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https://github.com/huggingface/transformers.git
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4ece3b9433
* add VITS model
* let's vits
* finish TextEncoder (mostly)
* rename VITS to Vits
* add StochasticDurationPredictor
* ads flow model
* add generator
* correctly set vocab size
* add tokenizer
* remove processor & feature extractor
* add PosteriorEncoder
* add missing weights to SDP
* also convert LJSpeech and VCTK checkpoints
* add training stuff in forward
* add placeholder tests for tokenizer
* add placeholder tests for model
* starting cleanup
* let the great renaming begin!
* use config
* global_conditioning
* more cleaning
* renaming variables
* more renaming
* more renaming
* it never ends
* reticulating the splines
* more renaming
* HiFi-GAN
* doc strings for main model
* fixup
* fix-copies
* don't make it a PreTrainedModel
* fixup
* rename config options
* remove training logic from forward pass
* simplify relative position
* use actual checkpoint
* style
* PR review fixes
* more review changes
* fixup
* more unit tests
* fixup
* fix doc test
* add integration test
* improve tokenizer tests
* add tokenizer integration test
* fix tests on GPU (gave OOM)
* conversion script can handle repos from hub
* add conversion script for all MMS-TTS checkpoints
* automatically create a README for the converted checkpoint
* small changes to config
* push README to hub
* only show uroman note for checkpoints that need it
* remove conversion script because code formatting breaks the readme
* make WaveNet layers configurable
* rename variables
* simplifying the math
* output attentions and hidden states
* remove VitsFlip in flow model
* also got rid of the other flip
* fix tests
* rename more variables
* rename tokenizer, add phonemization
* raise error when phonemizer missing
* re-order config docstrings to match method
* change config naming
* remove redundant str -> list
* fix copyright: vits authors -> kakao enterprise
* (mean, log_variances) -> (prior_mean, prior_log_variances)
* if return dict -> if not return dict
* speed -> speaking rate
* Apply suggestions from code review
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* update fused tanh sigmoid
* reduce dims in tester
* audio -> output_values
* audio -> output_values in tuple out
* fix return type
* fix return type
* make _unconstrained_rational_quadratic_spline a function
* all nn's to accept a config
* add spectro to output
* move {speaking rate, noise scale, noise scale duration} to config
* path -> attn_path
* idxs -> valid idxs -> padded idxs
* output values -> waveform
* use config for attention
* make generation work
* harden integration test
* add spectrogram to dict output
* tokenizer refactor
* make style
* remove 'fake' padding token
* harden tokenizer tests
* ron norm test
* fprop / save tests deterministic
* move uroman to tokenizer as much as possible
* better logger message
* fix vivit imports
* add uroman integration test
* make style
* up
* matthijs -> sanchit-gandhi
* fix tokenizer test
* make fix-copies
* fix dict comprehension
* fix config tests
* fix model tests
* make outputs consistent with reverse/not reverse
* fix key concat
* more model details
* add author
* return dict
* speaker error
* labels error
* Apply suggestions from code review
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/vits/convert_original_checkpoint.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* remove uromanize
* add docstrings
* add docstrings for tokenizer
* upper-case skip messages
* fix return dict
* style
* finish tests
* update checkpoints
* make style
* remove doctest file
* revert
* fix docstring
* fix tokenizer
* remove uroman integration test
* add sampling rate
* fix docs / docstrings
* style
* add sr to model output
* fix outputs
* style / copies
* fix docstring
* fix copies
* remove sr from model outputs
* Update utils/documentation_tests.txt
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* add sr as allowed attr
---------
Co-authored-by: sanchit-gandhi <sanchit@huggingface.co>
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
188 lines
13 KiB
Python
188 lines
13 KiB
Python
# coding=utf-8
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# Copyright 2023 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 VITS tokenizer."""
