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* make SpeechT5 model by copying Wav2Vec2 * add paper to docs * whoops added docs in wrong file * remove SpeechT5Tokenizer + put CTC back in the name * remove deprecated class * remove unused docstring * delete SpeechT5FeatureExtractor, use Wav2Vec2FeatureExtractor instead * remove classes we don't need right now * initial stab at speech encoder prenet * add more speech encoder prenet stuff * improve SpeechEncoderPrenet * add encoder (not finished yet) * add relative position bias to self-attention * add encoder CTC layers * fix formatting * add decoder from BART, doesn't work yet * make it work with generate loop * wrap the encoder into a speech encoder class * wrap the decoder in a text decoder class * changed my mind * changed my mind again ;-) * load decoder weights, make it work * add weights for text decoder postnet * add SpeechT5ForCTC model that uses only the encoder * clean up EncoderLayer and DecoderLayer * implement _init_weights in SpeechT5PreTrainedModel * cleanup config + Encoder and Decoder * add head + cross attention masks * improve doc comments * fixup * more cleanup * more fixup * TextDecoderPrenet works now, thanks Kendall * add CTC loss * add placeholders for other pre/postnets * add type annotation * fix freeze_feature_encoder * set padding tokens to 0 in decoder attention mask * encoder attention mask downsampling * remove features_pen calculation * disable the padding tokens thing again * fixup * more fixup * code review fixes * rename encoder/decoder wrapper classes * allow checkpoints to be loaded into SpeechT5Model * put encoder into wrapper for CTC model * clean up conversion script * add encoder for TTS model * add speech decoder prenet * add speech decoder post-net * attempt to reconstruct the generation loop * add speech generation loop * clean up generate_speech * small tweaks * fix forward pass * enable always dropout on speech decoder prenet * sort declaration * rename models * fixup * fix copies * more fixup * make consistency checker happy * add Seq2SeqSpectrogramOutput class * doc comments * quick note about loss and labels * add HiFi-GAN implementation (from Speech2Speech PR) * rename file * add vocoder to TTS model * improve vocoder * working on tokenizer * more better tokenizer * add CTC tokenizer * fix decode and batch_code in CTC tokenizer * fix processor * two processors and feature extractors * use SpeechT5WaveformFeatureExtractor instead of Wav2Vec2 * cleanup * more cleanup * even more fixup * notebooks * fix log-mel spectrograms * support reduction factor * fixup * shift spectrograms to right to create decoder inputs * return correct labels * add labels for stop token prediction * fix doc comments * fixup * remove SpeechT5ForPreTraining * more fixup * update copyright headers * add usage examples * add SpeechT5ProcessorForCTC * fixup * push unofficial checkpoints to hub * initial version of tokenizer unit tests * add slow test * fix failing tests * tests for CTC tokenizer * finish CTC tokenizer tests * processor tests * initial test for feature extractors * tests for spectrogram feature extractor * fixup * more fixup * add decorators * require speech for tests * modeling tests * more tests for ASR model * fix imports * add fake tests for the other models * fixup * remove jupyter notebooks * add missing SpeechT5Model tests * add missing tests for SpeechT5ForCTC * add missing tests for SpeechT5ForTextToSpeech * sort tests by name * fix Hi-Fi GAN tests * fixup * add speech-to-speech model * refactor duplicate speech generation code * add processor for SpeechToSpeech model * add usage example * add tests for speech-to-speech model * fixup * enable gradient checkpointing for SpeechT5FeatureEncoder * code review * push_to_hub now takes repo_id * improve doc comments for HiFi-GAN config * add missing test * add integration tests * make number of layers in speech decoder prenet configurable * rename variable * rename variables * add auto classes for TTS and S2S * REMOVE CTC!!! * S2S processor does not support save/load_pretrained * fixup * these models are now in an auto mapping * fix doc links * rename HiFiGAN to HifiGan, remove separate config file * REMOVE auto classes * there can be only one * fixup * replace assert * reformat * feature extractor can process input and target at same time * update checkpoint names * fix commit hash
187 lines
15 KiB
Python
187 lines
15 KiB
Python
# coding=utf-8
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# 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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"""Tests for the SpeechT5 tokenizers."""
