# Copyright 2023 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. import itertools import os import random import tempfile import unittest import numpy as np from datasets import Audio, load_dataset from transformers import UnivNetFeatureExtractor from transformers.testing_utils import check_json_file_has_correct_format, require_torch, slow 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 class UnivNetFeatureExtractionTester: def __init__( self, parent, batch_size=7, min_seq_length=400, max_seq_length=2000, feature_size=1, sampling_rate=24000, padding_value=0.0, do_normalize=True, num_mel_bins=100, hop_length=256, win_length=1024, win_function="hann_window", filter_length=1024, max_length_s=10, fmin=0.0, fmax=12000, mel_floor=1e-9, center=False, compression_factor=1.0, compression_clip_val=1e-5, normalize_min=-11.512925148010254, normalize_max=2.3143386840820312, model_in_channels=64, pad_end_length=10, ): 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.sampling_rate = sampling_rate self.padding_value = padding_value 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.filter_length = filter_length self.max_length_s = max_length_s self.fmin = fmin self.fmax = fmax self.mel_floor = mel_floor self.center = center self.compression_factor = compression_factor self.compression_clip_val = compression_clip_val self.normalize_min = normalize_min self.normalize_max = normalize_max self.model_in_channels = model_in_channels self.pad_end_length = pad_end_length def prepare_feat_extract_dict(self): return { "feature_size": self.feature_size, "sampling_rate": self.sampling_rate, "padding_value": self.padding_value, "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, "filter_length": self.filter_length, "max_length_s": self.max_length_s, "fmin": self.fmin, "fmax": self.fmax, "mel_floor": self.mel_floor, "center": self.center, "compression_factor": self.compression_factor, "compression_clip_val": self.compression_clip_val, "normalize_min": self.normalize_min, "normalize_max": self.normalize_max, "model_in_channels": self.model_in_channels, "pad_end_length": self.pad_end_length, } 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 UnivNetFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.TestCase): feature_extraction_class = UnivNetFeatureExtractor def setUp(self): self.feat_extract_tester = UnivNetFeatureExtractionTester(self) # Copied from tests.models.whisper.test_feature_extraction_whisper.WhisperFeatureExtractionTest.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() 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) # Copied from tests.models.whisper.test_feature_extraction_whisper.WhisperFeatureExtractionTest.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() 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", max_length=1600, return_tensors="np" ).input_features self.assertTrue(input_features.ndim == 3) # Note: for some reason I get a weird padding error when feature_size > 1 # self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size) # Note: we use the shape convention (batch_size, seq_len, num_mel_bins) self.assertTrue(input_features.shape[-1] == feature_extractor.num_mel_bins) # 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((feature_extractor.num_max_samples - 100), (feature_extractor.num_max_samples + 500), 200) ] np_speech_inputs = [np.asarray(speech_input) for speech_input in speech_inputs] speech_inputs_truncated = [x[: feature_extractor.num_max_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_batched_unbatched_consistency(self): feature_extractor = self.feature_extraction_class(**self.feat_extract_dict) speech_inputs = floats_list((1, 800))[0] np_speech_inputs = np.asarray(speech_inputs) # Test unbatched vs batched list encoded_sequences_1 = feature_extractor(speech_inputs, return_tensors="np").input_features encoded_sequences_2 = feature_extractor([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 np.ndarray vs list[np.ndarray] encoded_sequences_1 = feature_extractor(np_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 unbatched np.ndarray vs batched np.ndarray encoded_sequences_1 = feature_extractor(np_speech_inputs, return_tensors="np").input_features encoded_sequences_2 = feature_extractor( np.expand_dims(np_speech_inputs, axis=0), 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_generate_noise(self): feature_extractor = self.feature_extraction_class(**self.feat_extract_dict) speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)] features = feature_extractor(speech_inputs, return_noise=True) input_features = features.input_features noise_features = features.noise_sequence for spectrogram, noise in zip(input_features, noise_features): self.assertEqual(spectrogram.shape[0], noise.shape[0]) def test_pad_end(self): feature_extractor = self.feature_extraction_class(**self.feat_extract_dict) speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)] input_features1 = feature_extractor(speech_inputs, padding=False, pad_end=False).input_features input_features2 = feature_extractor(speech_inputs, padding=False, pad_end=True).input_features for spectrogram1, spectrogram2 in zip(input_features1, input_features2): self.assertEqual(spectrogram1.shape[0] + self.feat_extract_tester.pad_end_length, spectrogram2.shape[0]) def test_generate_noise_and_pad_end(self): feature_extractor = self.feature_extraction_class(**self.feat_extract_dict) speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)] features = feature_extractor(speech_inputs, padding=False, return_noise=True, pad_end=True) input_features = features.input_features noise_features = features.noise_sequence for spectrogram, noise in zip(input_features, noise_features): self.assertEqual(spectrogram.shape[0], noise.shape[0]) @require_torch def test_batch_decode(self): import torch feature_extractor = self.feature_extraction_class(**self.feat_extract_dict) input_lengths = list(range(800, 1400, 200)) pad_samples = feature_extractor.pad_end_length * feature_extractor.hop_length output_features = { "waveforms": torch.tensor(floats_list((3, max(input_lengths) + pad_samples))), "waveform_lengths": torch.tensor(input_lengths), } waveforms = feature_extractor.batch_decode(**output_features) for input_length, waveform in zip(input_lengths, waveforms): self.assertTrue(len(waveform.shape) == 1, msg="Individual output waveforms should be 1D") self.assertEqual(waveform.shape[0], input_length) @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_datasamples(self, num_samples): ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") ds = ds.cast_column("audio", Audio(sampling_rate=self.feat_extract_tester.sampling_rate)) # automatic decoding with librispeech speech_samples = ds.sort("id")[:num_samples]["audio"] return [x["array"] for x in speech_samples], [x["sampling_rate"] for x in speech_samples] @slow @require_torch def test_integration(self): # fmt: off EXPECTED_INPUT_FEATURES = torch.tensor( [ -5.0229, -6.1358, -5.8346, -5.4447, -5.6707, -5.8577, -5.0464, -5.0058, -5.6015, -5.6410, -5.4325, -5.6116, -5.3700, -5.7956, -5.3196, -5.3274, -5.9655, -5.6057, -5.8382, -5.9602, -5.9005, -5.9123, -5.7669, -6.1441, -5.5168, -5.1405, -5.3927, -6.0032, -5.5784, -5.3728 ], ) # fmt: on input_speech, sr = self._load_datasamples(1) feature_extractor = UnivNetFeatureExtractor() input_features = feature_extractor(input_speech, sampling_rate=sr[0], return_tensors="pt").input_features self.assertEqual(input_features.shape, (1, 548, 100)) input_features_mean = torch.mean(input_features) input_features_stddev = torch.std(input_features) EXPECTED_MEAN = torch.tensor(-6.18862009) EXPECTED_STDDEV = torch.tensor(2.80845642) torch.testing.assert_close(input_features_mean, EXPECTED_MEAN, rtol=5e-5, atol=5e-5) torch.testing.assert_close(input_features_stddev, EXPECTED_STDDEV) torch.testing.assert_close(input_features[0, :30, 0], EXPECTED_INPUT_FEATURES, rtol=1e-4, atol=1e-4)