# Copyright 2021-2023 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. """Tests for the SpeechT5 feature extractors.""" import itertools import random import unittest import numpy as np from transformers import BatchFeature, SpeechT5FeatureExtractor from transformers.testing_utils import require_torch from transformers.utils.import_utils import is_torch_available from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin if is_torch_available(): import torch 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 class SpeechT5FeatureExtractionTester: def __init__( self, parent, batch_size=7, min_seq_length=400, max_seq_length=2000, feature_size=1, padding_value=0.0, sampling_rate=16000, do_normalize=True, num_mel_bins=80, hop_length=16, win_length=64, win_function="hann_window", fmin=80, fmax=7600, mel_floor=1e-10, 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.feature_size = feature_size self.padding_value = padding_value self.sampling_rate = sampling_rate self.do_normalize = do_normalize self.num_mel_bins = num_mel_bins self.hop_length = hop_length self.win_length = win_length self.win_function = win_function self.fmin = fmin self.fmax = fmax self.mel_floor = mel_floor self.return_attention_mask = return_attention_mask def prepare_feat_extract_dict(self): return { "feature_size": self.feature_size, "padding_value": self.padding_value, "sampling_rate": self.sampling_rate, "do_normalize": self.do_normalize, "num_mel_bins": self.num_mel_bins, "hop_length": self.hop_length, "win_length": self.win_length, "win_function": self.win_function, "fmin": self.fmin, "fmax": self.fmax, "mel_floor": self.mel_floor, "return_attention_mask": self.return_attention_mask, } 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.batch_size, self.max_seq_length)) else: # make sure that inputs increase in size speech_inputs = [ _flatten(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 def prepare_inputs_for_target(self, equal_length=False, numpify=False): if equal_length: speech_inputs = [floats_list((self.max_seq_length, self.num_mel_bins)) for _ in range(self.batch_size)] else: # make sure that inputs increase in size speech_inputs = [ floats_list((x, self.num_mel_bins)) 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_torch class SpeechT5FeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.TestCase): feature_extraction_class = SpeechT5FeatureExtractor def setUp(self): self.feat_extract_tester = SpeechT5FeatureExtractionTester(self) def _check_zero_mean_unit_variance(self, input_vector): self.assertTrue(np.all(np.mean(input_vector, axis=0) < 1e-3)) self.assertTrue(np.all(np.abs(np.var(input_vector, axis=0) - 1) < 1e-3)) def test_call(self): # Tests that all call wrap to encode_plus and batch_encode_plus feat_extract = 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 not batched input encoded_sequences_1 = feat_extract(speech_inputs[0], return_tensors="np").input_values encoded_sequences_2 = feat_extract(np_speech_inputs[0], return_tensors="np").input_values self.assertTrue(np.allclose(encoded_sequences_1, encoded_sequences_2, atol=1e-3)) # Test batched encoded_sequences_1 = feat_extract(speech_inputs, return_tensors="np").input_values encoded_sequences_2 = feat_extract(np_speech_inputs, return_tensors="np").input_values 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)) def test_zero_mean_unit_variance_normalization_np(self): feat_extract = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()) speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)] paddings = ["longest", "max_length", "do_not_pad"] max_lengths = [None, 1600, None] for max_length, padding in zip(max_lengths, paddings): processed = feat_extract(speech_inputs, padding=padding, max_length=max_length, return_tensors="np") input_values = processed.input_values self._check_zero_mean_unit_variance(input_values[0][:800]) self.assertTrue(input_values[0][800:].sum() < 1e-6) self._check_zero_mean_unit_variance(input_values[1][:1000]) self.assertTrue(input_values[0][1000:].sum() < 1e-6) self._check_zero_mean_unit_variance(input_values[2][:1200]) def test_zero_mean_unit_variance_normalization(self): feat_extract = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()) lengths = range(800, 1400, 200) speech_inputs = [floats_list((1, x))[0] for x in lengths] paddings = ["longest", "max_length", "do_not_pad"] max_lengths = [None, 1600, None] for