Add the AudioClassificationPipeline (#13342)

* Add the audio classification pipeline * Remove autoconfig exception * Mark ffmpeg test as slow * Rearrange pipeline tests * Add small test * Replace asserts with ValueError
2021-09-01 11:03:48 +03:00
parent 02039352b2
commit b9c6a97694
12 changed files with 347 additions and 5 deletions
--- a/tests/test_pipelines_audio_classification.py
+++ b/tests/test_pipelines_audio_classification.py
@@ -0,0 +1,120 @@
+# 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 numpy as np
+
+from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, PreTrainedTokenizer
+from transformers.pipelines import AudioClassificationPipeline, pipeline
+from transformers.testing_utils import (
+    is_pipeline_test,
+    nested_simplify,
+    require_datasets,
+    require_tf,
+    require_torch,
+    slow,
+)
+
+from .test_pipelines_common import ANY, PipelineTestCaseMeta
+
+
+@is_pipeline_test
+@require_torch
+class AudioClassificationPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):
+    model_mapping = MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
+
+    @require_datasets
+    @slow
+    def run_pipeline_test(self, model, tokenizer, feature_extractor):
+        import datasets
+
+        audio_classifier = AudioClassificationPipeline(model=model, feature_extractor=feature_extractor)
+
+        # test with a raw waveform
+        audio = np.zeros((34000,))
+        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)},
+            ],
+        )
+
+        # test with a local file
+        dataset = datasets.load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
+        filename = dataset[0]["file"]
+        output = audio_classifier(filename)
+        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"
+        tokenizer = PreTrainedTokenizer()
+        audio_classifier = pipeline("audio-classification", model=model, tokenizer=tokenizer)
+
+        audio = np.ones((8000,))
+        output = audio_classifier(audio, top_k=4)
+        self.assertEqual(
+            nested_simplify(output, decimals=4),
+            [
+                {"score": 0.0843, "label": "on"},
+                {"score": 0.0840, "label": "left"},
+                {"score": 0.0837, "label": "off"},
+                {"score": 0.0835, "label": "yes"},
+            ],
+        )
+
+    @require_torch
+    @require_datasets
+    @slow
+    def test_large_model_pt(self):
+        import datasets
+
+        model = "superb/wav2vec2-base-superb-ks"
+        tokenizer = PreTrainedTokenizer()
+        audio_classifier = pipeline("audio-classification", model=model, tokenizer=tokenizer)
+        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=4),
+            [
+                {"score": 0.9809, "label": "go"},
+                {"score": 0.0073, "label": "up"},
+                {"score": 0.0064, "label": "_unknown_"},
+                {"score": 0.0015, "label": "down"},
+            ],
+        )
+
+    @require_tf
+    @unittest.skip("Audio classification is not implemented for TF")
+    def test_small_model_tf(self):
+        pass