Merge pull request #3853 from kinaryml:audio-embedder-python
PiperOrigin-RevId: 488434586
This commit is contained in:
Copybara-Service
commit
9a2af2f2a1
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@ -94,10 +94,11 @@ cc_library(
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"//mediapipe/tasks/cc/vision/object_detector:object_detector_graph",
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] + select({
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# TODO: Build text_classifier_graph and text_embedder_graph on Windows.
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# TODO: Build audio_classifier_graph on Windows.
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# TODO: Build audio_classifier_graph and audio_embedder_graph on Windows.
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"//mediapipe:windows": [],
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"//conditions:default": [
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"//mediapipe/tasks/cc/audio/audio_classifier:audio_classifier_graph",
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"//mediapipe/tasks/cc/audio/audio_embedder:audio_embedder_graph",
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"//mediapipe/tasks/cc/text/text_classifier:text_classifier_graph",
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"//mediapipe/tasks/cc/text/text_embedder:text_embedder_graph",
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],
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|
|
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@ -39,3 +39,26 @@ py_library(
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"//mediapipe/tasks/python/core:task_info",
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],
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)
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py_library(
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name = "audio_embedder",
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srcs = [
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"audio_embedder.py",
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],
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deps = [
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"//mediapipe/python:_framework_bindings",
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"//mediapipe/python:packet_creator",
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"//mediapipe/python:packet_getter",
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"//mediapipe/tasks/cc/audio/audio_embedder/proto:audio_embedder_graph_options_py_pb2",
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"//mediapipe/tasks/cc/components/containers/proto:embeddings_py_pb2",
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"//mediapipe/tasks/python/audio/core:audio_task_running_mode",
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"//mediapipe/tasks/python/audio/core:base_audio_task_api",
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"//mediapipe/tasks/python/components/containers:audio_data",
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"//mediapipe/tasks/python/components/containers:embedding_result",
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"//mediapipe/tasks/python/components/processors:embedder_options",
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"//mediapipe/tasks/python/components/utils:cosine_similarity",
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"//mediapipe/tasks/python/core:base_options",
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"//mediapipe/tasks/python/core:optional_dependencies",
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"//mediapipe/tasks/python/core:task_info",
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],
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)
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284
mediapipe/tasks/python/audio/audio_embedder.py
Normal file
284
mediapipe/tasks/python/audio/audio_embedder.py
Normal file
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@ -0,0 +1,284 @@
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# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""MediaPipe audio embedder task."""
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import dataclasses
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from typing import Callable, Mapping, List, Optional
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from mediapipe.python import packet_creator
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from mediapipe.python import packet_getter
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from mediapipe.python._framework_bindings import packet
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from mediapipe.tasks.cc.audio.audio_embedder.proto import audio_embedder_graph_options_pb2
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from mediapipe.tasks.cc.components.containers.proto import embeddings_pb2
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from mediapipe.tasks.python.audio.core import audio_task_running_mode as running_mode_module
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from mediapipe.tasks.python.audio.core import base_audio_task_api
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from mediapipe.tasks.python.components.containers import audio_data as audio_data_module
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from mediapipe.tasks.python.components.containers import embedding_result as embedding_result_module
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from mediapipe.tasks.python.components.processors import embedder_options as embedder_options_module
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from mediapipe.tasks.python.components.utils import cosine_similarity
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from mediapipe.tasks.python.core import base_options as base_options_module
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from mediapipe.tasks.python.core import task_info as task_info_module
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from mediapipe.tasks.python.core.optional_dependencies import doc_controls
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AudioEmbedderResult = embedding_result_module.EmbeddingResult
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_AudioEmbedderGraphOptionsProto = audio_embedder_graph_options_pb2.AudioEmbedderGraphOptions
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_AudioData = audio_data_module.AudioData
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_BaseOptions = base_options_module.BaseOptions
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_EmbedderOptions = embedder_options_module.EmbedderOptions
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_RunningMode = running_mode_module.AudioTaskRunningMode
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_TaskInfo = task_info_module.TaskInfo
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_AUDIO_IN_STREAM_NAME = 'audio_in'
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_AUDIO_TAG = 'AUDIO'
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_EMBEDDINGS_STREAM_NAME = 'embeddings_out'
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_EMBEDDINGS_TAG = 'EMBEDDINGS'
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_SAMPLE_RATE_IN_STREAM_NAME = 'sample_rate_in'
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_SAMPLE_RATE_TAG = 'SAMPLE_RATE'
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_TASK_GRAPH_NAME = 'mediapipe.tasks.audio.audio_embedder.AudioEmbedderGraph'
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_TIMESTAMPTED_EMBEDDINGS_STREAM_NAME = 'timestamped_embeddings_out'
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_TIMESTAMPTED_EMBEDDINGS_TAG = 'TIMESTAMPED_EMBEDDINGS'
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_MICRO_SECONDS_PER_MILLISECOND = 1000
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@dataclasses.dataclass
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class AudioEmbedderOptions:
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"""Options for the audio embedder task.
