# coding=utf-8 # Copyright 2022 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 is_speech_available from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio from transformers.utils.import_utils import is_torch_available from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin if is_speech_available(): from transformers import WhisperFeatureExtractor if is_torch_available(): import torch global_rng = random.Random() 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_torchaudio class WhisperFeatureExtractionTester(unittest.TestCase): def __init__( self, parent, batch_size=7, min_seq_length=400, max_seq_length=2000, feature_size=10, hop_length=160, chunk_length=8, padding_value=0.0, sampling_rate=4_000, return_attention_mask=False, 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.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.chunk_length = chunk_length self.hop_length = hop_length def prepare_feat_extract_dict(self): return { "feature_size": self.feature_size, "hop_length": self.hop_length, "chunk_length": self.chunk_length, "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.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 @require_torchaudio class WhisperFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.TestCase): feature_extraction_class = WhisperFeatureExtractor if is_speech_available() else None def setUp(self): self.feat_extract_tester = WhisperFeatureExtractionTester(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() mel_1 = feat_extract_first.mel_filters mel_2 = feat_extract_second.mel_filters self.assertTrue(np.allclose(mel_1, mel_2)) self.assertEqual(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() mel_1 = feat_extract_first.mel_filters mel_2 = feat_extract_second.mel_filters self.assertTrue(np.allclose(mel_1, mel_2)) 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="max_length", return_tensors="np").input_features self.assertTrue(input_features.ndim == 3) self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames) self.assertTrue(input_features.shape[-2] == 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)) # Test truncation required speech_inputs = [floats_list((1, x))[0] for x in range(200, (feature_extractor.n_samples + 500), 200)] np_speech_inputs = [np.asarray(speech_input) for speech_input in speech_inputs] speech_inputs_truncated = [x[: feature_extractor.n_samples] for x in speech_inputs] np_speech_inputs_truncated = [np.asarray(speech_input) for speech_input in speech_inputs_truncated] encoded_sequences_1 = feature_extractor(np_speech_inputs, return_tensors="np").input_features encoded_sequences_2 = feature_extractor(np_speech_inputs_truncated, 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_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_datasamples(self, num_samples): ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") # automatic decoding with librispeech speech_samples = ds.sort("id").select(range(num_samples))[:num_samples]["audio"] return [x["array"] for x in speech_samples] def test_integration(self): # fmt: off EXPECTED_INPUT_FEATURES = torch.tensor( [ 0.1193, -0.0946, -0.1098, -0.0196, 0.0225, -0.0690, -0.1736, 0.0951, 0.0971, -0.0817, -0.0702, 0.0162, 0.0260, 0.0017, -0.0192, -0.1678, 0.0709, -0.1867, -0.0655, -0.0274, -0.0234, -0.1884, -0.0516, -0.0554, -0.0274, -0.1425, -0.1423, 0.0837, 0.0377, -0.0854 ] ) # fmt: on input_speech = self._load_datasamples(1) feature_extractor = WhisperFeatureExtractor() input_features = feature_extractor(input_speech, return_tensors="pt").input_features self.assertEqual(input_features.shape, (1, 80, 3000)) self.assertTrue(torch.allclose(input_features[0, 0, :30], EXPECTED_INPUT_FEATURES, 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_datasamples(1)[0] 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(np.all(np.mean(audio) < 1e-3)) self.assertTrue(np.all(np.abs(np.var(audio) - 1) < 1e-3))