mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-10 08:10:05 +06:00
871c31a6f1
* 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>
119 lines
3.9 KiB
Python
119 lines
3.9 KiB
Python
# 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
|
|
from transformers.pipelines import AudioClassificationPipeline, pipeline
|
|
from transformers.testing_utils import nested_simplify, require_tf, require_torch, require_torchaudio, slow
|
|
|
|
from .test_pipelines_common import ANY
|
|
|
|
|
|
@require_torch
|
|
class AudioClassificationPipelineTests(unittest.TestCase):
|
|
model_mapping = MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
|
|
|
|
def get_test_pipeline(self, model, tokenizer, processor):
|
|
audio_classifier = AudioClassificationPipeline(model=model, feature_extractor=processor)
|
|
|
|
# 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)
|
|
|
|
@require_torchaudio
|
|
def run_torchaudio(self, audio_classifier):
|
|
import datasets
|
|
|
|
# test with a local file
|
|
dataset = datasets.load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
|
|
audio = 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)
|
|
self.assertEqual(
|
|
nested_simplify(output, decimals=4),
|
|
[
|
|
{"score": 0.0842, "label": "no"},
|
|
{"score": 0.0838, "label": "up"},
|
|
{"score": 0.0837, "label": "go"},
|
|
{"score": 0.0834, "label": "right"},
|
|
],
|
|
)
|
|
|
|
@require_torch
|
|
@slow
|
|
def test_large_model_pt(self):
|
|
import datasets
|
|
|
|
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_tf
|
|
@unittest.skip("Audio classification is not implemented for TF")
|
|
def test_small_model_tf(self):
|
|
pass
|