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ee4fa2e465
* [AutoProcessor] Add Wav2Vec2WithLM & small fix * revert line removal * Update src/transformers/__init__.py * add test * up * up * small fix
237 lines
9.0 KiB
Python
237 lines
9.0 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 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 multiprocessing import Pool
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import numpy as np
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from transformers.file_utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available
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from transformers.models.wav2vec2 import Wav2Vec2CTCTokenizer, Wav2Vec2FeatureExtractor
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from transformers.models.wav2vec2.tokenization_wav2vec2 import VOCAB_FILES_NAMES
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from transformers.testing_utils import require_pyctcdecode
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from .test_feature_extraction_wav2vec2 import floats_list
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if is_pyctcdecode_available():
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from pyctcdecode import BeamSearchDecoderCTC
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from transformers.models.wav2vec2_with_lm import Wav2Vec2ProcessorWithLM
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@require_pyctcdecode
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class Wav2Vec2ProcessorWithLMTest(unittest.TestCase):
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def setUp(self):
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vocab = "| <pad> <unk> <s> </s> a b c d e f g h i j k".split()
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vocab_tokens = dict(zip(vocab, range(len(vocab))))
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self.add_kwargs_tokens_map = {
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"unk_token": "<unk>",
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"bos_token": "<s>",
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"eos_token": "</s>",
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}
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feature_extractor_map = {
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"feature_size": 1,
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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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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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self.feature_extraction_file = os.path.join(self.tmpdirname, FEATURE_EXTRACTOR_NAME)
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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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with open(self.feature_extraction_file, "w", encoding="utf-8") as fp:
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fp.write(json.dumps(feature_extractor_map) + "\n")
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# load decoder from hub
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self.decoder_name = "hf-internal-testing/ngram-beam-search-decoder"
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def get_tokenizer(self, **kwargs_init):
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kwargs = self.add_kwargs_tokens_map.copy()
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kwargs.update(kwargs_init)
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return Wav2Vec2CTCTokenizer.from_pretrained(self.tmpdirname, **kwargs)
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def get_feature_extractor(self, **kwargs):
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return Wav2Vec2FeatureExtractor.from_pretrained(self.tmpdirname, **kwargs)
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def get_decoder(self, **kwargs):
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return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name, **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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decoder = self.get_decoder()
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processor = Wav2Vec2ProcessorWithLM(tokenizer=tokenizer, feature_extractor=feature_extractor, decoder=decoder)
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processor.save_pretrained(self.tmpdirname)
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processor = Wav2Vec2ProcessorWithLM.from_pretrained(self.tmpdirname)
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# tokenizer
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self.assertEqual(processor.tokenizer.get_vocab(), tokenizer.get_vocab())
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self.assertIsInstance(processor.tokenizer, Wav2Vec2CTCTokenizer)
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# feature extractor
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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, Wav2Vec2FeatureExtractor)
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# decoder
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self.assertEqual(processor.decoder._alphabet.labels, decoder._alphabet.labels)
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self.assertEqual(
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processor.decoder.model_container[decoder._model_key]._unigram_set,
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decoder.model_container[decoder._model_key]._unigram_set,
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)
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self.assertIsInstance(processor.decoder, BeamSearchDecoderCTC)
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def test_save_load_pretrained_additional_features(self):
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processor = Wav2Vec2ProcessorWithLM(
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tokenizer=self.get_tokenizer(), feature_extractor=self.get_feature_extractor(), decoder=self.get_decoder()
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)
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processor.save_pretrained(self.tmpdirname)
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# make sure that error is thrown when decoder alphabet doesn't match
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processor = Wav2Vec2ProcessorWithLM.from_pretrained(
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self.tmpdirname, alpha=5.0, beta=3.0, score_boundary=-7.0, unk_score_offset=3
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)
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# decoder
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self.assertEqual(processor.language_model.alpha, 5.0)
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self.assertEqual(processor.language_model.beta, 3.0)
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self.assertEqual(processor.language_model.score_boundary, -7.0)
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self.assertEqual(processor.language_model.unk_score_offset, 3)
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def test_load_decoder_tokenizer_mismatch_content(self):
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tokenizer = self.get_tokenizer()
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# add token to trigger raise
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tokenizer.add_tokens(["xx"])
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with self.assertRaisesRegex(ValueError, "include"):
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Wav2Vec2ProcessorWithLM(
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tokenizer=tokenizer, feature_extractor=self.get_feature_extractor(), decoder=self.get_decoder()
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)
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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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decoder = self.get_decoder()
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processor = Wav2Vec2ProcessorWithLM(tokenizer=tokenizer, feature_extractor=feature_extractor, decoder=decoder)
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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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decoder = self.get_decoder()
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processor = Wav2Vec2ProcessorWithLM(tokenizer=tokenizer, feature_extractor=feature_extractor, decoder=decoder)
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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 _get_dummy_logits(self, shape=(2, 10, 16), seed=77):
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np.random.seed(seed)
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return np.random.rand(*shape)
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def test_decoder(self):
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feature_extractor = self.get_feature_extractor()
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tokenizer = self.get_tokenizer()
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decoder = self.get_decoder()
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processor = Wav2Vec2ProcessorWithLM(tokenizer=tokenizer, feature_extractor=feature_extractor, decoder=decoder)
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logits = self._get_dummy_logits(shape=(10, 16), seed=13)
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decoded_processor = processor.decode(logits).text
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decoded_decoder = decoder.decode_beams(logits)[0][0]
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self.assertEqual(decoded_decoder, decoded_processor)
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self.assertEqual("</s> <s> </s>", decoded_processor)
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def test_decoder_batch(self):
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feature_extractor = self.get_feature_extractor()
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tokenizer = self.get_tokenizer()
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decoder = self.get_decoder()
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processor = Wav2Vec2ProcessorWithLM(tokenizer=tokenizer, feature_extractor=feature_extractor, decoder=decoder)
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logits = self._get_dummy_logits()
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decoded_processor = processor.batch_decode(logits).text
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logits_list = [array for array in logits]
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decoded_decoder = [d[0][0] for d in decoder.decode_beams_batch(Pool(), logits_list)]
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self.assertListEqual(decoded_decoder, decoded_processor)
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self.assertListEqual(["<s> <s> </s>", "<s> <s> <s>"], decoded_processor)
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def test_decoder_with_params(self):
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feature_extractor = self.get_feature_extractor()
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tokenizer = self.get_tokenizer()
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decoder = self.get_decoder()
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processor = Wav2Vec2ProcessorWithLM(tokenizer=tokenizer, feature_extractor=feature_extractor, decoder=decoder)
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logits = self._get_dummy_logits()
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beam_width = 20
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beam_prune_logp = -20.0
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token_min_logp = -4.0
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decoded_processor_out = processor.batch_decode(
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logits,
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beam_width=beam_width,
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beam_prune_logp=beam_prune_logp,
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token_min_logp=token_min_logp,
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)
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decoded_processor = decoded_processor_out.text
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logits_list = [array for array in logits]
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decoded_decoder_out = decoder.decode_beams_batch(
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Pool(),
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logits_list,
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beam_width=beam_width,
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beam_prune_logp=beam_prune_logp,
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token_min_logp=token_min_logp,
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)
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decoded_decoder = [d[0][0] for d in decoded_decoder_out]
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self.assertListEqual(decoded_decoder, decoded_processor)
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self.assertListEqual(["<s> </s> </s>", "<s> <s> </s>"], decoded_processor)
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