[Wav2Vec2] PyCTCDecode Integration to support language model boosted decoding (#14339)

* up * up * up * make it cleaner * correct * make styhahalal * add more tests * finish * small fix * make style * up * tryout to solve cicrle ci * up * fix more tests * fix more tests * apply sylvains suggestions * fix import * correct docs * add pyctcdecode only to speech tests * fix more tests * add tf, flax and pt tests * add pt * fix last tests * fix more tests * Apply suggestions from code review * change lines * Apply suggestions from code review Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com> * correct tests * correct tests * add doc string Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>
2021-12-08 12:07:54 +01:00
parent 2e12d90b9e
commit 961732c276
16 changed files with 831 additions and 19 deletions
--- a/tests/test_processor_wav2vec2_with_lm.py
+++ b/tests/test_processor_wav2vec2_with_lm.py
@@ -0,0 +1,236 @@
+# Copyright 2021 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import json
+import os
+import shutil
+import tempfile
+import unittest
+from multiprocessing import Pool
+
+import numpy as np
+
+from transformers.file_utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available
+from transformers.models.wav2vec2 import Wav2Vec2CTCTokenizer, Wav2Vec2FeatureExtractor
+from transformers.models.wav2vec2.tokenization_wav2vec2 import VOCAB_FILES_NAMES
+from transformers.testing_utils import require_pyctcdecode
+
+from .test_feature_extraction_wav2vec2 import floats_list
+
+
+if is_pyctcdecode_available():
+    from pyctcdecode import BeamSearchDecoderCTC
+    from transformers.models.wav2vec2 import Wav2Vec2ProcessorWithLM
+
+
+@require_pyctcdecode
+class Wav2Vec2ProcessorWithLMTest(unittest.TestCase):
+    def setUp(self):
+        vocab = "| <pad> <unk> <s> </s> a b c d e f g h i j k".split()
+        vocab_tokens = dict(zip(vocab, range(len(vocab))))
+
+        self.add_kwargs_tokens_map = {
+            "unk_token": "<unk>",
+            "bos_token": "<s>",
+            "eos_token": "</s>",
+        }
+        feature_extractor_map = {
+            "feature_size": 1,
+            "padding_value": 0.0,
+            "sampling_rate": 16000,
+            "return_attention_mask": False,
+            "do_normalize": True,
+        }
+
+        self.tmpdirname = tempfile.mkdtemp()
+        self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
+        self.feature_extraction_file = os.path.join(self.tmpdirname, FEATURE_EXTRACTOR_NAME)
+        with open(self.vocab_file, "w", encoding="utf-8") as fp:
+            fp.write(json.dumps(vocab_tokens) + "\n")
+
+        with open(self.feature_extraction_file, "w", encoding="utf-8") as fp:
+            fp.write(json.dumps(feature_extractor_map) + "\n")
+
+        # load decoder from hub
+        self.decoder_name = "hf-internal-testing/ngram-beam-search-decoder"
+
+    def get_tokenizer(self, **kwargs_init):
+        kwargs = self.add_kwargs_tokens_map.copy()
+        kwargs.update(kwargs_init)
+        return Wav2Vec2CTCTokenizer.from_pretrained(self.tmpdirname, **kwargs)
+
+    def get_feature_extractor(self, **kwargs):
+        return Wav2Vec2FeatureExtractor.from_pretrained(self.tmpdirname, **kwargs)
+
+    def get_decoder(self, **kwargs):
+        return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name, **kwargs)
+
+    def tearDown(self):
+        shutil.rmtree(self.tmpdirname)
+
+    def test_save_load_pretrained_default(self):
+        tokenizer = self.get_tokenizer()
+        feature_extractor = self.get_feature_extractor()
+        decoder = self.get_decoder()
+
+        processor = Wav2Vec2ProcessorWithLM(tokenizer=tokenizer, feature_extractor=feature_extractor, decoder=decoder)
+
+        processor.save_pretrained(self.tmpdirname)
+        processor = Wav2Vec2ProcessorWithLM.from_pretrained(self.tmpdirname)
+
+        # tokenizer
+        self.assertEqual(processor.tokenizer.get_vocab(), tokenizer.get_vocab())
+        self.assertIsInstance(processor.tokenizer, Wav2Vec2CTCTokenizer)
+
+        # feature extractor
+        self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor.to_json_string())
+        self.assertIsInstance(processor.feature_extractor, Wav2Vec2FeatureExtractor)
+
+        # decoder
+        self.assertEqual(processor.decoder._alphabet.labels, decoder._alphabet.labels)
+        self.assertEqual(
+            processor.decoder.model_container[decoder._model_key]._unigram_set,
+            decoder.model_container[decoder._model_key]._unigram_set,
+        )
+        self.assertIsInstance(processor.decoder, BeamSearchDecoderCTC)
+
+    def test_save_load_pretrained_additional_features(self):
+        processor = Wav2Vec2ProcessorWithLM(
+            tokenizer=self.get_tokenizer(), feature_extractor=self.get_feature_extractor(), decoder=self.get_decoder()
+        )
