mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-04 05:10:06 +06:00
ac2bc50a10
* wrong argument name * append eos_token_id * all tokenizers need mask and ctc_blank tokens * remove reduction factor from feature extractor * add proper TTS loss * did shifting the wrong way around * mask out padded portions * remove logits again (don't really need it) * fix unit tests * fixup * pad also returns the decoder attention mask, since that's useful to have * clean up feature extractor logic * pad can handle TTS task too * remove stop_labels from loss calculation * simplify logic * fixup * do -100 masking properly * small STFT optimization (calculate mel filterbanks only once) * replace torchaudio fbanks with audio_utils * remove torchaudio dependency * simplify & speed up the STFT * don't serialize window and mel filters * output cross attentions when generating speech * add guided attention loss * fix failing test * Update src/transformers/models/speecht5/feature_extraction_speecht5.py Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com> * Update src/transformers/models/speecht5/modeling_speecht5.py Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com> * change type annotation of attention_mask to LongTensor * extract loss into class * remove unused frame_signal_scale argument * use config object in loss class * fix type annotations in doc comments * change optional to just bool * implement missing tokenizer method * add deprecation warning * Update src/transformers/models/speecht5/feature_extraction_speecht5.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/speecht5/feature_extraction_speecht5.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * add deprecation warning for stop_labels --------- Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
207 lines
16 KiB
Python
207 lines
16 KiB
Python
# coding=utf-8
|
|
# Copyright 2022 The HuggingFace Team. All rights reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
"""Tests for the SpeechT5 tokenizers."""
|
|
|
|
import unittest
|
|
|
|
from transformers import SPIECE_UNDERLINE
|
|
from transformers.models.speecht5 import SpeechT5Tokenizer
|
|
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
|
|
from transformers.tokenization_utils import AddedToken
|
|
|
|
from ...test_tokenization_common import TokenizerTesterMixin
|
|
|
|
|
|
SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece_bpe_char.model")
|
|
|
|
|
|
@require_sentencepiece
|
|
@require_tokenizers
|
|
class SpeechT5TokenizerTest(TokenizerTesterMixin, unittest.TestCase):
|
|
tokenizer_class = SpeechT5Tokenizer
|
|
test_rust_tokenizer = False
|
|
test_sentencepiece = True
|
|
|
|
def setUp(self):
|
|
super().setUp()
|
|
|
|
# We have a SentencePiece fixture for testing
|
|
tokenizer = SpeechT5Tokenizer(SAMPLE_VOCAB)
|
|
|
|
mask_token = AddedToken("<mask>", lstrip=True, rstrip=False)
|
|
tokenizer.mask_token = mask_token
|
|
tokenizer.add_special_tokens({"mask_token": mask_token})
|
|
tokenizer.add_tokens(["<ctc_blank>"])
|
|
|
|
tokenizer.save_pretrained(self.tmpdirname)
|
|
|
|
def get_input_output_texts(self, tokenizer):
|
|
input_text = "this is a test"
|
|
output_text = "this is a test"
|
|
return input_text, output_text
|
|
|
|
def get_clean_sequence(self, tokenizer, with_prefix_space=False, max_length=20, min_length=5):
|
|
input_text, output_text = self.get_input_output_texts(tokenizer)
|
|
ids = tokenizer.encode(output_text, add_special_tokens=False)
|
|
text = tokenizer.decode(ids, clean_up_tokenization_spaces=False)
|
|
return text, ids
|
|
|
|
def test_convert_token_and_id(self):
|
|
"""Test ``_convert_token_to_id`` and ``_convert_id_to_token``."""
|
|
token = "<pad>"
|
|
token_id = 1
|
|
|
|
self.assertEqual(self.get_tokenizer()._convert_token_to_id(token), token_id)
|
|
self.assertEqual(self.get_tokenizer()._convert_id_to_token(token_id), token)
|
|
|
|
def test_get_vocab(self):
|
|
vocab_keys = list(self.get_tokenizer().get_vocab().keys())
|
|
|
|
self.assertEqual(vocab_keys[0], "<s>")
|
|
self.assertEqual(vocab_keys[1], "<pad>")
|
|
self.assertEqual(vocab_keys[-4], "œ")
|
|
self.assertEqual(vocab_keys[-2], "<mask>")
|
|
self.assertEqual(vocab_keys[-1], "<ctc_blank>")
|
|
self.assertEqual(len(vocab_keys), 81)
|
|
|
|
def test_vocab_size(self):
|
|
self.assertEqual(self.get_tokenizer().vocab_size, 79)
|
|
|
|
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)
|
|
|
|
def test_pickle_subword_regularization_tokenizer(self):
|
|
pass
|
|
|
|
def test_subword_regularization_tokenizer(self):
|
|
pass
|
|
|
|
def test_full_tokenizer(self):
|
|
tokenizer = self.get_tokenizer()
|
|
|
|
tokens = tokenizer.tokenize("This is a test")
|
|
# fmt: off
|
|
self.assertListEqual(tokens, [SPIECE_UNDERLINE, 'T', 'h', 'i', 's', SPIECE_UNDERLINE, 'i', 's', SPIECE_UNDERLINE, 'a', SPIECE_UNDERLINE, 't', 'e', 's', 't'])
|
|
# fmt: on
|
|
|
|
self.assertListEqual(
|
|
tokenizer.convert_tokens_to_ids(tokens),
|
|
[4, 32, 11, 10, 12, 4, 10, 12, 4, 7, 4, 6, 5, 12, 6],
|
|
)
|
|
|
|
tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.")
|
|
self.assertListEqual(
|
|
tokens,
|
|
# fmt: off
|
|
[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', '.']
|
|
# fmt: on
|
|
)
|
|
|
|
ids = tokenizer.convert_tokens_to_ids(tokens)
|
|
# fmt: off
|
|
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])
|
|
# fmt: on
|
|
|
|
back_tokens = tokenizer.convert_ids_to_tokens(ids)
|
|
self.assertListEqual(
|
|
back_tokens,
|
|
# fmt: off
|
|
[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', '.']
|
|
# fmt: on
|
|
)
|
|
|
|
@slow
|
|
def test_tokenizer_integration(self):
|
|
# Use custom sequence because this tokenizer does not handle numbers.
|
|
sequences = [
|
|
"Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides "
|
|
"general-purpose architectures (BERT, GPT, RoBERTa, XLM, DistilBert, XLNet...) for Natural "
|
|
"Language Understanding (NLU) and Natural Language Generation (NLG) with over thirty-two pretrained "
|
|
"models in one hundred plus languages and deep interoperability between Jax, PyTorch and TensorFlow.",
|
|
"BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly "
|
|
"conditioning on both left and right context in all layers.",
|
|
"The quick brown fox jumps over the lazy dog.",
|
|
]
|
|
|
|
# fmt: off
|
|
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, 2],
|
|
[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, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
]
|
|
}
|
|
# fmt: on
|
|
|
|
self.tokenizer_integration_test_util(
|
|
expected_encoding=expected_encoding,
|
|
model_name="microsoft/speecht5_asr",
|
|
revision="c5ef64c71905caeccde0e4462ef3f9077224c524",
|
|
sequences=sequences,
|
|
)
|