409 lines
20 KiB
Python
409 lines
20 KiB
Python
# coding=utf-8
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# Copyright 2021 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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"""Tests for the Wav2Vec2Phoneme tokenizer."""
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import json
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import os
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import unittest
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from typing import Tuple
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from transformers import Wav2Vec2PhonemeCTCTokenizer
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from transformers.models.wav2vec2.tokenization_wav2vec2 import VOCAB_FILES_NAMES
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from transformers.models.wav2vec2_phoneme.tokenization_wav2vec2_phoneme import Wav2Vec2PhonemeCTCTokenizerOutput
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from transformers.testing_utils import require_phonemizer
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from ...test_tokenization_common import TokenizerTesterMixin
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@require_phonemizer
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class Wav2Vec2PhonemeCTCTokenizerTest(TokenizerTesterMixin, unittest.TestCase):
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from_pretrained_id = "facebook/wav2vec2-lv-60-espeak-cv-ft"
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tokenizer_class = Wav2Vec2PhonemeCTCTokenizer
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test_rust_tokenizer = False
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def setUp(self):
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super().setUp()
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vocab = (
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"<s> <pad> </s> <unk> n s t ə l a i k d m ɛ ɾ e ɪ p o ɐ z ð f j v b ɹ ʁ ʊ iː r w ʌ u ɡ æ aɪ ʃ h ɔ ɑː "
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"ŋ ɚ eɪ β uː y ɑ̃ oʊ ᵻ eː θ aʊ ts oː ɔ̃ ɣ ɜ ɑ dʒ əl x ɜː ç ʒ tʃ ɔː ɑːɹ ɛ̃ ʎ ɔːɹ ʋ aː ɕ œ ø oːɹ ɲ yː "
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"ʔ iə i5 s. tɕ ?? nʲ ɛː œ̃ ɭ ɔø ʑ tʲ ɨ ɛɹ ts. rʲ ɪɹ ɭʲ i.5 ɔɪ q sʲ u5 ʊɹ iɜ a5 iɛ5 øː ʕ ja əɜ th ɑ5 "
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"oɪ dʲ ə5 tɕh ts.h mʲ ɯ dʑ vʲ e̞ tʃʲ ei5 o5 onɡ5 ɑu5 iɑ5 ai5 aɪɚ kh ə1 ʐ i2 ʉ ħ t[ aɪə ʲ ju ə2 u2 oɜ "
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"pː iɛɜ ou5 y5 uɜ tː uo5 d[ uoɜ tsh ɑɜ ɵ i̪5 uei5 ɟ aɜ ɑɨ i.ɜ eʊ o2 ɐ̃ ä pʲ kʲ n̩ ɒ ph ɑu2 uɨ əɪ ɫ ɬ "
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"yɜ bʲ ɑ2 s̪ aiɜ χ ɐ̃ʊ̃ 1 ə4 yæɜ a2 ɨː t̪ iouɜ ũ onɡɜ aɨ iɛ2 ɔɨ ɑuɜ o̞ ei2 iou2 c kː y2 ɖ oe dˤ yɛɜ "
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'əʊ S ɡʲ onɡ2 u" eiɜ ʈ ɯᵝ iou5 dZ r̝̊ i.2 tS s^ ʝ yə5 iɑɜ uə5 pf ɨu iɑ2 ou2 ər2 fʲ ai2 r̝ uəɜ ɳ əɨ '
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"ua5 uɪ ɽ bː yu5 uo2 yɛ5 l̩ ɻ ərɜ ʂ i̪2 ouɜ uaɜ a. a.ː yæ5 dː r̩ ee ɪu ər5 i̪ ɜ æi u: i.ː t^ o1 ɪ^ "
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"ai ueiɜ æː ɛɪ eə i. ɴ ie ua2 ɑ1 o4 tʃː o: ɑ: u1 N i̪1 au yæ2 u. qː yəɜ y: kʰ tʃʰ iʊ sx õ uo tʰ "
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"uai5 bʰ u.ː uə2 ʊə d^ s̪ː yiɜ dʰ r. oe: i1 ɟː yu2 nʲʲ i̪4 uei2 tsʲ ɸ ĩ ɑ4 t̪ː eɑ u4 e: tsː ʈʰ ɡʰ "
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"ɯɯ dʒʲ ʂʲ X ɵː uaiɜ tɕʲ ã t^ː ẽː yɛ2 cː i.1 ɛʊ dˤdˤ dʒː i4 ɡː yi ɕʲ ɟʰ pʰ dʑʲ yuɜ ua1 ua4 æiː ɐɐ "
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"ui iou1 ʊː a1 iou4 cʰ iɛ1 yə2 ɖʰ ẽ ʒʲ ää ər4 iːː ɪː iɑ1 ər1 œː øi ɪuː cʰcʰ əː1 iː1 ũ kʰː o̞o̞ xʲ "
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"ou1 iɛ4 e̞e̞ y1 dzː dʲʲ dʰː ɯᵝɯᵝ lː uo1 i.4 i: yɛ5ʲ a4"
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).split(" ")
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vocab_tokens = dict(zip(vocab, range(len(vocab))))
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self.special_tokens_map = {"pad_token": "<pad>", "unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"}
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self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
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with open(self.vocab_file, "w", encoding="utf-8") as fp:
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fp.write(json.dumps(vocab_tokens) + "\n")
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# overwrite since phonemes require specific creation
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def get_clean_sequence(self, tokenizer, with_prefix_space=False, max_length=20, min_length=5) -> Tuple[str, list]:
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toks = [(i, tokenizer.decode([i], clean_up_tokenization_spaces=False)) for i in range(len(tokenizer))]
