# 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_with_lm import Wav2Vec2ProcessorWithLM @require_pyctcdecode class Wav2Vec2ProcessorWithLMTest(unittest.TestCase): def setUp(self): vocab = "| 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": "", "bos_token": "", "eos_token": "", } 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(" ", 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([" ", " "], 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([" ", " "], decoded_processor)