828 lines
40 KiB
Python
828 lines
40 KiB
Python
# coding=utf-8
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# Copyright 2021 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Tests for the Wav2Vec2 tokenizer."""
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import inspect
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import json
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import os
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import random
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import shutil
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import tempfile
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import unittest
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import numpy as np
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from transformers import (
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AddedToken,
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Wav2Vec2Config,
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Wav2Vec2CTCTokenizer,
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Wav2Vec2Tokenizer,
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)
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from transformers.models.wav2vec2.tokenization_wav2vec2 import VOCAB_FILES_NAMES, Wav2Vec2CTCTokenizerOutput
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from transformers.testing_utils import require_torch, slow
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from ...test_tokenization_common import TokenizerTesterMixin
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global_rng = random.Random()
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# Copied from tests.models.whisper.test_feature_extraction_whisper.floats_list
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def floats_list(shape, scale=1.0, rng=None, name=None):
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"""Creates a random float32 tensor"""
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if rng is None:
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rng = global_rng
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values = []
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for batch_idx in range(shape[0]):
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values.append([])
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for _ in range(shape[1]):
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values[-1].append(rng.random() * scale)
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return values
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class Wav2Vec2TokenizerTest(unittest.TestCase):
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tokenizer_class = Wav2Vec2Tokenizer
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def setUp(self):
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super().setUp()
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vocab = "<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K ' X J Q Z".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.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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return Wav2Vec2Tokenizer.from_pretrained(self.tmpdirname, **kwargs)
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def test_tokenizer_decode(self):
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# TODO(PVP) - change to facebook
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tokenizer = Wav2Vec2Tokenizer.from_pretrained("facebook/wav2vec2-base-960h")
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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, tokenizer.word_delimiter_token_id, 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, ["HELLO<unk>", "BYE BYE<unk>"])
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def test_tokenizer_decode_special(self):
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# TODO(PVP) - change to facebook
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tokenizer = Wav2Vec2Tokenizer.from_pretrained("facebook/wav2vec2-base-960h")
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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, tokenizer.word_delimiter_token_id, 24, 22, 5, 77],
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]
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sample_ids_2 = [
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[11, 5, 5, 5, 5, 5, 15, 15, 15, tokenizer.pad_token_id, 15, 8, 98],
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[
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24,
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22,
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5,
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tokenizer.pad_token_id,
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tokenizer.pad_token_id,
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tokenizer.pad_token_id,
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tokenizer.word_delimiter_token_id,
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24,
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22,
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5,
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77,
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tokenizer.word_delimiter_token_id,
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],
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]
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batch_tokens = tokenizer.batch_decode(sample_ids)
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batch_tokens_2 = tokenizer.batch_decode(sample_ids_2)
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self.assertEqual(batch_tokens, batch_tokens_2)
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self.assertEqual(batch_tokens, ["HELLO<unk>", "BYE BYE<unk>"])
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def test_tokenizer_decode_added_tokens(self):
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tokenizer = Wav2Vec2Tokenizer.from_pretrained("facebook/wav2vec2-base-960h")
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tokenizer.add_tokens(["!", "?"])
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tokenizer.add_special_tokens({"cls_token": "$$$"})
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sample_ids = [
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[
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11,
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5,
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15,
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tokenizer.pad_token_id,
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15,
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8,
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98,
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32,
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32,
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33,
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tokenizer.word_delimiter_token_id,
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32,
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32,
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33,
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34,
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34,
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],
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[24, 22, 5, tokenizer.word_delimiter_token_id, 24, 22, 5, 77, tokenizer.pad_token_id, 34, 34],
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]
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batch_tokens = tokenizer.batch_decode(sample_ids)
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batch_tokens_2 = tokenizer.batch_decode(sample_ids, skip_special_tokens=True)
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self.assertEqual(batch_tokens, ["HELLO<unk>!?!?$$$", "BYE BYE<unk>$$$"])
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self.assertEqual(batch_tokens_2, ["HELO!?!?", "BYE BYE"])
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def test_call(self):
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# Tests that all call wrap to encode_plus and batch_encode_plus
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tokenizer = self.get_tokenizer()
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# create three inputs of length 800, 1000, and 1200
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speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
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np_speech_inputs = [np.asarray(speech_input) for speech_input in speech_inputs]
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# Test not batched input
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encoded_sequences_1 = tokenizer(speech_inputs[0], return_tensors="np").input_values
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encoded_sequences_2 = tokenizer(np_speech_inputs[0], return_tensors="np").input_values
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self.assertTrue(np.allclose(encoded_sequences_1, encoded_sequences_2, atol=1e-3))
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# Test batched
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encoded_sequences_1 = tokenizer(speech_inputs, return_tensors="np").input_values
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encoded_sequences_2 = tokenizer(np_speech_inputs, return_tensors="np").input_values
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for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2):
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self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=1e-3))
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# Test 2-D numpy arrays are batched.
