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# coding=utf-8 | |
# 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. | |
""" Testing suite for the TVLT feature extraction. """ | |
import itertools | |
import os | |
import random | |
import tempfile | |
import unittest | |
import numpy as np | |
from transformers import is_datasets_available, 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_torch_available(): | |
import torch | |
if is_datasets_available(): | |
from datasets import load_dataset | |
if is_speech_available(): | |
from transformers import TvltFeatureExtractor | |
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 | |
class TvltFeatureExtractionTester(unittest.TestCase): | |
def __init__( | |
self, | |
parent, | |
batch_size=7, | |
min_seq_length=400, | |
max_seq_length=2000, | |
spectrogram_length=2048, | |
feature_size=128, | |
num_audio_channels=1, | |
hop_length=512, | |
chunk_length=30, | |
sampling_rate=44100, | |
): | |
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.spectrogram_length = spectrogram_length | |
self.feature_size = feature_size | |
self.num_audio_channels = num_audio_channels | |
self.hop_length = hop_length | |
self.chunk_length = chunk_length | |
self.sampling_rate = sampling_rate | |
def prepare_feat_extract_dict(self): | |
return { | |
"spectrogram_length": self.spectrogram_length, | |
"feature_size": self.feature_size, | |
"num_audio_channels": self.num_audio_channels, | |
"hop_length": self.hop_length, | |
"chunk_length": self.chunk_length, | |
"sampling_rate": self.sampling_rate, | |
} | |
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 | |
class TvltFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.TestCase): | |
feature_extraction_class = TvltFeatureExtractor if is_speech_available() else None | |
def setUp(self): | |
self.feat_extract_tester = TvltFeatureExtractionTester(self) | |
def test_feat_extract_properties(self): | |
feature_extractor = self.feature_extraction_class(**self.feat_extract_dict) | |
self.assertTrue(hasattr(feature_extractor, "spectrogram_length")) | |
self.assertTrue(hasattr(feature_extractor, "feature_size")) | |
self.assertTrue(hasattr(feature_extractor, "num_audio_channels")) | |
self.assertTrue(hasattr(feature_extractor, "hop_length")) | |
self.assertTrue(hasattr(feature_extractor, "chunk_length")) | |
self.assertTrue(hasattr(feature_extractor, "sampling_rate")) | |
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 = dict_first.pop("mel_filters") | |
mel_2 = dict_second.pop("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 = dict_first.pop("mel_filters") | |
mel_2 = dict_second.pop("mel_filters") | |
self.assertTrue(np.allclose(mel_1, mel_2)) | |
self.assertEqual(dict_first, dict_second) | |
def test_call(self): | |
# Initialize feature_extractor | |
feature_extractor = self.feature_extraction_class(**self.feat_extract_dict) | |
# create three inputs of length 800, 1000, and 1200 | |
speech_inputs = [floats_list((1, x))[0] for x in range(8000, 14000, 20000)] | |
np_speech_inputs = [np.asarray(speech_input) for speech_input in speech_inputs] | |
# Test not batched input | |
encoded_audios = feature_extractor(np_speech_inputs[0], return_tensors="np", sampling_rate=44100).audio_values | |
self.assertTrue(encoded_audios.ndim == 4) | |
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size) | |
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length) | |
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels) | |
# Test batched | |
encoded_audios = feature_extractor(np_speech_inputs, return_tensors="np", sampling_rate=44100).audio_values | |
self.assertTrue(encoded_audios.ndim == 4) | |
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size) | |
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length) | |
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels) | |
# Test audio masking | |
encoded_audios = feature_extractor( | |
np_speech_inputs, return_tensors="np", sampling_rate=44100, mask_audio=True | |
).audio_values | |
self.assertTrue(encoded_audios.ndim == 4) | |
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size) | |
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length) | |
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels) | |
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): | |
input_speech = self._load_datasamples(1) | |
feaure_extractor = TvltFeatureExtractor() | |
audio_values = feaure_extractor(input_speech, return_tensors="pt").audio_values | |
self.assertTrue(audio_values.shape, [1, 1, 192, 128]) | |
expected_slice = torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]]) | |
self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2], expected_slice, atol=1e-4)) | |