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* Update modeling tests * update tokenization test * typo * nit * fix expected attention outputs * Apply suggestions from code review Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com> * Update tests from review Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com> Co-authored-by: ydshieh <ydshieh@users.noreply.github.com> * remove problematics kwargs passed to the padding function Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com> Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
230 lines
10 KiB
Python
230 lines
10 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 unittest
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from transformers.models.whisper import WhisperTokenizer
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from transformers.testing_utils import slow
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from ...test_tokenization_common import TokenizerTesterMixin
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ES_CODE = 50262
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EN_CODE = 50259
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END_OF_TRANSCRIPT = 50257
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START_OF_TRANSCRIPT = 50258
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TRANSLATE = 50358
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TRANSCRIBE = 50359
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NOTIMESTAMPS = 50363
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class WhisperTokenizerTest(TokenizerTesterMixin, unittest.TestCase):
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tokenizer_class = WhisperTokenizer
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test_rust_tokenizer = False
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test_sentencepiece = False
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test_seq2seq = False
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def setUp(self):
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super().setUp()
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tokenizer = WhisperTokenizer.from_pretrained("openai/whisper-tiny")
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tokenizer.pad_token_id = 50256
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tokenizer.pad_token = "<|endoftext|>"
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tokenizer.save_pretrained(self.tmpdirname)
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def test_convert_token_and_id(self):
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"""Test ``_convert_token_to_id`` and ``_convert_id_to_token``."""
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token = "Where"
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token_id = 14436
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self.assertEqual(self.get_tokenizer()._convert_token_to_id(token), token_id)
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self.assertEqual(self.get_tokenizer()._convert_id_to_token(token_id), token)
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def test_get_vocab(self):
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vocab_keys = list(self.get_tokenizer().get_vocab().keys())
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self.assertEqual(vocab_keys[0], "!")
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self.assertEqual(vocab_keys[1], '"')
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self.assertEqual(vocab_keys[-1], "<|notimestamps|>")
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self.assertEqual(len(vocab_keys), 50364)
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def test_vocab_size(self):
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self.assertEqual(self.get_tokenizer().vocab_size, 50257)
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def test_full_tokenizer(self):
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tokenizer = WhisperTokenizer.from_pretrained(self.tmpdirname)
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tokens = tokenizer.tokenize("This is a test")
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self.assertListEqual(tokens, ["This", "Ġis", "Ġa", "Ġ", "test"])
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self.assertListEqual(
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tokenizer.convert_tokens_to_ids(tokens),
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[5723, 307, 257, 220, 31636],
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)
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tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.")
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self.assertListEqual(
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tokens,
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# fmt: off
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['I', 'Ġwas', 'Ġborn', 'Ġin', 'Ġ9', '2000', ',', 'Ġand', 'Ġ', 'this', 'Ġis', 'Ġfals', 'é', '.'],
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# fmt: on
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)
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ids = tokenizer.convert_tokens_to_ids(tokens)
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self.assertListEqual(ids, [40, 390, 4232, 294, 1722, 25743, 11, 293, 220, 11176, 307, 16720, 526, 13])
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back_tokens = tokenizer.convert_ids_to_tokens(ids)
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self.assertListEqual(
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back_tokens,
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# fmt: off
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['I', 'Ġwas', 'Ġborn', 'Ġin', 'Ġ9', '2000', ',', 'Ġand', 'Ġ', 'this', 'Ġis', 'Ġfals', 'é', '.'],
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# fmt: on
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)
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def test_tokenizer_slow_store_full_signature(self):
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pass
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@slow
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def test_tokenizer_integration(self):
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# fmt: off
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expected_encoding = {'input_ids': [[50257, 50362, 41762, 364, 357, 36234, 1900, 355, 12972, 13165, 354, 12, 35636, 364, 290, 12972, 13165, 354, 12, 5310, 13363, 12, 4835, 8, 3769, 2276, 12, 29983, 45619, 357, 13246, 51, 11, 402, 11571, 12, 17, 11, 5564, 13246, 38586, 11, 16276, 44, 11, 4307, 346, 33, 861, 11, 16276, 7934, 23029, 329, 12068, 15417, 28491, 357, 32572, 52, 8, 290, 12068, 15417, 16588, 357, 32572, 38, 8, 351, 625, 3933, 10, 2181, 13363, 4981, 287, 1802, 10, 8950, 290, 2769, 48817, 1799, 1022, 449, 897, 11, 9485, 15884, 354, 290, 309, 22854, 37535, 13, 50256], [50257, 50362, 13246, 51, 318, 3562, 284, 662, 12, 27432, 2769, 8406, 4154, 282, 24612, 422, 9642, 9608, 276, 2420, 416, 26913, 21143, 319, 1111, 1364, 290, 826, 4732, 287, 477, 11685, 13, 50256], [50257, 50362, 464, 2068, 7586, 21831, 18045, 625, 262, 16931, 3290, 13, 50256]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
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# fmt: on
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self.tokenizer_integration_test_util(
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expected_encoding=expected_encoding, model_name="openai/whisper-tiny.en", padding=False
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)
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class SpeechToTextTokenizerMultilinguialTest(unittest.TestCase):
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checkpoint_name = "openai/whisper-small.en"
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@classmethod
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def setUpClass(cls):
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cls.tokenizer: WhisperTokenizer = WhisperTokenizer.from_pretrained(cls.checkpoint_name)
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return cls
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def test_tokenizer_equivalence(self):
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text = "다람쥐 헌 쳇바퀴에 타고파"
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multilingual_tokenizer = WhisperTokenizer.from_pretrained("openai/whisper-tiny", language="korean")
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monolingual_tokenizer = WhisperTokenizer.from_pretrained("openai/whisper-tiny.en")
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monolingual_tokens = monolingual_tokenizer.encode(text, add_special_tokens=False)