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Attributes:
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base_options: Base options for the audio embedder task.
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running_mode: The running mode of the task. Default to the audio clips mode.
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Audio embedder task has two running modes: 1) The audio clips mode for
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running embedding extraction on independent audio clips. 2) The audio
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stream mode for running embedding extraction on the audio stream, such as
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from microphone. In this mode, the "result_callback" below must be
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specified to receive the embedding results asynchronously.
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embedder_options: Options for configuring the embedder behavior, such as
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l2_normalize and quantize.
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result_callback: The user-defined result callback for processing audio
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stream data. The result callback should only be specified when the running
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mode is set to the audio stream mode.
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"""
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base_options: _BaseOptions
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running_mode: _RunningMode = _RunningMode.AUDIO_CLIPS
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embedder_options: _EmbedderOptions = _EmbedderOptions()
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result_callback: Optional[Callable[[AudioEmbedderResult, int], None]] = None
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@doc_controls.do_not_generate_docs
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def to_pb2(self) -> _AudioEmbedderGraphOptionsProto:
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"""Generates an AudioEmbedderOptions protobuf object."""
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base_options_proto = self.base_options.to_pb2()
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base_options_proto.use_stream_mode = False if self.running_mode == _RunningMode.AUDIO_CLIPS else True
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embedder_options_proto = self.embedder_options.to_pb2()
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return _AudioEmbedderGraphOptionsProto(
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base_options=base_options_proto,
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embedder_options=embedder_options_proto)
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class AudioEmbedder(base_audio_task_api.BaseAudioTaskApi):
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"""Class that performs embedding extraction on audio clips or audio stream."""
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@classmethod
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def create_from_model_path(cls, model_path: str) -> 'AudioEmbedder':
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"""Creates an `AudioEmbedder` object from a TensorFlow Lite model and the default `AudioEmbedderOptions`.
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Note that the created `AudioEmbedder` instance is in audio clips mode, for
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embedding extraction on the independent audio clips.
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Args:
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model_path: Path to the model.
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Returns:
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`AudioEmbedder` object that's created from the model file and the
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default `AudioEmbedderOptions`.
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Raises:
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ValueError: If failed to create `AudioEmbedder` object from the provided
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file such as invalid file path.
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RuntimeError: If other types of error occurred.
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"""
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base_options = _BaseOptions(model_asset_path=model_path)
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options = AudioEmbedderOptions(
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base_options=base_options, running_mode=_RunningMode.AUDIO_CLIPS)
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return cls.create_from_options(options)
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@classmethod
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def create_from_options(cls,
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options: AudioEmbedderOptions) -> 'AudioEmbedder':
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"""Creates the `AudioEmbedder` object from audio embedder options.
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Args:
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options: Options for the audio embedder task.
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Returns:
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`AudioEmbedder` object that's created from `options`.
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Raises:
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ValueError: If failed to create `AudioEmbedder` object from
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`AudioEmbedderOptions` such as missing the model.
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RuntimeError: If other types of error occurred.
