# Copyright 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. import itertools import os import random import tempfile import unittest import numpy as np from datasets import load_dataset from transformers import SeamlessM4TFeatureExtractor, is_speech_available from transformers.testing_utils import check_json_file_has_correct_format, 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 SeamlessM4TFeatureExtractionTester: def __init__( self, parent, batch_size=7, min_seq_length=400, max_seq_length=2000, feature_size=10, padding_value=0.0, sampling_rate=4_000, return_attention_mask=True, do_normalize=True, stride=2, ): 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.do_normalize = do_normalize self.feature_size = feature_size self.stride = stride self.num_mel_bins = feature_size def prepare_feat_extract_dict(self): return { "feature_size": self.feature_size, "num_mel_bins": self.num_mel_bins, "padding_value": self.padding_value, "sampling_rate": self.sampling_rate, "stride": self.stride, "return_attention_mask": self.return_attention_mask, "do_normalize": self.do_normalize, } # 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_torch class SeamlessM4TFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.TestCase): feature_extraction_class = SeamlessM4TFeatureExtractor if is_speech_available() else None def setUp(self): self.feat_extract_tester = SeamlessM4TFeatureExtractionTester(self) 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) 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_numpy(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) self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size * feature_extractor.stride) # 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)) def test_call_with_padded_input_not_multiple_of_stride(self): # same as test_call_numpy but with stride=6 and pad_to_multiple_of=8 # the input sizes 800, 1400 and 200 are a multiple of pad_to_multiple_of but not a multiple of stride # therefore remainder = num_frames % self.stride will not be zero and must be subtracted from num_frames stride = 6 pad_to_multiple_of = 8 feature_extractor_args = self.feat_extract_tester.prepare_feat_extract_dict() feature_extractor_args["stride"] = stride feature_extractor = self.feature_extraction_class(**feature_extractor_args) 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 and attention mask size output = feature_extractor(np_speech_inputs, pad_to_multiple_of=pad_to_multiple_of, return_tensors="np") input_features = output.input_features self.assertTrue(input_features.ndim == 3) self.assertTrue(input_features.shape[0] == 3) self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size * feature_extractor.stride) # same as test_attention_mask attention_mask = output.attention_mask self.assertTrue(attention_mask.ndim == 2) self.assertTrue(attention_mask.shape[0] == 3) self.assertTrue(attention_mask.shape[-1] == input_features.shape[1]) # Test not batched input encoded_sequences_1 = feature_extractor( speech_inputs[0], pad_to_multiple_of=pad_to_multiple_of, return_tensors="np" ).input_features encoded_sequences_2 = feature_extractor( np_speech_inputs[0], pad_to_multiple_of=pad_to_multiple_of, 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, pad_to_multiple_of=pad_to_multiple_of, return_tensors="np" ).input_features encoded_sequences_2 = feature_extractor( np_speech_inputs, pad_to_multiple_of=pad_to_multiple_of, 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, pad_to_multiple_of=pad_to_multiple_of, return_tensors="np" ).input_features encoded_sequences_2 = feature_extractor( np_speech_inputs, pad_to_multiple_of=pad_to_multiple_of, 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)) def test_call_without_attention_mask(self): feature_extractor_args = self.feat_extract_tester.prepare_feat_extract_dict() feature_extractor = self.feature_extraction_class(**feature_extractor_args) # 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 attention mask when passing no attention mask to forward call output = feature_extractor(np_speech_inputs, padding=True, return_tensors="np", return_attention_mask=False) self.assertTrue("attention_mask" not