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multilingual_tokens = multilingual_tokenizer.encode(text, add_special_tokens=False)
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assert monolingual_tokenizer.decode(monolingual_tokens) == text
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assert multilingual_tokenizer.decode(multilingual_tokens) == text
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assert len(monolingual_tokens) > len(multilingual_tokens)
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# fmt: off
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EXPECTED_ENG = [
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46695, 97, 167, 252, 234, 168, 98, 238, 220, 169,
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245, 234, 23821, 111, 229, 167, 108, 242, 169, 222,
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112, 168, 245, 238, 220, 169, 225, 222, 166, 111,
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254, 169, 234, 234
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]
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EXPECTED_MULTI = [
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9835, 22855, 168, 98, 238, 13431, 234, 43517, 229, 47053,
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169, 222, 19086, 19840, 1313, 17974
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]
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# fmt: on
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self.assertListEqual(monolingual_tokens, EXPECTED_ENG)
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self.assertListEqual(multilingual_tokens, EXPECTED_MULTI)
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def test_tokenizer_special(self):
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multilingual_tokenizer = WhisperTokenizer.from_pretrained(
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"openai/whisper-tiny", language="english", task="transcribe"
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)
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text = "Hey! How are you feeling? J'ai l'impression que 郷さん est prêt"
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multilingual_tokens = multilingual_tokenizer.encode(text)
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# fmt: off
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# format: <|startoftranscript|> <|lang-id|> <|task|> <|notimestamps|> ... transcription ids ... <|endoftext|>
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EXPECTED_MULTI = [
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START_OF_TRANSCRIPT, EN_CODE, TRANSCRIBE, NOTIMESTAMPS, 7057, 0, 1012, 366, 291,
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2633, 30, 508, 6, 1301, 287, 6, 36107, 631, 220, 11178,
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115, 15567, 871, 44393, END_OF_TRANSCRIPT
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]
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EXPECTED_SPECIAL_TEXT = (
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"<|startoftranscript|><|en|><|transcribe|><|notimestamps|>Hey! How are you feeling? "
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"J'ai l'impression que 郷さん est prêt<|endoftext|>"
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)
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# fmt: on
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self.assertListEqual(multilingual_tokens, EXPECTED_MULTI)
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special_transcript = multilingual_tokenizer.decode(multilingual_tokens, skip_special_tokens=False)
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self.assertEqual(special_transcript, EXPECTED_SPECIAL_TEXT)
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transcript = multilingual_tokenizer.decode(multilingual_tokens, skip_special_tokens=True)
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self.assertEqual(transcript, text)
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def test_vocab_size(self):
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self.assertEqual(self.tokenizer.vocab_size, 50257)
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# Copied from transformers.tests.speech_to_test.test_tokenization_speech_to_text.py
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def test_tokenizer_decode_ignores_language_codes(self):
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self.assertIn(ES_CODE, self.tokenizer.all_special_ids)
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generated_ids = [ES_CODE, 4, 1601, 47, 7647, 2]
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result = self.tokenizer.decode(generated_ids, skip_special_tokens=True)
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expected_spanish = self.tokenizer.decode(generated_ids[1:], skip_special_tokens=True)
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self.assertEqual(result, expected_spanish)
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self.assertNotIn(self.tokenizer.eos_token, result)
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def test_batch_encoding(self):
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multilingual_tokenizer = WhisperTokenizer.from_pretrained(
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"openai/whisper-tiny", language="spanish", task="translate"
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)
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batch = ["El gato ", "El gato se sentó"]
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batch_output = multilingual_tokenizer.batch_encode_plus(batch, padding=True).input_ids
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# fmt: off
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EXPECTED_MULTI = [
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[START_OF_TRANSCRIPT, ES_CODE, TRANSLATE, NOTIMESTAMPS, 17356, 290, 2513, 220,
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END_OF_TRANSCRIPT, END_OF_TRANSCRIPT, END_OF_TRANSCRIPT],
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[START_OF_TRANSCRIPT, ES_CODE, TRANSLATE, NOTIMESTAMPS, 17356, 290, 2513, 369,
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2279, 812, END_OF_TRANSCRIPT]
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]
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# fmt: on
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self.assertListEqual(batch_output, EXPECTED_MULTI)
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def test_set_prefix_tokens(self):
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multilingual_tokenizer = WhisperTokenizer.from_pretrained(
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"openai/whisper-tiny", language="spanish", task="translate"
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)
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# change the language prefix token from Spanish to English
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multilingual_tokenizer.set_prefix_tokens(language="english")
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batch = ["the cat", "the cat sat"]
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batch_output = multilingual_tokenizer.batch_encode_plus(batch, padding=True).input_ids
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# fmt: off
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EXPECTED_MULTI = [
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[START_OF_TRANSCRIPT, EN_CODE, TRANSLATE, NOTIMESTAMPS, 3322, 3857,
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END_OF_TRANSCRIPT, END_OF_TRANSCRIPT],
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[START_OF_TRANSCRIPT, EN_CODE, TRANSLATE, NOTIMESTAMPS, 3322, 3857,
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3227, END_OF_TRANSCRIPT]
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]
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# fmt: on
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self.assertListEqual(batch_output, EXPECTED_MULTI)
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def test_batch_encoding_decoding(self):
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multilingual_tokenizer = WhisperTokenizer.from_pretrained("openai/whisper-tiny", language="spanish")
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batch = ["hola güey", "que onda"]
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batch_encoding = multilingual_tokenizer.batch_encode_plus(batch, padding=True).input_ids
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transcription = multilingual_tokenizer.batch_decode(batch_encoding, skip_special_tokens=True)
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self.assertListEqual(batch, transcription)
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