Added AudioEmbedder API and tests along with fixing a couple of typos in AudioClassifier

mediapipe/tasks/python/audio/BUILD

@@ -39,3 +39,26 @@ py_library(
         "//mediapipe/tasks/python/core:task_info",
     ],
 )
+
+py_library(
+    name = "audio_embedder",
+    srcs = [
+        "audio_embedder.py",
+    ],
+    deps = [
+        "//mediapipe/python:_framework_bindings",
+        "//mediapipe/python:packet_creator",
+        "//mediapipe/python:packet_getter",
+        "//mediapipe/tasks/cc/audio/audio_embedder/proto:audio_embedder_graph_options_py_pb2",
+        "//mediapipe/tasks/cc/components/containers/proto:embeddings_py_pb2",
+        "//mediapipe/tasks/python/audio/core:audio_task_running_mode",
+        "//mediapipe/tasks/python/audio/core:base_audio_task_api",
+        "//mediapipe/tasks/python/components/containers:audio_data",
+        "//mediapipe/tasks/python/components/containers:embedding_result",
+        "//mediapipe/tasks/python/components/processors:embedder_options",
+        "//mediapipe/tasks/python/components/utils:cosine_similarity",
+        "//mediapipe/tasks/python/core:base_options",
+        "//mediapipe/tasks/python/core:optional_dependencies",
+        "//mediapipe/tasks/python/core:task_info",
+    ],
+)

mediapipe/tasks/python/audio/audio_classifier.py

@@ -257,7 +257,7 @@ class AudioClassifier(base_audio_task_api.BaseAudioTaskApi):
     Raises:
       ValueError: If any of the followings:
         1) The sample rate is not provided in the `AudioData` object or the
-        provided sample rate is inconsisent with the previously recevied.
+        provided sample rate is inconsistent with the previously received.
         2) The current input timestamp is smaller than what the audio
         classifier has already processed.
     """
@@ -270,7 +270,7 @@ class AudioClassifier(base_audio_task_api.BaseAudioTaskApi):
     elif audio_block.audio_format.sample_rate != self._default_sample_rate:
       raise ValueError(
           f'The audio sample rate provided in audio data: '
-          f'{audio_block.audio_format.sample_rate} is inconsisent with '
+          f'{audio_block.audio_format.sample_rate} is inconsistent with '
           f'the previously received: {self._default_sample_rate}.')
 
     self._send_audio_stream_data({

mediapipe/tasks/python/audio/audio_embedder.py

@@ -0,0 +1,285 @@
+# Copyright 2022 The MediaPipe 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.
+"""MediaPipe audio embedder task."""
+
+import dataclasses
+from typing import Callable, Mapping, List, Optional
+
+from mediapipe.python import packet_creator
+from mediapipe.python import packet_getter
+from mediapipe.python._framework_bindings import packet
+from mediapipe.tasks.cc.audio.audio_embedder.proto import audio_embedder_graph_options_pb2
+from mediapipe.tasks.cc.components.containers.proto import embeddings_pb2
+from mediapipe.tasks.python.audio.core import audio_task_running_mode as running_mode_module
+from mediapipe.tasks.python.audio.core import base_audio_task_api
+from mediapipe.tasks.python.components.containers import audio_data as audio_data_module
+from mediapipe.tasks.python.components.containers import embedding_result as embedding_result_module
+from mediapipe.tasks.python.components.processors import embedder_options as embedder_options_module
+from mediapipe.tasks.python.components.utils import cosine_similarity
+from mediapipe.tasks.python.core import base_options as base_options_module
+from mediapipe.tasks.python.core import task_info as task_info_module
+from mediapipe.tasks.python.core.optional_dependencies import doc_controls
+
+AudioEmbedderResult = embedding_result_module.EmbeddingResult
+_AudioEmbedderGraphOptionsProto = audio_embedder_graph_options_pb2.AudioEmbedderGraphOptions
+_AudioData = audio_data_module.AudioData
+_BaseOptions = base_options_module.BaseOptions
+_EmbedderOptions = embedder_options_module.EmbedderOptions
+_RunningMode = running_mode_module.AudioTaskRunningMode
+_TaskInfo = task_info_module.TaskInfo
+
+_AUDIO_IN_STREAM_NAME = 'audio_in'
+_AUDIO_TAG = 'AUDIO'
+_EMBEDDINGS_STREAM_NAME = 'embeddings_out'
+_EMBEDDINGS_TAG = 'EMBEDDINGS'
+_SAMPLE_RATE_IN_STREAM_NAME = 'sample_rate_in'
+_SAMPLE_RATE_TAG = 'SAMPLE_RATE'
+_TASK_GRAPH_NAME = 'mediapipe.tasks.audio.audio_embedder.AudioEmbedderGraph'
+_TIMESTAMPTED_EMBEDDINGS_STREAM_NAME = 'timestamped_embeddings_out'
+_TIMESTAMPTED_EMBEDDINGS_TAG = 'TIMESTAMPED_EMBEDDINGS'
+_MICRO_SECONDS_PER_MILLISECOND = 1000
+
+
+@dataclasses.dataclass
+class AudioEmbedderOptions:
+  """Options for the audio embedder task.
