=3,
+    encoding=None,
+    description=None,
+):
+    """Write an audio summary.
+
+    Arguments:
+      name: A name for this summary. The summary tag used for TensorBoard will
+        be this name prefixed by any active name scopes.
+      data: A `Tensor` representing audio data with shape `[k, t, c]`,
+        where `k` is the number of audio clips, `t` is the number of
+        frames, and `c` is the number of channels. Elements should be
+        floating-point values in `[-1.0, 1.0]`. Any of the dimensions may
+        be statically unknown (i.e., `None`).
+      sample_rate: An `int` or rank-0 `int32` `Tensor` that represents the
+        sample rate, in Hz. Must be positive.
+      step: Explicit `int64`-castable monotonic step value for this summary. If
+        omitted, this defaults to `tf.summary.experimental.get_step()`, which must
+        not be None.
+      max_outputs: Optional `int` or rank-0 integer `Tensor`. At most this
+        many audio clips will be emitted at each step. When more than
+        `max_outputs` many clips are provided, the first `max_outputs`
+        many clips will be used and the rest silently discarded.
+      encoding: Optional constant `str` for the desired encoding. Only "wav"
+        is currently supported, but this is not guaranteed to remain the
+        default, so if you want "wav" in particular, set this explicitly.
+      description: Optional long-form description for this summary, as a
+        constant `str`. Markdown is supported. Defaults to empty.
+
+    Returns:
+      True on success, or false if no summary was emitted because no default
+      summary writer was available.
+
+    Raises:
+      ValueError: if a default writer exists, but no step was provided and
+        `tf.summary.experimental.get_step()` is None.
+    """
+    audio_ops = getattr(tf, "audio", None)
+    if audio_ops is None:
+        # Fallback for older versions of TF without tf.audio.
+        from tensorflow.python.ops import gen_audio_ops as audio_ops
+
+    if encoding is None:
+        encoding = "wav"
+    if encoding != "wav":
+        raise ValueError("Unknown encoding: %r" % encoding)
+    summary_metadata = metadata.create_summary_metadata(
+        display_name=None,
+        description=description,
+        encoding=metadata.Encoding.Value("WAV"),
+    )
+    inputs = [data, sample_rate, max_outputs, step]
+    # TODO(https://github.com/tensorflow/tensorboard/issues/2109): remove fallback
+    summary_scope = (
+        getattr(tf.summary.experimental, "summary_scope", None)
+        or tf.summary.summary_scope
+    )
+    with summary_scope(name, "audio_summary", values=inputs) as (tag, _):
+        # Defer audio encoding preprocessing by passing it as a callable to write(),
+        # wrapped in a LazyTensorCreator for backwards compatibility, so that we
+        # only do this work when summaries are actually written.
+        @lazy_tensor_creator.LazyTensorCreator
+        def lazy_tensor():
+            tf.debugging.assert_rank(data, 3)
+            tf.debugging.assert_non_negative(max_outputs)
+            limited_audio = data[:max_outputs]
+            encode_fn = functools.partial(
+                audio_ops.encode_wav, sample_rate=sample_rate
+            )
+            encoded_audio = tf.map_fn(
+                encode_fn,
+                limited_audio,
+                dtype=tf.string,
+                name="encode_each_audio",
+            )
+            # Workaround for map_fn returning float dtype for an empty elems input.
+            encoded_audio = tf.cond(
+                tf.shape(input=encoded_audio)[0] > 0,
+                lambda: encoded_audio,
+                lambda: tf.constant([], tf.string),
+            )
+            limited_labels = tf.tile([""], tf.shape(input=limited_audio)[:1])
+            return tf.transpose(a=tf.stack([encoded_audio, limited_labels]))
+
+        # To ensure that audio encoding logic is only executed when summaries
+        # are written, we pass callable to `tensor` parameter.
+        return tf.summary.write(
+            tag=tag, tensor=lazy_tensor, step=step, metadata=summary_metadata
+        )