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214 lines
8.7 KiB
Python
214 lines
8.7 KiB
Python
# coding=utf-8
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# Copyright 2023 HuggingFace Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" Testing suite for the TVLT feature extraction. """
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import itertools
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import os
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import random
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import tempfile
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import unittest
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import numpy as np
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from transformers import TvltFeatureExtractor, is_datasets_available
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from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
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from transformers.utils.import_utils import is_torch_available
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from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
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if is_torch_available():
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import torch
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if is_datasets_available():
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from datasets import load_dataset
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global_rng = random.Random()
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def floats_list(shape, scale=1.0, rng=None, name=None):
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"""Creates a random float32 tensor"""
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if rng is None:
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rng = global_rng
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values = []
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for batch_idx in range(shape[0]):
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values.append([])
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for _ in range(shape[1]):
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values[-1].append(rng.random() * scale)
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return values
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class TvltFeatureExtractionTester(unittest.TestCase):
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def __init__(
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self,
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parent,
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batch_size=7,
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min_seq_length=400,
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max_seq_length=2000,
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spectrogram_length=2048,
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feature_size=128,
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num_audio_channels=1,
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hop_length=512,
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chunk_length=30,
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sampling_rate=44100,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.min_seq_length = min_seq_length
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self.max_seq_length = max_seq_length
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self.seq_length_diff = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
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self.spectrogram_length = spectrogram_length
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self.feature_size = feature_size
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self.num_audio_channels = num_audio_channels
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self.hop_length = hop_length
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self.chunk_length = chunk_length
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self.sampling_rate = sampling_rate
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def prepare_feat_extract_dict(self):
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return {
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"spectrogram_length": self.spectrogram_length,
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"feature_size": self.feature_size,
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"num_audio_channels": self.num_audio_channels,
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"hop_length": self.hop_length,
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"chunk_length": self.chunk_length,
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"sampling_rate": self.sampling_rate,
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}
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def prepare_inputs_for_common(self, equal_length=False, numpify=False):
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def _flatten(list_of_lists):
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return list(itertools.chain(*list_of_lists))
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if equal_length:
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speech_inputs = [floats_list((self.max_seq_length, self.feature_size)) for _ in range(self.batch_size)]
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else:
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# make sure that inputs increase in size
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speech_inputs = [
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floats_list((x, self.feature_size))
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for x in range(self.min_seq_length, self.max_seq_length, self.seq_length_diff)
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]
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if numpify:
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speech_inputs = [np.asarray(x) for x in speech_inputs]
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return speech_inputs
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@require_torch
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@require_torchaudio
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class TvltFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.TestCase):
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feature_extraction_class = TvltFeatureExtractor
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def setUp(self):
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self.feat_extract_tester = TvltFeatureExtractionTester(self)
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def test_feat_extract_properties(self):
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feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
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self.assertTrue(hasattr(feature_extractor, "spectrogram_length"))
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self.assertTrue(hasattr(feature_extractor, "feature_size"))
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self.assertTrue(hasattr(feature_extractor, "num_audio_channels"))
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self.assertTrue(hasattr(feature_extractor, "hop_length"))
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self.assertTrue(hasattr(feature_extractor, "chunk_length"))
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self.assertTrue(hasattr(feature_extractor, "sampling_rate"))
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def test_feat_extract_from_and_save_pretrained(self):
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feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict)
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with tempfile.TemporaryDirectory() as tmpdirname:
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saved_file = feat_extract_first.save_pretrained(tmpdirname)[0]
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check_json_file_has_correct_format(saved_file)
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feat_extract_second = self.feature_extraction_class.from_pretrained(tmpdirname)
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dict_first = feat_extract_first.to_dict()
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dict_second = feat_extract_second.to_dict()
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mel_1 = dict_first.pop("mel_filters")
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mel_2 = dict_second.pop("mel_filters")
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self.assertTrue(np.allclose(mel_1, mel_2))
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self.assertEqual(dict_first, dict_second)
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def test_feat_extract_to_json_file(self):
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feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict)
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with tempfile.TemporaryDirectory() as tmpdirname:
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json_file_path = os.path.join(tmpdirname, "feat_extract.json")
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feat_extract_first.to_json_file(json_file_path)
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feat_extract_second = self.feature_extraction_class.from_json_file(json_file_path)
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dict_first = feat_extract_first.to_dict()
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dict_second = feat_extract_second.to_dict()
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mel_1 = dict_first.pop("mel_filters")
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mel_2 = dict_second.pop("mel_filters")
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self.assertTrue(np.allclose(mel_1, mel_2))
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self.assertEqual(dict_first, dict_second)
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def test_call(self):
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# Initialize feature_extractor
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feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
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# create three inputs of length 800, 1000, and 1200
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speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
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np_speech_inputs = [np.asarray(speech_input) for speech_input in speech_inputs]
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# Test not batched input
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encoded_audios = feature_extractor(np_speech_inputs[0], return_tensors="np", sampling_rate=44100).audio_values
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self.assertTrue(encoded_audios.ndim == 4)
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self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size)
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self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length)
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self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels)
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# Test batched
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encoded_audios = feature_extractor(np_speech_inputs, return_tensors="np", sampling_rate=44100).audio_values
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self.assertTrue(encoded_audios.ndim == 4)
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self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size)
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self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length)
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self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels)
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# Test audio masking
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encoded_audios = feature_extractor(
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np_speech_inputs, return_tensors="np", sampling_rate=44100, mask_audio=True
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).audio_values
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self.assertTrue(encoded_audios.ndim == 4)
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self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size)
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self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length)
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self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels)
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# Test 2-D numpy arrays are batched.
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speech_inputs = [floats_list((1, x))[0] for x in (800, 800, 800)]
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np_speech_inputs = np.asarray(speech_inputs)
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encoded_audios = feature_extractor(np_speech_inputs, return_tensors="np", sampling_rate=44100).audio_values
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self.assertTrue(encoded_audios.ndim == 4)
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self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size)
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self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length)
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self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels)
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def _load_datasamples(self, num_samples):
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ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
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# automatic decoding with librispeech
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speech_samples = ds.sort("id").select(range(num_samples))[:num_samples]["audio"]
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return [x["array"] for x in speech_samples]
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def test_integration(self):
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input_speech = self._load_datasamples(1)
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feature_extractor = TvltFeatureExtractor()
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audio_values = feature_extractor(input_speech, return_tensors="pt").audio_values
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self.assertEquals(audio_values.shape, (1, 1, 192, 128))
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expected_slice = torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]])
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self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2], expected_slice, atol=1e-4))
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