+
+  Attributes:
+    base_options: Base options for the audio embedder task.
+    running_mode: The running mode of the task. Default to the audio clips mode.
+      Audio embedder task has two running modes: 1) The audio clips mode for
+      running embedding extraction on independent audio clips. 2) The audio
+      stream mode for running embedding extraction on the audio stream, such as
+      from microphone. In this mode,  the "result_callback" below must be
+      specified to receive the embedding results asynchronously.
+    embedder_options: Options for configuring the embedder behavior, such as
+      l2_normalize and quantize.
+    result_callback: The user-defined result callback for processing audio
+      stream data. The result callback should only be specified when the running
+      mode is set to the audio stream mode.
+  """
+  base_options: _BaseOptions
+  running_mode: _RunningMode = _RunningMode.AUDIO_CLIPS
+  embedder_options: _EmbedderOptions = _EmbedderOptions()
+  result_callback: Optional[Callable[[AudioEmbedderResult, int], None]] = None
+
+  @doc_controls.do_not_generate_docs
+  def to_pb2(self) -> _AudioEmbedderGraphOptionsProto:
+    """Generates an AudioEmbedderOptions protobuf object."""
+    base_options_proto = self.base_options.to_pb2()
+    base_options_proto.use_stream_mode = False if self.running_mode == _RunningMode.AUDIO_CLIPS else True
+    embedder_options_proto = self.embedder_options.to_pb2()
+
+    return _AudioEmbedderGraphOptionsProto(
+        base_options=base_options_proto,
+        embedder_options=embedder_options_proto)
+
+
+class AudioEmbedder(base_audio_task_api.BaseAudioTaskApi):
+  """Class that performs embedding extraction on audio clips or audio stream."""
+
+  @classmethod
+  def create_from_model_path(cls, model_path: str) -> 'AudioEmbedder':
+    """Creates an `AudioEmbedder` object from a TensorFlow Lite model and the default `AudioEmbedderOptions`.
+
+    Note that the created `AudioEmbedder` instance is in audio clips mode, for
+    embedding extraction on the independent audio clips.
+
+    Args:
+      model_path: Path to the model.
+
+    Returns:
+      `AudioEmbedder` object that's created from the model file and the
+      default `AudioEmbedderOptions`.
+
+    Raises:
+      ValueError: If failed to create `AudioEmbedder` object from the provided
+        file such as invalid file path.
+      RuntimeError: If other types of error occurred.
+    """
+    base_options = _BaseOptions(model_asset_path=model_path)
+    options = AudioEmbedderOptions(
+        base_options=base_options, running_mode=_RunningMode.AUDIO_CLIPS)
+    return cls.create_from_options(options)
+
+  @classmethod
+  def create_from_options(cls,
+                          options: AudioEmbedderOptions) -> 'AudioEmbedder':
+    """Creates the `AudioEmbedder` object from audio embedder options.
