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tests/models/wav2vec2_bert/test_processing_wav2vec2_bert.py

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+# Copyright 2024 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 unittest
+
+from transformers.models.wav2vec2.tokenization_wav2vec2 import VOCAB_FILES_NAMES
+from transformers.models.wav2vec2_bert import Wav2Vec2BertProcessor
+
+from ...test_processing_common import ProcessorTesterMixin
+from ..wav2vec2.test_feature_extraction_wav2vec2 import floats_list
+
+
+class Wav2Vec2BertProcessorTest(ProcessorTesterMixin, unittest.TestCase):
+    processor_class = Wav2Vec2BertProcessor
+    text_input_name = "labels"
+
+    @classmethod
+    def _setup_feature_extractor(cls):
+        feature_extractor_class = cls._get_component_class_from_processor("feature_extractor")
+
+        feature_extractor_map = {
+            "feature_size": 80,
+            "padding_value": 0.0,
+            "sampling_rate": 16000,
+            "return_attention_mask": False,
+            "do_normalize": True,
+        }
+        return feature_extractor_class(**feature_extractor_map)
+
+    @classmethod
+    def _setup_tokenizer(cls):
+        tokenizer_class = cls._get_component_class_from_processor("tokenizer")
+        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))))
+        vocab_file = os.path.join(cls.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
+        with open(vocab_file, "w", encoding="utf-8") as fp:
+            fp.write(json.dumps(vocab_tokens) + "\n")
+        add_kwargs_tokens_map = {
+            "pad_token": "<pad>",
+            "unk_token": "<unk>",
+            "bos_token": "<s>",
+            "eos_token": "</s>",
+        }
+        return tokenizer_class.from_pretrained(cls.tmpdirname, **add_kwargs_tokens_map)
+
+    @unittest.skip("Wav2Vec2BertProcessor changes input_features")
+    def test_processor_with_multiple_inputs(self):
+        pass
+
+    @unittest.skip("Wav2Vec2BertProcessor changes input_features")
+    def test_overlapping_text_audio_kwargs_handling(self):
+        pass
+
+    def test_feature_extractor(self):
+        feature_extractor = self.get_component("feature_extractor")
+        processor = self.get_processor()
+        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_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))