mirror of
https://github.com/huggingface/transformers.git
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257 lines
8.7 KiB
Python
257 lines
8.7 KiB
Python
# Copyright 2021 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import datasets
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import numpy as np
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from huggingface_hub import AudioClassificationOutputElement
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from transformers import (
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MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
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TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
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is_torch_available,
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)
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from transformers.pipelines import AudioClassificationPipeline, pipeline
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from transformers.testing_utils import (
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compare_pipeline_output_to_hub_spec,
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is_pipeline_test,
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nested_simplify,
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require_tf,
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require_torch,
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require_torchaudio,
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slow,
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)
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from .test_pipelines_common import ANY
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if is_torch_available():
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import torch
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@is_pipeline_test
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class AudioClassificationPipelineTests(unittest.TestCase):
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model_mapping = MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
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tf_model_mapping = TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
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_dataset = None
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@classmethod
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def _load_dataset(cls):
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# Lazy loading of the dataset. Because it is a class method, it will only be loaded once per pytest process.
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if cls._dataset is None:
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cls._dataset = datasets.load_dataset(
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"hf-internal-testing/librispeech_asr_dummy", "clean", split="validation"
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)
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def get_test_pipeline(
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self,
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model,
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tokenizer=None,
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image_processor=None,
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feature_extractor=None,
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processor=None,
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torch_dtype="float32",
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):
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audio_classifier = AudioClassificationPipeline(
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model=model,
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tokenizer=tokenizer,
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feature_extractor=feature_extractor,
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image_processor=image_processor,
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processor=processor,
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torch_dtype=torch_dtype,
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)
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# test with a raw waveform
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audio = np.zeros((34000,))
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audio2 = np.zeros((14000,))
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return audio_classifier, [audio2, audio]
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def run_pipeline_test(self, audio_classifier, examples):
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audio2, audio = examples
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output = audio_classifier(audio)
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# by default a model is initialized with num_labels=2
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self.assertEqual(
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output,
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[
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{"score": ANY(float), "label": ANY(str)},
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{"score": ANY(float), "label": ANY(str)},
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],
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)
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output = audio_classifier(audio, top_k=1)
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self.assertEqual(
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output,
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[
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{"score": ANY(float), "label": ANY(str)},
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],
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)
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self.run_torchaudio(audio_classifier)
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for single_output in output:
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compare_pipeline_output_to_hub_spec(single_output, AudioClassificationOutputElement)
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@require_torchaudio
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def run_torchaudio(self, audio_classifier):
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self._load_dataset()
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# test with a local file
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audio = self._dataset[0]["audio"]["array"]
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output = audio_classifier(audio)
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self.assertEqual(
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output,
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[
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{"score": ANY(float), "label": ANY(str)},
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{"score": ANY(float), "label": ANY(str)},
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],
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)
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@require_torch
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def test_small_model_pt(self):
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model = "anton-l/wav2vec2-random-tiny-classifier"
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audio_classifier = pipeline("audio-classification", model=model)
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audio = np.ones((8000,))
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output = audio_classifier(audio, top_k=4)
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EXPECTED_OUTPUT = [
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{"score": 0.0842, "label": "no"},
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{"score": 0.0838, "label": "up"},
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{"score": 0.0837, "label": "go"},
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{"score": 0.0834, "label": "right"},
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]
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EXPECTED_OUTPUT_PT_2 = [
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{"score": 0.0845, "label": "stop"},
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{"score": 0.0844, "label": "on"},
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{"score": 0.0841, "label": "right"},
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{"score": 0.0834, "label": "left"},
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]
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self.assertIn(nested_simplify(output, decimals=4), [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2])
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audio_dict = {"array": np.ones((8000,)), "sampling_rate": audio_classifier.feature_extractor.sampling_rate}
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output = audio_classifier(audio_dict, top_k=4)
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self.assertIn(nested_simplify(output, decimals=4), [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2])
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@require_torch
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def test_small_model_pt_fp16(self):
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model = "anton-l/wav2vec2-random-tiny-classifier"
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audio_classifier = pipeline("audio-classification", model=model, torch_dtype=torch.float16)
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audio = np.ones((8000,))
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output = audio_classifier(audio, top_k=4)
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# Expected outputs are collected running the test on torch 2.6 in few scenarios.
