Move test model folders (#17034)
* move test model folders (TODO: fix imports and others) * fix (potentially partially) imports (in model test modules) * fix (potentially partially) imports (in tokenization test modules) * fix (potentially partially) imports (in feature extraction test modules) * fix import utils.test_modeling_tf_core * fix path ../fixtures/ * fix imports about generation.test_generation_flax_utils * fix more imports * fix fixture path * fix get_test_dir * update module_to_test_file * fix get_tests_dir from wrong transformers.utils * update config.yml (CircleCI) * fix style * remove missing imports * update new model script * update check_repo * update SPECIAL_MODULE_TO_TEST_MAP * fix style * add __init__ * update self-scheduled * fix add_new_model scripts * check one way to get location back * python setup.py build install * fix import in test auto * update self-scheduled.yml * update slack notification script * Add comments about artifact names * fix for yolos Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
Yih-Dar
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tests/models/wav2vec2_with_lm/__init__.py
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tests/models/wav2vec2_with_lm/__init__.py
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tests/models/wav2vec2_with_lm/test_processor_wav2vec2_with_lm.py
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tests/models/wav2vec2_with_lm/test_processor_wav2vec2_with_lm.py
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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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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 get_context
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from pathlib import Path
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import datasets
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import numpy as np
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from datasets import load_dataset
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from transformers import AutoProcessor
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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, require_torch, require_torchaudio, slow
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from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available
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from ..wav2vec2.test_feature_extraction_wav2vec2 import floats_list
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if is_pyctcdecode_available():
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from huggingface_hub import snapshot_download
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from pyctcdecode import BeamSearchDecoderCTC
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from transformers.models.wav2vec2_with_lm import Wav2Vec2ProcessorWithLM
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from transformers.models.wav2vec2_with_lm.processing_wav2vec2_with_lm import Wav2Vec2DecoderWithLMOutput
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if is_torch_available():
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from transformers import Wav2Vec2ForCTC
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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)
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decoded_decoder = decoder.decode_beams(logits)[0]
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self.assertEqual(decoded_decoder[0], decoded_processor.text)
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self.assertEqual("</s> <s> </s>", decoded_processor.text)
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self.assertEqual(decoded_decoder[-2], decoded_processor.logit_score)
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self.assertEqual(decoded_decoder[-1], decoded_processor.lm_score)
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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)
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logits_list = [array for array in logits]
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pool = get_context("fork").Pool()
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decoded_beams = decoder.decode_beams_batch(pool, logits_list)
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texts_decoder, logit_scores_decoder, lm_scores_decoder = [], [], []
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for beams in decoded_beams:
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texts_decoder.append(beams[0][0])
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logit_scores_decoder.append(beams[0][-2])
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lm_scores_decoder.append(beams[0][-1])
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pool.close()
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self.assertListEqual(texts_decoder, decoded_processor.text)
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self.assertListEqual(["<s> <s> </s>", "<s> <s> <s>"], decoded_processor.text)
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self.assertListEqual(logit_scores_decoder, decoded_processor.logit_score)
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self.assertListEqual(lm_scores_decoder, decoded_processor.lm_score)
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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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pool = get_context("fork").Pool()
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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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pool.close()
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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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def test_decoder_with_params_of_lm(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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alpha = 2.0
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beta = 5.0
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unk_score_offset = -20.0
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lm_score_boundary = True
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decoded_processor_out = processor.batch_decode(
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logits,
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alpha=alpha,
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beta=beta,
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unk_score_offset=unk_score_offset,
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lm_score_boundary=lm_score_boundary,
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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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decoder.reset_params(
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alpha=alpha,
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beta=beta,
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unk_score_offset=unk_score_offset,
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lm_score_boundary=lm_score_boundary,
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)
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pool = get_context("fork").Pool()
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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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)
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pool.close()
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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> </s> <s> </s> </s>"], decoded_processor)
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lm_model = processor.decoder.model_container[processor.decoder._model_key]
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self.assertEqual(lm_model.alpha, 2.0)
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self.assertEqual(lm_model.beta, 5.0)
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self.assertEqual(lm_model.unk_score_offset, -20.0)
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self.assertEqual(lm_model.score_boundary, True)
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def test_decoder_download_ignores_files(self):
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processor = Wav2Vec2ProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm")
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language_model = processor.decoder.model_container[processor.decoder._model_key]
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path_to_cached_dir = Path(language_model._kenlm_model.path.decode("utf-8")).parent.parent.absolute()
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downloaded_decoder_files = os.listdir(path_to_cached_dir)
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expected_decoder_files = ["alphabet.json", "language_model"]
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downloaded_decoder_files.sort()
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expected_decoder_files.sort()
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# test that only decoder relevant files from
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# https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
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# are downloaded and none of the rest (e.g. README.md, ...)