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toks = list(filter(lambda t: [t[0]] == tokenizer.encode(t[1], do_phonemize=False), toks))
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if max_length is not None and len(toks) > max_length:
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toks = toks[:max_length]
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if min_length is not None and len(toks) < min_length and len(toks) > 0:
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while len(toks) < min_length:
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toks = toks + toks
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# toks_str = [t[1] for t in toks]
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toks_ids = [t[0] for t in toks]
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# Ensure consistency
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output_txt = tokenizer.decode(toks_ids, clean_up_tokenization_spaces=False)
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if " " not in output_txt and len(toks_ids) > 1:
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output_txt = (
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tokenizer.decode([toks_ids[0]], clean_up_tokenization_spaces=False)
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+ " "
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+ tokenizer.decode(toks_ids[1:], clean_up_tokenization_spaces=False)
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)
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if with_prefix_space:
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output_txt = " " + output_txt
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output_ids = tokenizer.encode(output_txt, add_special_tokens=False)
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return output_txt, output_ids
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def get_tokenizer(self, **kwargs):
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kwargs.update(self.special_tokens_map)
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return Wav2Vec2PhonemeCTCTokenizer.from_pretrained(self.tmpdirname, **kwargs)
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def test_tokenizer_add_new_tokens(self):
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tokenizer = self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft")
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# check adding a single token
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tokenizer.add_tokens("xxx")
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token_ids = tokenizer("m xxx ɪ", do_phonemize=False).input_ids
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self.assertEqual(token_ids, [13, 392, 17]) # xxx should be last token
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tokenizer.add_tokens(["aaa", "bbb", "ccc"])
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token_ids = tokenizer("m aaa ɪ ccc", do_phonemize=False).input_ids
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self.assertEqual(token_ids, [13, 393, 17, 395]) # aaa and ccc should be after xxx and 2 after aaa
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token_ids = tokenizer("maɪ c", do_phonemize=False).input_ids
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self.assertEqual(token_ids, [3, 200]) # mai should be <unk> (=3)
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def test_phonemize(self):
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tokenizer = self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft")
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input_text = "Hello how are you"
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phonemes = tokenizer.phonemize(input_text, phonemizer_lang="en-us")
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self.assertEqual(phonemes, "h ə l oʊ h aʊ ɑːɹ j uː")
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def test_encode(self):
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tokenizer = self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft")
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input_text = "Hello how are you"
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phonemes = tokenizer.phonemize(input_text, phonemizer_lang="en-us")
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self.assertEqual(tokenizer(input_text).input_ids, tokenizer(phonemes, do_phonemize=False).input_ids)
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def test_encode_decode(self):
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tokenizer = self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft")
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input_text = "Hello how are you"
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phonemes = tokenizer.phonemize(input_text, phonemizer_lang="en-us")
