tests / test_feature_extraction_speech_to_text.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 is_speech_available
	from transformers.testing_utils import require_torch, require_torchaudio

	from .test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


	if is_speech_available():
	from transformers import Speech2TextFeatureExtractor

	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


	@require_torch
	@require_torchaudio
	class Speech2TextFeatureExtractionTester(unittest.TestCase):
	def __init__(
	self,
	parent,
	batch_size=7,
	min_seq_length=400,
	max_seq_length=2000,
	feature_size=24,
	num_mel_bins=24,
	padding_value=0.0,
	sampling_rate=16_000,
	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.num_mel_bins = num_mel_bins
	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,
	"num_mel_bins": self.num_mel_bins,
	"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.max_seq_length, self.feature_size)) for _ in range(self.batch_size)]
	else:
	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


	@require_torch
	@require_torchaudio
	class Speech2TextFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.TestCase):

	feature_extraction_class = Speech2TextFeatureExtractor if is_speech_available() else None

	def setUp(self):
	self.feat_extract_tester = Speech2TextFeatureExtractionTester(self)

	def test_call(self):
	# Tests that all call wrap to encode_plus and batch_encode_plus
	feature_extractor = 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 feature size
	input_features = feature_extractor(np_speech_inputs, padding=True, return_tensors="np").input_features
	self.assertTrue(input_features.ndim == 3)
	self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size)

	# Test not batched input
	encoded_sequences_1 = feature_extractor(speech_inputs[0], return_tensors="np").input_features
	encoded_sequences_2 = feature_extractor(np_speech_inputs[0], return_tensors="np").input_features
	self.assertTrue(np.allclose(encoded_sequences_1, encoded_sequences_2, atol=1e-3))

	# Test batched
	encoded_sequences_1 = feature_extractor(speech_inputs, return_tensors="np").input_features
	encoded_sequences_2 = feature_extractor(np_speech_inputs, return_tensors="np").input_features
	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_cepstral_mean_and_variance_normalization(self):
	feature_extractor = 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)]
	inputs = feature_extractor(speech_inputs, padding=True, return_tensors="np", return_attention_mask=True)
	input_features = inputs.input_features
	attention_mask = inputs.attention_mask
	fbank_feat_lengths = np.sum(attention_mask == 1, axis=1)

	def _check_zero_mean_unit_variance(input_vector):
	self.assertTrue(np.all(np.mean(input_vector, axis=0) < 1e-3))
	self.assertTrue(np.all(np.abs(np.var(input_vector, axis=0) - 1) < 1e-3))

	_check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]])
	_check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]])
	_check_zero_mean_unit_variance(input_features[2, : fbank_feat_lengths[2]])