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transformers/tests/models/speecht5/test_processing_speecht5.py

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+# Copyright 2022 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.
+"""Tests for the SpeechT5 processors."""
+
+import json
+import os
+import shutil
+import tempfile
+import unittest
+
+from transformers import is_speech_available, is_torch_available
+from transformers.models.speecht5 import SpeechT5Tokenizer
+from transformers.testing_utils import get_tests_dir, require_speech, require_torch
+from transformers.utils import FEATURE_EXTRACTOR_NAME
+
+
+if is_speech_available() and is_torch_available():
+    from transformers import SpeechT5FeatureExtractor, SpeechT5Processor
+
+    from .test_feature_extraction_speecht5 import floats_list
+
+
+SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece_bpe_char.model")
+
+
+@require_torch
+@require_speech
+class SpeechT5ProcessorTest(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        cls.tmpdirname = tempfile.mkdtemp()
+
+        tokenizer = SpeechT5Tokenizer(SAMPLE_VOCAB)
+        tokenizer.save_pretrained(cls.tmpdirname)
+
+        feature_extractor_map = {
+            "feature_size": 1,
+            "padding_value": 0.0,
+            "sampling_rate": 16000,
+            "do_normalize": False,
+            "num_mel_bins": 80,
+            "hop_length": 16,
+            "win_length": 64,
+            "win_function": "hann_window",
+            "fmin": 80,
+            "fmax": 7600,
+            "mel_floor": 1e-10,
+            "reduction_factor": 2,
+            "return_attention_mask": True,
+        }
+
+        cls.feature_extraction_file = os.path.join(cls.tmpdirname, FEATURE_EXTRACTOR_NAME)
+        with open(cls.feature_extraction_file, "w", encoding="utf-8") as fp:
+            fp.write(json.dumps(feature_extractor_map) + "\n")
+
+    def get_tokenizer(self, **kwargs):
+        return SpeechT5Tokenizer.from_pretrained(self.tmpdirname, **kwargs)
+
+    def get_feature_extractor(self, **kwargs):
+        return SpeechT5FeatureExtractor.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 = SpeechT5Processor(tokenizer=tokenizer, feature_extractor=feature_extractor)
+
+        processor.save_pretrained(self.tmpdirname)
+        processor = SpeechT5Processor.from_pretrained(self.tmpdirname)
+
+        self.assertEqual(processor.tokenizer.get_vocab(), tokenizer.get_vocab())
+        self.assertIsInstance(processor.tokenizer, SpeechT5Tokenizer)
+
+        self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor.to_json_string())
+        self.assertIsInstance(processor.feature_extractor, SpeechT5FeatureExtractor)
+
+    def test_save_load_pretrained_additional_features(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            processor = SpeechT5Processor(
+                tokenizer=self.get_tokenizer(), feature_extractor=self.get_feature_extractor()
+            )
+            processor.save_pretrained(tmpdir)
+
+            tokenizer_add_kwargs = SpeechT5Tokenizer.from_pretrained(tmpdir, bos_token="(BOS)", eos_token="(EOS)")
+            feature_extractor_add_kwargs = SpeechT5FeatureExtractor.from_pretrained(
+                tmpdir, do_normalize=False, padding_value=1.0
+            )
+
+            processor = SpeechT5Processor.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, SpeechT5Tokenizer)
+
+        self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor_add_kwargs.to_json_string())
+        self.assertIsInstance(processor.feature_extractor, SpeechT5FeatureExtractor)
+
+    def test_feature_extractor(self):
+        feature_extractor = self.get_feature_extractor()
+        tokenizer = self.get_tokenizer()
+
+        processor = SpeechT5Processor(tokenizer=tokenizer, feature_extractor=feature_extractor)
+
+        raw_speech = floats_list((3, 1000))
+
+        input_feat_extract = feature_extractor(audio=raw_speech, return_tensors="np")
+        input_processor = processor(audio=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_feature_extractor_target(self):
+        feature_extractor = self.get_feature_extractor()
+        tokenizer = self.get_tokenizer()
+
+        processor = SpeechT5Processor(tokenizer=tokenizer, feature_extractor=feature_extractor)
+
+        raw_speech = floats_list((3, 1000))
+
+        input_feat_extract = feature_extractor(audio_target=raw_speech, return_tensors="np")
+        input_processor = processor(audio_target=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 = SpeechT5Processor(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_target(self):
+        feature_extractor = self.get_feature_extractor()
+        tokenizer = self.get_tokenizer()
+
+        processor = SpeechT5Processor(tokenizer=tokenizer, feature_extractor=feature_extractor)
+
+        input_str = "This is a test string"
+
+        encoded_processor = processor(text_target=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 = SpeechT5Processor(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)