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speech_inputs = [floats_list((1, x))[0] for x in (800, 800, 800)]
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np_speech_inputs = np.asarray(speech_inputs)
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encoded_sequences_1 = tokenizer(speech_inputs, return_tensors="np").input_values
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encoded_sequences_2 = tokenizer(np_speech_inputs, return_tensors="np").input_values
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for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2):
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self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=1e-3))
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def test_padding(self, max_length=50):
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def _input_values_have_equal_length(input_values):
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length = len(input_values[0])
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for input_values_slice in input_values[1:]:
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if len(input_values_slice) != length:
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return False
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return True
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def _input_values_are_equal(input_values_1, input_values_2):
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if len(input_values_1) != len(input_values_2):
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return False
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for input_values_slice_1, input_values_slice_2 in zip(input_values_1, input_values_2):
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if not np.allclose(np.asarray(input_values_slice_1), np.asarray(input_values_slice_2), atol=1e-3):
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return False
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return True
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tokenizer = self.get_tokenizer()
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speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
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input_values_1 = tokenizer(speech_inputs).input_values
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input_values_2 = tokenizer(speech_inputs, padding="longest").input_values
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input_values_3 = tokenizer(speech_inputs, padding="longest", max_length=1600).input_values
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self.assertFalse(_input_values_have_equal_length(input_values_1))
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self.assertTrue(_input_values_have_equal_length(input_values_2))
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self.assertTrue(_input_values_have_equal_length(input_values_3))
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self.assertTrue(_input_values_are_equal(input_values_2, input_values_3))
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self.assertTrue(len(input_values_1[0]) == 800)
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self.assertTrue(len(input_values_2[0]) == 1200)
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# padding should be 0.0
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self.assertTrue(abs(sum(np.asarray(input_values_2[0])[800:])) < 1e-3)
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self.assertTrue(abs(sum(np.asarray(input_values_2[1])[1000:])) < 1e-3)
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input_values_4 = tokenizer(speech_inputs, padding="max_length").input_values
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input_values_5 = tokenizer(speech_inputs, padding="max_length", max_length=1600).input_values
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self.assertTrue(_input_values_are_equal(input_values_1, input_values_4))
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self.assertEqual(input_values_5.shape, (3, 1600))
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# padding should be 0.0
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self.assertTrue(abs(sum(np.asarray(input_values_5[0])[800:1200])) < 1e-3)
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input_values_6 = tokenizer(speech_inputs, pad_to_multiple_of=500).input_values
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input_values_7 = tokenizer(speech_inputs, padding="longest", pad_to_multiple_of=500).input_values
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input_values_8 = tokenizer(
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speech_inputs, padding="max_length", pad_to_multiple_of=500, max_length=2400
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).input_values
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self.assertTrue(_input_values_are_equal(input_values_1, input_values_6))
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self.assertEqual(input_values_7.shape, (3, 1500))
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self.assertEqual(input_values_8.shape, (3, 2500))
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# padding should be 0.0
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self.assertTrue(abs(sum(np.asarray(input_values_7[0])[800:])) < 1e-3)
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self.assertTrue(abs(sum(np.asarray(input_values_7[1])[1000:])) < 1e-3)
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self.assertTrue(abs(sum(np.asarray(input_values_7[2])[1200:])) < 1e-3)
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self.assertTrue(abs(sum(np.asarray(input_values_8[0])[800:])) < 1e-3)
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self.assertTrue(abs(sum(np.asarray(input_values_8[1])[1000:])) < 1e-3)
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self.assertTrue(abs(sum(np.asarray(input_values_8[2])[1200:])) < 1e-3)
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def test_save_pretrained(self):
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pretrained_name = list(self.tokenizer_class.pretrained_vocab_files_map["vocab_file"].keys())[0]
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tokenizer = self.tokenizer_class.from_pretrained(pretrained_name)
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tmpdirname2 = tempfile.mkdtemp()
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tokenizer_files = tokenizer.save_pretrained(tmpdirname2)
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self.assertSequenceEqual(
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sorted(tuple(VOCAB_FILES_NAMES.values()) + ("special_tokens_map.json", "added_tokens.json")),
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sorted(x.split(os.path.sep)[-1] for x in tokenizer_files),
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)
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# Checks everything loads correctly in the same way
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tokenizer_p = self.tokenizer_class.from_pretrained(tmpdirname2)
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# Check special tokens are set accordingly on Rust and Python
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for key in tokenizer.special_tokens_map:
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self.assertTrue(key in tokenizer_p.special_tokens_map)
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shutil.rmtree(tmpdirname2)
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def test_get_vocab(self):
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tokenizer = self.get_tokenizer()
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vocab_dict = tokenizer.get_vocab()
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self.assertIsInstance(vocab_dict, dict)
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self.assertGreaterEqual(len(tokenizer), len(vocab_dict))
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vocab = [tokenizer.convert_ids_to_tokens(i) for i in range(len(tokenizer))]
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self.assertEqual(len(vocab), len(tokenizer))
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tokenizer.add_tokens(["asdfasdfasdfasdf"])
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vocab = [tokenizer.convert_ids_to_tokens(i) for i in range(len(tokenizer))]
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self.assertEqual(len(vocab), len(tokenizer))
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def test_save_and_load_tokenizer(self):
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tokenizer = self.get_tokenizer()
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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_ids = [0, 1, 4, 8, 9, 0, 12]
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before_tokens = tokenizer.decode(sample_ids)
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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.decode(sample_ids)
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after_vocab = after_tokenizer.get_vocab()
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self.assertEqual(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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tokenizer = self.get_tokenizer()
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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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before_len = len(tokenizer)
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sample_ids = [0, 1, 4, 8, 9, 0, 12, before_len, before_len + 1, before_len + 2]
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tokenizer.add_tokens(["?", "!"])
