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tests/pipelines/test_pipelines_zero_shot_audio_classification.py

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+# Copyright 2023 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
+
+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):
+    # 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}
+
+    @require_torch
+    def test_small_model_pt(self, dtype="float32"):
+        audio_classifier = pipeline(
+            task="zero-shot-audio-classification",
+            model="hf-internal-testing/tiny-clap-htsat-unfused",
+            dtype=dtype,
+        )
+        dataset = load_dataset("hf-internal-testing/ashraq-esc50-1-dog-example")
+        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"}],
+        )
+
+    @require_torch
+    def test_small_model_pt_fp16(self):
+        self.test_small_model_pt(dtype="float16")
+
+    @slow
+    @require_torch
+    def test_large_model_pt(self):
+        audio_classifier = pipeline(
+            task="zero-shot-audio-classification",
+            model="laion/clap-htsat-unfused",
+        )
+        # This is an audio of a dog
+        dataset = load_dataset("hf-internal-testing/ashraq-esc50-1-dog-example")
+        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": 1.0, "label": "Sound of a dog"},
+                {"score": 0.0, "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": 1.0, "label": "Sound of a dog"},
+                    {"score": 0.0, "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": 1.0, "label": "Sound of a dog"},
+                    {"score": 0.0, "label": "Sound of vaccum cleaner"},
+                ],
+            ]
+            * 5,
+        )