+
+    Args:
+      options: Options for the audio embedder task.
+
+    Returns:
+      `AudioEmbedder` object that's created from `options`.
+
+    Raises:
+      ValueError: If failed to create `AudioEmbedder` object from
+        `AudioEmbedderOptions` such as missing the model.
+      RuntimeError: If other types of error occurred.
+    """
+
+    def packets_callback(output_packets: Mapping[str, packet.Packet]):
+      timestamp_ms = output_packets[
+          _EMBEDDINGS_STREAM_NAME].timestamp.value // _MICRO_SECONDS_PER_MILLISECOND
+      if output_packets[_EMBEDDINGS_STREAM_NAME].is_empty():
+        options.result_callback(
+            AudioEmbedderResult(embeddings=[]), timestamp_ms)
+        return
+      embedding_result_proto = embeddings_pb2.EmbeddingResult()
+      embedding_result_proto.CopyFrom(
+          packet_getter.get_proto(output_packets[_EMBEDDINGS_STREAM_NAME]))
+      options.result_callback(
+          AudioEmbedderResult.create_from_pb2(embedding_result_proto),
+          timestamp_ms)
+
+    task_info = _TaskInfo(
+        task_graph=_TASK_GRAPH_NAME,
+        input_streams=[
+            ':'.join([_AUDIO_TAG, _AUDIO_IN_STREAM_NAME]),
+            ':'.join([_SAMPLE_RATE_TAG, _SAMPLE_RATE_IN_STREAM_NAME])
+        ],
+        output_streams=[
+            ':'.join([_EMBEDDINGS_TAG, _EMBEDDINGS_STREAM_NAME]),
+            ':'.join([
+                _TIMESTAMPTED_EMBEDDINGS_TAG,
+                _TIMESTAMPTED_EMBEDDINGS_STREAM_NAME
+            ])
+        ],
+        task_options=options)
+    return cls(
+        # Audio tasks should not drop input audio due to flow limiting, which
+        # may cause data inconsistency.
+        task_info.generate_graph_config(enable_flow_limiting=False),
+        options.running_mode,
+        packets_callback if options.result_callback else None)
+
+  def embed(self, audio_clip: _AudioData) -> List[AudioEmbedderResult]:
+    """Performs embedding extraction on the provided audio clips.
+
+    The audio clip is represented as a MediaPipe AudioData. The method accepts
+    audio clips with various length and audio sample rate. It's required to
+    provide the corresponding audio sample rate within the `AudioData` object.
+
+    The input audio clip may be longer than what the model is able to process
+    in a single inference. When this occurs, the input audio clip is split into
+    multiple chunks starting at different timestamps. For this reason, this
+    function returns a vector of EmbeddingResult objects, each associated
+    ith a timestamp corresponding to the start (in milliseconds) of the chunk
+    data on which embedding extraction was carried out.
+
+    Args:
+      audio_clip: MediaPipe AudioData.
+
+    Returns:
+      An `AudioEmbedderResult` object that contains a list of embedding result
+      objects, each associated with a timestamp corresponding to the start
+      (in milliseconds) of the chunk data on which embedding extraction was
+      carried out.
+
+    Raises:
+      ValueError: If any of the input arguments is invalid, such as the sample
+        rate is not provided in the `AudioData` object.
+      RuntimeError: If audio embedding extraction failed to run.
+    """
+    if not audio_clip.audio_format.sample_rate:
+      raise ValueError('Must provide the audio sample rate in audio data.')
+    output_packets = self._process_audio_clip({
+        _AUDIO_IN_STREAM_NAME:
+            packet_creator.create_matrix(audio_clip.buffer, transpose=True),
+        _SAMPLE_RATE_IN_STREAM_NAME:
+            packet_creator.create_double(audio_clip.audio_format.sample_rate)
+    })
+    output_list = []
+    embeddings_proto_list = packet_getter.get_proto_list(
+        output_packets[_TIMESTAMPTED_EMBEDDINGS_STREAM_NAME])
+    for proto in embeddings_proto_list:
+      embedding_result_proto = embeddings_pb2.EmbeddingResult()
+      embedding_result_proto.CopyFrom(proto)
+      output_list.append(
+          AudioEmbedderResult.create_from_pb2(embedding_result_proto))
+    return output_list
+
+  def embed_async(self, audio_block: _AudioData, timestamp_ms: int) -> None:
+    """Sends audio data (a block in a continuous audio stream) to perform audio embedding extraction.