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additional_special_tokens = tokenizer.additional_special_tokens
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additional_special_tokens.append("&")
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tokenizer.add_special_tokens(
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{"additional_special_tokens": additional_special_tokens}, replace_additional_special_tokens=False
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)
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before_tokens = tokenizer.decode(sample_ids)
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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.decode(sample_ids)
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after_vocab = after_tokenizer.get_vocab()
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self.assertEqual(before_tokens, after_tokens)
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self.assertDictEqual(before_vocab, after_vocab)
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self.assertTrue(len(tokenizer), before_len + 3)
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self.assertTrue(len(tokenizer), len(after_tokenizer))
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shutil.rmtree(tmpdirname)
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def test_tokenizer_slow_store_full_signature(self):
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signature = inspect.signature(self.tokenizer_class.__init__)
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tokenizer = self.get_tokenizer()
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for parameter_name, parameter in signature.parameters.items():
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if parameter.default != inspect.Parameter.empty:
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self.assertIn(parameter_name, tokenizer.init_kwargs)
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def test_zero_mean_unit_variance_normalization(self):
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tokenizer = self.get_tokenizer(do_normalize=True)
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speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
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processed = tokenizer(speech_inputs, padding="longest")
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input_values = processed.input_values
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def _check_zero_mean_unit_variance(input_vector):
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self.assertTrue(np.abs(np.mean(input_vector)) < 1e-3)
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self.assertTrue(np.abs(np.var(input_vector) - 1) < 1e-3)
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_check_zero_mean_unit_variance(input_values[0, :800])
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_check_zero_mean_unit_variance(input_values[1, :1000])
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_check_zero_mean_unit_variance(input_values[2])
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def test_return_attention_mask(self):
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speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
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# default case -> no attention_mask is returned
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tokenizer = self.get_tokenizer()
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processed = tokenizer(speech_inputs)
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self.assertNotIn("attention_mask", processed)
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# wav2vec2-lv60 -> return attention_mask
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tokenizer = self.get_tokenizer(return_attention_mask=True)
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processed = tokenizer(speech_inputs, padding="longest")
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self.assertIn("attention_mask", processed)
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self.assertListEqual(list(processed.attention_mask.shape), list(processed.input_values.shape))
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self.assertListEqual(processed.attention_mask.sum(-1).tolist(), [800, 1000, 1200])
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@slow
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@require_torch
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def test_pretrained_checkpoints_are_set_correctly(self):
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# this test makes sure that models that are using
