2021 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 test with a raw waveform by default a model is initialized with numlabels2 test with a local file 2021 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 test with a raw waveform by default a model is initialized with num_labels 2 test with a local file
import unittest

import numpy as np

from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torchaudio,
    slow,
)

from .test_pipelines_common import ANY


@is_pipeline_test
class AudioClassificationPipelineTests(unittest.TestCase):
    model_mapping = MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
    tf_model_mapping = TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING

    def get_test_pipeline(self, model, tokenizer, processor):
        audio_classifier = AudioClassificationPipeline(model=model, feature_extractor=processor)

        
        audio = np.zeros((34000,))
        audio2 = np.zeros((14000,))
        return audio_classifier, [audio2, audio]

    def run_pipeline_test(self, audio_classifier, examples):
        audio2, audio = examples
        output = audio_classifier(audio)
        
        self.assertEqual(
            output,
            [
                {"score": ANY(float), "label": ANY(str)},
                {"score": ANY(float), "label": ANY(str)},
            ],
        )
        output = audio_classifier(audio, top_k=1)
        self.assertEqual(
            output,
            [
                {"score": ANY(float), "label": ANY(str)},
            ],
        )

        self.run_torchaudio(audio_classifier)

    @require_torchaudio
    def run_torchaudio(self, audio_classifier):
        import datasets

        
        dataset = datasets.load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
        audio = dataset[0]["audio"]["array"]
        output = audio_classifier(audio)
        self.assertEqual(
            output,
            [
                {"score": ANY(float), "label": ANY(str)},
                {"score": ANY(float), "label": ANY(str)},
            ],
        )

    @require_torch
    def test_small_model_pt(self):
        model = "anton-l/wav2vec2-random-tiny-classifier"

        audio_classifier = pipeline("audio-classification", model=model)

        audio = np.ones((8000,))
        output = audio_classifier(audio, top_k=4)

        EXPECTED_OUTPUT = [
            {"score": 0.0842, "label": "no"},
            {"score": 0.0838, "label": "up"},
            {"score": 0.0837, "label": "go"},
            {"score": 0.0834, "label": "right"},
        ]
        EXPECTED_OUTPUT_PT_2 = [
            {"score": 0.0845, "label": "stop"},
            {"score": 0.0844, "label": "on"},
            {"score": 0.0841, "label": "right"},
            {"score": 0.0834, "label": "left"},
        ]
        self.assertIn(nested_simplify(output, decimals=4), [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2])

        audio_dict = {"array": np.ones((8000,)), "sampling_rate": audio_classifier.feature_extractor.sampling_rate}
        output = audio_classifier(audio_dict, top_k=4)
        self.assertIn(nested_simplify(output, decimals=4), [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2])

    @require_torch
    @slow
    def test_large_model_pt(self):
        import datasets

        model = "superb/wav2vec2-base-superb-ks"

        audio_classifier = pipeline("audio-classification", model=model)
        dataset = datasets.load_dataset("anton-l/superb_dummy", "ks", split="test")

        audio = np.array(dataset[3]["speech"], dtype=np.float32)
        output = audio_classifier(audio, top_k=4)
        self.assertEqual(
            nested_simplify(output, decimals=3),
            [
                {"score": 0.981, "label": "go"},
                {"score": 0.007, "label": "up"},
                {"score": 0.006, "label": "_unknown_"},
                {"score": 0.001, "label": "down"},
            ],
        )

    @require_tf
    @unittest.skip("Audio classification is not implemented for TF")
    def test_small_model_tf(self):
        pass