+
+    Only use this method when the AudioEmbedder is created with the audio
+    stream running mode. The input timestamps should be monotonically increasing
+    for adjacent calls of this method. This method will return immediately after
+    the input audio data is accepted. The results will be available via the
+    `result_callback` provided in the `AudioEmbedderOptions`. The
+    `embed_async` method is designed to process auido stream data such as
+    microphone input.
+
+    The input audio data may be longer than what the model is able to process
+    in a single inference. When this occurs, the input audio block is split
+    into multiple chunks. For this reason, the callback may be called multiple
+    times (once per chunk) for each call to this function.
+
+    The `result_callback` provides:
+      - An `AudioEmbedderResult` object that contains a list of
+        embeddings.
+      - The input timestamp in milliseconds.
+
+    Args:
+      audio_block: MediaPipe AudioData.
+      timestamp_ms: The timestamp of the input audio data in milliseconds.
+
+    Raises:
+      ValueError: If any of the followings:
+        1) The sample rate is not provided in the `AudioData` object or the
+        provided sample rate is inconsistent with the previously received.
+        2) The current input timestamp is smaller than what the audio
+        embedder has already processed.
+    """
+    if not audio_block.audio_format.sample_rate:
+      raise ValueError('Must provide the audio sample rate in audio data.')
+    if not self._default_sample_rate:
+      self._default_sample_rate = audio_block.audio_format.sample_rate
+      self._set_sample_rate(_SAMPLE_RATE_IN_STREAM_NAME,
+                            self._default_sample_rate)
+    elif audio_block.audio_format.sample_rate != self._default_sample_rate:
+      raise ValueError(
+          f'The audio sample rate provided in audio data: '
+          f'{audio_block.audio_format.sample_rate} is inconsistent with '
+          f'the previously received: {self._default_sample_rate}.')
+
+    self._send_audio_stream_data({
+        _AUDIO_IN_STREAM_NAME:
+            packet_creator.create_matrix(audio_block.buffer, transpose=True).at(
+                timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND)
+    })
+
+  @classmethod
+  def cosine_similarity(cls, u: embedding_result_module.Embedding,
+                        v: embedding_result_module.Embedding) -> float:
+    """Utility function to compute cosine similarity between two embedding entries.
+
+    May return an InvalidArgumentError if e.g. the feature vectors are
+    of different types (quantized vs. float), have different sizes, or have a
+    an L2-norm of 0.
+
+    Args:
+      u: An embedding entry.
+      v: An embedding entry.
+
+    Returns:
+      The cosine similarity for the two embeddings.
+
+    Raises:
+      ValueError: May return an error if e.g. the feature vectors are of
+        different types (quantized vs. float), have different sizes, or have
+        an L2-norm of 0.
+    """
+    return cosine_similarity.cosine_similarity(u, v)

mediapipe/tasks/python/test/audio/BUILD

@@ -35,3 +35,21 @@ py_test(
         "//mediapipe/tasks/python/test:test_utils",
     ],
 )
+
+py_test(
+    name = "audio_embedder_test",
+    srcs = ["audio_embedder_test.py"],
+    data = [
+        "//mediapipe/tasks/testdata/audio:test_audio_clips",
+        "//mediapipe/tasks/testdata/audio:test_models",
+    ],
+    deps = [
+        "//mediapipe/tasks/python/audio:audio_embedder",
+        "//mediapipe/tasks/python/audio/core:audio_task_running_mode",
+        "//mediapipe/tasks/python/components/containers:audio_data",
+        "//mediapipe/tasks/python/components/containers:embedding_result",
+        "//mediapipe/tasks/python/components/processors:embedder_options",
+        "//mediapipe/tasks/python/core:base_options",
+        "//mediapipe/tasks/python/test:test_utils",
+    ],
+)

mediapipe/tasks/python/test/audio/audio_embedder_test.py

@@ -0,0 +1,317 @@
+# Copyright 2022 The MediaPipe 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.