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# group norm don't have their tokenizer return the
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# attention_mask
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model_id = "facebook/wav2vec2-base-960h"
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config = Wav2Vec2Config.from_pretrained(model_id)
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tokenizer = Wav2Vec2Tokenizer.from_pretrained(model_id)
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# only "layer" feature extraction norm should make use of
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# attention_mask
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self.assertEqual(tokenizer.return_attention_mask, config.feat_extract_norm == "layer")
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class Wav2Vec2CTCTokenizerTest(TokenizerTesterMixin, unittest.TestCase):
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from_pretrained_id = "facebook/wav2vec2-base-960h"
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tokenizer_class = Wav2Vec2CTCTokenizer
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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 = "<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K ' X J Q Z".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.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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return Wav2Vec2CTCTokenizer.from_pretrained(self.tmpdirname, **kwargs)
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def test_tokenizer_add_token_chars(self):
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tokenizer = self.tokenizer_class.from_pretrained("facebook/wav2vec2-base-960h")
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# check adding a single token
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tokenizer.add_tokens("x")
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token_ids = tokenizer("C x A").input_ids
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self.assertEqual(token_ids, [19, 4, 32, 4, 7])
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tokenizer.add_tokens(["a", "b", "c"])
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token_ids = tokenizer("C a A c").input_ids
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self.assertEqual(token_ids, [19, 4, 33, 4, 7, 4, 35])
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tokenizer.add_tokens(["a", "b", "c"])
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token_ids = tokenizer("CaA c").input_ids
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self.assertEqual(token_ids, [19, 33, 7, 4, 35])
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def test_tokenizer_add_token_words(self):
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tokenizer = self.tokenizer_class.from_pretrained("facebook/wav2vec2-base-960h")
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# check adding a single token
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tokenizer.add_tokens("xxx")
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token_ids = tokenizer("C xxx A B").input_ids
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self.assertEqual(token_ids, [19, 4, 32, 4, 7, 4, 24])
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tokenizer.add_tokens(["aaa", "bbb", "ccc"])
|
|
token_ids = tokenizer("C aaa A ccc B B").input_ids
|
|
self.assertEqual(token_ids, [19, 4, 33, 4, 7, 4, 35, 4, 24, 4, 24])
|
|
|
|
tokenizer.add_tokens(["aaa", "bbb", "ccc"])
|
|
token_ids = tokenizer("CaaaA ccc B B").input_ids
|
|
self.assertEqual(token_ids, [19, 33, 7, 4, 35, 4, 24, 4, 24])
|
|
|
|
def test_tokenizer_decode(self):
|
|
tokenizer = self.tokenizer_class.from_pretrained("facebook/wav2vec2-base-960h")
|
|
|
|
sample_ids = [
|
|
[11, 5, 15, tokenizer.pad_token_id, 15, 8, 98],
|
|
[24, 22, 5, tokenizer.word_delimiter_token_id, 24, 22, 5, 77],
|
|
]
|
|
tokens = tokenizer.decode(sample_ids[0])
|
|
batch_tokens = tokenizer.batch_decode(sample_ids)
|
|
self.assertEqual(tokens, batch_tokens[0])
|
|
self.assertEqual(batch_tokens, ["HELLO<unk>", "BYE BYE<unk>"])
|
|
|
|
def test_tokenizer_decode_special(self):
|
|
tokenizer = self.tokenizer_class.from_pretrained("facebook/wav2vec2-base-960h")
|
|
|
|
# fmt: off
|
|
sample_ids = [
|
|
[11, 5, 15, tokenizer.pad_token_id, 15, 8, 98],
|
|
[24, 22, 5, tokenizer.word_delimiter_token_id, 24, 22, 5, 77],
|
|
]
|
|
sample_ids_2 = [
|
|
[11, 5, 5, 5, 5, 5, 15, 15, 15, tokenizer.pad_token_id, 15, 8, 98],
|
|
[24, 22, 5, tokenizer.pad_token_id, tokenizer.pad_token_id, tokenizer.pad_token_id, tokenizer.word_delimiter_token_id, 24, 22, 5, 77, tokenizer.word_delimiter_token_id],
|
|
]
|
|
