init

transformers/tests/models/musicgen_melody/test_feature_extraction_musicgen_melody.py

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+# Copyright 2024 HuggingFace Inc.
+#
+# 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 itertools
+import math
+import os
+import random
+import tempfile
+import unittest
+
+import numpy as np
+
+from transformers.testing_utils import (
+    check_json_file_has_correct_format,
+    require_torch,
+    require_torchaudio,
+)
+from transformers.utils.import_utils import is_torchaudio_available
+
+from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
+
+
+if is_torchaudio_available():
+    import torch
+
+    from transformers import MusicgenMelodyFeatureExtractor
+
+
+global_rng = random.Random()
+
+
+# Copied from tests.models.whisper.test_feature_extraction_whisper.floats_list
+def floats_list(shape, scale=1.0, rng=None, name=None):
+    """Creates a random float32 tensor"""
+    if rng is None:
+        rng = global_rng
+
+    values = []
+    for batch_idx in range(shape[0]):
+        values.append([])
+        for _ in range(shape[1]):
+            values[-1].append(rng.random() * scale)
+
+    return values
+
+
+# Copied from tests.models.musicgen.test_modeling_musicgen.get_bip_bip
+def get_bip_bip(bip_duration=0.125, duration=0.5, sample_rate=32000):
+    """Produces a series of 'bip bip' sounds at a given frequency."""
+    timesteps = np.arange(int(duration * sample_rate)) / sample_rate
+    wav = np.cos(2 * math.pi * 440 * timesteps)
+    time_period = (timesteps % (2 * bip_duration)) / (2 * bip_duration)
+    envelope = time_period >= 0.5
+    return wav * envelope
+
+
+@require_torch
+@require_torchaudio
+class MusicgenMelodyFeatureExtractionTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=7,
+        min_seq_length=400,
+        max_seq_length=2000,
+        feature_size=12,
+        padding_value=0.0,
+        sampling_rate=4_000,
+        return_attention_mask=True,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.min_seq_length = min_seq_length
+        self.max_seq_length = max_seq_length
+        self.seq_length_diff = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
+        self.padding_value = padding_value
+        self.sampling_rate = sampling_rate
+        self.return_attention_mask = return_attention_mask
+        self.feature_size = feature_size
+        self.num_chroma = feature_size
+
+    def prepare_feat_extract_dict(self):
+        return {
+            "feature_size": self.feature_size,
+            "padding_value": self.padding_value,
+            "sampling_rate": self.sampling_rate,
+            "return_attention_mask": self.return_attention_mask,
+        }
+
+    # Copied from tests.models.whisper.test_feature_extraction_whisper.WhisperFeatureExtractionTester.prepare_inputs_for_common
+    def prepare_inputs_for_common(self, equal_length=False, numpify=False):
+        def _flatten(list_of_lists):
+            return list(itertools.chain(*list_of_lists))
+
+        if equal_length:
+            speech_inputs = [floats_list((self.max_seq_length, self.feature_size)) for _ in range(self.batch_size)]
+        else:
+            # make sure that inputs increase in size
+            speech_inputs = [
+                floats_list((x, self.feature_size))
+                for x in range(self.min_seq_length, self.max_seq_length, self.seq_length_diff)
+            ]
+        if numpify:
+            speech_inputs = [np.asarray(x) for x in speech_inputs]
+        return speech_inputs
+
+
+@require_torchaudio
+@require_torch
+class MusicgenMelodyFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.TestCase):
+    feature_extraction_class = MusicgenMelodyFeatureExtractor if is_torchaudio_available() else None
+
+    def setUp(self):
+        self.feat_extract_tester = MusicgenMelodyFeatureExtractionTester(self)
+
+    # Copied from tests.models.seamless_m4t.test_feature_extraction_seamless_m4t.SeamlessM4TFeatureExtractionTest.test_feat_extract_from_and_save_pretrained
+    def test_feat_extract_from_and_save_pretrained(self):
+        feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict)
+
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            saved_file = feat_extract_first.save_pretrained(tmpdirname)[0]
+            check_json_file_has_correct_format(saved_file)
+            feat_extract_second = self.feature_extraction_class.from_pretrained(tmpdirname)
+
+        dict_first = feat_extract_first.to_dict()
+        dict_second = feat_extract_second.to_dict()
+        self.assertDictEqual(dict_first, dict_second)
+
+    # Copied from tests.models.seamless_m4t.test_feature_extraction_seamless_m4t.SeamlessM4TFeatureExtractionTest.test_feat_extract_to_json_file
+    def test_feat_extract_to_json_file(self):
+        feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict)