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import unittest
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from transformers import SPIECE_UNDERLINE
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from transformers.models.speecht5 import SpeechT5Tokenizer
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from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
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from ...test_tokenization_common import TokenizerTesterMixin
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SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece_bpe_char.model")
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@require_sentencepiece
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@require_tokenizers
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class SpeechT5TokenizerTest(TokenizerTesterMixin, unittest.TestCase):
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tokenizer_class = SpeechT5Tokenizer
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test_rust_tokenizer = False
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test_sentencepiece = True
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def setUp(self):
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super().setUp()
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# We have a SentencePiece fixture for testing
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tokenizer = SpeechT5Tokenizer(SAMPLE_VOCAB)
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tokenizer.save_pretrained(self.tmpdirname)
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def get_input_output_texts(self, tokenizer):
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input_text = "this is a test"
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output_text = "this is a test"
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return input_text, output_text
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def get_clean_sequence(self, tokenizer, with_prefix_space=False, max_length=20, min_length=5):
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input_text, output_text = self.get_input_output_texts(tokenizer)
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ids = tokenizer.encode(output_text, add_special_tokens=False)
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text = tokenizer.decode(ids, clean_up_tokenization_spaces=False)
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return text, ids
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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 = "<pad>"
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token_id = 1
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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], "<s>")
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self.assertEqual(vocab_keys[1], "<pad>")
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self.assertEqual(vocab_keys[-2], "œ")
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self.assertEqual(len(vocab_keys), 79)
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def test_vocab_size(self):
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self.assertEqual(self.get_tokenizer().vocab_size, 79)
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def test_add_tokens_tokenizer(self):
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tokenizers = self.get_tokenizers(do_lower_case=False)
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for tokenizer in tokenizers:
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with self.subTest(f"{tokenizer.__class__.__name__}"):
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vocab_size = tokenizer.vocab_size
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all_size = len(tokenizer)
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self.assertNotEqual(vocab_size, 0)
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# We usually have added tokens from the start in tests because our vocab fixtures are
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# smaller than the original vocabs - let's not assert this
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# self.assertEqual(vocab_size, all_size)
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new_toks = ["aaaaa bbbbbb", "cccccccccdddddddd"]
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added_toks = tokenizer.add_tokens(new_toks)
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vocab_size_2 = tokenizer.vocab_size
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all_size_2 = len(tokenizer)
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self.assertNotEqual(vocab_size_2, 0)
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self.assertEqual(vocab_size, vocab_size_2)
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self.assertEqual(added_toks, len(new_toks))
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self.assertEqual(all_size_2, all_size + len(new_toks))
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tokens = tokenizer.encode("aaaaa bbbbbb low cccccccccdddddddd l", add_special_tokens=False)
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self.assertGreaterEqual(len(tokens), 4)
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self.assertGreater(tokens[0], tokenizer.vocab_size - 1)
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self.assertGreater(tokens[-3], tokenizer.vocab_size - 1)
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new_toks_2 = {"eos_token": ">>>>|||<||<<|<<", "pad_token": "<<<<<|||>|>>>>|>"}
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added_toks_2 = tokenizer.add_special_tokens(new_toks_2)
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vocab_size_3 = tokenizer.vocab_size
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all_size_3 = len(tokenizer)
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self.assertNotEqual(vocab_size_3, 0)
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self.assertEqual(vocab_size, vocab_size_3)
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self.assertEqual(added_toks_2, len(new_toks_2))
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self.assertEqual(all_size_3, all_size_2 + len(new_toks_2))
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tokens = tokenizer.encode(
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">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l", add_special_tokens=False
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)
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self.assertGreaterEqual(len(tokens), 6)
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self.assertGreater(tokens[0], tokenizer.vocab_size - 1)
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self.assertGreater(tokens[0], tokens[1])
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self.assertGreater(tokens[-3], tokenizer.vocab_size - 1)
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self.assertGreater(tokens[-3], tokens[-4])
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self.assertEqual(tokens[0], tokenizer.eos_token_id)
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self.assertEqual(tokens[-3], tokenizer.pad_token_id)
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def test_pickle_subword_regularization_tokenizer(self):
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pass
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def test_subword_regularization_tokenizer(self):
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pass
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def test_full_tokenizer(self):
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tokenizer = self.get_tokenizer()
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tokens = tokenizer.tokenize("This is a test")
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# fmt: off
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self.assertListEqual(tokens, [SPIECE_UNDERLINE, 'T', 'h', 'i', 's', SPIECE_UNDERLINE, 'i', 's', SPIECE_UNDERLINE, 'a', SPIECE_UNDERLINE, 't', 'e', 's', 't'])
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# fmt: on
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self.assertListEqual(
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tokenizer.convert_tokens_to_ids(tokens),
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[4, 32, 11, 10, 12, 4, 10, 12, 4, 7, 4, 6, 5, 12, 6],
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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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[SPIECE_UNDERLINE, 'I', SPIECE_UNDERLINE, 'w', 'a', 's', SPIECE_UNDERLINE, 'b', 'o', 'r', 'n', SPIECE_UNDERLINE, 'i', 'n', SPIECE_UNDERLINE, '92000', ',', SPIECE_UNDERLINE, 'a', 'n', 'd', SPIECE_UNDERLINE, 't', 'h', 'i', 's', SPIECE_UNDERLINE, 'i', 's', SPIECE_UNDERLINE, 'f', 'a', 'l', 's', '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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# fmt: off
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self.assertListEqual(ids, [4, 30, 4, 20, 7, 12, 4, 25, 8, 13, 9, 4, 10, 9, 4, 3, 23, 4, 7, 9, 14, 4, 6, 11, 10, 12, 4, 10, 12, 4, 19, 7, 15, 12, 73, 26])
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# fmt: on
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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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[SPIECE_UNDERLINE, 'I', SPIECE_UNDERLINE, 'w', 'a', 's', SPIECE_UNDERLINE, 'b', 'o', 'r', 'n', SPIECE_UNDERLINE, 'i', 'n', SPIECE_UNDERLINE, '<unk>', ',', SPIECE_UNDERLINE, 'a', 'n', 'd', SPIECE_UNDERLINE, 't', 'h', 'i', 's', SPIECE_UNDERLINE, 'i', 's', SPIECE_UNDERLINE, 'f', 'a', 'l', 's', 'é', '.']