in output) # Test attention mask when no attention mask by default feature_extractor_args["return_attention_mask"] = False feature_extractor = self.feature_extraction_class(**feature_extractor_args) output = feature_extractor(np_speech_inputs, padding=True, return_tensors="np", return_attention_mask=False) self.assertTrue("attention_mask" not in output) def test_attention_mask(self): # test attention mask has the right output shape feature_extractor_args = self.feat_extract_tester.prepare_feat_extract_dict() feature_extractor = self.feature_extraction_class(**feature_extractor_args) # 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 attention mask when passing it to forward call output = feature_extractor(np_speech_inputs, padding=True, return_tensors="np") input_features = output.input_features attention_mask = output.attention_mask self.assertTrue(attention_mask.ndim == 2) self.assertTrue(attention_mask.shape[0] == 3) self.assertTrue(attention_mask.shape[-1] == input_features.shape[1]) @require_torch def test_call_torch(self): import torch # 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)] pt_speech_inputs = [torch.tensor(speech_input) for speech_input in speech_inputs] # Test feature size input_features = feature_extractor(pt_speech_inputs, padding=True, return_tensors="pt").input_features self.assertTrue(input_features.ndim == 3) self.assertTrue(input_features.shape[0] == 3) self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size * feature_extractor.stride) # Test not batched input encoded_sequences_1 = feature_extractor(speech_inputs[0], return_tensors="pt").input_features encoded_sequences_2 = feature_extractor(pt_speech_inputs[0], return_tensors="pt").input_features torch.testing.assert_close(encoded_sequences_1, encoded_sequences_2, rtol=1e-3, atol=1e-3) # Test batched encoded_sequences_1 = feature_extractor(speech_inputs, return_tensors="pt").input_features encoded_sequences_2 = feature_extractor(pt_speech_inputs, return_tensors="pt").input_features for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2): torch.testing.assert_close(enc_seq_1, enc_seq_2, rtol=1e-3, atol=1e-3) # Test 2-D numpy arrays are batched. speech_inputs = [floats_list((1, x))[0] for x in (800, 800, 800)] pt_speech_inputs = torch.tensor(speech_inputs) encoded_sequences_1 = feature_extractor(speech_inputs, return_tensors="pt").input_features encoded_sequences_2 = feature_extractor(pt_speech_inputs, return_tensors="pt").input_features for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2): torch.testing.assert_close(enc_seq_1, enc_seq_2, rtol=1e-3, atol=1e-3) @require_torch # Copied from tests.models.whisper.test_feature_extraction_whisper.WhisperFeatureExtractionTest.test_double_precision_pad 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 _load_datasample(self, id): ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") # automatic decoding with librispeech speech_sample = ds.sort("id")[id]["audio"]["array"] return torch.from_numpy(speech_sample).unsqueeze(0) def test_integration(self): # fmt: off EXPECTED_INPUT_FEATURES = torch.tensor( [ -1.5621, -1.4236, -1.3335, -1.3991, -1.2881, -1.1133, -0.9710, -0.8895, -0.8280, -0.7376, -0.7194, -0.6896, -0.6849, -0.6788, -0.6545, -0.6610, -0.6566, -0.5738, -0.5252, -0.5533, -0.5887, -0.6116, -0.5971, -0.4956, -0.2881, -0.1512, 0.0299, 0.1762, 0.2728, 0.2236 ] ) # fmt: on input_speech = self._load_datasample(10) feature_extractor = SeamlessM4TFeatureExtractor() input_features = feature_extractor(input_speech, return_tensors="pt").input_features feature_extractor(input_speech, return_tensors="pt").input_features[0, 5, :30] self.assertEqual(input_features.shape, (1, 279, 160)) torch.testing.assert_close(input_features[0, 5, :30], EXPECTED_INPUT_FEATURES, rtol=1e-4, atol=1e-4) 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()) audio = self._load_datasample(1) audio = ((audio - audio.min()) / (audio.max() - audio.min())) * 65535 # Rescale to [0, 65535] to show issue audio = feat_extract.zero_mean_unit_var_norm([audio], attention_mask=None)[0] self.assertTrue((audio.mean() < 1e-3).all()) self.assertTrue(((audio.var() - 1).abs() < 1e-3).all())