# 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 EnCodec feature extractor.""" import itertools import random import unittest import numpy as np from transformers import EncodecFeatureExtractor 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 EnCodecFeatureExtractionTester: def __init__( self, parent, batch_size=7, min_seq_length=400, max_seq_length=2000, feature_size=1, padding_value=0.0, sampling_rate=24000, 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.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, "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: audio_inputs = floats_list((self.batch_size, self.max_seq_length)) else: # make sure that inputs increase in size audio_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: audio_inputs = [np.asarray(x) for x in audio_inputs] return audio_inputs @require_torch class EnCodecFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.TestCase): feature_extraction_class = EncodecFeatureExtractor def setUp(self): self.feat_extract_tester = EnCodecFeatureExtractionTester(self) 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 audio_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)] np_audio_inputs = [np.asarray(audio_input) for audio_input in audio_inputs] # Test not batched input encoded_sequences_1 = feat_extract(audio_inputs[0], return_tensors="np").input_values encoded_sequences_2 = feat_extract(np_audio_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(audio_inputs, padding=True, return_tensors="np").input_values encoded_sequences_2 = feat_extract(np_audio_inputs, padding=True, 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_double_precision_pad(self): feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()) np_audio_inputs = np.random.rand(100).astype(np.float64) py_audio_inputs = np_audio_inputs.tolist() for inputs in [py_audio_inputs, np_audio_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 _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 audio_samples = ds.sort("id")[:num_samples]["audio"] return [x["array"] for x in audio_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_audio = self._load_datasamples(1) feature_extractor = EncodecFeatureExtractor() input_values = feature_extractor(input_audio, return_tensors="pt").input_values self.assertEqual(input_values.shape, (1, 1, 93680)) torch.testing.assert_close(input_values[0, 0, :30], EXPECTED_INPUT_VALUES, rtol=1e-6, atol=1e-6) def test_integration_stereo(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_audio = self._load_datasamples(1) input_audio = [np.tile(input_audio[0][None], reps=(2, 1))] input_audio[0][1] *= 0.5 feature_extractor = EncodecFeatureExtractor(feature_size=2) input_values = feature_extractor(input_audio, return_tensors="pt").input_values self.assertEqual(input_values.shape, (1, 2, 93680)) torch.testing.assert_close(input_values[0, 0, :30], EXPECTED_INPUT_VALUES, rtol=1e-6, atol=1e-6) torch.testing.assert_close(input_values[0, 1, :30], EXPECTED_INPUT_VALUES * 0.5, rtol=1e-6, atol=1e-6) def test_truncation_and_padding(self): input_audio = self._load_datasamples(2) # would be easier if the stride was like feature_extractor = EncodecFeatureExtractor(feature_size=1, chunk_length_s=1, overlap=0.01) # pad and trunc raise an error ? with self.assertRaisesRegex( ValueError, "^Both padding and truncation were set. Make sure you only set one.$", ): truncated_outputs = feature_extractor( input_audio, padding="max_length", truncation=True, return_tensors="pt" ).input_values # truncate to chunk truncated_outputs = feature_extractor(input_audio, truncation=True, return_tensors="pt").input_values self.assertEqual(truncated_outputs.shape, (2, 1, 71520)) # 2 chunks # force truncate to max_length truncated_outputs = feature_extractor( input_audio, truncation=True, max_length=48000, return_tensors="pt" ).input_values self.assertEqual(truncated_outputs.shape, (2, 1, 48000)) # pad to chunk padded_outputs = feature_extractor(input_audio, padding=True, return_tensors="pt").input_values self.assertEqual(padded_outputs.shape, (2, 1, 95280)) # pad to chunk truncated_outputs = feature_extractor(input_audio, return_tensors="pt").input_values self.assertEqual(truncated_outputs.shape, (2, 1, 95280)) # force pad to max length truncated_outputs = feature_extractor( input_audio, padding="max_length", max_length=100000, return_tensors="pt" ).input_values self.assertEqual(truncated_outputs.shape, (2, 1, 100000)) # force no pad with self.assertRaisesRegex( ValueError, "^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$", ): truncated_outputs = feature_extractor(input_audio, padding=False, return_tensors="pt").input_values truncated_outputs = feature_extractor(input_audio[0], padding=False, return_tensors="pt").input_values self.assertEqual(truncated_outputs.shape, (1, 1, 93680)) # no pad if no chunk_length_s feature_extractor.chunk_length_s = None with self.assertRaisesRegex( ValueError, "^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$", ): truncated_outputs = feature_extractor(input_audio, padding=False, return_tensors="pt").input_values truncated_outputs = feature_extractor(input_audio[0], padding=False, return_tensors="pt").input_values self.assertEqual(truncated_outputs.shape, (1, 1, 93680)) # no pad if no overlap feature_extractor.chunk_length_s = 2 feature_extractor.overlap = None with self.assertRaisesRegex( ValueError, "^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$", ): truncated_outputs = feature_extractor(input_audio, padding=False, return_tensors="pt").input_values truncated_outputs = feature_extractor(input_audio[0], padding=False, return_tensors="pt").input_values self.assertEqual(truncated_outputs.shape, (1, 1, 93680))