tests / 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")