+
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            json_file_path = os.path.join(tmpdirname, "feat_extract.json")
+            feat_extract_first.to_json_file(json_file_path)
+            feat_extract_second = self.feature_extraction_class.from_json_file(json_file_path)
+
+        dict_first = feat_extract_first.to_dict()
+        dict_second = feat_extract_second.to_dict()
+        self.assertEqual(dict_first, dict_second)
+
+    def test_call(self):
+        # Tests that all call wrap to encode_plus and batch_encode_plus
+        feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
+        # create three inputs of length 800, 1000, and 1200
+        speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
+        np_speech_inputs = [np.asarray(speech_input) for speech_input in speech_inputs]
+
+        # Test feature size
+        input_features = feature_extractor(np_speech_inputs, padding=True, return_tensors="np").input_features
+        self.assertTrue(input_features.ndim == 3)
+        self.assertTrue(input_features.shape[0] == 3)
+        # Ignore copy
+        self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size)
+
+        # Test not batched input
+        encoded_sequences_1 = feature_extractor(speech_inputs[0], return_tensors="np").input_features
+        encoded_sequences_2 = feature_extractor(np_speech_inputs[0], return_tensors="np").input_features
+        self.assertTrue(np.allclose(encoded_sequences_1, encoded_sequences_2, atol=1e-3))
+
+        # Test batched
+        encoded_sequences_1 = feature_extractor(speech_inputs, return_tensors="np").input_features
+        encoded_sequences_2 = feature_extractor(np_speech_inputs, return_tensors="np").input_features
+        for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2):
+            self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=1e-3))
+
+        # Test 2-D numpy arrays are batched.
+        speech_inputs = [floats_list((1, x))[0] for x in (800, 800, 800)]
+        np_speech_inputs = np.asarray(speech_inputs)
+        encoded_sequences_1 = feature_extractor(speech_inputs, return_tensors="np").input_features
+        encoded_sequences_2 = feature_extractor(np_speech_inputs, return_tensors="np").input_features
+        for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2):
+            self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=1e-3))
+
+    @require_torchaudio
+    def test_call_from_demucs(self):
+        # Tests that all call wrap to encode_plus and batch_encode_plus
+        feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
+
+        # (batch_size, num_stems, channel_size, audio_length)
+        inputs = torch.rand([4, 5, 2, 44000])
+
+        # Test feature size
+        input_features = feature_extractor(inputs, padding=True, return_tensors="np").input_features
+        self.assertTrue(input_features.ndim == 3)
+        self.assertTrue(input_features.shape[0] == 4)
+        self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size)
+
+        # Test single input
+        encoded_sequences_1 = feature_extractor(inputs[[0]], return_tensors="np").input_features
+        self.assertTrue(np.allclose(encoded_sequences_1[0], input_features[0], atol=1e-3))
+
+    # Copied from tests.models.whisper.test_feature_extraction_whisper.WhisperFeatureExtractionTest.test_double_precision_pad with input_features->input_features
+    def test_double_precision_pad(self):
+        import torch
+
+        feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
+        np_speech_inputs = np.random.rand(100, 32).astype(np.float64)
+        py_speech_inputs = np_speech_inputs.tolist()
+
+        for inputs in [py_speech_inputs, np_speech_inputs]:
+            np_processed = feature_extractor.pad([{"input_features": inputs}], return_tensors="np")
+            self.assertTrue(np_processed.input_features.dtype == np.float32)
+            pt_processed = feature_extractor.pad([{"input_features": inputs}], return_tensors="pt")
+            self.assertTrue(pt_processed.input_features.dtype == torch.float32)
+
+    def test_integration(self):
+        EXPECTED_INPUT_FEATURES = torch.zeros([2, 8, 12])
+        EXPECTED_INPUT_FEATURES[0, :6, 9] = 1
+        EXPECTED_INPUT_FEATURES[0, 6:, 0] = 1
+        EXPECTED_INPUT_FEATURES[1, :, 9] = 1
+
+        input_speech = [get_bip_bip(duration=0.5), get_bip_bip(duration=1.0)]
+        feature_extractor = MusicgenMelodyFeatureExtractor()
+        input_features = feature_extractor(input_speech, return_tensors="pt").input_features
+
+        self.assertEqual(input_features.shape, (2, 8, 12))
+        self.assertTrue((input_features == EXPECTED_INPUT_FEATURES).all())

transformers/tests/models/musicgen_melody/test_processing_musicgen_melody.py

@@ -0,0 +1,167 @@
+# 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.
