149 lines
5.9 KiB
Python
149 lines
5.9 KiB
Python
# 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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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 pathlib import Path
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from shutil import copyfile
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from transformers import Speech2TextTokenizer, is_speech_available
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from transformers.file_utils import FEATURE_EXTRACTOR_NAME
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from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
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from transformers.testing_utils import require_sentencepiece, require_torch, require_torchaudio
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from .test_feature_extraction_speech_to_text import floats_list
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if is_speech_available():
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from transformers import Speech2TextFeatureExtractor, Speech2TextProcessor
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SAMPLE_SP = os.path.join(os.path.dirname(os.path.abspath(__file__)), "fixtures/test_sentencepiece.model")
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@require_torch
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@require_torchaudio
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@require_sentencepiece
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class Speech2TextProcessorTest(unittest.TestCase):
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def setUp(self):
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self.tmpdirname = tempfile.mkdtemp()
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vocab = ["<s>", "<pad>", "</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est"]
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vocab_tokens = dict(zip(vocab, range(len(vocab))))
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save_dir = Path(self.tmpdirname)
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save_json(vocab_tokens, save_dir / VOCAB_FILES_NAMES["vocab_file"])
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if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
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copyfile(SAMPLE_SP, save_dir / VOCAB_FILES_NAMES["spm_file"])
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tokenizer = Speech2TextTokenizer.from_pretrained(self.tmpdirname)
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tokenizer.save_pretrained(self.tmpdirname)
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feature_extractor_map = {
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"feature_size": 24,
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"num_mel_bins": 24,
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"padding_value": 0.0,
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"sampling_rate": 16000,
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"return_attention_mask": False,
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"do_normalize": True,
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}
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save_json(feature_extractor_map, save_dir / FEATURE_EXTRACTOR_NAME)
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def get_tokenizer(self, **kwargs):
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return Speech2TextTokenizer.from_pretrained(self.tmpdirname, **kwargs)
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def get_feature_extractor(self, **kwargs):
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return Speech2TextFeatureExtractor.from_pretrained(self.tmpdirname, **kwargs)
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def tearDown(self):
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shutil.rmtree(self.tmpdirname)
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def test_save_load_pretrained_default(self):
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tokenizer = self.get_tokenizer()
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feature_extractor = self.get_feature_extractor()
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processor = Speech2TextProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
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processor.save_pretrained(self.tmpdirname)
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processor = Speech2TextProcessor.from_pretrained(self.tmpdirname)
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self.assertEqual(processor.tokenizer.get_vocab(), tokenizer.get_vocab())
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self.assertIsInstance(processor.tokenizer, Speech2TextTokenizer)
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self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor.to_json_string())
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self.assertIsInstance(processor.feature_extractor, Speech2TextFeatureExtractor)
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def test_save_load_pretrained_additional_features(self):
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processor = Speech2TextProcessor(
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tokenizer=self.get_tokenizer(), feature_extractor=self.get_feature_extractor()
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)
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processor.save_pretrained(self.tmpdirname)
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tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)")
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feature_extractor_add_kwargs = self.get_feature_extractor(do_normalize=False, padding_value=1.0)
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processor = Speech2TextProcessor.from_pretrained(
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self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False, padding_value=1.0
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)
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self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
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self.assertIsInstance(processor.tokenizer, Speech2TextTokenizer)
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self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor_add_kwargs.to_json_string())
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self.assertIsInstance(processor.feature_extractor, Speech2TextFeatureExtractor)
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def test_feature_extractor(self):
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feature_extractor = self.get_feature_extractor()
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tokenizer = self.get_tokenizer()
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processor = Speech2TextProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
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raw_speech = floats_list((3, 1000))
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input_feat_extract = feature_extractor(raw_speech, return_tensors="np")
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input_processor = processor(raw_speech, return_tensors="np")
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for key in input_feat_extract.keys():
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self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2)
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def test_tokenizer(self):
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feature_extractor = self.get_feature_extractor()
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tokenizer = self.get_tokenizer()
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processor = Speech2TextProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
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input_str = "This is a test string"
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with processor.as_target_processor():
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encoded_processor = processor(input_str)
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encoded_tok = tokenizer(input_str)
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for key in encoded_tok.keys():
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self.assertListEqual(encoded_tok[key], encoded_processor[key])
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def test_tokenizer_decode(self):
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feature_extractor = self.get_feature_extractor()
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tokenizer = self.get_tokenizer()
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processor = Speech2TextProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
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predicted_ids = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
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decoded_processor = processor.batch_decode(predicted_ids)
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decoded_tok = tokenizer.batch_decode(predicted_ids)
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self.assertListEqual(decoded_tok, decoded_processor)
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