2023 the huggingface team 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 orglicenseslicense2 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 deactivating auto tests since we don t have a good modelforxx mapping and only clap would be there for now modelmapping clapconfig clapmodel this is an audio of a dog 2023 the huggingface team 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 deactivating auto tests since we don t have a good model_for_xx mapping and only clap would be there for now model_mapping clapconfig clapmodel this is an audio of a dog
import unittest

from datasets import load_dataset

from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow


@is_pipeline_test
@require_torch
class ZeroShotAudioClassificationPipelineTests(unittest.TestCase):
    
    
    

    @require_torch
    def test_small_model_pt(self):
        audio_classifier = pipeline(
            task="zero-shot-audio-classification", model="hf-internal-testing/tiny-clap-htsat-unfused"
        )
        dataset = load_dataset("ashraq/esc50")
        audio = dataset["train"]["audio"][-1]["array"]
        output = audio_classifier(audio, candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"])
        self.assertEqual(
            nested_simplify(output),
            [{"score": 0.501, "label": "Sound of a dog"}, {"score": 0.499, "label": "Sound of vaccum cleaner"}],
        )

    @unittest.skip("No models are available in TF")
    def test_small_model_tf(self):
        pass

    @slow
    @require_torch
    def test_large_model_pt(self):
        audio_classifier = pipeline(
            task="zero-shot-audio-classification",
            model="laion/clap-htsat-unfused",
        )
        
        dataset = load_dataset("ashraq/esc50")
        audio = dataset["train"]["audio"][-1]["array"]
        output = audio_classifier(audio, candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"])

        self.assertEqual(
            nested_simplify(output),
            [
                {"score": 0.999, "label": "Sound of a dog"},
                {"score": 0.001, "label": "Sound of vaccum cleaner"},
            ],
        )

        output = audio_classifier([audio] * 5, candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"])
        self.assertEqual(
            nested_simplify(output),
            [
                [
                    {"score": 0.999, "label": "Sound of a dog"},
                    {"score": 0.001, "label": "Sound of vaccum cleaner"},
                ],
            ]
            * 5,
        )
        output = audio_classifier(
            [audio] * 5, candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"], batch_size=5
        )
        self.assertEqual(
            nested_simplify(output),
            [
                [
                    {"score": 0.999, "label": "Sound of a dog"},
                    {"score": 0.001, "label": "Sound of vaccum cleaner"},
                ],
            ]
            * 5,
        )

    @unittest.skip("No models are available in TF")
    def test_large_model_tf(self):
        pass