# fmt: on
|
|
|
|
batch_tokens = tokenizer.batch_decode(sample_ids)
|
|
batch_tokens_2 = tokenizer.batch_decode(sample_ids_2)
|
|
self.assertEqual(batch_tokens, batch_tokens_2)
|
|
self.assertEqual(batch_tokens, ["HELLO<unk>", "BYE BYE<unk>"])
|
|
|
|
def test_tokenizer_decode_added_tokens(self):
|
|
tokenizer = self.tokenizer_class.from_pretrained("facebook/wav2vec2-base-960h")
|
|
tokenizer.add_tokens(["!", "?", "<new_tokens>"])
|
|
tokenizer.add_special_tokens({"cls_token": "$$$"})
|
|
|
|
# fmt: off
|
|
sample_ids = [
|
|
[11, 5, 15, tokenizer.pad_token_id, 15, 8, 98, 32, 32, 33, tokenizer.word_delimiter_token_id, 32, 32, 33, 34, 34, 35, 35],
|
|
[24, 22, 5, tokenizer.word_delimiter_token_id, 24, 22, 5, 77, tokenizer.pad_token_id, 34, 34, 35, 35],
|
|
]
|
|
# fmt: on
|
|
batch_tokens = tokenizer.batch_decode(sample_ids)
|
|
batch_tokens_2 = tokenizer.batch_decode(sample_ids, skip_special_tokens=True)
|
|
|
|
self.assertEqual(batch_tokens, ["HELLO<unk>!?!?<new_tokens>$$$", "BYE BYE<unk><new_tokens>$$$"])
|
|
self.assertEqual(batch_tokens_2, ["HELO!?!?<new_tokens>", "BYE BYE<new_tokens>"])
|
|
|
|
def test_special_characters_in_vocab(self):
|
|
sent = "ʈʰ æ æ̃ ˧ kʰ"
|
|
|
|
vocab_dict = {k: v for v, k in enumerate(set(sent.split()))}
|
|
vocab_file = os.path.join(self.tmpdirname, "vocab_special.json")
|
|
|
|
with open(vocab_file, "w") as f:
|
|
json.dump(vocab_dict, f)
|
|
|
|
tokenizer = Wav2Vec2CTCTokenizer(vocab_file) # , unk_token="<unk>")
|
|
|
|
expected_sent = tokenizer.decode(tokenizer(sent).input_ids, spaces_between_special_tokens=True)
|
|
self.assertEqual(sent, expected_sent)
|
|
|
|
tokenizer.save_pretrained(os.path.join(self.tmpdirname, "special_tokenizer"))
|
|
tokenizer = Wav2Vec2CTCTokenizer.from_pretrained(os.path.join(self.tmpdirname, "special_tokenizer"))
|
|
|
|
expected_sent = tokenizer.decode(tokenizer(sent).input_ids, spaces_between_special_tokens=True)
|
|
self.assertEqual(sent, expected_sent)
|
|
|
|
@staticmethod
|
|
def get_from_offsets(offsets, key):
|
|
retrieved_list = [d[key] for d in offsets]
|
|
return retrieved_list
|
|
|
|
def test_offsets(self):
|
|
tokenizer = self.get_tokenizer()
|
|
|
|
# fmt: off
|
|
# HEEEEE||LLL<pad>LO<unk> => HE LLO<unk>
|
|
# 1H + 5E + 2| + 3L + 1<pad> + 1L + 1O + 1<unk>
|
|
sample_ids = [11, 5, 5, 5, 5, 5, 4, 4, 15, 15, 15, tokenizer.pad_token_id, 15, 8, 98]
|
|
# fmt: on
|
|
|
|
outputs_char = tokenizer.decode(sample_ids, output_char_offsets=True)
|
|
# check Wav2Vec2CTCTokenizerOutput keys for char
|
|
self.assertEqual(len(outputs_char.keys()), 2)
|
|
self.assertTrue("text" in outputs_char)
|
|
self.assertTrue("char_offsets" in outputs_char)
|
|
self.assertTrue(isinstance(outputs_char, Wav2Vec2CTCTokenizerOutput))
|
|
|
|
outputs_word = tokenizer.decode(sample_ids, output_word_offsets=True)
|
|
# check Wav2Vec2CTCTokenizerOutput keys for word
|
|
self.assertEqual(len(outputs_word.keys()), 2)
|
|
self.assertTrue("text" in outputs_word)
|
|
self.assertTrue("word_offsets" in outputs_word)
|
|
self.assertTrue(isinstance(outputs_word, Wav2Vec2CTCTokenizerOutput))
|
|
|
|
outputs = tokenizer.decode(sample_ids, output_char_offsets=True, output_word_offsets=True)
|
|
# check Wav2Vec2CTCTokenizerOutput keys for both
|
|
self.assertEqual(len(outputs.keys()), 3)
|
|
self.assertTrue("text" in outputs)
|
|
self.assertTrue("char_offsets" in outputs)
|
|
self.assertTrue("word_offsets" in outputs)
|
|
self.assertTrue(isinstance(outputs, Wav2Vec2CTCTokenizerOutput))
|
|
|
|
# check that order of chars is correct and identical for both outputs
|
|
self.assertEqual("".join(self.get_from_offsets(outputs["char_offsets"], "char")), outputs.text)
|
|
self.assertEqual(
|
|
self.get_from_offsets(outputs["char_offsets"], "char"), ["H", "E", " ", "L", "L", "O", "<unk>"]
|
|
)
|
|
self.assertListEqual(
|
|
self.get_from_offsets(outputs["char_offsets"], "char"),
|
|
self.get_from_offsets(outputs_char["char_offsets"], "char"),
|
|
)
|
|
|
|
# check that order of words is correct and identical to both outputs
|
|
self.assertEqual(" ".join(self.get_from_offsets(outputs["word_offsets"], "word")), outputs.text)
|
|
self.assertListEqual(self.get_from_offsets(outputs["word_offsets"], "word"), ["HE", "LLO<unk>"])
|
|
self.assertListEqual(
|
|
self.get_from_offsets(outputs["word_offsets"], "word"),
|
|
self.get_from_offsets(outputs_word["word_offsets"], "word"),
|
|
)
|
|
|
|
# check that offsets are actually correct for char
|
|
# 0 is H, 1 is E, 6 is | (" "), 8 is 1st L, 12 is 2nd L, 13 is O, 14 is <unk>
|
|
self.assertListEqual(self.get_from_offsets(outputs["char_offsets"], "start_offset"), [0, 1, 6, 8, 12, 13, 14])
|
|
# 1 is H, 6 is E, 8 is | (" "), 11 is 1st L (note due to <pad>
|
|