+"""Tests for audio embedder."""
+import enum
+import os
+from typing import List, Tuple
+from unittest import mock
+
+from absl.testing import absltest
+from absl.testing import parameterized
+
+import numpy as np
+from scipy.io import wavfile
+
+from mediapipe.tasks.python.audio import audio_embedder
+from mediapipe.tasks.python.audio.core import audio_task_running_mode
+from mediapipe.tasks.python.components.containers import audio_data as audio_data_module
+from mediapipe.tasks.python.components.containers import embedding_result as embedding_result_module
+from mediapipe.tasks.python.components.processors import embedder_options
+from mediapipe.tasks.python.core import base_options as base_options_module
+from mediapipe.tasks.python.test import test_utils
+
+_AudioEmbedder = audio_embedder.AudioEmbedder
+_AudioEmbedderOptions = audio_embedder.AudioEmbedderOptions
+_AudioEmbedderResult = embedding_result_module.EmbeddingResult
+_AudioData = audio_data_module.AudioData
+_BaseOptions = base_options_module.BaseOptions
+_EmbedderOptions = embedder_options.EmbedderOptions
+_RUNNING_MODE = audio_task_running_mode.AudioTaskRunningMode
+
+_YAMNET_MODEL_FILE = 'yamnet_embedding_metadata.tflite'
+_YAMNET_MODEL_SAMPLE_RATE = 16000
+_SPEECH_WAV_16K_MONO = 'speech_16000_hz_mono.wav'
+_SPEECH_WAV_48K_MONO = 'speech_48000_hz_mono.wav'
+_TWO_HEADS_WAV_16K_MONO = 'two_heads_16000_hz_mono.wav'
+_TEST_DATA_DIR = 'mediapipe/tasks/testdata/audio'
+_SPEECH_SIMILARITIES = [0.985359, 0.994349, 0.993227, 0.996658, 0.996384]
+_YAMNET_NUM_OF_SAMPLES = 15600
+_MILLSECONDS_PER_SECOND = 1000
+# Tolerance for embedding vector coordinate values.
+_EPSILON = 3e-6
+# Tolerance for cosine similarity evaluation.
+_SIMILARITY_TOLERANCE = 1e-6
+
+
+class ModelFileType(enum.Enum):
+  FILE_CONTENT = 1
+  FILE_NAME = 2
+
+
+class AudioEmbedderTest(parameterized.TestCase):
+
+  def setUp(self):
+    super().setUp()
+    self.yamnet_model_path = test_utils.get_test_data_path(
+        os.path.join(_TEST_DATA_DIR, _YAMNET_MODEL_FILE))
+
+  def _read_wav_file(self, file_name) -> _AudioData:
+    sample_rate, buffer = wavfile.read(
+        test_utils.get_test_data_path(os.path.join(_TEST_DATA_DIR, file_name)))
+    return _AudioData.create_from_array(
+        buffer.astype(float) / np.iinfo(np.int16).max, sample_rate)
+
+  def _read_wav_file_as_stream(self, file_name) -> List[Tuple[_AudioData, int]]:
+    sample_rate, buffer = wavfile.read(
+        test_utils.get_test_data_path(os.path.join(_TEST_DATA_DIR, file_name)))
+    audio_data_list = []
+    start = 0
+    step_size = _YAMNET_NUM_OF_SAMPLES * sample_rate / _YAMNET_MODEL_SAMPLE_RATE
+    while start < len(buffer):
+      end = min(start + (int)(step_size), len(buffer))
+      audio_data_list.append((_AudioData.create_from_array(
+          buffer[start:end].astype(float) / np.iinfo(np.int16).max,
+          sample_rate), (int)(start / sample_rate * _MILLSECONDS_PER_SECOND)))
+      start = end
+    return audio_data_list
+
+  def _check_embedding_value(self, result, expected_first_value):
+    # Check embedding first value.
