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https://github.com/huggingface/transformers.git
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251 lines
11 KiB
Python
251 lines
11 KiB
Python
# coding=utf-8
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# Copyright 2021 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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import itertools
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import random
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import unittest
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import numpy as np
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from transformers import is_speech_available
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from transformers.testing_utils import require_torch, require_torchaudio
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from .test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
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if is_speech_available():
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from transformers import Speech2TextFeatureExtractor
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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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@require_torch
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@require_torchaudio
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class Speech2TextFeatureExtractionTester(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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feature_size=24,
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num_mel_bins=24,
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padding_value=0.0,
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sampling_rate=16_000,
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return_attention_mask=True,
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do_normalize=True,
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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.feature_size = feature_size
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self.num_mel_bins = num_mel_bins
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self.padding_value = padding_value
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self.sampling_rate = sampling_rate
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self.return_attention_mask = return_attention_mask
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self.do_normalize = do_normalize
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def prepare_feat_extract_dict(self):
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return {
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"feature_size": self.feature_size,
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"num_mel_bins": self.num_mel_bins,
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"padding_value": self.padding_value,
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"sampling_rate": self.sampling_rate,
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"return_attention_mask": self.return_attention_mask,
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"do_normalize": self.do_normalize,
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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 Speech2TextFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.TestCase):
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feature_extraction_class = Speech2TextFeatureExtractor if is_speech_available() else None
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def setUp(self):
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self.feat_extract_tester = Speech2TextFeatureExtractionTester(self)
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def _check_zero_mean_unit_variance(self, input_vector):
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self.assertTrue(np.all(np.mean(input_vector, axis=0) < 1e-3))
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self.assertTrue(np.all(np.abs(np.var(input_vector, axis=0) - 1) < 1e-3))
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def test_call(self):
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# Tests that all call wrap to encode_plus and batch_encode_plus
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feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_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 feature size
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input_features = feature_extractor(np_speech_inputs, padding=True, return_tensors="np").input_features
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self.assertTrue(input_features.ndim == 3)
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self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size)
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# Test not batched input
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encoded_sequences_1 = feature_extractor(speech_inputs[0], return_tensors="np").input_features
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encoded_sequences_2 = feature_extractor(np_speech_inputs[0], return_tensors="np").input_features
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self.assertTrue(np.allclose(encoded_sequences_1, encoded_sequences_2, atol=1e-3))
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# Test batched
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encoded_sequences_1 = feature_extractor(speech_inputs, return_tensors="np").input_features
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encoded_sequences_2 = feature_extractor(np_speech_inputs, return_tensors="np").input_features
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for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2):
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self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=1e-3))
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def test_cepstral_mean_and_variance_normalization(self):
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feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
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speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
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paddings = ["longest", "max_length", "do_not_pad"]
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max_lengths = [None, 16, None]
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for max_length, padding in zip(max_lengths, paddings):
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inputs = feature_extractor(
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speech_inputs, padding=padding, max_length=max_length, return_attention_mask=True
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)
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input_features = inputs.input_features
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attention_mask = inputs.attention_mask
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fbank_feat_lengths = [np.sum(x) for x in attention_mask]
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self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]])
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self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]])
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self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]])
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def test_cepstral_mean_and_variance_normalization_np(self):
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feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
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speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
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paddings = ["longest", "max_length", "do_not_pad"]
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max_lengths = [None, 16, None]
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for max_length, padding in zip(max_lengths, paddings):
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inputs = feature_extractor(
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speech_inputs, max_length=max_length, padding=padding, return_tensors="np", return_attention_mask=True
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)
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input_features = inputs.input_features
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attention_mask = inputs.attention_mask
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fbank_feat_lengths = [np.sum(x) for x in attention_mask]
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self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]])
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self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1e-6)
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self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]])
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self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1e-6)
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self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]])
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def test_cepstral_mean_and_variance_normalization_trunc_max_length(self):
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feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
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speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
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inputs = feature_extractor(
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speech_inputs,
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padding="max_length",
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max_length=4,
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truncation=True,
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return_tensors="np",
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return_attention_mask=True,
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)
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input_features = inputs.input_features
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attention_mask = inputs.attention_mask
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fbank_feat_lengths = np.sum(attention_mask == 1, axis=1)
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self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]])
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self._check_zero_mean_unit_variance(input_features[1])
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self._check_zero_mean_unit_variance(input_features[2])
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def test_cepstral_mean_and_variance_normalization_trunc_longest(self):
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feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
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speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
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inputs = feature_extractor(
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speech_inputs,
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padding="longest",
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max_length=4,
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truncation=True,
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return_tensors="np",
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return_attention_mask=True,
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)
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input_features = inputs.input_features
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attention_mask = inputs.attention_mask
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fbank_feat_lengths = np.sum(attention_mask == 1, axis=1)
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self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]])
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self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]])
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self._check_zero_mean_unit_variance(input_features[2])
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# make sure that if max_length < longest -> then pad to max_length
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self.assertEqual(input_features.shape, (3, 4, 24))
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speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
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inputs = feature_extractor(
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speech_inputs,
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padding="longest",
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max_length=16,
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truncation=True,
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return_tensors="np",
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return_attention_mask=True,
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)
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input_features = inputs.input_features
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attention_mask = inputs.attention_mask
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fbank_feat_lengths = np.sum(attention_mask == 1, axis=1)
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self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]])
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self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]])
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self._check_zero_mean_unit_variance(input_features[2])
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# make sure that if max_length < longest -> then pad to max_length
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self.assertEqual(input_features.shape, (3, 6, 24))
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def test_double_precision_pad(self):
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import torch
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feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
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np_speech_inputs = np.random.rand(100, 32).astype(np.float64)
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py_speech_inputs = np_speech_inputs.tolist()
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for inputs in [py_speech_inputs, np_speech_inputs]:
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np_processed = feature_extractor.pad([{"input_features": inputs}], return_tensors="np")
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self.assertTrue(np_processed.input_features.dtype == np.float32)
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pt_processed = feature_extractor.pad([{"input_features": inputs}], return_tensors="pt")
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self.assertTrue(pt_processed.input_features.dtype == torch.float32)
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