max_length, padding in zip(max_lengths, paddings): processed = feat_extract(speech_inputs, max_length=max_length, padding=padding) input_values = processed.input_values self._check_zero_mean_unit_variance(input_values[0][:800]) self._check_zero_mean_unit_variance(input_values[1][:1000]) self._check_zero_mean_unit_variance(input_values[2][:1200]) def test_zero_mean_unit_variance_normalization_trunc_np_max_length(self): feat_extract = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()) speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)] processed = feat_extract( speech_inputs, truncation=True, max_length=1000, padding="max_length", return_tensors="np" ) input_values = processed.input_values self._check_zero_mean_unit_variance(input_values[0, :800]) self._check_zero_mean_unit_variance(input_values[1]) self._check_zero_mean_unit_variance(input_values[2]) def test_zero_mean_unit_variance_normalization_trunc_np_longest(self): feat_extract = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()) speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)] processed = feat_extract( speech_inputs, truncation=True, max_length=1000, padding="longest", return_tensors="np" ) input_values = processed.input_values self._check_zero_mean_unit_variance(input_values[0, :800]) self._check_zero_mean_unit_variance(input_values[1, :1000]) self._check_zero_mean_unit_variance(input_values[2]) # make sure that if max_length < longest -> then pad to max_length self.assertTrue(input_values.shape == (3, 1000)) speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)] processed = feat_extract( speech_inputs, truncation=True, max_length=2000, padding="longest", return_tensors="np" ) input_values = processed.input_values self._check_zero_mean_unit_variance(input_values[0, :800]) self._check_zero_mean_unit_variance(input_values[1, :1000]) self._check_zero_mean_unit_variance(input_values[2]) # make sure that if max_length > longest -> then pad to longest self.assertTrue(input_values.shape == (3, 1200)) def test_double_precision_pad(self): feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()) np_speech_inputs = np.random.rand(100).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_values": inputs}], return_tensors="np") self.assertTrue(np_processed.input_values.dtype == np.float32) pt_processed = feature_extractor.pad([{"input_values": inputs}], return_tensors="pt") self.assertTrue(pt_processed.input_values.dtype == torch.float32) def test_call_target(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_values = feature_extractor(audio_target=np_speech_inputs, padding=True, return_tensors="np").input_values self.assertTrue(input_values.ndim == 3) self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins) # Test not batched input encoded_sequences_1 = feature_extractor(speech_inputs[0], return_tensors="np").input_values encoded_sequences_2 = feature_extractor(np_speech_inputs[0], return_tensors="np").input_values 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_values encoded_sequences_2 = feature_extractor(np_speech_inputs, return_tensors="np").input_values 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_values encoded_sequences_2 = feature_extractor(np_speech_inputs, return_tensors="np").input_values 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)) def test_batch_feature_target(self): speech_inputs = self.feat_extract_tester.prepare_inputs_for_target() feat_extract = self.feature_extraction_class(**self.feat_extract_dict) input_name = feat_extract.model_input_names[0] processed_features = BatchFeature({input_name: speech_inputs}) self.assertTrue(all(len(x) == len(y) for x, y in zip(speech_inputs, processed_features[input_name]))) speech_inputs = self.feat_extract_tester.prepare_inputs_for_target(equal_length=True) processed_features = BatchFeature({input_name: speech_inputs}, tensor_type="np") batch_features_input = processed_features[input_name] if len(batch_features_input.shape) < 3: batch_features_input = batch_features_input[:, :, None] self.assertTrue( batch_features_input.shape == (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.num_mel_bins) ) @require_torch def test_batch_feature_target_pt(self): speech_inputs = self.feat_extract_tester.prepare_inputs_for_target(equal_length=True) feat_extract = self.feature_extraction_class(**self.feat_extract_dict) input_name = feat_extract.model_input_names[0] processed_features = BatchFeature({input_name: speech_inputs}, tensor_type="pt") batch_features_input = processed_features[input_name] if len(batch_features_input.shape) < 3: batch_features_input = batch_features_input[:, :, None] self.assertTrue( batch_features_input.shape == (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.num_mel_bins) ) @require_torch def test_padding_accepts_tensors_target_pt(self): feat_extract = self.feature_extraction_class(**self.feat_extract_dict) speech_inputs = self.feat_extract_tester.prepare_inputs_for_target() input_name = feat_extract.model_input_names[0] processed_features = BatchFeature({input_name: speech_inputs}) feat_extract.feature_size = feat_extract.num_mel_bins # hack! input_np = feat_extract.pad(processed_features, padding="longest", return_tensors="np")[input_name] input_pt = feat_extract.pad(processed_features, padding="longest", return_tensors="pt")[input_name] self.assertTrue(abs(input_np.astype(np.float32).sum() - input_pt.numpy().astype(np.float32).sum()) < 1e-2) def test_attention_mask_target(self): feat_dict = self.feat_extract_dict feat_dict["return_attention_mask"] = True feat_extract = self.feature_extraction_class(**feat_dict) speech_inputs = self.feat_extract_tester.prepare_inputs_for_target() input_lengths = [len(x) for x in speech_inputs] input_name = feat_extract.model_input_names[0] processed = BatchFeature({input_name: speech_inputs}) feat_extract.feature_size = feat_extract.num_mel_bins # hack! processed = feat_extract.pad(processed, padding="longest", return_tensors="np") self.assertIn("attention_mask", processed) self.assertListEqual(list(processed.attention_mask.shape), list(processed[input_name].shape[:2])) self.assertListEqual(processed.attention_mask.sum(-1).tolist(), input_lengths) def test_attention_mask_with_truncation_target(self): feat_dict = self.feat_extract_dict feat_dict["return_attention_mask"] = True feat_extract = self.feature_extraction_class(**feat_dict) speech_inputs = self.feat_extract_tester.prepare_inputs_for_target() input_lengths = [len(x) for x in speech_inputs] input_name = feat_extract.model_input_names[0] processed = BatchFeature({input_name: speech_inputs}) max_length = min(input_lengths) feat_extract.feature_size = feat_extract.num_mel_bins # hack! processed_pad = feat_extract.pad( processed, padding="max_length", max_length=max_length, truncation=True, return_tensors="np" ) self.assertIn("attention_mask", processed_pad) self.assertListEqual( list(processed_pad.attention_mask.shape), [processed_pad[input_name].shape[0], max_length] ) self.assertListEqual( processed_pad.attention_mask[:, :max_length].sum(-1).tolist(), [max_length for x in speech_inputs] ) def _load_datasamples(self, num_samples): from datasets import load_dataset ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") # automatic decoding with librispeech speech_samples = ds.sort("id")[:num_samples]["audio"] return [x["array"] for x in speech_samples] def test_integration(self): # fmt: off EXPECTED_INPUT_VALUES = torch.tensor( [2.3804e-03, 2.0752e-03, 1.9836e-03, 2.1057e-03, 1.6174e-03, 3.0518e-04, 9.1553e-05, 3.3569e-04, 9.7656e-04, 1.8311e-03, 2.0142e-03, 2.1057e-03, 1.7395e-03, 4.5776e-04, -3.9673e-04, 4.5776e-04, 1.0071e-03, 9.1553e-05, 4.8828e-04, 1.1597e-03, 7.3242e-04, 9.4604e-04, 1.8005e-03, 1.8311e-03, 8.8501e-04, 4.2725e-04, 4.8828e-04, 7.3242e-04, 1.0986e-03, 2.1057e-03] ) # fmt: on input_speech = self._load_datasamples(1) feature_extractor = SpeechT5FeatureExtractor() input_values = feature_extractor(input_speech, return_tensors="pt").input_values self.assertEqual(input_values.shape, (1, 93680)) torch.testing.assert_close(input_values[0, :30], EXPECTED_INPUT_VALUES, rtol=1e-6, atol=1e-6) def test_integration_target(self): # fmt: off EXPECTED_INPUT_VALUES = torch.tensor( [-2.6870, -3.0104, -3.1356, -3.5352, -3.0044, -3.0353, -3.4719, -3.6777, -3.1520, -2.9435, -2.6553, -2.8795, -2.9944, -2.5921, -3.0279, -3.0386, -3.0864, -3.1291, -3.2353, -2.7444, -2.6831, -2.7287, -3.1761, -3.1571, -3.2726, -3.0582, -3.1007, -3.4533, -3.4695, -3.0998] ) # fmt: on input_speech = self._load_datasamples(1) feature_extractor = SpeechT5FeatureExtractor() input_values = feature_extractor(audio_target=input_speech, return_tensors="pt").input_values self.assertEqual(input_values.shape, (1, 366, 80)) torch.testing.assert_close(input_values[0, 0, :30], EXPECTED_INPUT_VALUES, rtol=1e-4, atol=1e-4)