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transformers/tests/models/wav2vec2/test_processing_wav2vec2.py

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+# 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 transformers.models.wav2vec2 import Wav2Vec2CTCTokenizer, Wav2Vec2FeatureExtractor, Wav2Vec2Processor
+from transformers.models.wav2vec2.tokenization_wav2vec2 import VOCAB_FILES_NAMES
+from transformers.utils import FEATURE_EXTRACTOR_NAME
+
+from ...test_processing_common import ProcessorTesterMixin
+from .test_feature_extraction_wav2vec2 import floats_list
+
+
+class Wav2Vec2ProcessorTest(ProcessorTesterMixin, unittest.TestCase):
+    processor_class = Wav2Vec2Processor
+    audio_input_name = "input_values"
+    text_input_name = "labels"
+
+    @classmethod
+    def setUpClass(cls):
+        vocab = "<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K ' X J Q Z".split(" ")
+        vocab_tokens = dict(zip(vocab, range(len(vocab))))
+
+        cls.add_kwargs_tokens_map = {
+            "pad_token": "<pad>",
+            "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,
+        }
+
+        cls.tmpdirname = tempfile.mkdtemp()
+        cls.vocab_file = os.path.join(cls.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
+        cls.feature_extraction_file = os.path.join(cls.tmpdirname, FEATURE_EXTRACTOR_NAME)
+        with open(cls.vocab_file, "w", encoding="utf-8") as fp:
+            fp.write(json.dumps(vocab_tokens) + "\n")
+
+        with open(cls.feature_extraction_file, "w", encoding="utf-8") as fp:
+            fp.write(json.dumps(feature_extractor_map) + "\n")
+
+        tokenizer = cls.get_tokenizer()
+        tokenizer.save_pretrained(cls.tmpdirname)
+
+    @classmethod
+    def get_tokenizer(cls, **kwargs_init):
+        kwargs = cls.add_kwargs_tokens_map.copy()
+        kwargs.update(kwargs_init)
+        return Wav2Vec2CTCTokenizer.from_pretrained(cls.tmpdirname, **kwargs)
+
+    def get_feature_extractor(self, **kwargs):
+        return Wav2Vec2FeatureExtractor.from_pretrained(self.tmpdirname, **kwargs)
+
+    @classmethod
+    def tearDownClass(cls):
+        shutil.rmtree(cls.tmpdirname, ignore_errors=True)
+
+    def test_save_load_pretrained_default(self):
+        tokenizer = self.get_tokenizer()
+        feature_extractor = self.get_feature_extractor()
+
+        processor = Wav2Vec2Processor(tokenizer=tokenizer, feature_extractor=feature_extractor)
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            processor.save_pretrained(tmpdir)
+            processor = Wav2Vec2Processor.from_pretrained(tmpdir)
+
+        self.assertEqual(processor.tokenizer.get_vocab(), tokenizer.get_vocab())
+        self.assertIsInstance(processor.tokenizer, Wav2Vec2CTCTokenizer)
+
+        self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor.to_json_string())
+        self.assertIsInstance(processor.feature_extractor, Wav2Vec2FeatureExtractor)
+
+    def test_save_load_pretrained_additional_features(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            processor = Wav2Vec2Processor(
+                tokenizer=self.get_tokenizer(), feature_extractor=self.get_feature_extractor()
+            )
+            processor.save_pretrained(tmpdir)
+
+            tokenizer_add_kwargs = Wav2Vec2CTCTokenizer.from_pretrained(
+                tmpdir, **(self.add_kwargs_tokens_map | {"bos_token": "(BOS)", "eos_token": "(EOS)"})
+            )
+            feature_extractor_add_kwargs = Wav2Vec2FeatureExtractor.from_pretrained(
+                tmpdir, do_normalize=False, padding_value=1.0
+            )
+
+            processor = Wav2Vec2Processor.from_pretrained(
+                tmpdir, 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, Wav2Vec2CTCTokenizer)
+
+        self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor_add_kwargs.to_json_string())
+        self.assertIsInstance(processor.feature_extractor, Wav2Vec2FeatureExtractor)
+
+    def test_feature_extractor(self):
+        feature_extractor = self.get_feature_extractor()
+        tokenizer = self.get_tokenizer()
+
+        processor = Wav2Vec2Processor(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:
+            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 = Wav2Vec2Processor(tokenizer=tokenizer, feature_extractor=feature_extractor)
+
+        input_str = "This is a test string"
+        encoded_processor = processor(text=input_str)
+
+        encoded_tok = tokenizer(input_str)
+
+        for key in encoded_tok:
+            self.assertListEqual(encoded_tok[key], encoded_processor[key])
+
+    def test_tokenizer_decode(self):
+        feature_extractor = self.get_feature_extractor()
+        tokenizer = self.get_tokenizer()
+
+        processor = Wav2Vec2Processor(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)
+
+    def test_model_input_names(self):
+        processor = self.get_processor()
+
+        text = "lower newer"
+        audio_inputs = self.prepare_audio_inputs()
+
+        inputs = processor(text=text, audio=audio_inputs, return_attention_mask=True, return_tensors="pt")
+
+        self.assertSetEqual(set(inputs.keys()), set(processor.model_input_names))