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| # Copyright 2022 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 shutil | |
| import tempfile | |
| import unittest | |
| from transformers import WhisperTokenizer, is_speech_available | |
| from transformers.testing_utils import require_sentencepiece, require_torch, require_torchaudio | |
| from .test_feature_extraction_whisper import floats_list | |
| if is_speech_available(): | |
| from transformers import WhisperFeatureExtractor, WhisperProcessor | |
| TRANSCRIBE = 50358 | |
| NOTIMESTAMPS = 50362 | |
| class WhisperProcessorTest(unittest.TestCase): | |
| def setUp(self): | |
| self.checkpoint = "openai/whisper-small.en" | |
| self.tmpdirname = tempfile.mkdtemp() | |
| def get_tokenizer(self, **kwargs): | |
| return WhisperTokenizer.from_pretrained(self.checkpoint, **kwargs) | |
| def get_feature_extractor(self, **kwargs): | |
| return WhisperFeatureExtractor.from_pretrained(self.checkpoint, **kwargs) | |
| def tearDown(self): | |
| shutil.rmtree(self.tmpdirname) | |
| def test_save_load_pretrained_default(self): | |
| tokenizer = self.get_tokenizer() | |
| feature_extractor = self.get_feature_extractor() | |
| processor = WhisperProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor) | |
| processor.save_pretrained(self.tmpdirname) | |
| processor = WhisperProcessor.from_pretrained(self.tmpdirname) | |
| self.assertEqual(processor.tokenizer.get_vocab(), tokenizer.get_vocab()) | |
| self.assertIsInstance(processor.tokenizer, WhisperTokenizer) | |
| self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor.to_json_string()) | |
| self.assertIsInstance(processor.feature_extractor, WhisperFeatureExtractor) | |
| def test_save_load_pretrained_additional_features(self): | |
| processor = WhisperProcessor(tokenizer=self.get_tokenizer(), feature_extractor=self.get_feature_extractor()) | |
| processor.save_pretrained(self.tmpdirname) | |
| tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)") | |
| feature_extractor_add_kwargs = self.get_feature_extractor(do_normalize=False, padding_value=1.0) | |
| processor = WhisperProcessor.from_pretrained( | |
| self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False, padding_value=1.0 | |
| ) | |
| self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab()) | |
| self.assertIsInstance(processor.tokenizer, WhisperTokenizer) | |
| self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor_add_kwargs.to_json_string()) | |
| self.assertIsInstance(processor.feature_extractor, WhisperFeatureExtractor) | |
| def test_feature_extractor(self): | |
| feature_extractor = self.get_feature_extractor() | |
| tokenizer = self.get_tokenizer() | |
| processor = WhisperProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor) | |
| raw_speech = floats_list((3, 1000)) | |
| input_feat_extract = feature_extractor(raw_speech, return_tensors="np") | |
| input_processor = processor(raw_speech, return_tensors="np") | |
| for key in input_feat_extract.keys(): | |
| self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2) | |
| def test_tokenizer(self): | |
| feature_extractor = self.get_feature_extractor() | |
| tokenizer = self.get_tokenizer() | |
| processor = WhisperProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor) | |
| input_str = "This is a test string" | |
| encoded_processor = processor(text=input_str) | |
| encoded_tok = tokenizer(input_str) | |
| for key in encoded_tok.keys(): | |
| self.assertListEqual(encoded_tok[key], encoded_processor[key]) | |
| def test_tokenizer_decode(self): | |
| feature_extractor = self.get_feature_extractor() | |
| tokenizer = self.get_tokenizer() | |
| processor = WhisperProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor) | |
| predicted_ids = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]] | |
| decoded_processor = processor.batch_decode(predicted_ids) | |
| decoded_tok = tokenizer.batch_decode(predicted_ids) | |
| self.assertListEqual(decoded_tok, decoded_processor) | |
| def test_model_input_names(self): | |
| feature_extractor = self.get_feature_extractor() | |
| tokenizer = self.get_tokenizer() | |
| processor = WhisperProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor) | |
| self.assertListEqual( | |
| processor.model_input_names, | |
| feature_extractor.model_input_names, | |
| msg="`processor` and `feature_extractor` model input names do not match", | |
| ) | |
| def test_get_decoder_prompt_ids(self): | |
| feature_extractor = self.get_feature_extractor() | |
| tokenizer = self.get_tokenizer() | |
| processor = WhisperProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor) | |
| forced_decoder_ids = processor.get_decoder_prompt_ids(task="transcribe", no_timestamps=True) | |
| self.assertIsInstance(forced_decoder_ids, list) | |
| for ids in forced_decoder_ids: | |
| self.assertIsInstance(ids, (list, tuple)) | |
| expected_ids = [TRANSCRIBE, NOTIMESTAMPS] | |
| self.assertListEqual([ids[-1] for ids in forced_decoder_ids], expected_ids) | |