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import json
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import os
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import shutil
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import tempfile
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import unittest
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from transformers import VitsTokenizer
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from transformers.models.vits.tokenization_vits import VOCAB_FILES_NAMES
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from transformers.testing_utils import slow
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from ...test_tokenization_common import TokenizerTesterMixin
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class VitsTokenizerTest(TokenizerTesterMixin, unittest.TestCase):
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tokenizer_class = VitsTokenizer
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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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"k ' z y u d h e s w – 3 c p - 1 j m i X f l o 0 b r a 4 2 n _ x v t q 5 6 g ț ţ < > | <pad> <unk>".split(
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" "
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)
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)
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vocab_tokens = dict(zip(vocab, range(len(vocab))))
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vocab_tokens[" "] = vocab_tokens["X"]
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del vocab_tokens["X"]
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self.special_tokens_map = {"pad_token": "<pad>", "unk_token": "<unk>"}
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self.tmpdirname = tempfile.mkdtemp()
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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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def get_tokenizer(self, **kwargs):
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kwargs.update(self.special_tokens_map)
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kwargs["phonemize"] = False
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kwargs["normalize"] = False
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return VitsTokenizer.from_pretrained(self.tmpdirname, **kwargs)
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def get_clean_sequence(self, tokenizer, with_prefix_space=False, max_length=20, min_length=5):
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txt = "beyonce lives in los angeles"
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ids = tokenizer.encode(txt, add_special_tokens=False)
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return txt, ids
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@unittest.skip("Adding multicharacter tokens does not work with the VITS tokenizer")
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def test_add_tokens_tokenizer(self):
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pass
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@unittest.skip("Adding multicharacter tokens does not work with the VITS tokenizer")
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def test_encode_decode_with_spaces(self):
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pass
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@unittest.skip("The VITS tokenizer does not support `is_split_into_words`")
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def test_pretokenized_inputs(self):
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pass
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def test_save_and_load_tokenizer(self):
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# safety check on max_len default value so we are sure the test works
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tokenizers = self.get_tokenizers()
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for tokenizer in tokenizers:
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with self.subTest(f"{tokenizer.__class__.__name__}"):
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self.assertNotEqual(tokenizer.model_max_length, 42)
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# Now let's start the test
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tokenizers = self.get_tokenizers()
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for tokenizer in tokenizers:
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with self.subTest(f"{tokenizer.__class__.__name__}"):
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# Isolate this from the other tests because we save additional tokens/etc
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tmpdirname = tempfile.mkdtemp()
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sample_text = " He is very happy, UNwant\u00E9d,running"
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before_tokens = tokenizer.encode(sample_text, add_special_tokens=False)
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before_vocab = tokenizer.get_vocab()
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tokenizer.save_pretrained(tmpdirname)
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after_tokenizer = tokenizer.__class__.from_pretrained(tmpdirname)
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after_tokens = after_tokenizer.encode(sample_text, add_special_tokens=False)
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after_vocab = after_tokenizer.get_vocab()
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self.assertListEqual(before_tokens, after_tokens)
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self.assertDictEqual(before_vocab, after_vocab)
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shutil.rmtree(tmpdirname)
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@unittest.skip("Adding multicharacter tokens does not work the VITS tokenizer")
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def test_special_tokens_initialization_with_non_empty_additional_special_tokens(self):
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pass
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def test_ron_normalization(self):
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tokenizer = self.get_tokenizer()
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tokenizer.language = "ron"
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sequences = ["vițs"]
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normalized_sequences = ["viţs"]
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encoded_ids = tokenizer(sequences, normalize=True)["input_ids"]
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decoded_sequences = tokenizer.batch_decode(encoded_ids)
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self.assertEqual(normalized_sequences, decoded_sequences)
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def test_normalization(self):
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tokenizer = self.get_tokenizer()
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sequences = ["VITS; is a model for t-t-s!"]
