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[whisper] small changes for faster tests (#38236)

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Joao Gante 2025-05-21 14:11:08 +01:00 committed by GitHub
parent ddf67d2d73
commit e4decee9c0
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1 changed files with 11 additions and 37 deletions
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48
tests/models/whisper/test_modeling_whisper.py
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@ -27,7 +27,6 @@ import pytest
from huggingface_hub import hf_hub_download
from parameterized import parameterized
import transformers
from transformers import WhisperConfig
from transformers.testing_utils import (
is_flaky,
@ -41,7 +40,7 @@ from transformers.testing_utils import (
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_torch_xpu_available, is_torchaudio_available
from transformers.utils import is_torch_available, is_torch_xpu_available, is_torchaudio_available
from transformers.utils.import_utils import is_datasets_available
from ...generation.test_utils import GenerationTesterMixin
@ -1432,33 +1431,22 @@ class WhisperModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMi
@require_torch
@require_torchaudio
class WhisperModelIntegrationTests(unittest.TestCase):
def setUp(self):
self._unpatched_generation_mixin_generate = transformers.GenerationMixin.generate
_dataset = None
def tearDown(self):
transformers.GenerationMixin.generate = self._unpatched_generation_mixin_generate
@cached_property
def default_processor(self):
return WhisperProcessor.from_pretrained("openai/whisper-base")
@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 _load_datasamples(self, num_samples):
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
# automatic decoding with librispeech
self._load_dataset()
ds = self._dataset
speech_samples = ds.sort("id").select(range(num_samples))[:num_samples]["audio"]
return [x["array"] for x in speech_samples]
def _patch_generation_mixin_generate(self, check_args_fn=None):
test = self
def generate(self, *args, **kwargs):
if check_args_fn is not None:
check_args_fn(*args, **kwargs)
return test._unpatched_generation_mixin_generate(self, *args, **kwargs)
transformers.GenerationMixin.generate = generate
@slow
def test_tiny_logits_librispeech(self):
torch_device = "cpu"
@ -1586,8 +1574,6 @@ class WhisperModelIntegrationTests(unittest.TestCase):
@slow
def test_tiny_en_generation(self):
torch_device = "cpu"
set_seed(0)
processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en")
model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en")
model.to(torch_device)
@ -1605,8 +1591,6 @@ class WhisperModelIntegrationTests(unittest.TestCase):
@slow
def test_tiny_generation(self):
torch_device = "cpu"
set_seed(0)
processor = WhisperProcessor.from_pretrained("openai/whisper-tiny")
model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny")
model.to(torch_device)
@ -1623,8 +1607,6 @@ class WhisperModelIntegrationTests(unittest.TestCase):
@slow
def test_large_generation(self):
torch_device = "cpu"
set_seed(0)
processor = WhisperProcessor.from_pretrained("openai/whisper-large-v3")
model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large-v3")
model.to(torch_device)
@ -1643,7 +1625,6 @@ class WhisperModelIntegrationTests(unittest.TestCase):
@slow
def test_large_generation_multilingual(self):
set_seed(0)
processor = WhisperProcessor.from_pretrained("openai/whisper-large-v3")
model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large-v3")
model.to(torch_device)
@ -1710,8 +1691,6 @@ class WhisperModelIntegrationTests(unittest.TestCase):
@slow
def test_large_batched_generation_multilingual(self):
torch_device = "cpu"
set_seed(0)
processor = WhisperProcessor.from_pretrained("openai/whisper-large")
model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large")
model.to(torch_device)
@ -2727,11 +2706,6 @@ class WhisperModelIntegrationTests(unittest.TestCase):
"renormalize_logits": True, # necessary to match OAI beam search implementation
}
def check_gen_kwargs(inputs, generation_config, *args, **kwargs):
self.assertEqual(generation_config.num_beams, gen_kwargs["num_beams"])
self._patch_generation_mixin_generate(check_args_fn=check_gen_kwargs)
torch.manual_seed(0)
result = model.generate(input_features, **gen_kwargs)
decoded = processor.batch_decode(result, skip_special_tokens=True)