\"\n",
"SENTIMENT_CLASSIFIED_PATHWAY_NAME = \"Sentiment Classified\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Ontology Structure\n",
"\n",
"For this project, the following ontology is appropriate to use.\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can use classifications and not objects as we are labelling the whole clip, not sections of it."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Defining the Runner\n",
" The runner object is what we use to link our functions to our project."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"runner = Runner(project_hash=PROJECT_HASH)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Defining the Function\n",
"The function is what is run on the agent stage in our workflow.\n",
"\n",
"The decorator `@runner.stage()` means that the function `classify_by_sentiment` runs at the appropriate agent stage and it passes in the parameters of `lr`, `asset` and `project` for us to use.\n",
"\n",
"The function is explained in greater depth with the comments in the script."
]
},
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"# Common variables used to create Audio classifications in Encord\n",
"radio_ontology_classification = runner.project.ontology_structure.get_child_by_title(\n",
" title=\"emotion\", type_=Classification\n",
")\n",
"\n",
"# Collects attributes of the classifcation radio options and then creates a dictionary to\n",
"# translate the label to an Option object for the API to accept.\n",
"attr = radio_ontology_classification.attributes[0]\n",
"dict_lbl_to_opt = {option.label: option for option in attr.options}"
]
},
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"source": [
"@runner.stage(STAGE_ID, overwrite=True)\n",
"def classify_by_sentiment(lr: LabelRowV2, asset: Annotated[Path, Depends(dep_asset)]):\n",
" # Using the model to predict the emotion\n",
" predicted_emotion = predict_emotion(asset, model, feature_extractor, id2label)\n",
"\n",
" # Prepares an instance to use to add a classification to the audio.\n",
" # As we're classifying audio, we need the range_only=True variable\n",
" classification_instance = radio_ontology_classification.create_instance(range_only=True)\n",
"\n",
" # To determine which classifcation is added to the asset\n",
" # [ dict_lbl_to_opt(\"sad\"), attr ] `attr` tells the API to focus\n",
" # on top level classifications only\n",
"\n",
" match predicted_emotion:\n",
" case \"sad\":\n",
" classification_instance.set_answer(dict_lbl_to_opt[\"sad\"], attr)\n",
" case \"happy\":\n",
" classification_instance.set_answer(dict_lbl_to_opt[\"happy\"], attr)\n",
" case \"angry\":\n",
" classification_instance.set_answer(dict_lbl_to_opt[\"angry\"], attr)\n",
" case \"neutral\":\n",
" classification_instance.set_answer(dict_lbl_to_opt[\"neutral\"], attr)\n",
" case \"disgust\":\n",
" classification_instance.set_answer(dict_lbl_to_opt[\"disgust\"], attr)\n",
" case \"fearful\":\n",
" classification_instance.set_answer(dict_lbl_to_opt[\"fearful\"], attr)\n",
" case \"surprised\":\n",
" classification_instance.set_answer(dict_lbl_to_opt[\"surprised\"], attr)\n",
" case \"calm\":\n",
" classification_instance.set_answer(dict_lbl_to_opt[\"calm\"], attr)\n",
"\n",
" # Indicates that the whole audio should be classified, and old classifications should be overwritten\n",
" classification_instance.set_for_frames()\n",
" lr.add_classification_instance(classification_instance)\n",
" lr.save()\n",
"\n",
" return SENTIMENT_CLASSIFIED_PATHWAY_NAME # Sentiment Classified Path"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Running the agent\n",
"\n",
"To run the agent in our Workflow, we can call the runner object."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
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"source": [
"runner()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Outcome\n",
"This AI-powered agent performs reliable sentiment and emotion classification on short, single-speaker audio clips. The Encord Workflow in this example shows how the model's accuracy can be evaluated with human review of its generated labels."
]
}
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