# Copyright 2021 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 random import unittest import numpy as np from transformers import Wav2Vec2Config, Wav2Vec2FeatureExtractor from transformers.testing_utils import require_torch, slow from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin 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 class Wav2Vec2FeatureExtractionTester: 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, return_attention_mask=True, do_normalize=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.return_attention_mask = return_attention_mask self.do_normalize = do_normalize 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, "do_normalize": self.do_normalize, } 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 class Wav2Vec2FeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.TestCase): feature_extraction_class = Wav2Vec2FeatureExtractor def setUp(self): self.feat_extract_tester = Wav2Vec2FeatureExtractionTester(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)) # 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 = 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)) @require_torch 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).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) @slow @require_torch def test_pretrained_checkpoints_are_set_correctly(self): # this test makes sure that models that are using # group norm don't have their feature extractor return the # attention_mask model_id = "facebook/wav2vec2-base-960h" config = Wav2Vec2Config.from_pretrained(model_id) feat_extract = Wav2Vec2FeatureExtractor.from_pretrained(model_id) # only "layer" feature extraction norm should make use of # attention_mask self.assertEqual(feat_extract.return_attention_mask, config.feat_extract_norm == "layer")