Wav2Vec2 meets phonemes (#14353)
* up * add tokenizer * improve more * finish tokenizer * finish * adapt speech recognition script * adapt convert * more fixes * more fixes * update phonemizer wav2vec2 * better naming * fix more tests * more fixes swedish * correct tests * finish * improve script * remove file * up * lets get those 100 model architectures until the end of the month * make fix-copies * correct more * correct script * more fixes * more fixes * add to docs * Apply suggestions from code review Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * replace assert * fix copies * fix docs * new try docs * boom boom * update * add phonemizer to audio tests * make fix-copies * up * upload models * some changes * Update tests/test_tokenization_wav2vec2_phoneme.py Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com> * more fixes * remove @ Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>
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Patrick von Platen
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@@ -1399,6 +1399,38 @@ class Wav2Vec2ModelIntegrationTest(unittest.TestCase):
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self.assertListEqual(predicted_ids.tolist(), expected_labels)
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self.assertTrue(torch.allclose(predicted_logits, expected_logits, atol=1e-2))
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def test_phoneme_recognition(self):
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model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft").to(torch_device)
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processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft")
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input_speech = self._load_datasamples(4)
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inputs = processor(input_speech, return_tensors="pt", padding=True)
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input_values = inputs.input_values.to(torch_device)
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attention_mask = inputs.attention_mask.to(torch_device)
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with torch.no_grad():
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logits = model(input_values, attention_mask=attention_mask).logits
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predicted_ids = torch.argmax(logits, dim=-1)
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predicted_trans = processor.batch_decode(predicted_ids)
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EXPECTED_TRANSCRIPTIONS = [
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"ɐ m æ n s ɛ d t ə ð ə j uː n ɪ v ɚ s s ɚ aɪ ɛ ɡ z ɪ s t",
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"s w ɛ t k ʌ v ɚ d b ɹ iː ɔ n z b ɑː d i t ɹ ɪ k l ɪ ŋ ɪ n t ə ð ə t aɪ t l oɪ n k l ɑː θ ð æ w ʌ z ð ɪ oʊ n l i ɡ ɑːɹ m ə n t h iː w ɔːɹ",
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"ð ə k aɪ t ɔ n h ɪ z tʃ ɛ s t s t ɪ l d ɹ ɪ p ɪ ŋ b l ʌ d ð ɪ eɪ k ʌ v h ɪ z oʊ v ɚ s t ɹ eɪ n d aɪ z iː v ə n ð ə s ɔːɹ ɹ ɪ ŋ ɐ ɹ iː n ɐ ɚ ɹ aʊ n d h ɪ m w ɪ ð ə θ aʊ z ə n d z ʌ v s p ɛ k t eɪ ɾ ɚ z w ɜː t ɹ ɪ v ɪ æ l ᵻ ɾ i z n ɑː t w ɜː θ θ ɪ ŋ k ɪ ŋ ɐ b aʊ t",
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"h ɪ z ɪ n s t ə n t v p æ n ɪ k w ʌ z f ɑː l oʊ d b aɪ ɐ s m ɔː l ʃ ɑːɹ p b l oʊ h aɪ ɔ n h ɪ z tʃ ɛ s t",
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]
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# should correspond to =>:
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# [
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# "a man said to the universe sir i exist",
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# "sweat covered brion's body trickling into the tight loin cloth that was the only garment he wore",
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# "the cut on his chest still dripping blood the ache of his overstrained eyes even the soaring arena around him with the thousands of spectators were trivialities not worth thinking about",
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# "his instant panic was followed by a small sharp blow high on his chest",
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# ]
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self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS)
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@require_pyctcdecode
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@require_torchaudio
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def test_wav2vec2_with_lm(self):
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328
tests/test_tokenization_wav2vec2_phoneme.py
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328
tests/test_tokenization_wav2vec2_phoneme.py
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@@ -0,0 +1,328 @@
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# 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.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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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": "$$$"})
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# fmt: off
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sample_ids = [
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[11, 5, 15, tokenizer.pad_token_id, 15, 8, 98, 392, 392, 393, 392, 392, 393, 394, 394],
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[24, 22, 5, 24, 22, 5, 77, tokenizer.pad_token_id, 394, 394],
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]
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# fmt: on
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|
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batch_tokens = tokenizer.batch_decode(sample_ids)
|
||||
self.assertEqual(batch_tokens, ["k s ɾ ɾ l ɭʲ!?!? $$$", "j ð s j ð s oːɹ $$$"])
|
||||
|
||||
# overwrite common test
|
||||
def test_added_tokens_do_lower_case(self):
|
||||
# Wav2Vec2PhonemeTokenizer always lower cases letters to correctly map to phonemes
|
||||
pass
|
||||
|
||||
# overwrite common test
|
||||
def test_encode_decode_with_spaces(self):
|
||||
# Wav2Vec2PhonemeTokenizer always puts spaces between phonemes
|
||||
pass
|
||||
|
||||
# overwrite common test
|
||||
def test_internal_consistency(self):
|
||||
# encodes to text to ids, but decodes ids to phonemes -> not possible to have internal consistency
|
||||
pass
|
||||
|
||||
def test_pretrained_model_lists(self):
|
||||
# Wav2Vec2PhonemeModel has no max model length => no testing
|
||||
pass
|
||||
|
||||
# overwrite common
|
||||
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
|
||||
Reference in New Issue
Block a user