Speech2TextTransformer (#10175)

* s2t * fix config * conversion script * fix import * add tokenizer * fix tok init * fix tokenizer * first version working * fix embeds * fix lm head * remove extra heads * fix convert script * handle encoder attn mask * style * better enc attn mask * override _prepare_attention_mask_for_generation * handle attn_maks in encoder and decoder * input_ids => input_features * enable use_cache * remove old code * expand embeddings if needed * remove logits bias * masked_lm_loss => loss * hack tokenizer to support feature processing * fix model_input_names * style * fix error message * doc * remove inputs_embeds * remove input_embeds * remove unnecessary docstring * quality * SpeechToText => Speech2Text * style * remove shared_embeds * subsample => conv * remove Speech2TextTransformerDecoderWrapper * update output_lengths formula * fix table * remove max_position_embeddings * update conversion scripts * add possibility to do upper case for now * add FeatureExtractor and Processor * add tests for extractor * require_torch_audio => require_torchaudio * add processor test * update import * remove classification head * attention mask is now 1D * update docstrings * attention mask should be of type long * handle attention mask from generate * alwyas return attention_mask * fix test * style * doc * Speech2TextTransformer => Speech2Text * Speech2TextTransformerConfig => Speech2TextConfig * remove dummy_inputs * nit * style * multilinguial tok * fix tokenizer * add tgt_lang setter * save lang_codes * fix tokenizer * add forced_bos_token_id to tokenizer * apply review suggestions * add torchaudio to extra deps * add speech deps to CI * fix dep * add libsndfile to ci * libsndfile1 * add speech to extras all * libsndfile1 -> libsndfile1 * libsndfile * libsndfile1-dev * apt update * add sudo to install * update deps table * install libsndfile1-dev on CI * tuple to list * init conv layer * add model tests * quality * add integration tests * skip_special_tokens * add speech_to_text_transformer in toctree * fix tokenizer * fix fp16 tests * add tokenizer tests * fix copyright * input_values => input_features * doc * add model in readme * doc * change checkpoint names * fix copyright * fix code example * add max_model_input_sizes in tokenizer * fix integration tests * add do_lower_case to tokenizer * remove clamp trick * fix "Add modeling imports here" * fix copyrights * fix tests * SpeechToTextTransformer => SpeechToText * fix naming * fix table formatting * fix typo * style * fix typos * remove speech dep from extras[testing] * fix copies * rename doc file, * put imports under is_torch_available * run feat extract tests when torch is available * dummy objects for processor and extractor * fix imports in tests * fix import in modeling test * fxi imports * fix torch import * fix imports again * fix positional embeddings * fix typo in import * adapt new extractor refactor * style * fix torchscript test * doc * doc * Apply suggestions from code review Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * fix docs, copied from, style * fix docstring * handle imports * remove speech from all extra deps * remove s2t from seq2seq lm mapping * better names * skip training tests * add install instructions * List => Tuple * doc * fix conversion script * fix urls * add instruction for libsndfile * fix fp16 test Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
2021-03-10 21:42:04 +05:30
parent efb5c0a453
commit d26b37e744
30 changed files with 3833 additions and 24 deletions
--- a/tests/test_processor_speech_to_text.py
+++ b/tests/test_processor_speech_to_text.py
@@ -0,0 +1,144 @@
+# 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 os
+import shutil
+import tempfile
+import unittest
+from pathlib import Path
+from shutil import copyfile
+
+from transformers import Speech2TextFeatureExtractor, Speech2TextProcessor, Speech2TextTokenizer
+from transformers.file_utils import FEATURE_EXTRACTOR_NAME
+from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
+from transformers.testing_utils import require_sentencepiece, require_torch, require_torchaudio
+
+from .test_feature_extraction_speech_to_text import floats_list
+
+
+SAMPLE_SP = os.path.join(os.path.dirname(os.path.abspath(__file__)), "fixtures/test_sentencepiece.model")
+
+
+@require_torch
+@require_torchaudio
+@require_sentencepiece
+class Speech2TextProcessorTest(unittest.TestCase):
+    def setUp(self):
+        self.tmpdirname = tempfile.mkdtemp()
+
+        vocab = ["<s>", "<pad>", "</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est"]
+        vocab_tokens = dict(zip(vocab, range(len(vocab))))
+        save_dir = Path(self.tmpdirname)
+        save_json(vocab_tokens, save_dir / VOCAB_FILES_NAMES["vocab_file"])
+        if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
+            copyfile(SAMPLE_SP, save_dir / VOCAB_FILES_NAMES["spm_file"])
+
+        tokenizer = Speech2TextTokenizer.from_pretrained(self.tmpdirname)
+        tokenizer.save_pretrained(self.tmpdirname)
+
+        feature_extractor_map = {
+            "feature_size": 24,
+            "num_mel_bins": 24,
+            "padding_value": 0.0,
+            "sampling_rate": 16000,
+            "return_attention_mask": False,
+            "do_normalize": True,
+        }
+        save_json(feature_extractor_map, save_dir / FEATURE_EXTRACTOR_NAME)
+
+    def get_tokenizer(self, **kwargs):
+        return Speech2TextTokenizer.from_pretrained(self.tmpdirname, **kwargs)
+
+    def get_feature_extractor(self, **kwargs):
+        return Speech2TextFeatureExtractor.from_pretrained(self.tmpdirname, **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()
+
+        processor = Speech2TextProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
+
+        processor.save_pretrained(self.tmpdirname)
+        processor = Speech2TextProcessor.from_pretrained(self.tmpdirname)
+
+        self.assertEqual(processor.tokenizer.get_vocab(), tokenizer.get_vocab())
+        self.assertIsInstance(processor.tokenizer, Speech2TextTokenizer)
+
+        self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor.to_json_string())
+        self.assertIsInstance(processor.feature_extractor, Speech2TextFeatureExtractor)
+
+    def test_save_load_pretrained_additional_features(self):
+        processor = Speech2TextProcessor(
+            tokenizer=self.get_tokenizer(), feature_extractor=self.get_feature_extractor()
+        )
+        processor.save_pretrained(self.tmpdirname)
+
+        tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)")
+        feature_extractor_add_kwargs = self.get_feature_extractor(do_normalize=False, padding_value=1.0)
+
+        processor = Speech2TextProcessor.from_pretrained(
+            self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False, padding_value=1.0
+        )
+
+        self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
+        self.assertIsInstance(processor.tokenizer, Speech2TextTokenizer)
+
+        self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor_add_kwargs.to_json_string())
+        self.assertIsInstance(processor.feature_extractor, Speech2TextFeatureExtractor)
+
+    def test_feature_extractor(self):
+        feature_extractor = self.get_feature_extractor()
+        tokenizer = self.get_tokenizer()
+
+        processor = Speech2TextProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
+
+        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()
+
+        processor = Speech2TextProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
+
+        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 test_tokenizer_decode(self):
+        feature_extractor = self.get_feature_extractor()
+        tokenizer = self.get_tokenizer()
+
+        processor = Speech2TextProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
+
+        predicted_ids = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
+
+        decoded_processor = processor.batch_decode(predicted_ids)
+        decoded_tok = tokenizer.batch_decode(predicted_ids)
+
+        self.assertListEqual(decoded_tok, decoded_processor)