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self.assertListEqual(downloaded_decoder_files, expected_decoder_files)
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def test_decoder_local_files(self):
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local_dir = snapshot_download("hf-internal-testing/processor_with_lm")
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processor = Wav2Vec2ProcessorWithLM.from_pretrained(local_dir)
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language_model = processor.decoder.model_container[processor.decoder._model_key]
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path_to_cached_dir = Path(language_model._kenlm_model.path.decode("utf-8")).parent.parent.absolute()
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local_decoder_files = os.listdir(local_dir)
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expected_decoder_files = os.listdir(path_to_cached_dir)
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local_decoder_files.sort()
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expected_decoder_files.sort()
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# test that both decoder form hub and local files in cache are the same
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self.assertListEqual(local_decoder_files, expected_decoder_files)
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def test_processor_from_auto_processor(self):
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processor_wav2vec2 = Wav2Vec2ProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm")
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processor_auto = AutoProcessor.from_pretrained("hf-internal-testing/processor_with_lm")
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raw_speech = floats_list((3, 1000))
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input_wav2vec2 = processor_wav2vec2(raw_speech, return_tensors="np")
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input_auto = processor_auto(raw_speech, return_tensors="np")
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for key in input_wav2vec2.keys():
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self.assertAlmostEqual(input_wav2vec2[key].sum(), input_auto[key].sum(), delta=1e-2)
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logits = self._get_dummy_logits()
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decoded_wav2vec2 = processor_wav2vec2.batch_decode(logits)
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decoded_auto = processor_auto.batch_decode(logits)
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self.assertListEqual(decoded_wav2vec2.text, decoded_auto.text)
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@staticmethod
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def get_from_offsets(offsets, key):
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retrieved_list = [d[key] for d in offsets]
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return retrieved_list
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def test_offsets_integration_fast(self):
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processor = Wav2Vec2ProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm")
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logits = self._get_dummy_logits()[0]
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outputs = processor.decode(logits, output_word_offsets=True)
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# check Wav2Vec2CTCTokenizerOutput keys for word
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self.assertEqual(len(outputs.keys()), 4)
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self.assertTrue("text" in outputs)
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self.assertTrue("word_offsets" in outputs)
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self.assertTrue(isinstance(outputs, Wav2Vec2DecoderWithLMOutput))
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self.assertEqual(" ".join(self.get_from_offsets(outputs["word_offsets"], "word")), outputs.text)
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self.assertListEqual(self.get_from_offsets(outputs["word_offsets"], "word"), ["<s>", "<s>", "</s>"])
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self.assertListEqual(self.get_from_offsets(outputs["word_offsets"], "start_offset"), [0, 2, 4])
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self.assertListEqual(self.get_from_offsets(outputs["word_offsets"], "end_offset"), [1, 3, 5])
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def test_offsets_integration_fast_batch(self):
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processor = Wav2Vec2ProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm")
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||||
logits = self._get_dummy_logits()
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||||
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||||
outputs = processor.batch_decode(logits, output_word_offsets=True)
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||||
|
||||
# check Wav2Vec2CTCTokenizerOutput keys for word
|
||||
self.assertEqual(len(outputs.keys()), 4)
|
||||
self.assertTrue("text" in outputs)
|
||||
self.assertTrue("word_offsets" in outputs)
|
||||
self.assertTrue(isinstance(outputs, Wav2Vec2DecoderWithLMOutput))
|
||||
|
||||
self.assertListEqual(
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||||
[" ".join(self.get_from_offsets(o, "word")) for o in outputs["word_offsets"]], outputs.text
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||||
)
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||||
self.assertListEqual(self.get_from_offsets(outputs["word_offsets"][0], "word"), ["<s>", "<s>", "</s>"])
|
||||
self.assertListEqual(self.get_from_offsets(outputs["word_offsets"][0], "start_offset"), [0, 2, 4])
|
||||
self.assertListEqual(self.get_from_offsets(outputs["word_offsets"][0], "end_offset"), [1, 3, 5])
|
||||
|
||||
@slow
|
||||
@require_torch
|
||||
@require_torchaudio
|
||||
def test_word_time_stamp_integration(self):
|
||||
import torch
|
||||
|
||||
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)
|
||||
|
||||
processor = AutoProcessor.from_pretrained("patrickvonplaten/wav2vec2-base-100h-with-lm")
|
||||
model = Wav2Vec2ForCTC.from_pretrained("patrickvonplaten/wav2vec2-base-100h-with-lm")
|
||||
|
||||
# compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
|
||||
input_values = processor(sample["audio"]["array"], return_tensors="pt").input_values
|
||||
|
||||
with torch.no_grad():
|
||||
logits = model(input_values).logits.cpu().numpy()
|
||||
|
||||
output = processor.decode(logits[0], output_word_offsets=True)
|
||||
|
||||
time_offset = model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
|
||||
word_time_stamps = [
|
||||
{
|
||||
"start_time": d["start_offset"] * time_offset,
|
||||
"end_time": d["end_offset"] * time_offset,
|
||||
"word": d["word"],
|
||||
}
|
||||
for d in output["word_offsets"]
|
||||
]
|
||||
|
||||
EXPECTED_TEXT = "WHY DOES A MILE SANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL"
|
||||
|
||||
# output words
|
||||
self.assertEqual(" ".join(self.get_from_offsets(word_time_stamps, "word")), EXPECTED_TEXT)
|
||||
self.assertEqual(" ".join(self.get_from_offsets(word_time_stamps, "word")), output.text)
|
||||
|
||||
# output times
|
||||
start_times = [round(x, 2) for x in self.get_from_offsets(word_time_stamps, "start_time")]
|
||||
end_times = [round(x, 2) for x in self.get_from_offsets(word_time_stamps, "end_time")]
|
||||
|
||||
# fmt: off
|
||||
self.assertListEqual(
|
||||
start_times,
|
||||
[
|
||||
1.42, 1.64, 2.12, 2.26, 2.54, 3.0, 3.24, 3.6, 3.8, 4.1, 4.26, 4.94, 5.28, 5.66, 5.78, 5.94, 6.32, 6.54, 6.66,
|
||||
],
|
||||
)
|
||||
|
||||
self.assertListEqual(
|
||||
end_times,
|
||||
[
|
||||
1.54, 1.88, 2.14, 2.46, 2.9, 3.18, 3.54, 3.72, 4.02, 4.18, 4.76, 5.16, 5.56, 5.7, 5.86, 6.2, 6.38, 6.62, 6.94,
|
||||
],
|
||||
)
|
||||
# fmt: on
|
||||
Reference in New Issue
Block a user