137 lines
5.8 KiB
Python
137 lines
5.8 KiB
Python
# Copyright 2023 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import gc
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import shutil
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import tempfile
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import unittest
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from transformers import ClvpFeatureExtractor, ClvpProcessor, ClvpTokenizer
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from transformers.testing_utils import require_torch
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from .test_feature_extraction_clvp import floats_list
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@require_torch
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class ClvpProcessorTest(unittest.TestCase):
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def setUp(self):
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self.checkpoint = "susnato/clvp_dev"
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self.tmpdirname = tempfile.mkdtemp()
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def tearDown(self):
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super().tearDown()
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shutil.rmtree(self.tmpdirname)
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gc.collect()
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# Copied from transformers.tests.models.whisper.test_processor_whisper.WhisperProcessorTest.get_tokenizer with Whisper->Clvp
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def get_tokenizer(self, **kwargs):
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return ClvpTokenizer.from_pretrained(self.checkpoint, **kwargs)
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# Copied from transformers.tests.models.whisper.test_processor_whisper.WhisperProcessorTest.get_feature_extractor with Whisper->Clvp
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def get_feature_extractor(self, **kwargs):
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return ClvpFeatureExtractor.from_pretrained(self.checkpoint, **kwargs)
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# Copied from transformers.tests.models.whisper.test_processor_whisper.WhisperProcessorTest.test_save_load_pretrained_default with Whisper->Clvp
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def test_save_load_pretrained_default(self):
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tokenizer = self.get_tokenizer()
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feature_extractor = self.get_feature_extractor()
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processor = ClvpProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
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processor.save_pretrained(self.tmpdirname)
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processor = ClvpProcessor.from_pretrained(self.tmpdirname)
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self.assertEqual(processor.tokenizer.get_vocab(), tokenizer.get_vocab())
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self.assertIsInstance(processor.tokenizer, ClvpTokenizer)
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self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor.to_json_string())
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self.assertIsInstance(processor.feature_extractor, ClvpFeatureExtractor)
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# Copied from transformers.tests.models.whisper.test_processor_whisper.WhisperProcessorTest.test_feature_extractor with Whisper->Clvp,processor(raw_speech->processor(raw_speech=raw_speech
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def test_feature_extractor(self):
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feature_extractor = self.get_feature_extractor()
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tokenizer = self.get_tokenizer()
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processor = ClvpProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
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raw_speech = floats_list((3, 1000))
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input_feat_extract = feature_extractor(raw_speech, return_tensors="np")
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input_processor = processor(raw_speech=raw_speech, return_tensors="np")
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for key in input_feat_extract.keys():
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self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2)
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# Copied from transformers.tests.models.whisper.test_processor_whisper.WhisperProcessorTest.test_tokenizer with Whisper->Clvp
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def test_tokenizer(self):
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feature_extractor = self.get_feature_extractor()
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tokenizer = self.get_tokenizer()
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processor = ClvpProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
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input_str = "This is a test string"
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encoded_processor = processor(text=input_str)
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encoded_tok = tokenizer(input_str)
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for key in encoded_tok.keys():
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self.assertListEqual(encoded_tok[key], encoded_processor[key])
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# Copied from transformers.tests.models.whisper.test_processor_whisper.WhisperProcessorTest.test_tokenizer_decode with Whisper->Clvp
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def test_tokenizer_decode(self):
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feature_extractor = self.get_feature_extractor()
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tokenizer = self.get_tokenizer()
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processor = ClvpProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
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predicted_ids = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
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decoded_processor = processor.batch_decode(predicted_ids)
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decoded_tok = tokenizer.batch_decode(predicted_ids)
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self.assertListEqual(decoded_tok, decoded_processor)
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def test_save_load_pretrained_additional_features(self):
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processor = ClvpProcessor(tokenizer=self.get_tokenizer(), feature_extractor=self.get_feature_extractor())
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processor.save_pretrained(self.tmpdirname)
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tokenizer_add_kwargs = self.get_tokenizer(pad_token="(PAD)")
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feature_extractor_add_kwargs = self.get_feature_extractor(sampling_rate=16000)
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processor = ClvpProcessor.from_pretrained(
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self.tmpdirname,
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pad_token="(PAD)",
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sampling_rate=16000,
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)
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self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
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self.assertIsInstance(processor.tokenizer, ClvpTokenizer)
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self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor_add_kwargs.to_json_string())
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self.assertIsInstance(processor.feature_extractor, ClvpFeatureExtractor)
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def test_model_input_names(self):
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feature_extractor = self.get_feature_extractor()
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tokenizer = self.get_tokenizer()
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processor = ClvpProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
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self.assertListEqual(
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sorted(processor.model_input_names),
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sorted(set(feature_extractor.model_input_names + tokenizer.model_input_names)),
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msg="`processor` and `feature_extractor` model input names do not match",
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)
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