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# fmt: on
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)
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@slow
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def test_tokenizer_integration(self):
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# Use custom sequence because this tokenizer does not handle numbers.
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sequences = [
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"Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides "
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"general-purpose architectures (BERT, GPT, RoBERTa, XLM, DistilBert, XLNet...) for Natural "
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"Language Understanding (NLU) and Natural Language Generation (NLG) with over thirty-two pretrained "
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"models in one hundred plus languages and deep interoperability between Jax, PyTorch and TensorFlow.",
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"BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly "
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"conditioning on both left and right context in all layers.",
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"The quick brown fox jumps over the lazy dog.",
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]
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# fmt: off
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expected_encoding = {'input_ids': [[4, 32, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 64, 19, 8, 13, 18, 5, 13, 15, 22, 4, 28, 9, 8, 20, 9, 4, 7, 12, 4, 24, 22, 6, 8, 13, 17, 11, 39, 6, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 7, 9, 14, 4, 24, 22, 6, 8, 13, 17, 11, 39, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 39, 25, 5, 13, 6, 63, 4, 24, 13, 8, 27, 10, 14, 5, 12, 4, 21, 5, 9, 5, 13, 7, 15, 39, 24, 16, 13, 24, 8, 12, 5, 4, 7, 13, 17, 11, 10, 6, 5, 17, 6, 16, 13, 5, 12, 4, 64, 40, 47, 54, 32, 23, 4, 53, 49, 32, 23, 4, 54, 8, 40, 47, 54, 32, 7, 23, 4, 69, 52, 43, 23, 4, 51, 10, 12, 6, 10, 15, 40, 5, 13, 6, 23, 4, 69, 52, 48, 5, 6, 26, 26, 26, 63, 4, 19, 8, 13, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 61, 9, 14, 5, 13, 12, 6, 7, 9, 14, 10, 9, 21, 4, 64, 48, 52, 61, 63, 4, 7, 9, 14, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 53, 5, 9, 5, 13, 7, 6, 10, 8, 9, 4, 64, 48, 52, 53, 63, 4, 20, 10, 6, 11, 4, 8, 27, 5, 13, 4, 6, 11, 10, 13, 6, 22, 39, 6, 20, 8, 4, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 4, 18, 8, 14, 5, 15, 12, 4, 10, 9, 4, 8, 9, 5, 4, 11, 16, 9, 14, 13, 5, 14, 4, 24, 15, 16, 12, 4, 15, 7, 9, 21, 16, 7, 21, 5, 12, 4, 7, 9, 14, 4, 14, 5, 5, 24, 4, 10, 9, 6, 5, 13, 8, 24, 5, 13, 7, 25, 10, 15, 10, 6, 22, 4, 25, 5, 6, 20, 5, 5, 9, 4, 58, 7, 37, 23, 4, 49, 22, 32, 8, 13, 17, 11, 4, 7, 9, 14, 4, 32, 5, 9, 12, 8, 13, 55, 15, 8, 20, 26], [4, 40, 47, 54, 32, 4, 10, 12, 4, 14, 5, 12, 10, 21, 9, 5, 14, 4, 6, 8, 4, 24, 13, 5, 39, 6, 13, 7, 10, 9, 4, 14, 5, 5, 24, 4, 25, 10, 14, 10, 13, 5, 17, 6, 10, 8, 9, 7, 15, 4, 13, 5, 24, 13, 5, 12, 5, 9, 6, 7, 6, 10, 8, 9, 12, 4, 19, 13, 8, 18, 4, 16, 9, 15, 7, 25, 5, 15, 5, 14, 4, 6, 5, 37, 6, 4, 25, 22, 4, 46, 8, 10, 9, 6, 15, 22, 4, 17, 8, 9, 14, 10, 6, 10, 8, 9, 10, 9, 21, 4, 8, 9, 4, 25, 8, 6, 11, 4, 15, 5, 19, 6, 4, 7, 9, 14, 4, 13, 10, 21, 11, 6, 4, 17, 8, 9, 6, 5, 37, 6, 4, 10, 9, 4, 7, 15, 15, 4, 15, 7, 22, 5, 13, 12, 26, 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, 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], [4, 32, 11, 5, 4, 45, 16, 10, 17, 28, 4, 25, 13, 8, 20, 9, 4, 19, 8, 37, 4, 46, 16, 18, 24, 12, 4, 8, 27, 5, 13, 4, 6, 11, 5, 4, 15, 7, 57, 22, 4, 14, 8, 21, 26, 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, 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, 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]], '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, 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, 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, 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# fmt: on
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self.tokenizer_integration_test_util(
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expected_encoding=expected_encoding,
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model_name="microsoft/speecht5_asr",
|
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revision="c5ef64c71905caeccde0e4462ef3f9077224c524",
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sequences=sequences,
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)
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