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normalized_sequences = ["vits is a model for t-t-s"]
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unnormalized_sequences = [
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"<unk><unk><unk><unk><unk> is a model for t-t-s<unk>"
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] # can't handle upper-case or certain punctuations
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encoded_normalized_ids = tokenizer(sequences, normalize=True)
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encoded_unnormalized_ids = tokenizer(sequences, normalize=False)
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decoded_normalized_sequences = [
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tokenizer.decode(seq, skip_special_tokens=False) for seq in encoded_normalized_ids["input_ids"]
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]
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decoded_unnormalized_sequences = [
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tokenizer.decode(seq, skip_special_tokens=False) for seq in encoded_unnormalized_ids["input_ids"]
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]
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self.assertEqual(decoded_normalized_sequences, normalized_sequences)
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self.assertEqual(decoded_unnormalized_sequences, unnormalized_sequences)
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@slow
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def test_tokenizer_integration(self):
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sequences = [
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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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"We use k as our padding token",
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]
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normalized_sequences = [
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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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"we use k as our padding token",
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]
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# fmt: off
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expected_encoding = {
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'input_ids': [
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[0, 24, 0, 7, 0, 25, 0, 33, 0, 19, 0, 18, 0, 8, 0, 19, 0, 5, 0, 7, 0, 8, 0, 18, 0, 37, 0, 29, 0, 7, 0, 5, 0, 19, 0, 33, 0, 22, 0, 19, 0, 13, 0, 25, 0, 7, 0, 14, 0, 33, 0, 25, 0, 26, 0, 18, 0, 29, 0, 19, 0, 5, 0, 7, 0, 7, 0, 13, 0, 19, 0, 24, 0, 18, 0, 5, 0, 18, 0, 25, 0, 7, 0, 12, 0, 33, 0, 18, 0, 22, 0, 29, 0, 26, 0, 21, 0, 19, 0, 25, 0, 7, 0, 13, 0, 25, 0, 7, 0, 8, 0, 7, 0, 29, 0, 33, 0, 26, 0, 33, 0, 18, 0, 22, 0, 29, 0, 8, 0, 19, 0, 20, 0, 25, 0, 22, 0, 17, 0, 19, 0, 4, 0, 29, 0, 21, 0, 26, 0, 24, 0, 7, 0, 21, 0, 7, 0, 5, 0, 19, 0, 33, 0, 7, 0, 31, 0, 33, 0, 19, 0, 24, 0, 3, 0, 19, 0, 16, 0, 22, 0, 18, 0, 29, 0, 33, 0, 21, 0, 3, 0, 19, 0, 12, 0, 22, 0, 29, 0, 5, 0, 18, 0, 33, 0, 18, 0, 22, 0, 29, 0, 18, 0, 29, 0, 37, 0, 19, 0, 22, 0, 29, 0, 19, 0, 24, 0, 22, 0, 33, 0, 6, 0, 19, 0, 21, 0, 7, 0, 20, 0, 33, 0, 19, 0, 26, 0, 29, 0, 5, 0, 19, 0, 25, 0, 18, 0, 37, 0, 6, 0, 33, 0, 19, 0, 12, 0, 22, 0, 29, 0, 33, 0, 7, 0, 31, 0, 33, 0, 19, 0, 18, 0, 29, 0, 19, 0, 26, 0, 21, 0, 21, 0, 19, 0, 21, 0, 26, 0, 3, 0, 7, 0, 25, 0, 8, 0],
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[0, 33, 0, 6, 0, 7, 0, 19, 0, 34, 0, 4, 0, 18, 0, 12, 0, 0, 0, 19, 0, 24, 0, 25, 0, 22, 0, 9, 0, 29, 0, 19, 0, 20, 0, 22, 0, 31, 0, 19, 0, 16, 0, 4, 0, 17, 0, 13, 0, 8, 0, 19, 0, 22, 0, 32, 0, 7, 0, 25, 0, 19, 0, 33, 0, 6, 0, 7, 0, 19, 0, 21, 0, 26, 0, 2, 0, 3, 0, 19, 0, 5, 0, 22, 0, 37, 0, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38],
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[0, 9, 0, 7, 0, 19, 0, 4, 0, 8, 0, 7, 0, 19, 0, 0, 0, 19, 0, 26, 0, 8, 0, 19, 0, 22, 0, 4, 0, 25, 0, 19, 0, 13, 0, 26, 0, 5, 0, 5, 0, 18, 0, 29, 0, 37, 0, 19, 0, 33, 0, 22, 0, 0, 0, 7, 0, 29, 0, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38],
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],
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'attention_mask': [
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,1, 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],
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]
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}
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# fmt: on
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tokenizer_classes = [self.tokenizer_class]
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if self.test_rust_tokenizer:
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tokenizer_classes.append(self.rust_tokenizer_class)
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for tokenizer_class in tokenizer_classes:
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tokenizer = tokenizer_class.from_pretrained(
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"facebook/mms-tts-eng",
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revision="d188a254c84ae6cfd24deb7a8f5c0c1d349d7d9f", # to pin the tokenizer version
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)
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encoding = tokenizer(sequences, padding=True, normalize=True)
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||
decoded_sequences = [tokenizer.decode(seq, skip_special_tokens=True) for seq in encoding["input_ids"]]
|
||
|
||
encoding_data = encoding.data
|
||
self.assertDictEqual(encoding_data, expected_encoding)
|
||
|
||
for expected, decoded in zip(normalized_sequences, decoded_sequences):
|
||
self.assertEqual(expected, decoded)
|