transformers/tests/pipelines/test_pipelines_zero_shot_audio_classification.py
Yih-Dar 871c31a6f1
🔥Rework pipeline testing by removing PipelineTestCaseMeta 🚀 (#21516)
* Add PipelineTesterMixin

* remove class PipelineTestCaseMeta

* move validate_test_components

* Add for ViT

* Add to SPECIAL_MODULE_TO_TEST_MAP

* style and quality

* Add feature-extraction

* update

* raise instead of skip

* add tiny_model_summary.json

* more explicit

* skip tasks not in mapping

* add availability check

* Add Copyright

* A way to diable irrelevant tests

* update with main

* remove disable_irrelevant_tests

* skip tests

* better skip message

* better skip message

* Add all pipeline task tests

* revert

* Import PipelineTesterMixin

* subclass test classes with PipelineTesterMixin

* Add pipieline_model_mapping

* Fix import after adding pipieline_model_mapping

* Fix style and quality after adding pipieline_model_mapping

* Fix one more import after adding pipieline_model_mapping

* Fix style and quality after adding pipieline_model_mapping

* Fix test issues

* Fix import requirements

* Fix mapping for MobileViTModelTest

* Update

* Better skip message

* pipieline_model_mapping could not be None

* Remove some PipelineTesterMixin

* Fix typo

* revert tests_fetcher.py

* update

* rename

* revert

* Remove PipelineTestCaseMeta from ZeroShotAudioClassificationPipelineTests

* style and quality

* test fetcher for all pipeline/model tests

---------

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
2023-02-28 19:40:57 +01:00

94 lines
3.3 KiB
Python

# 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 nested_simplify, require_torch, slow
@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):
audio_classifier = pipeline(
task="zero-shot-audio-classification", model="hf-internal-testing/tiny-clap-htsat-unfused"
)
dataset = load_dataset("ashraq/esc50")
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"}],
)
@unittest.skip("No models are available in TF")
def test_small_model_tf(self):
pass
@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("ashraq/esc50")
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": 0.999, "label": "Sound of a dog"},
{"score": 0.001, "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": 0.999, "label": "Sound of a dog"},
{"score": 0.001, "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": 0.999, "label": "Sound of a dog"},
{"score": 0.001, "label": "Sound of vaccum cleaner"},
],
]
* 5,
)
@unittest.skip("No models are available in TF")
def test_large_model_tf(self):
pass