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"""
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def packets_callback(output_packets: Mapping[str, packet.Packet]):
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timestamp_ms = output_packets[
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_EMBEDDINGS_STREAM_NAME].timestamp.value // _MICRO_SECONDS_PER_MILLISECOND
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if output_packets[_EMBEDDINGS_STREAM_NAME].is_empty():
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options.result_callback(
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AudioEmbedderResult(embeddings=[]), timestamp_ms)
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return
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embedding_result_proto = embeddings_pb2.EmbeddingResult()
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embedding_result_proto.CopyFrom(
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packet_getter.get_proto(output_packets[_EMBEDDINGS_STREAM_NAME]))
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options.result_callback(
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AudioEmbedderResult.create_from_pb2(embedding_result_proto),
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timestamp_ms)
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task_info = _TaskInfo(
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task_graph=_TASK_GRAPH_NAME,
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input_streams=[
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':'.join([_AUDIO_TAG, _AUDIO_IN_STREAM_NAME]),
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':'.join([_SAMPLE_RATE_TAG, _SAMPLE_RATE_IN_STREAM_NAME])
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],
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output_streams=[
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':'.join([_EMBEDDINGS_TAG, _EMBEDDINGS_STREAM_NAME]), ':'.join([
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_TIMESTAMPTED_EMBEDDINGS_TAG,
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_TIMESTAMPTED_EMBEDDINGS_STREAM_NAME
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])
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],
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task_options=options)
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return cls(
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# Audio tasks should not drop input audio due to flow limiting, which
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# may cause data inconsistency.
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task_info.generate_graph_config(enable_flow_limiting=False),
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options.running_mode,
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packets_callback if options.result_callback else None)
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def embed(self, audio_clip: _AudioData) -> List[AudioEmbedderResult]:
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"""Performs embedding extraction on the provided audio clips.
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The audio clip is represented as a MediaPipe AudioData. The method accepts
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audio clips with various length and audio sample rate. It's required to
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provide the corresponding audio sample rate within the `AudioData` object.
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The input audio clip may be longer than what the model is able to process
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in a single inference. When this occurs, the input audio clip is split into
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multiple chunks starting at different timestamps. For this reason, this
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function returns a vector of EmbeddingResult objects, each associated
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ith a timestamp corresponding to the start (in milliseconds) of the chunk
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data on which embedding extraction was carried out.
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Args:
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audio_clip: MediaPipe AudioData.
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Returns:
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An `AudioEmbedderResult` object that contains a list of embedding result
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objects, each associated with a timestamp corresponding to the start
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(in milliseconds) of the chunk data on which embedding extraction was
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carried out.
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Raises:
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ValueError: If any of the input arguments is invalid, such as the sample
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rate is not provided in the `AudioData` object.
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RuntimeError: If audio embedding extraction failed to run.
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"""
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if not audio_clip.audio_format.sample_rate:
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raise ValueError('Must provide the audio sample rate in audio data.')
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output_packets = self._process_audio_clip({
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_AUDIO_IN_STREAM_NAME:
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packet_creator.create_matrix(audio_clip.buffer, transpose=True),
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_SAMPLE_RATE_IN_STREAM_NAME:
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packet_creator.create_double(audio_clip.audio_format.sample_rate)
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})
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output_list = []
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embeddings_proto_list = packet_getter.get_proto_list(
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output_packets[_TIMESTAMPTED_EMBEDDINGS_STREAM_NAME])
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for proto in embeddings_proto_list:
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embedding_result_proto = embeddings_pb2.EmbeddingResult()
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embedding_result_proto.CopyFrom(proto)
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output_list.append(
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AudioEmbedderResult.create_from_pb2(embedding_result_proto))
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return output_list
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def embed_async(self, audio_block: _AudioData, timestamp_ms: int) -> None:
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"""Sends audio data (a block in a continuous audio stream) to perform audio embedding extraction.
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Only use this method when the AudioEmbedder is created with the audio
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stream running mode. The input timestamps should be monotonically increasing
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for adjacent calls of this method. This method will return immediately after
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the input audio data is accepted. The results will be available via the
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`result_callback` provided in the `AudioEmbedderOptions`. The
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`embed_async` method is designed to process auido stream data such as
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microphone input.