+        processor.save_pretrained(self.tmpdirname)
+
+        # make sure that error is thrown when decoder alphabet doesn't match
+        processor = Wav2Vec2ProcessorWithLM.from_pretrained(
+            self.tmpdirname, alpha=5.0, beta=3.0, score_boundary=-7.0, unk_score_offset=3
+        )
+
+        # decoder
+        self.assertEqual(processor.language_model.alpha, 5.0)
+        self.assertEqual(processor.language_model.beta, 3.0)
+        self.assertEqual(processor.language_model.score_boundary, -7.0)
+        self.assertEqual(processor.language_model.unk_score_offset, 3)
+
+    def test_load_decoder_tokenizer_mismatch_content(self):
+        tokenizer = self.get_tokenizer()
+        # add token to trigger raise
+        tokenizer.add_tokens(["xx"])
+        with self.assertRaisesRegex(ValueError, "include"):
+            Wav2Vec2ProcessorWithLM(
+                tokenizer=tokenizer, feature_extractor=self.get_feature_extractor(), decoder=self.get_decoder()
+            )
+
+    def test_feature_extractor(self):
+        feature_extractor = self.get_feature_extractor()
+        tokenizer = self.get_tokenizer()
+        decoder = self.get_decoder()
+
+        processor = Wav2Vec2ProcessorWithLM(tokenizer=tokenizer, feature_extractor=feature_extractor, decoder=decoder)
+
+        raw_speech = floats_list((3, 1000))
+
+        input_feat_extract = feature_extractor(raw_speech, return_tensors="np")
+        input_processor = processor(raw_speech, return_tensors="np")
+
+        for key in input_feat_extract.keys():
+            self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2)
+
+    def test_tokenizer(self):
+        feature_extractor = self.get_feature_extractor()
+        tokenizer = self.get_tokenizer()
+        decoder = self.get_decoder()
+
+        processor = Wav2Vec2ProcessorWithLM(tokenizer=tokenizer, feature_extractor=feature_extractor, decoder=decoder)
+
+        input_str = "This is a test string"
+
+        with processor.as_target_processor():
+            encoded_processor = processor(input_str)
+
+        encoded_tok = tokenizer(input_str)
+
+        for key in encoded_tok.keys():
+            self.assertListEqual(encoded_tok[key], encoded_processor[key])
+
+    def _get_dummy_logits(self, shape=(2, 10, 16), seed=77):
+        np.random.seed(seed)
+        return np.random.rand(*shape)
+
+    def test_decoder(self):
+        feature_extractor = self.get_feature_extractor()
+        tokenizer = self.get_tokenizer()
+        decoder = self.get_decoder()
+
+        processor = Wav2Vec2ProcessorWithLM(tokenizer=tokenizer, feature_extractor=feature_extractor, decoder=decoder)
+
+        logits = self._get_dummy_logits(shape=(10, 16), seed=13)
+
+        decoded_processor = processor.decode(logits).text
+
+        decoded_decoder = decoder.decode_beams(logits)[0][0]
+
+        self.assertEqual(decoded_decoder, decoded_processor)
+        self.assertEqual("</s> <s> </s>", decoded_processor)
+
+    def test_decoder_batch(self):
+        feature_extractor = self.get_feature_extractor()
+        tokenizer = self.get_tokenizer()
+        decoder = self.get_decoder()
+
+        processor = Wav2Vec2ProcessorWithLM(tokenizer=tokenizer, feature_extractor=feature_extractor, decoder=decoder)
+
+        logits = self._get_dummy_logits()
+
+        decoded_processor = processor.batch_decode(logits).text
+
+        logits_list = [array for array in logits]
+        decoded_decoder = [d[0][0] for d in decoder.decode_beams_batch(Pool(), logits_list)]
+
+        self.assertListEqual(decoded_decoder, decoded_processor)
+        self.assertListEqual(["<s> <s> </s>", "<s> <s> <s>"], decoded_processor)
+
+    def test_decoder_with_params(self):
+        feature_extractor = self.get_feature_extractor()
+        tokenizer = self.get_tokenizer()
+        decoder = self.get_decoder()
+
+        processor = Wav2Vec2ProcessorWithLM(tokenizer=tokenizer, feature_extractor=feature_extractor, decoder=decoder)
+
+        logits = self._get_dummy_logits()
+
+        beam_width = 20
+        beam_prune_logp = -20.0
+        token_min_logp = -4.0
+
+        decoded_processor_out = processor.batch_decode(
+            logits,
+            beam_width=beam_width,
+            beam_prune_logp=beam_prune_logp,
+            token_min_logp=token_min_logp,
+        )
+        decoded_processor = decoded_processor_out.text
+
+        logits_list = [array for array in logits]
+        decoded_decoder_out = decoder.decode_beams_batch(
+            Pool(),
+            logits_list,
+            beam_width=beam_width,
+            beam_prune_logp=beam_prune_logp,
+            token_min_logp=token_min_logp,
+        )
+
+        decoded_decoder = [d[0][0] for d in decoded_decoder_out]
+
+        self.assertListEqual(decoded_decoder, decoded_processor)
+        self.assertListEqual(["<s> </s> </s>", "<s> <s> </s>"], decoded_processor)