test_feature_extraction_wav2vec2.py
0164de5c5cd4main

test_feature_extraction_wav2vec2.py
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			# coding=utf-8
			# Copyright 2021 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.


			import itertools
			import random
			import unittest

			import numpy as np

			from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, Wav2Vec2Config, Wav2Vec2FeatureExtractor
			from transformers.testing_utils import require_torch, slow

			from .test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


			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 Wav2Vec2FeatureExtractionTester(unittest.TestCase):
			def __init__(
			self,
			parent,
			batch_size=7,
			min_seq_length=400,
			max_seq_length=2000,
			feature_size=1,
			padding_value=0.0,
			sampling_rate=16000,
			return_attention_mask=True,
			do_normalize=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
			self.do_normalize = do_normalize

			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,
			"do_normalize": self.do_normalize,
			}

			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:
			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 Wav2Vec2FeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.TestCase):

			feature_extraction_class = Wav2Vec2FeatureExtractor

			def setUp(self):
			self.feat_extract_tester = Wav2Vec2FeatureExtractionTester(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
			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 not batched input
			encoded_sequences_1 = feat_extract(speech_inputs[0], return_tensors="np").input_values
			encoded_sequences_2 = feat_extract(np_speech_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(speech_inputs, return_tensors="np").input_values
			encoded_sequences_2 = feat_extract(np_speech_inputs, 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_zero_mean_unit_variance_normalization(self):
			feat_extract = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
			speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
			processed = feat_extract(speech_inputs, padding="longest")
			input_values = processed.input_values

			def _check_zero_mean_unit_variance(input_vector):
			self.assertTrue(np.abs(np.mean(input_vector)) < 1e-3)
			self.assertTrue(np.abs(np.var(input_vector) - 1) < 1e-3)

			_check_zero_mean_unit_variance(input_values[0, :800])
			_check_zero_mean_unit_variance(input_values[1, :1000])
			_check_zero_mean_unit_variance(input_values[2])

			@slow
			@require_torch
			def test_pretrained_checkpoints_are_set_correctly(self):
			# this test makes sure that models that are using
			# group norm don't have their feature extractor return the
			# attention_mask
			for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
			config = Wav2Vec2Config.from_pretrained(model_id)
			feat_extract = Wav2Vec2FeatureExtractor.from_pretrained(model_id)

			# only "layer" feature extraction norm should make use of
			# attention_mask
			self.assertEqual(feat_extract.return_attention_mask, config.feat_extract_norm == "layer")

R11484/source_code/transformers/tests/test_feature_extraction_wav2vec2.py0164de5c5cd4main

test_feature_extraction_wav2vec2.pyView Options

R11484/source_code/transformers/tests/test_feature_extraction_wav2vec2.py
0164de5c5cd4main

test_feature_extraction_wav2vec2.py
View Options