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phonemes_enc_dec = tokenizer.decode(tokenizer(input_text).input_ids)
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self.assertEqual(phonemes, phonemes_enc_dec)
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def test_decode(self):
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tokenizer = self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft")
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sample_ids = [
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[11, 5, 15, tokenizer.pad_token_id, 15, 8, 98],
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[24, 22, 5, 24, 22, 5, 77],
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]
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tokens = tokenizer.decode(sample_ids[0])
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batch_tokens = tokenizer.batch_decode(sample_ids)
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self.assertEqual(tokens, batch_tokens[0])
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self.assertEqual(batch_tokens, ["k s ɾ ɾ l ɭʲ", "j ð s j ð s oːɹ"])
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def test_phonemize_with_word_del(self):
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tokenizer = self.tokenizer_class.from_pretrained(
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"facebook/wav2vec2-lv-60-espeak-cv-ft", word_delimiter_token="|"
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)
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tokenizer.add_tokens("|")
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input_text = "Hello how are you"
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phonemes = tokenizer.phonemize(input_text, phonemizer_lang="en-us")
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self.assertEqual(phonemes, "h ə l oʊ | h aʊ | ɑːɹ | j uː |")
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def test_encode_with_del(self):
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tokenizer = self.tokenizer_class.from_pretrained(
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"facebook/wav2vec2-lv-60-espeak-cv-ft", word_delimiter_token="|"
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)
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tokenizer.add_tokens("|")
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input_text = "Hello how are you"
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phonemes = tokenizer.phonemize(input_text, phonemizer_lang="en-us")
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self.assertEqual(tokenizer(input_text).input_ids, tokenizer(phonemes, do_phonemize=False).input_ids)
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def test_decode_with_del(self):
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tokenizer = self.tokenizer_class.from_pretrained(
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"facebook/wav2vec2-lv-60-espeak-cv-ft", word_delimiter_token="|"
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)
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tokenizer.add_tokens("|")
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# fmt: off
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sample_ids = [
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[11, 5, 15, tokenizer.pad_token_id, tokenizer.word_delimiter_token_id, 15, 8, tokenizer.word_delimiter_token_id, 98],
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[tokenizer.word_delimiter_token_id, 24, 22, tokenizer.word_delimiter_token_id, 5, 24, 22, 5, 77],
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]
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# fmt: on
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# decode with word_del_token filter
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tokens = tokenizer.decode(sample_ids[0])
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batch_tokens = tokenizer.batch_decode(sample_ids)
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self.assertEqual(tokens, batch_tokens[0])
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self.assertEqual(batch_tokens, ["k s ɾ ɾ l ɭʲ", "j ð s j ð s oːɹ"])
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# decode with no word_del_token filter
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tokens = tokenizer.decode(sample_ids[0], filter_word_delimiter_token=False)
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batch_tokens = tokenizer.batch_decode(sample_ids, filter_word_delimiter_token=False)
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self.assertEqual(tokens, batch_tokens[0])
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self.assertEqual(batch_tokens, ["k s ɾ | ɾ l | ɭʲ", "| j ð | s j ð s oːɹ"])
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def test_encode_decode_with_del(self):
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tokenizer = self.tokenizer_class.from_pretrained(
