tests/pipelines/test_pipelines_audio_classification.py

# Copyright 2021 The HuggingFace Team. 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.

import unittest

import datasets
import numpy as np
from huggingface_hub import AudioClassificationOutputElement

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

from .test_pipelines_common import ANY


if is_torch_available():
    import torch


@is_pipeline_test
class AudioClassificationPipelineTests(unittest.TestCase):
    model_mapping = MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
    _dataset = None

    @classmethod
    def _load_dataset(cls):
        # Lazy loading of the dataset. Because it is a class method, it will only be loaded once per pytest process.
        if cls._dataset is None:
            cls._dataset = datasets.load_dataset(
                "hf-internal-testing/librispeech_asr_dummy", "clean", split="validation"
            )

    def get_test_pipeline(
        self,
        model,
        tokenizer=None,
        image_processor=None,
        feature_extractor=None,
        processor=None,
        dtype="float32",
    ):
        audio_classifier = AudioClassificationPipeline(
            model=model,
            tokenizer=tokenizer,
            feature_extractor=feature_extractor,
            image_processor=image_processor,
            processor=processor,
            dtype=dtype,
        )

        # test with a raw waveform
        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)
        # by default a model is initialized with num_labels=2
        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)

        for single_output in output:
            compare_pipeline_output_to_hub_spec(single_output, AudioClassificationOutputElement)

    @require_torchaudio
    def run_torchaudio(self, audio_classifier):
        self._load_dataset()
        # test with a local file
        audio = self._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
    def test_small_model_pt_fp16(self):
        model = "anton-l/wav2vec2-random-tiny-classifier"

        audio_classifier = pipeline("audio-classification", model=model, dtype=torch.float16)

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

        # Expected outputs are collected running the test on torch 2.6 in few scenarios.
        # Running on CUDA T4/A100 and on XPU PVC (note: using stock torch xpu, NOT using IPEX):
        EXPECTED_OUTPUT = [
            {"score": 0.0833, "label": "go"},
            {"score": 0.0833, "label": "off"},
            {"score": 0.0833, "label": "stop"},
            {"score": 0.0833, "label": "on"},
        ]
        # Running on CPU:
        EXPECTED_OUTPUT_PT_2 = [
            {"score": 0.0839, "label": "no"},
            {"score": 0.0837, "label": "go"},
            {"score": 0.0836, "label": "yes"},
            {"score": 0.0835, "label": "right"},
        ]
        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):
        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_torch
    @slow
    def test_top_k_none_returns_all_labels(self):
        model_name = "superb/wav2vec2-base-superb-ks"  # model with more than 5 labels
        classification_pipeline = pipeline(
            "audio-classification",
            model=model_name,
            top_k=None,
        )

        # Create dummy input
        sampling_rate = 16000
        signal = np.zeros((sampling_rate,), dtype=np.float32)

        result = classification_pipeline(signal)
        num_labels = classification_pipeline.model.config.num_labels

        self.assertEqual(len(result), num_labels, "Should return all labels when top_k is None")

    @require_torch
    @slow
    def test_top_k_none_with_few_labels(self):
        model_name = "superb/hubert-base-superb-er"  # model with fewer labels
        classification_pipeline = pipeline(
            "audio-classification",
            model=model_name,
            top_k=None,
        )

        # Create dummy input
        sampling_rate = 16000
        signal = np.zeros((sampling_rate,), dtype=np.float32)

        result = classification_pipeline(signal)
        num_labels = classification_pipeline.model.config.num_labels

        self.assertEqual(len(result), num_labels, "Should handle models with fewer labels correctly")

    @require_torch
    @slow
    def test_top_k_greater_than_labels(self):
        model_name = "superb/hubert-base-superb-er"
        classification_pipeline = pipeline(
            "audio-classification",
            model=model_name,
            top_k=100,  # intentionally large number
        )

        # Create dummy input
        sampling_rate = 16000
        signal = np.zeros((sampling_rate,), dtype=np.float32)

        result = classification_pipeline(signal)
        num_labels = classification_pipeline.model.config.num_labels

        self.assertEqual(len(result), num_labels, "Should cap top_k to number of labels")