+    self.assertAlmostEqual(
+      result.embeddings[0].embedding[0], expected_first_value, delta=_EPSILON)
+
+  def _check_embedding_size(self, result, quantize, expected_embedding_size):
+    # Check embedding size.
+    self.assertLen(result.embeddings, 1)
+    embedding_result = result.embeddings[0]
+    self.assertLen(embedding_result.embedding, expected_embedding_size)
+    if quantize:
+      self.assertEqual(embedding_result.embedding.dtype, np.uint8)
+    else:
+      self.assertEqual(embedding_result.embedding.dtype, float)
+
+  def _check_cosine_similarity(self, result0, result1, expected_similarity):
+    # Checks cosine similarity.
+    similarity = _AudioEmbedder.cosine_similarity(result0.embeddings[0],
+                                                  result1.embeddings[0])
+    self.assertAlmostEqual(
+      similarity, expected_similarity, delta=_SIMILARITY_TOLERANCE)
+
+  # TODO: Compares the exact score values to capture unexpected
+  # changes in the inference pipeline.
+  def _check_yamnet_result(
+      self,
+      embedding_result0_list: List[_AudioEmbedderResult],
+      embedding_result1_list: List[_AudioEmbedderResult],
+      expected_similarities: List[float]):
+    expected_size = len(expected_similarities)
+    self.assertLen(embedding_result0_list, expected_size)
+    self.assertLen(embedding_result1_list, expected_size)
+
+    for idx in range(expected_size):
+      embedding_result0 = embedding_result0_list[idx]
+      embedding_result1 = embedding_result1_list[idx]
+      self._check_cosine_similarity(embedding_result0, embedding_result1,
+                                    expected_similarities[idx])
+
+  def test_create_from_file_succeeds_with_valid_model_path(self):
+    # Creates with default option and valid model file successfully.
+    with _AudioEmbedder.create_from_model_path(
+        self.yamnet_model_path) as embedder:
+      self.assertIsInstance(embedder, _AudioEmbedder)
+
+  def test_create_from_options_succeeds_with_valid_model_path(self):
+    # Creates with options containing model file successfully.
+    with _AudioEmbedder.create_from_options(
+        _AudioEmbedderOptions(
+            base_options=_BaseOptions(
+                model_asset_path=self.yamnet_model_path))) as embedder:
+      self.assertIsInstance(embedder, _AudioEmbedder)
+
+  def test_create_from_options_fails_with_invalid_model_path(self):
+    with self.assertRaisesRegex(
+        RuntimeError, 'Unable to open file at /path/to/invalid/model.tflite'):
+      base_options = _BaseOptions(
+          model_asset_path='/path/to/invalid/model.tflite')
+      options = _AudioEmbedderOptions(base_options=base_options)
+      _AudioEmbedder.create_from_options(options)
+
+  def test_create_from_options_succeeds_with_valid_model_content(self):
+    # Creates with options containing model content successfully.
+    with open(self.yamnet_model_path, 'rb') as f:
+      base_options = _BaseOptions(model_asset_buffer=f.read())
+      options = _AudioEmbedderOptions(base_options=base_options)
+      embedder = _AudioEmbedder.create_from_options(options)
+      self.assertIsInstance(embedder, _AudioEmbedder)
+
+  @parameterized.parameters(
+      # Same audio inputs but different sample rates.
+      (False, False, ModelFileType.FILE_NAME, _SPEECH_WAV_16K_MONO,
+       _SPEECH_WAV_48K_MONO, 1024, (0, 0)),
+      (False, False, ModelFileType.FILE_CONTENT, _SPEECH_WAV_16K_MONO,
+       _SPEECH_WAV_48K_MONO, 1024, (0, 0)))
+  def test_embed_with_yamnet_model(
+      self, l2_normalize, quantize, model_file_type, audio_file0, audio_file1,
+      expected_size, expected_first_values):
+    # Creates embedder.