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"facebook/wav2vec2-lv-60-espeak-cv-ft", word_delimiter_token="|"
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)
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tokenizer.add_tokens("|")
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input_text = "Hello how are you"
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phonemes = tokenizer.phonemize(input_text, phonemizer_lang="en-us")
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phonemes_enc_dec = tokenizer.decode(tokenizer(input_text).input_ids, filter_word_delimiter_token=False)
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self.assertEqual(phonemes, phonemes_enc_dec)
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def test_encode_decode_with_del_filter(self):
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tokenizer = self.tokenizer_class.from_pretrained(
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"facebook/wav2vec2-lv-60-espeak-cv-ft", word_delimiter_token="|"
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)
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tokenizer.add_tokens("|")
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input_text = "Hello how are you"
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phonemes = tokenizer.phonemize(input_text, phonemizer_lang="en-us")
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phonemes_enc_dec = tokenizer.decode(tokenizer(input_text).input_ids, filter_word_delimiter_token=True)
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self.assertEqual(" ".join([p.strip() for p in phonemes.split(" |")]).strip(), phonemes_enc_dec)
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def test_change_phonemizer_lang(self):
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tokenizer = self.tokenizer_class.from_pretrained(
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"facebook/wav2vec2-lv-60-espeak-cv-ft", word_delimiter_token=None
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)
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input_text = "Hello how are you"
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input_ids_en = tokenizer(input_text, phonemizer_lang="en-us").input_ids
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input_ids_fr = tokenizer(input_text, phonemizer_lang="fr-fr").input_ids
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self.assertNotEqual(input_ids_en, input_ids_fr)
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text_en = tokenizer.decode(input_ids_en)
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text_fr = tokenizer.decode(input_ids_fr)
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self.assertEqual(text_en, "h ə l oʊ h aʊ ɑːɹ j uː")
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self.assertEqual(text_fr, "ɛ l o h aʊ a ʁ j u")
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def test_case_insensitive(self):
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tokenizer = self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft")
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input_text_up = "Hello how Are you"
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input_text_low = "hello how are you"
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input_ids_up = tokenizer(input_text_up).input_ids
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input_ids_low = tokenizer(input_text_low).input_ids
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self.assertEqual(input_ids_up, input_ids_low)
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def test_tokenizer_decode_added_tokens(self):
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tokenizer = self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft")
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tokenizer.add_tokens(["!", "?"])
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tokenizer.add_special_tokens({"cls_token": "$$$"})
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# fmt: off
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sample_ids = [
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[11, 5, 15, tokenizer.pad_token_id, 15, 8, 98, 392, 392, 393, 392, 392, 393, 394, 394],
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[24, 22, 5, 24, 22, 5, 77, tokenizer.pad_token_id, 394, 394],
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]
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# fmt: on
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batch_tokens = tokenizer.batch_decode(sample_ids)
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self.assertEqual(batch_tokens, ["k s ɾ ɾ l ɭʲ!?!? $$$", "j ð s j ð s oːɹ $$$"])
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@staticmethod
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def get_from_offsets(offsets, key):
