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# 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 datasets
import numpy as np
from huggingface_hub import AudioClassificationOutputElement
from transformers import (
MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
is_torch_available,
)
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
compare_pipeline_output_to_hub_spec,
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_torchaudio,
slow,
)
from .test_pipelines_common import ANY
if is_torch_available():
import torch
@is_pipeline_test
class AudioClassificationPipelineTests(unittest.TestCase):
model_mapping = MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
tf_model_mapping = TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
_dataset = None
@classmethod
def _load_dataset(cls):
# Lazy loading of the dataset. Because it is a class method, it will only be loaded once per pytest process.
if cls._dataset is None:
cls._dataset = datasets.load_dataset(
"hf-internal-testing/librispeech_asr_dummy", "clean", split="validation"
)
def get_test_pipeline(
self,
model,
tokenizer=None,
image_processor=None,
feature_extractor=None,
processor=None,
torch_dtype="float32",
):
audio_classifier = AudioClassificationPipeline(
model=model,
tokenizer=tokenizer,
feature_extractor=feature_extractor,
image_processor=image_processor,
processor=processor,
torch_dtype=torch_dtype,
)
# 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)
for single_output in output:
compare_pipeline_output_to_hub_spec(single_output, AudioClassificationOutputElement)
@require_torchaudio
def run_torchaudio(self, audio_classifier):
self._load_dataset()
# test with a local file
audio = self._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)
EXPECTED_OUTPUT = [
{"score": 0.0842, "label": "no"},
{"score": 0.0838, "label": "up"},
{"score": 0.0837, "label": "go"},
{"score": 0.0834, "label": "right"},
]
EXPECTED_OUTPUT_PT_2 = [
{"score": 0.0845, "label": "stop"},
{"score": 0.0844, "label": "on"},
{"score": 0.0841, "label": "right"},
{"score": 0.0834, "label": "left"},
]
self.assertIn(nested_simplify(output, decimals=4), [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2])
audio_dict = {"array": np.ones((8000,)), "sampling_rate": audio_classifier.feature_extractor.sampling_rate}
output = audio_classifier(audio_dict, top_k=4)
self.assertIn(nested_simplify(output, decimals=4), [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2])
@require_torch
def test_small_model_pt_fp16(self):
model = "anton-l/wav2vec2-random-tiny-classifier"
audio_classifier = pipeline("audio-classification", model=model, torch_dtype=torch.float16)
audio = np.ones((8000,))
output = audio_classifier(audio, top_k=4)
# Expected outputs are collected running the test on torch 2.6 in few scenarios.
# Running on CUDA T4/A100 and on XPU PVC (note: using stock torch xpu, NOT using IPEX):
EXPECTED_OUTPUT = [
{"score": 0.0833, "label": "go"},
{"score": 0.0833, "label": "off"},
{"score": 0.0833, "label": "stop"},
{"score": 0.0833, "label": "on"},
]
# Running on CPU:
EXPECTED_OUTPUT_PT_2 = [
{"score": 0.0839, "label": "no"},
{"score": 0.0837, "label": "go"},
{"score": 0.0836, "label": "yes"},
{"score": 0.0835, "label": "right"},
]
self.assertIn(nested_simplify(output, decimals=4), [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2])
audio_dict = {"array": np.ones((8000,)), "sampling_rate": audio_classifier.feature_extractor.sampling_rate}
output = audio_classifier(audio_dict, top_k=4)
self.assertIn(nested_simplify(output, decimals=4), [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2])
@require_torch
@slow
def test_large_model_pt(self):
model = "superb/wav2vec2-base-superb-ks"
audio_classifier = pipeline("audio-classification", model=model)
dataset = datasets.load_dataset("anton-l/superb_dummy", "ks", split="test", trust_remote_code=True)
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(reason="Audio classification is not implemented for TF")
def test_small_model_tf(self):
pass
@require_torch
@slow
def test_top_k_none_returns_all_labels(self):
model_name = "superb/wav2vec2-base-superb-ks" # model with more than 5 labels
classification_pipeline = pipeline(
"audio-classification",
model=model_name,
top_k=None,
)
# Create dummy input
sampling_rate = 16000
signal = np.zeros((sampling_rate,), dtype=np.float32)
result = classification_pipeline(signal)
num_labels = classification_pipeline.model.config.num_labels
self.assertEqual(len(result), num_labels, "Should return all labels when top_k is None")
@require_torch
@slow
def test_top_k_none_with_few_labels(self):
model_name = "superb/hubert-base-superb-er" # model with fewer labels
classification_pipeline = pipeline(
"audio-classification",
model=model_name,
top_k=None,
)
# Create dummy input
sampling_rate = 16000
signal = np.zeros((sampling_rate,), dtype=np.float32)
result = classification_pipeline(signal)
num_labels = classification_pipeline.model.config.num_labels
self.assertEqual(len(result), num_labels, "Should handle models with fewer labels correctly")
@require_torch
@slow
def test_top_k_greater_than_labels(self):
model_name = "superb/hubert-base-superb-er"
classification_pipeline = pipeline(
"audio-classification",
model=model_name,
top_k=100, # intentionally large number
)
# Create dummy input
sampling_rate = 16000
signal = np.zeros((sampling_rate,), dtype=np.float32)
result = classification_pipeline(signal)
num_labels = classification_pipeline.model.config.num_labels
self.assertEqual(len(result), num_labels, "Should cap top_k to number of labels")
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