+    if model_file_type is ModelFileType.FILE_NAME:
+      base_options = _BaseOptions(model_asset_path=self.yamnet_model_path)
+    elif model_file_type is ModelFileType.FILE_CONTENT:
+      with open(self.yamnet_model_path, 'rb') as f:
+        model_content = f.read()
+      base_options = _BaseOptions(model_asset_buffer=model_content)
+    else:
+      # Should never happen
+      raise ValueError('model_file_type is invalid.')
+
+    embedder_options = _EmbedderOptions(
+      l2_normalize=l2_normalize, quantize=quantize)
+    options = _AudioEmbedderOptions(
+      base_options=base_options, embedder_options=embedder_options)
+
+    with _AudioEmbedder.create_from_options(options) as embedder:
+      embedding_result0_list = embedder.embed(self._read_wav_file(audio_file0))
+      embedding_result1_list = embedder.embed(self._read_wav_file(audio_file1))
+
+      # Checks embeddings and cosine similarity.
+      expected_result0_value, expected_result1_value = expected_first_values
+      self._check_embedding_size(embedding_result0_list[0], quantize,
+                                 expected_size)
+      self._check_embedding_size(embedding_result1_list[0], quantize,
+                                 expected_size)
+      self._check_embedding_value(embedding_result0_list[0],
+                                  expected_result0_value)
+      self._check_embedding_value(embedding_result1_list[0],
+                                  expected_result1_value)
+      self._check_yamnet_result(embedding_result0_list, embedding_result1_list,
+                                expected_similarities=_SPEECH_SIMILARITIES)
+
+  def test_embed_with_yamnet_model_and_different_inputs(self):
+    with _AudioEmbedder.create_from_model_path(
+        self.yamnet_model_path) as embedder:
+      embedding_result0_list = embedder.embed(
+          self._read_wav_file(_SPEECH_WAV_16K_MONO))
+      embedding_result1_list = embedder.embed(
+          self._read_wav_file(_TWO_HEADS_WAV_16K_MONO))
+      self.assertLen(embedding_result0_list, 5)
+      self.assertLen(embedding_result1_list, 1)
+      self._check_cosine_similarity(embedding_result0_list[0],
+                                    embedding_result1_list[0],
+                                    expected_similarity=0.09017)
+
+  def test_missing_sample_rate_in_audio_clips_mode(self):
+    options = _AudioEmbedderOptions(
+        base_options=_BaseOptions(model_asset_path=self.yamnet_model_path),
+        running_mode=_RUNNING_MODE.AUDIO_CLIPS)
+    with self.assertRaisesRegex(ValueError,
+                                r'Must provide the audio sample rate'):
+      with _AudioEmbedder.create_from_options(options) as embedder:
+        embedder.embed(_AudioData(buffer_length=100))
+
+  def test_missing_sample_rate_in_audio_stream_mode(self):
+    options = _AudioEmbedderOptions(
+        base_options=_BaseOptions(model_asset_path=self.yamnet_model_path),
+        running_mode=_RUNNING_MODE.AUDIO_STREAM,
+        result_callback=mock.MagicMock())
+    with self.assertRaisesRegex(ValueError,
+                                r'provide the audio sample rate in audio data'):
+      with _AudioEmbedder.create_from_options(options) as embedder:
+        embedder.embed(_AudioData(buffer_length=100))
+
+  def test_missing_result_callback(self):
+    options = _AudioEmbedderOptions(
+        base_options=_BaseOptions(model_asset_path=self.yamnet_model_path),
+        running_mode=_RUNNING_MODE.AUDIO_STREAM)
+    with self.assertRaisesRegex(ValueError,
+                                r'result callback must be provided'):
+      with _AudioEmbedder.create_from_options(options) as unused_embedder:
+        pass
+
+  def test_illegal_result_callback(self):