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retrieved_list = [d[key] for d in offsets]
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return retrieved_list
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def test_offsets(self):
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tokenizer = self.get_tokenizer(word_delimiter_token="|")
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tokenizer.add_tokens("|")
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# fmt: off
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# ksssɾɾ|ɾɾ<pad>ɾɾ|<pad>ɾlll|ɭʲ -> k s ɾ ɾ | ɾ l | ɭʲ"
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sample_ids = [11, 5, 5, 5, 15, 15, tokenizer.pad_token_id, 15, 15, tokenizer.word_delimiter_token_id, tokenizer.pad_token_id, 15, 8, 8, 8, tokenizer.word_delimiter_token_id, 98]
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# fmt: on
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outputs = tokenizer.decode(sample_ids, output_char_offsets=True, filter_word_delimiter_token=False)
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# check Wav2Vec2CTCTokenizerOutput keys for char
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self.assertEqual(len(outputs.keys()), 2)
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self.assertTrue("text" in outputs)
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self.assertTrue("char_offsets" in outputs)
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self.assertTrue(isinstance(outputs, Wav2Vec2PhonemeCTCTokenizerOutput))
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# check that order of chars is correct and identical for both outputs
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self.assertEqual(" ".join(self.get_from_offsets(outputs["char_offsets"], "char")), outputs.text)
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self.assertListEqual(
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self.get_from_offsets(outputs["char_offsets"], "char"), ["k", "s", "ɾ", "ɾ", "|", "ɾ", "l", "|", "ɭʲ"]
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)
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# check that offsets are actually correct for char
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# 0-1 is 11, 1-4 is 5, 4-6 is first 15, 6-7 is <pad> (thus not shown), 7-9 is second 15, 9-10 is word_delimiter_token,
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# 10-11 is <pad> (thus not shown), 11-12 is third 15, 12-15 is 8, 15-16 is word_delimiter_token, 16-17 is 98
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self.assertListEqual(
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self.get_from_offsets(outputs["char_offsets"], "start_offset"), [0, 1, 4, 7, 9, 11, 12, 15, 16]
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)
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self.assertListEqual(
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self.get_from_offsets(outputs["char_offsets"], "end_offset"), [1, 4, 6, 9, 10, 12, 15, 16, 17]
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)
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def test_offsets_batch(self):
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tokenizer = self.get_tokenizer(word_delimiter_token="|")
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def check_list_tuples_equal(outputs_batch, outputs_list):
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self.assertTrue(isinstance(outputs_batch, Wav2Vec2PhonemeCTCTokenizerOutput))
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self.assertTrue(isinstance(outputs_list[0], Wav2Vec2PhonemeCTCTokenizerOutput))
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# transform list to ModelOutput
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outputs_batch_2 = Wav2Vec2PhonemeCTCTokenizerOutput(
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{k: [d[k] for d in outputs_list] for k in outputs_list[0]}
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)
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self.assertListEqual(outputs_batch["text"], outputs_batch_2["text"])
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def recursive_check(list_or_dict_1, list_or_dict_2):
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if isinstance(list_or_dict_1, list):
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[recursive_check(l1, l2) for l1, l2 in zip(list_or_dict_1, list_or_dict_2)]
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self.assertEqual(list_or_dict_1, list_or_dict_2)
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if "char_offsets" in outputs_batch:
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recursive_check(outputs_batch["char_offsets"], outputs_batch_2["char_offsets"])
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# fmt: off
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sample_ids = [