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The input audio data may be longer than what the model is able to process
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in a single inference. When this occurs, the input audio block is split
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into multiple chunks. For this reason, the callback may be called multiple
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times (once per chunk) for each call to this function.
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The `result_callback` provides:
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- An `AudioEmbedderResult` object that contains a list of
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embeddings.
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- The input timestamp in milliseconds.
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Args:
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audio_block: MediaPipe AudioData.
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timestamp_ms: The timestamp of the input audio data in milliseconds.
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Raises:
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ValueError: If any of the followings:
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1) The sample rate is not provided in the `AudioData` object or the
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provided sample rate is inconsistent with the previously received.
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2) The current input timestamp is smaller than what the audio
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embedder has already processed.
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"""
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if not audio_block.audio_format.sample_rate:
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raise ValueError('Must provide the audio sample rate in audio data.')
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if not self._default_sample_rate:
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self._default_sample_rate = audio_block.audio_format.sample_rate
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self._set_sample_rate(_SAMPLE_RATE_IN_STREAM_NAME,
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self._default_sample_rate)
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elif audio_block.audio_format.sample_rate != self._default_sample_rate:
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raise ValueError(
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f'The audio sample rate provided in audio data: '
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f'{audio_block.audio_format.sample_rate} is inconsistent with '
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f'the previously received: {self._default_sample_rate}.')
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self._send_audio_stream_data({
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_AUDIO_IN_STREAM_NAME:
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packet_creator.create_matrix(audio_block.buffer, transpose=True).at(
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timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND)
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})
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@classmethod
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def cosine_similarity(cls, u: embedding_result_module.Embedding,
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v: embedding_result_module.Embedding) -> float:
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"""Utility function to compute cosine similarity between two embedding entries.
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May return an InvalidArgumentError if e.g. the feature vectors are
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of different types (quantized vs. float), have different sizes, or have a
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an L2-norm of 0.
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Args:
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u: An embedding entry.
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v: An embedding entry.
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Returns:
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The cosine similarity for the two embeddings.
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Raises:
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ValueError: May return an error if e.g. the feature vectors are of
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different types (quantized vs. float), have different sizes, or have
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an L2-norm of 0.
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"""
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return cosine_similarity.cosine_similarity(u, v)
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|
|
@ -35,3 +35,21 @@ py_test(
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"//mediapipe/tasks/python/test:test_utils",
|
||||
],
|
||||
)
|
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|
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py_test(
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name = "audio_embedder_test",
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srcs = ["audio_embedder_test.py"],
|
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data = [
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"//mediapipe/tasks/testdata/audio:test_audio_clips",
|
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"//mediapipe/tasks/testdata/audio:test_models",
|
||||
],
|
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deps = [
|
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"//mediapipe/tasks/python/audio:audio_embedder",
|
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"//mediapipe/tasks/python/audio/core:audio_task_running_mode",
|
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"//mediapipe/tasks/python/components/containers:audio_data",
|
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"//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",
|
||||
],
|
||||
)
|
||||
|
|
|
|||
318
mediapipe/tasks/python/test/audio/audio_embedder_test.py
Normal file
318
mediapipe/tasks/python/test/audio/audio_embedder_test.py
Normal file
|
|
@ -0,0 +1,318 @@
|
|||
# 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
|
||||
|
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import numpy as np
|
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from scipy.io import wavfile
|
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|
||||
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.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
|
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_AudioEmbedderOptions = audio_embedder.AudioEmbedderOptions
|
||||
_AudioEmbedderResult = audio_embedder.AudioEmbedderResult
|
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_AudioData = audio_data_module.AudioData
|
||||
_BaseOptions = base_options_module.BaseOptions
|
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_EmbedderOptions = embedder_options.EmbedderOptions
|
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_RUNNING_MODE = audio_task_running_mode.AudioTaskRunningMode
|
||||
|
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_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)
|
||||
|
||||
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.')
|
||||
|
||||
options = _AudioEmbedderOptions(
|
||||
base_options=base_options,
|
||||
embedder_options=_EmbedderOptions(
|
||||
l2_normalize=l2_normalize, quantize=quantize))
|
||||
|
||||
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()
|
||||
Loading…
Reference in New Issue
Block a user