180 lines
7.3 KiB
Python
180 lines
7.3 KiB
Python
# Copyright 2022 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 shutil
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import tempfile
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import unittest
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import pytest
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from transformers import WhisperTokenizer, is_speech_available
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from transformers.testing_utils import require_sentencepiece, require_torch, require_torchaudio
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from .test_feature_extraction_whisper import floats_list
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if is_speech_available():
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from transformers import WhisperFeatureExtractor, WhisperProcessor
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TRANSCRIBE = 50358
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NOTIMESTAMPS = 50362
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@require_torch
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@require_torchaudio
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@require_sentencepiece
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class WhisperProcessorTest(unittest.TestCase):
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def setUp(self):
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self.checkpoint = "openai/whisper-small.en"
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self.tmpdirname = tempfile.mkdtemp()
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def get_tokenizer(self, **kwargs):
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return WhisperTokenizer.from_pretrained(self.checkpoint, **kwargs)
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def get_feature_extractor(self, **kwargs):
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return WhisperFeatureExtractor.from_pretrained(self.checkpoint, **kwargs)
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def tearDown(self):
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shutil.rmtree(self.tmpdirname)
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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 = WhisperProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
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processor.save_pretrained(self.tmpdirname)
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processor = WhisperProcessor.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, WhisperTokenizer)
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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, WhisperFeatureExtractor)
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def test_save_load_pretrained_additional_features(self):
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processor = WhisperProcessor(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(bos_token="(BOS)", eos_token="(EOS)")
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feature_extractor_add_kwargs = self.get_feature_extractor(do_normalize=False, padding_value=1.0)
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processor = WhisperProcessor.from_pretrained(
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self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False, padding_value=1.0
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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, WhisperTokenizer)
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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, WhisperFeatureExtractor)
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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 = WhisperProcessor(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, 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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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 = WhisperProcessor(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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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 = WhisperProcessor(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_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 = WhisperProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
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self.assertListEqual(
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processor.model_input_names,
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feature_extractor.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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def test_get_decoder_prompt_ids(self):
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feature_extractor = self.get_feature_extractor()
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tokenizer = self.get_tokenizer()
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processor = WhisperProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
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forced_decoder_ids = processor.get_decoder_prompt_ids(task="transcribe", no_timestamps=True)
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self.assertIsInstance(forced_decoder_ids, list)
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for ids in forced_decoder_ids:
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self.assertIsInstance(ids, (list, tuple))
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expected_ids = [TRANSCRIBE, NOTIMESTAMPS]
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self.assertListEqual([ids[-1] for ids in forced_decoder_ids], expected_ids)
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def test_get_prompt_ids(self):
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processor = WhisperProcessor(tokenizer=self.get_tokenizer(), feature_extractor=self.get_feature_extractor())
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prompt_ids = processor.get_prompt_ids("Mr. Quilter")
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decoded_prompt = processor.tokenizer.decode(prompt_ids)
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self.assertListEqual(prompt_ids.tolist(), [50360, 1770, 13, 2264, 346, 353])
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self.assertEqual(decoded_prompt, "<|startofprev|> Mr. Quilter")
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def test_empty_get_prompt_ids(self):
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processor = WhisperProcessor(tokenizer=self.get_tokenizer(), feature_extractor=self.get_feature_extractor())
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prompt_ids = processor.get_prompt_ids("")
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decoded_prompt = processor.tokenizer.decode(prompt_ids)
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self.assertListEqual(prompt_ids.tolist(), [50360, 220])
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self.assertEqual(decoded_prompt, "<|startofprev|> ")
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def test_get_prompt_ids_with_special_tokens(self):
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processor = WhisperProcessor(tokenizer=self.get_tokenizer(), feature_extractor=self.get_feature_extractor())
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def _test_prompt_error_raised_helper(prompt, special_token):
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with pytest.raises(ValueError) as excinfo:
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processor.get_prompt_ids(prompt)
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expected = f"Encountered text in the prompt corresponding to disallowed special token: {special_token}."
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self.assertEqual(expected, str(excinfo.value))
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_test_prompt_error_raised_helper("<|startofprev|> test", "<|startofprev|>")
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_test_prompt_error_raised_helper("test <|notimestamps|>", "<|notimestamps|>")
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_test_prompt_error_raised_helper("test <|zh|> test <|transcribe|>", "<|zh|>")
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