# different begin of 2nd L), 13 is 2nd L, 14 is O, 15 is <unk>
|
|
self.assertListEqual(self.get_from_offsets(outputs["char_offsets"], "end_offset"), [1, 6, 8, 11, 13, 14, 15])
|
|
|
|
# check that offsets are actually correct for word
|
|
# H is at 1st position of first word, first L is at 8th position of second word
|
|
self.assertListEqual(self.get_from_offsets(outputs["word_offsets"], "start_offset"), [0, 8])
|
|
# last E is at 6th position of first word, first L is at last (15th) position of second word
|
|
self.assertListEqual(self.get_from_offsets(outputs["word_offsets"], "end_offset"), [6, 15])
|
|
|
|
def test_word_offsets_from_char_offsets(self):
|
|
tokenizer = self.get_tokenizer()
|
|
|
|
char_offsets = [
|
|
{"char": "H", "start_offset": 0, "end_offset": 1},
|
|
{"char": "I", "start_offset": 1, "end_offset": 2},
|
|
{"char": " ", "start_offset": 2, "end_offset": 3},
|
|
{"char": "L", "start_offset": 3, "end_offset": 4},
|
|
{"char": "I", "start_offset": 4, "end_offset": 5},
|
|
]
|
|
word_offsets = tokenizer._get_word_offsets(char_offsets, tokenizer.replace_word_delimiter_char)
|
|
|
|
self.assertEqual(
|
|
word_offsets,
|
|
[{"word": "HI", "start_offset": 0, "end_offset": 2}, {"word": "LI", "start_offset": 3, "end_offset": 5}],
|
|
)
|
|
|
|
# Double spaces don't get counted
|
|
char_offsets = [
|
|
{"char": " ", "start_offset": 0, "end_offset": 1},
|
|
{"char": "H", "start_offset": 1, "end_offset": 2},
|
|
{"char": "I", "start_offset": 2, "end_offset": 3},
|
|
{"char": " ", "start_offset": 3, "end_offset": 4},
|
|
{"char": " ", "start_offset": 4, "end_offset": 5},
|
|
{"char": "L", "start_offset": 5, "end_offset": 6},
|
|
{"char": "I", "start_offset": 6, "end_offset": 7},
|
|
{"char": "I", "start_offset": 7, "end_offset": 8},
|
|
{"char": " ", "start_offset": 8, "end_offset": 9},
|
|
{"char": " ", "start_offset": 9, "end_offset": 10},
|
|
]
|
|
word_offsets = tokenizer._get_word_offsets(char_offsets, tokenizer.replace_word_delimiter_char)
|
|
self.assertEqual(
|
|
word_offsets,
|
|
[{"word": "HI", "start_offset": 1, "end_offset": 3}, {"word": "LII", "start_offset": 5, "end_offset": 8}],
|
|
)
|
|
|
|
def test_offsets_batch(self):
|
|
tokenizer = self.get_tokenizer()
|
|
|
|
def check_list_tuples_equal(outputs_batch, outputs_list):
|
|
self.assertTrue(isinstance(outputs_batch, Wav2Vec2CTCTokenizerOutput))
|
|
self.assertTrue(isinstance(outputs_list[0], Wav2Vec2CTCTokenizerOutput))
|
|
|
|
# transform list to ModelOutput
|
|
outputs_batch_2 = Wav2Vec2CTCTokenizerOutput({k: [d[k] for d in outputs_list] for k in outputs_list[0]})
|
|
|
|
self.assertListEqual(outputs_batch["text"], outputs_batch_2["text"])
|
|
|
|
def recursive_check(list_or_dict_1, list_or_dict_2):
|
|
if isinstance(list_or_dict_1, list):
|
|
[recursive_check(l1, l2) for l1, l2 in zip(list_or_dict_1, list_or_dict_2)]
|
|
self.assertEqual(list_or_dict_1, list_or_dict_2)
|
|
|
|
if "char_offsets" in outputs_batch:
|
|
recursive_check(outputs_batch["char_offsets"], outputs_batch_2["char_offsets"])
|
|
|
|
if "word_offsets" in outputs_batch:
|
|
recursive_check(outputs_batch["word_offsets"], outputs_batch_2["word_offsets"])
|
|
|
|
# fmt: off
|
|
sample_ids = [
|
|
[11, 5, 15, tokenizer.pad_token_id, 15, 4, 8, 98, 32, 32, 32, 32, 4, 33, tokenizer.word_delimiter_token_id, 32, 32, 33, 34, 34],
|
|
[24, 22, 5, tokenizer.word_delimiter_token_id, tokenizer.word_delimiter_token_id, 24, 22, 22, 22, 4, 5, 77, tokenizer.pad_token_id, 22, 22, 4, 34, 34, 34, 34],
|
|
]
|
|
# fmt: on
|
|
|
|
# We assume that `decode` works as expected. All we will check now is
|
|
# the output type is correct and the output is identical to `decode`
|
|
|
|
# char
|
|
outputs_char_batch = tokenizer.batch_decode(sample_ids, output_char_offsets=True)
|
|
outputs_char = [tokenizer.decode(ids, output_char_offsets=True) for ids in sample_ids]
|
|
check_list_tuples_equal(outputs_char_batch, outputs_char)
|
|
|
|
# word
|
|
outputs_word_batch = tokenizer.batch_decode(sample_ids, output_word_offsets=True)
|
|
outputs_word = [tokenizer.decode(ids, output_word_offsets=True) for ids in sample_ids]
|
|
check_list_tuples_equal(outputs_word_batch, outputs_word)
|
|
|
|
# both
|
|
outputs_batch = tokenizer.batch_decode(sample_ids, output_char_offsets=True, output_word_offsets=True)
|
|
outputs = [tokenizer.decode(ids, output_word_offsets=True, output_char_offsets=True) for ids in sample_ids]
|
|
check_list_tuples_equal(outputs_batch, outputs)
|
|
|
|
def test_offsets_integration(self):
|
|
tokenizer = self.tokenizer_class.from_pretrained("facebook/wav2vec2-base-960h")
|
|
# pred_ids correspond to the following code
|
|
# ```
|
|
# from transformers import AutoTokenizer, AutoFeatureExtractor, AutoModelForCTC
|
|