+"""Tests for the MusicGen processor."""
+
+import random
+import shutil
+import tempfile
+import unittest
+
+import numpy as np
+
+from transformers import T5Tokenizer, T5TokenizerFast
+from transformers.testing_utils import require_sentencepiece, require_torch, require_torchaudio
+from transformers.utils.import_utils import is_torchaudio_available
+
+
+if is_torchaudio_available():
+    from transformers import MusicgenMelodyFeatureExtractor, MusicgenMelodyProcessor
+
+
+global_rng = random.Random()
+
+
+# Copied from tests.models.whisper.test_feature_extraction_whisper.floats_list
+def floats_list(shape, scale=1.0, rng=None, name=None):
+    """Creates a random float32 tensor"""
+    if rng is None:
+        rng = global_rng
+
+    values = []
+    for batch_idx in range(shape[0]):
+        values.append([])
+        for _ in range(shape[1]):
+            values[-1].append(rng.random() * scale)
+
+    return values
+
+
+@require_torch
+@require_sentencepiece
+@require_torchaudio
+# Copied from tests.models.musicgen.test_processing_musicgen.MusicgenProcessorTest with Musicgen->MusicgenMelody, Encodec->MusicgenMelody, padding_mask->attention_mask, input_values->input_features
+class MusicgenMelodyProcessorTest(unittest.TestCase):
+    def setUp(self):
+        # Ignore copy
+        self.checkpoint = "facebook/musicgen-melody"
+        self.tmpdirname = tempfile.mkdtemp()
+
+    def get_tokenizer(self, **kwargs):
+        return T5Tokenizer.from_pretrained(self.checkpoint, **kwargs)
+
+    def get_feature_extractor(self, **kwargs):
+        return MusicgenMelodyFeatureExtractor.from_pretrained(self.checkpoint, **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 = MusicgenMelodyProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
+
+        processor.save_pretrained(self.tmpdirname)
+        processor = MusicgenMelodyProcessor.from_pretrained(self.tmpdirname)
+
+        self.assertEqual(processor.tokenizer.get_vocab(), tokenizer.get_vocab())
+        self.assertIsInstance(processor.tokenizer, T5TokenizerFast)
+
+        self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor.to_json_string())
+        self.assertIsInstance(processor.feature_extractor, MusicgenMelodyFeatureExtractor)
+
+    def test_save_load_pretrained_additional_features(self):
+        processor = MusicgenMelodyProcessor(
+            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 = MusicgenMelodyProcessor.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, T5TokenizerFast)
+
+        self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor_add_kwargs.to_json_string())
+        self.assertIsInstance(processor.feature_extractor, MusicgenMelodyFeatureExtractor)
+
+    def test_feature_extractor(self):
+        feature_extractor = self.get_feature_extractor()
+        tokenizer = self.get_tokenizer()
+
+        processor = MusicgenMelodyProcessor(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 = MusicgenMelodyProcessor(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 = MusicgenMelodyProcessor(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(sequences=predicted_ids)
+        decoded_tok = tokenizer.batch_decode(predicted_ids)
+
+        self.assertListEqual(decoded_tok, decoded_processor)
+
+    # Ignore copy
+    def test_decode_audio(self):
+        feature_extractor = self.get_feature_extractor(padding_side="left")
+        tokenizer = self.get_tokenizer()
+
+        processor = MusicgenMelodyProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
+
+        attention_mask = np.zeros((3, 20))
+        attention_mask[0, -5:] = 1
+        attention_mask[1, -20:] = 1
+        attention_mask[2, -10:] = 1
+
+        generated_speech = np.asarray(floats_list((3, 20)))[:, None, :]
+        decoded_audios = processor.batch_decode(generated_speech, attention_mask=attention_mask)
+
+        self.assertIsInstance(decoded_audios, list)
+
+        for audio in decoded_audios:
+            self.assertIsInstance(audio, np.ndarray)
+
+        self.assertTrue(decoded_audios[0].shape == (1, 5))
+        self.assertTrue(decoded_audios[1].shape == (1, 20))
+        self.assertTrue(decoded_audios[2].shape == (1, 10))