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[11, 5, 15, tokenizer.pad_token_id, 15, 4, 8, 98, 32, 32, 32, 32, 4, 33, tokenizer.word_delimiter_token_id, 32, 32, 33, 34, 34],
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[24, 22, 5, tokenizer.word_delimiter_token_id, tokenizer.word_delimiter_token_id, 24, 22, 22, 22, 4, 5, 77, tokenizer.pad_token_id, 22, 22, 4, 34, 34, 34, 34],
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]
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# fmt: on
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# We assume that `decode` works as expected. All we will check now is
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# the output type is correct and the output is identical to `decode`
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# char
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outputs_char_batch = tokenizer.batch_decode(sample_ids, output_char_offsets=True)
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outputs_char = [tokenizer.decode(ids, output_char_offsets=True) for ids in sample_ids]
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check_list_tuples_equal(outputs_char_batch, outputs_char)
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@unittest.skip("Wav2Vec2PhonemeTokenizer always lower cases letters to correctly map to phonemes")
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def test_added_tokens_do_lower_case(self):
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pass
|
||
|
||
@unittest.skip("Wav2Vec2PhonemeTokenizer always puts spaces between phonemes")
|
||
def test_encode_decode_with_spaces(self):
|
||
pass
|
||
|
||
@unittest.skip("encodes to text to ids, but decodes ids to phonemes -> not possible to have internal consistency")
|
||
def test_internal_consistency(self):
|
||
pass
|
||
|
||
@unittest.skip("Wav2Vec2PhonemeModel has no max model length => no testing")
|
||
def test_add_tokens_tokenizer(self):
|
||
tokenizers = self.get_tokenizers(do_lower_case=False)
|
||
for tokenizer in tokenizers:
|
||
with self.subTest(f"{tokenizer.__class__.__name__}"):
|
||
vocab_size = tokenizer.vocab_size
|
||
all_size = len(tokenizer)
|
||
|
||
self.assertNotEqual(vocab_size, 0)
|
||
|
||
# We usually have added tokens from the start in tests because our vocab fixtures are
|
||
# smaller than the original vocabs - let's not assert this
|
||
# self.assertEqual(vocab_size, all_size)
|
||
|
||
new_toks = ["aaaaa bbbbbb", "cccccccccdddddddd"]
|
||
added_toks = tokenizer.add_tokens(new_toks)
|
||
vocab_size_2 = tokenizer.vocab_size
|
||
all_size_2 = len(tokenizer)
|
||
|
||
self.assertNotEqual(vocab_size_2, 0)
|
||
self.assertEqual(vocab_size, vocab_size_2)
|
||
self.assertEqual(added_toks, len(new_toks))
|
||
self.assertEqual(all_size_2, all_size + len(new_toks))
|
||
|
||
tokens = tokenizer.encode("aaaaa bbbbbb low cccccccccdddddddd l", add_special_tokens=False)
|
||
|
||
self.assertGreaterEqual(len(tokens), 4)
|
||
self.assertGreater(tokens[0], tokenizer.vocab_size - 1)
|
||
self.assertGreater(tokens[-3], tokenizer.vocab_size - 1)
|
||
|
||
new_toks_2 = {"eos_token": ">>>>|||<||<<|<<", "pad_token": "<<<<<|||>|>>>>|>"}
|
||
added_toks_2 = tokenizer.add_special_tokens(new_toks_2)
|
||
vocab_size_3 = tokenizer.vocab_size
|
||
all_size_3 = len(tokenizer)
|
||
|
||
self.assertNotEqual(vocab_size_3, 0)
|
||
self.assertEqual(vocab_size, vocab_size_3)
|
||
self.assertEqual(added_toks_2, len(new_toks_2))
|
||
self.assertEqual(all_size_3, all_size_2 + len(new_toks_2))
|
||
|
||
tokens = tokenizer.encode(
|
||
">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l", add_special_tokens=False
|
||
)
|
||
|
||
self.assertGreaterEqual(len(tokens), 6)
|
||
self.assertGreater(tokens[0], tokenizer.vocab_size - 1)
|
||
self.assertGreater(tokens[0], tokens[1])
|
||
self.assertGreater(tokens[-3], tokenizer.vocab_size - 1)
|
||
self.assertGreater(tokens[-3], tokens[-4])
|
||
self.assertEqual(tokens[0], tokenizer.eos_token_id)
|
||
self.assertEqual(tokens[-3], tokenizer.pad_token_id)
|
||
|
||
@unittest.skip("The tokenizer shouldn't be used to encode input IDs (except for labels), only to decode.")
|
||
def test_tf_encode_plus_sent_to_model(self):
|
||
pass
|
||
|
||
@unittest.skip("The tokenizer shouldn't be used to encode input IDs (except for labels), only to decode.")
|
||
def test_torch_encode_plus_sent_to_model(self):
|
||
pass
|
||
|
||
def test_convert_tokens_to_string_format(self):
|
||
# The default common tokenizer tests assumes that the output of `convert_tokens_to_string` is a string which
|
||
# is not the case for Wav2Vec2PhonemeCTCTokenizer.
|
||
tokenizers = self.get_tokenizers(fast=True, do_lower_case=True)
|
||
for tokenizer in tokenizers:
|
||
with self.subTest(f"{tokenizer.__class__.__name__}"):
|
||
tokens = ["ð", "ɪ", "s", "ɪ", "z", "ɐ", "t", "ɛ", "k", "s", "t"]
|
||
output = tokenizer.convert_tokens_to_string(tokens)
|
||
|
||
self.assertIsInstance(output["text"], str)
|