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# Running on CUDA T4/A100 and on XPU PVC (note: using stock torch xpu, NOT using IPEX):
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EXPECTED_OUTPUT = [
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{"score": 0.0833, "label": "go"},
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{"score": 0.0833, "label": "off"},
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{"score": 0.0833, "label": "stop"},
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{"score": 0.0833, "label": "on"},
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]
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# Running on CPU:
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EXPECTED_OUTPUT_PT_2 = [
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{"score": 0.0839, "label": "no"},
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{"score": 0.0837, "label": "go"},
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{"score": 0.0836, "label": "yes"},
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{"score": 0.0835, "label": "right"},
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]
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self.assertIn(nested_simplify(output, decimals=4), [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2])
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audio_dict = {"array": np.ones((8000,)), "sampling_rate": audio_classifier.feature_extractor.sampling_rate}
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output = audio_classifier(audio_dict, top_k=4)
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self.assertIn(nested_simplify(output, decimals=4), [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2])
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@require_torch
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@slow
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def test_large_model_pt(self):
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model = "superb/wav2vec2-base-superb-ks"
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audio_classifier = pipeline("audio-classification", model=model)
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dataset = datasets.load_dataset("anton-l/superb_dummy", "ks", split="test", trust_remote_code=True)
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audio = np.array(dataset[3]["speech"], dtype=np.float32)
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output = audio_classifier(audio, top_k=4)
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self.assertEqual(
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nested_simplify(output, decimals=3),
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[
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{"score": 0.981, "label": "go"},
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{"score": 0.007, "label": "up"},
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{"score": 0.006, "label": "_unknown_"},
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{"score": 0.001, "label": "down"},
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],
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)
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@require_tf
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@unittest.skip(reason="Audio classification is not implemented for TF")
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def test_small_model_tf(self):
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pass
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@require_torch
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@slow
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def test_top_k_none_returns_all_labels(self):
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model_name = "superb/wav2vec2-base-superb-ks" # model with more than 5 labels
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classification_pipeline = pipeline(
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"audio-classification",
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model=model_name,
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top_k=None,
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)
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# Create dummy input
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sampling_rate = 16000
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signal = np.zeros((sampling_rate,), dtype=np.float32)
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result = classification_pipeline(signal)
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num_labels = classification_pipeline.model.config.num_labels
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self.assertEqual(len(result), num_labels, "Should return all labels when top_k is None")
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@require_torch
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@slow
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def test_top_k_none_with_few_labels(self):
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model_name = "superb/hubert-base-superb-er" # model with fewer labels
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classification_pipeline = pipeline(
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"audio-classification",
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model=model_name,
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top_k=None,
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)
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# Create dummy input
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sampling_rate = 16000
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signal = np.zeros((sampling_rate,), dtype=np.float32)
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result = classification_pipeline(signal)
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num_labels = classification_pipeline.model.config.num_labels
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self.assertEqual(len(result), num_labels, "Should handle models with fewer labels correctly")
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@require_torch
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@slow
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def test_top_k_greater_than_labels(self):
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model_name = "superb/hubert-base-superb-er"
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classification_pipeline = pipeline(
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"audio-classification",
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model=model_name,
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top_k=100, # intentionally large number
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)
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# Create dummy input
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sampling_rate = 16000
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signal = np.zeros((sampling_rate,), dtype=np.float32)
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result = classification_pipeline(signal)
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num_labels = classification_pipeline.model.config.num_labels
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self.assertEqual(len(result), num_labels, "Should cap top_k to number of labels")
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