# from datasets import load_dataset
|
|
# import datasets
|
|
# import torch
|
|
# model = AutoModelForCTC.from_pretrained("facebook/wav2vec2-base-960h")
|
|
# feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base-960h")
|
|
#
|
|
# ds = load_dataset("common_voice", "en", split="train", streaming=True)
|
|
# ds = ds.cast_column("audio", datasets.Audio(sampling_rate=16_000))
|
|
# ds_iter = iter(ds)
|
|
# sample = next(ds_iter)
|
|
#
|
|
# input_values = feature_extractor(sample["audio"]["array"], return_tensors="pt").input_values
|
|
# logits = model(input_values).logits
|
|
# pred_ids = torch.argmax(logits, axis=-1).cpu().tolist()
|
|
# ```
|
|
# fmt: off
|
|
pred_ids = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 18, 11, 0, 0, 0, 22, 0, 0, 4, 4, 4, 14, 0, 0, 0, 0, 0, 8, 8, 0, 5, 5, 0, 12, 0, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 17, 0, 0, 10, 0, 0, 0, 15, 0, 0, 10, 0, 0, 0, 12, 0, 0, 0, 0, 0, 7, 0, 9, 0, 0, 14, 0, 0, 0, 13, 0, 7, 0, 0, 4, 4, 0, 15, 8, 8, 0, 0, 8, 0, 26, 0, 0, 4, 4, 0, 0, 15, 0, 0, 0, 0, 0, 0, 10, 0, 26, 5, 5, 0, 4, 4, 0, 0, 12, 11, 0, 0, 5, 4, 4, 4, 0, 18, 0, 0, 0, 7, 9, 9, 0, 6, 0, 12, 12, 4, 4, 0, 6, 0, 0, 8, 0, 4, 4, 4, 0, 19, 0, 0, 8, 9, 9, 0, 0, 0, 0, 12, 12, 0, 0, 0, 0, 0, 0, 0, 16, 16, 0, 0, 17, 5, 5, 5, 0, 4, 4, 4, 0, 0, 29, 29, 0, 0, 0, 0, 8, 11, 0, 9, 9, 0, 0, 0, 4, 4, 0, 12, 12, 0, 0, 0, 9, 0, 0, 0, 0, 0, 8, 18, 0, 0, 0, 4, 4, 0, 0, 8, 9, 0, 4, 4, 0, 6, 11, 5, 0, 4, 4, 0, 13, 13, 0, 0, 0, 10, 0, 0, 25, 0, 0, 6, 0, 4, 4, 0, 0, 0, 0, 7, 0, 0, 23, 0, 0, 4, 4, 0, 0, 0, 6, 11, 0, 5, 4, 4, 18, 0, 0, 0, 0, 0, 0, 7, 15, 0, 0, 0, 15, 15, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
|
|
|
|
# wav2vec2-base downsamples input audio by a factor of 320
|
|
# sampling rate for wav2vec2-base is 16_000
|
|
time_offset_wav2vec2_base = 320 / 16_000
|
|
|
|
expected_char_time_stamps_text = ['W', 'H', 'Y', ' ', 'D', 'O', 'E', 'S', ' ', 'M', 'I', 'L', 'I', 'S', 'A', 'N', 'D', 'R', 'A', ' ', 'L', 'O', 'O', 'K', ' ', 'L', 'I', 'K', 'E', ' ', 'S', 'H', 'E', ' ', 'W', 'A', 'N', 'T', 'S', ' ', 'T', 'O', ' ', 'C', 'O', 'N', 'S', 'U', 'M', 'E', ' ', 'J', 'O', 'H', 'N', ' ', 'S', 'N', 'O', 'W', ' ', 'O', 'N', ' ', 'T', 'H', 'E', ' ', 'R', 'I', 'V', 'T', ' ', 'A', 'P', ' ', 'T', 'H', 'E', ' ', 'W', 'A', 'L', 'L', ' ']
|
|
expected_char_time_stamps_start = [1.42, 1.44, 1.52, 1.58, 1.64, 1.76, 1.82, 1.88, 1.92, 2.26, 2.32, 2.4, 2.46, 2.54, 2.66, 2.7, 2.76, 2.84, 2.88, 2.94, 3.0, 3.02, 3.1, 3.14, 3.2, 3.28, 3.42, 3.46, 3.48, 3.54, 3.62, 3.64, 3.7, 3.72, 3.8, 3.88, 3.9, 3.96, 4.0, 4.04, 4.1, 4.16, 4.2, 4.28, 4.34, 4.36, 4.48, 4.66, 4.74, 4.76, 4.84, 4.94, 5.06, 5.08, 5.12, 5.22, 5.28, 5.38, 5.5, 5.52, 5.6, 5.68, 5.7, 5.74, 5.8, 5.82, 5.84, 5.88, 5.94, 6.04, 6.1, 6.16, 6.2, 6.32, 6.38, 6.44, 6.54, 6.56, 6.6, 6.62, 6.66, 6.8, 6.82, 6.9, 6.96]
|
|
expected_char_time_stamps_end = [1.44, 1.46, 1.54, 1.64, 1.66, 1.8, 1.86, 1.9, 2.06, 2.28, 2.34, 2.42, 2.48, 2.56, 2.68, 2.72, 2.78, 2.86, 2.9, 2.98, 3.02, 3.06, 3.12, 3.16, 3.24, 3.3, 3.44, 3.48, 3.52, 3.58, 3.64, 3.66, 3.72, 3.78, 3.82, 3.9, 3.94, 3.98, 4.04, 4.08, 4.12, 4.18, 4.26, 4.3, 4.36, 4.4, 4.52, 4.7, 4.76, 4.82, 4.9, 4.98, 5.08, 5.1, 5.16, 5.26, 5.32, 5.4, 5.52, 5.54, 5.64, 5.7, 5.72, 5.78, 5.82, 5.84, 5.86, 5.92, 5.98, 6.06, 6.12, 6.18, 6.24, 6.34, 6.4, 6.48, 6.56, 6.58, 6.62, 6.66, 6.68, 6.82, 6.84, 6.94, 7.02]
|
|
|
|
expected_word_time_stamps_text = ['WHY', 'DOES', 'MILISANDRA', 'LOOK', 'LIKE', 'SHE', 'WANTS', 'TO', 'CONSUME', 'JOHN', 'SNOW', 'ON', 'THE', 'RIVT', 'AP', 'THE', 'WALL']
|
|
expected_word_time_stamps_start = [1.42, 1.64, 2.26, 3.0, 3.28, 3.62, 3.8, 4.1, 4.28, 4.94, 5.28, 5.68, 5.8, 5.94, 6.32, 6.54, 6.66]
|
|
expected_word_time_stamps_end = [1.54, 1.9, 2.9, 3.16, 3.52, 3.72, 4.04, 4.18, 4.82, 5.16, 5.54, 5.72, 5.86, 6.18, 6.4, 6.62, 6.94]
|
|
# fmt: on
|
|
|
|
output = tokenizer.batch_decode(pred_ids, output_char_offsets=True, output_word_offsets=True)
|
|
|
|
char_offsets_text = self.get_from_offsets(output["char_offsets"][0], "char")
|
|
char_offsets_start = self.get_from_offsets(output["char_offsets"][0], "start_offset")
|
|
char_offsets_end = self.get_from_offsets(output["char_offsets"][0], "end_offset")
|
|
|
|
word_offsets_text = self.get_from_offsets(output["word_offsets"][0], "word")
|
|
word_offsets_start = self.get_from_offsets(output["word_offsets"][0], "start_offset")
|
|
word_offsets_end = self.get_from_offsets(output["word_offsets"][0], "end_offset")
|
|
|
|
# let's transform offsets to time stamps in seconds
|
|
char_time_stamps_start = [round(c * time_offset_wav2vec2_base, 2) for c in char_offsets_start]
|
|
char_time_stamps_end = [round(c * time_offset_wav2vec2_base, 2) for c in char_offsets_end]
|
|
|
|