+    options = _AudioEmbedderOptions(
+        base_options=_BaseOptions(model_asset_path=self.yamnet_model_path),
+        running_mode=_RUNNING_MODE.AUDIO_CLIPS,
+        result_callback=mock.MagicMock())
+    with self.assertRaisesRegex(ValueError,
+                                r'result callback should not be provided'):
+      with _AudioEmbedder.create_from_options(options) as unused_embedder:
+        pass
+
+  def test_calling_embed_in_audio_stream_mode(self):
+    options = _AudioEmbedderOptions(
+        base_options=_BaseOptions(model_asset_path=self.yamnet_model_path),
+        running_mode=_RUNNING_MODE.AUDIO_STREAM,
+        result_callback=mock.MagicMock())
+    with _AudioEmbedder.create_from_options(options) as embedder:
+      with self.assertRaisesRegex(ValueError,
+                                  r'not initialized with the audio clips mode'):
+        embedder.embed(self._read_wav_file(_SPEECH_WAV_16K_MONO))
+
+  def test_calling_embed_async_in_audio_clips_mode(self):
+    options = _AudioEmbedderOptions(
+        base_options=_BaseOptions(model_asset_path=self.yamnet_model_path),
+        running_mode=_RUNNING_MODE.AUDIO_CLIPS)
+    with _AudioEmbedder.create_from_options(options) as embedder:
+      with self.assertRaisesRegex(
+          ValueError, r'not initialized with the audio stream mode'):
+        embedder.embed_async(self._read_wav_file(_SPEECH_WAV_16K_MONO), 0)
+
+  def test_embed_async_calls_with_illegal_timestamp(self):
+    options = _AudioEmbedderOptions(
+        base_options=_BaseOptions(model_asset_path=self.yamnet_model_path),
+        running_mode=_RUNNING_MODE.AUDIO_STREAM,
+        result_callback=mock.MagicMock())
+    with _AudioEmbedder.create_from_options(options) as embedder:
+      embedder.embed_async(self._read_wav_file(_SPEECH_WAV_16K_MONO), 100)
+      with self.assertRaisesRegex(
+          ValueError, r'Input timestamp must be monotonically increasing'):
+        embedder.embed_async(self._read_wav_file(_SPEECH_WAV_16K_MONO), 0)
+
+  @parameterized.parameters(
+      # Same audio inputs but different sample rates.
+      (False, False, _SPEECH_WAV_16K_MONO, _SPEECH_WAV_48K_MONO))
+  def test_embed_async(self, l2_normalize, quantize, audio_file0, audio_file1):
+    embedding_result_list = []
+    embedding_result_list_copy = embedding_result_list.copy()
+
+    def save_result(result: _AudioEmbedderResult, timestamp_ms: int):
+      result.timestamp_ms = timestamp_ms
+      embedding_result_list.append(result)
+
+    options = _AudioEmbedderOptions(
+        base_options=_BaseOptions(model_asset_path=self.yamnet_model_path),
+        running_mode=_RUNNING_MODE.AUDIO_STREAM,
+        embedder_options=_EmbedderOptions(l2_normalize=l2_normalize,
+                                          quantize=quantize),
+        result_callback=save_result)
+
+    with _AudioEmbedder.create_from_options(options) as embedder:
+      audio_data0_list = self._read_wav_file_as_stream(audio_file0)
+      for audio_data, timestamp_ms in audio_data0_list:
+        embedder.embed_async(audio_data, timestamp_ms)
+      embedding_result0_list = embedding_result_list
+
+    with _AudioEmbedder.create_from_options(options) as embedder:
+      audio_data1_list = self._read_wav_file_as_stream(audio_file1)
+      embedding_result_list = embedding_result_list_copy
+      for audio_data, timestamp_ms in audio_data1_list:
+        embedder.embed_async(audio_data, timestamp_ms)
+      embedding_result1_list = embedding_result_list
+
+    self._check_yamnet_result(embedding_result0_list, embedding_result1_list,
+                              expected_similarities=_SPEECH_SIMILARITIES)
+
+
+if __name__ == '__main__':
+  absltest.main()