mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-04 13:20:12 +06:00
257 lines
8.7 KiB
Python
257 lines
8.7 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 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")
|