word_time_stamps_start = [round(w * time_offset_wav2vec2_base, 2) for w in word_offsets_start]
|
|
word_time_stamps_end = [round(w * time_offset_wav2vec2_base, 2) for w in word_offsets_end]
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|
|
|
# NOTE: you can verify the above results by checking out the dataset viewer
|
|
# on https://huggingface.co/datasets/common_voice/viewer/en/train and
|
|
# downloading / playing the sample `common_voice_en_100038.mp3`. As
|
|
# you can hear the time-stamps match more or less
|
|
|
|
self.assertListEqual(expected_char_time_stamps_text, char_offsets_text)
|
|
self.assertListEqual(expected_char_time_stamps_start, char_time_stamps_start)
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|
self.assertListEqual(expected_char_time_stamps_end, char_time_stamps_end)
|
|
|
|
self.assertListEqual(expected_word_time_stamps_text, word_offsets_text)
|
|
self.assertListEqual(expected_word_time_stamps_start, word_time_stamps_start)
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|
self.assertListEqual(expected_word_time_stamps_end, word_time_stamps_end)
|
|
|
|
# overwrite from test_tokenization_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": AddedToken(">>>>|||<||<<|<<", lstrip=False, rstrip=False),
|
|
"pad_token": AddedToken("<<<<<|||>|>>>>|>", rstrip=False, lstrip=False),
|
|
}
|
|
added_toks_2 = tokenizer.add_special_tokens(new_toks_2)
|
|
vocab_size_3 = tokenizer.vocab_size
|
|
all_size_3 = len(tokenizer)
|
|
|
|
self.assertNotEqual(vocab_size_3, 0)
|
|
self.assertEqual(vocab_size, vocab_size_3)
|
|
self.assertEqual(added_toks_2, len(new_toks_2))
|
|
self.assertEqual(all_size_3, all_size_2 + len(new_toks_2))
|
|
|
|
tokens = tokenizer.encode(
|
|
">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l", add_special_tokens=False
|
|
)
|
|
|
|
self.assertGreaterEqual(len(tokens), 6)
|
|
self.assertGreater(tokens[0], tokenizer.vocab_size - 1)
|
|
self.assertGreater(tokens[0], tokens[1])
|
|
self.assertGreater(tokens[-3], tokenizer.vocab_size - 1)
|
|
self.assertGreater(tokens[-3], tokens[-4])
|
|
self.assertEqual(tokens[0], tokenizer.eos_token_id)
|
|
self.assertEqual(tokens[-3], tokenizer.pad_token_id)
|
|
|
|
@unittest.skip("The tokenizer shouldn't be used to encode input IDs (except for labels), only to decode.")
|
|
def test_tf_encode_plus_sent_to_model(self):
|
|
pass
|
|
|
|
@unittest.skip("The tokenizer shouldn't be used to encode input IDs (except for labels), only to decode.")
|
|
def test_torch_encode_plus_sent_to_model(self):
|
|
pass
|
|
|
|
def test_convert_tokens_to_string_format(self):
|
|
# The default common tokenizer tests assumes that the output of `convert_tokens_to_string` is a string which
|
|
# is not the case for Wav2vec2.
|
|
tokenizers = self.get_tokenizers(fast=True, do_lower_case=True)
|
|
for tokenizer in tokenizers:
|
|
with self.subTest(f"{tokenizer.__class__.__name__}"):
|
|
tokens = ["T", "H", "I", "S", "|", "I", "S", "|", "A", "|", "T", "E", "X", "T"]
|
|
output = tokenizer.convert_tokens_to_string(tokens)
|
|
|
|
self.assertIsInstance(output["text"], str)
|
|
|
|
def test_nested_vocab(self):
|
|
eng_vocab = {"a": 7, "b": 8}
|
|
spa_vocab = {"a": 23, "c": 88}
|
|
ita_vocab = {"a": 6, "d": 9}
|
|
|
|
nested_vocab = {"eng": eng_vocab, "spa": spa_vocab, "ita": ita_vocab}
|
|
|
|
def check_tokenizer(tokenizer, check_ita_first=False):
|
|
if check_ita_first:
|
|
self.assertEqual(tokenizer.decode([6, 9, 9]), "ad")
|
|
self.assertEqual(tokenizer.encoder, ita_vocab)
|
|
tokenizer.set_target_lang("eng")
|
|
|
|
self.assertEqual(tokenizer.encoder, eng_vocab)
|
|
self.assertEqual(tokenizer.decode([7, 8, 7]), "aba")
|
|
|
|
tokenizer.set_target_lang("spa")
|
|
self.assertEqual(tokenizer.decode([23, 88, 23]), "aca")
|
|
self.assertEqual(tokenizer.encoder, spa_vocab)
|
|
|
|
tokenizer.set_target_lang("eng")
|
|
self.assertEqual(tokenizer.encoder, eng_vocab)
|
|
self.assertEqual(tokenizer.decode([7, 7, 8]), "ab")
|
|
|
|
tokenizer.set_target_lang("ita")
|
|
self.assertEqual(tokenizer.decode([6, 9, 9]), "ad")
|
|
self.assertEqual(tokenizer.encoder, ita_vocab)
|
|
|
|
with tempfile.TemporaryDirectory() as tempdir:
|
|
tempfile_path = os.path.join(tempdir, "vocab.json")
|
|
with open(tempfile_path, "w") as temp_file:
|
|
json.dump(nested_vocab, temp_file)
|
|
|
|
tokenizer = Wav2Vec2CTCTokenizer.from_pretrained(tempdir, target_lang="eng")
|
|
|
|
check_tokenizer(tokenizer)
|
|
|
|
with tempfile.TemporaryDirectory() as tempdir:
|
|
# should have saved target lang as "ita" since it was last one
|
|
tokenizer.save_pretrained(tempdir)
|
|
tokenizer = Wav2Vec2CTCTokenizer.from_pretrained(tempdir)
|
|
|
|
self.assertEqual(tokenizer.target_lang, "ita")
|
|
check_tokenizer(tokenizer, check_ita_first=True)
|