[Test refactor 1/5] Per-folder tests reorganization (#15725)
* Per-folder tests reorganization Co-authored-by: sgugger <sylvain.gugger@gmail.com> Co-authored-by: Stas Bekman <stas@stason.org>
This commit is contained in:
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tests/speech_to_text/__init__.py
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tests/speech_to_text/__init__.py
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tests/speech_to_text/test_feature_extraction_speech_to_text.py
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tests/speech_to_text/test_feature_extraction_speech_to_text.py
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# 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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763
tests/speech_to_text/test_modeling_speech_to_text.py
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tests/speech_to_text/test_modeling_speech_to_text.py
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# coding=utf-8
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# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
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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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""" Testing suite for the PyTorch Speech2Text model. """
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import copy
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import inspect
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import os
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import tempfile
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import unittest
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from transformers import Speech2TextConfig
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from transformers.file_utils import cached_property
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from transformers.testing_utils import (
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is_torch_available,
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require_sentencepiece,
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require_tokenizers,
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require_torch,
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require_torchaudio,
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slow,
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torch_device,
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)
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from ..generation.test_generation_utils import GenerationTesterMixin
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from ..test_configuration_common import ConfigTester
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from ..test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
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if is_torch_available():
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import torch
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from transformers import Speech2TextForConditionalGeneration, Speech2TextModel, Speech2TextProcessor
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from transformers.models.speech_to_text.modeling_speech_to_text import Speech2TextDecoder, Speech2TextEncoder
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def prepare_speech_to_text_inputs_dict(
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config,
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input_features,
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decoder_input_ids,
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attention_mask=None,
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decoder_attention_mask=None,
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head_mask=None,
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decoder_head_mask=None,
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cross_attn_head_mask=None,
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):
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if attention_mask is None:
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attention_mask = input_features.ne(0)
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if decoder_attention_mask is None:
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decoder_attention_mask = decoder_input_ids.ne(config.pad_token_id)
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if head_mask is None:
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head_mask = torch.ones(config.encoder_layers, config.encoder_attention_heads, device=torch_device)
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if decoder_head_mask is None:
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decoder_head_mask = torch.ones(config.decoder_layers, config.decoder_attention_heads, device=torch_device)
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if cross_attn_head_mask is None:
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cross_attn_head_mask = torch.ones(config.decoder_layers, config.decoder_attention_heads, device=torch_device)
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return {
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# "input_ids": input_features,
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"input_features": input_features,
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"decoder_input_ids": decoder_input_ids,
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"attention_mask": attention_mask,
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"decoder_attention_mask": attention_mask,
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"head_mask": head_mask,
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"decoder_head_mask": decoder_head_mask,
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"cross_attn_head_mask": cross_attn_head_mask,
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}
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@require_torch
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class Speech2TextModelTester:
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def __init__(
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self,
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parent,
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batch_size=13,
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seq_length=7,
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is_training=True,
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use_labels=False,
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vocab_size=99,
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hidden_size=16,
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num_hidden_layers=2,
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num_attention_heads=4,
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intermediate_size=4,
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num_conv_layers=2,
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conv_kernel_sizes=(5, 5),
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conv_channels=32,
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input_feat_per_channel=24,
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input_channels=1,
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hidden_act="relu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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max_position_embeddings=20,
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max_source_positions=20,
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max_target_positions=20,
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eos_token_id=2,
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pad_token_id=1,
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bos_token_id=0,
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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.seq_length = seq_length
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self.is_training = is_training
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self.use_labels = use_labels
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.num_conv_layers = num_conv_layers
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self.conv_kernel_sizes = conv_kernel_sizes
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self.conv_channels = conv_channels
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self.input_feat_per_channel = input_feat_per_channel
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self.input_channels = input_channels
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self.hidden_act = hidden_act
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.max_position_embeddings = max_position_embeddings
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self.max_source_positions = max_source_positions
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self.max_target_positions = max_target_positions
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self.eos_token_id = eos_token_id
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self.pad_token_id = pad_token_id
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self.bos_token_id = bos_token_id
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def prepare_config_and_inputs(self):
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input_features = floats_tensor(
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[self.batch_size, self.seq_length, self.input_feat_per_channel], self.vocab_size
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)
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attention_mask = torch.ones([self.batch_size, self.seq_length], dtype=torch.long, device=torch_device)
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decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size).clamp(2)
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config = self.get_config()
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inputs_dict = prepare_speech_to_text_inputs_dict(
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config,
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input_features=input_features,
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decoder_input_ids=decoder_input_ids,
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attention_mask=attention_mask,
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)
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return config, inputs_dict
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def get_config(self):
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return Speech2TextConfig(
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vocab_size=self.vocab_size,
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d_model=self.hidden_size,
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encoder_layers=self.num_hidden_layers,
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decoder_layers=self.num_hidden_layers,
|
||||
encoder_attention_heads=self.num_attention_heads,
|
||||
decoder_attention_heads=self.num_attention_heads,
|
||||
encoder_ffn_dim=self.intermediate_size,
|
||||
decoder_ffn_dim=self.intermediate_size,
|
||||
num_conv_layers=self.num_conv_layers,
|
||||
conv_kernel_sizes=self.conv_kernel_sizes,
|
||||
conv_channels=self.conv_channels,
|
||||
input_feat_per_channel=self.input_feat_per_channel,
|
||||
input_channels=self.input_channels,
|
||||
dropout=self.hidden_dropout_prob,
|
||||
attention_dropout=self.attention_probs_dropout_prob,
|
||||
max_position_embeddings=self.max_position_embeddings,
|
||||
max_source_positions=self.max_source_positions,
|
||||
max_target_positions=self.max_target_positions,
|
||||
eos_token_id=self.eos_token_id,
|
||||
bos_token_id=self.bos_token_id,
|
||||
pad_token_id=self.pad_token_id,
|
||||
)
|
||||
|
||||
def prepare_config_and_inputs_for_common(self):
|
||||
config, inputs_dict = self.prepare_config_and_inputs()
|
||||
return config, inputs_dict
|
||||
|
||||
def get_subsampled_output_lengths(self, input_lengths):
|
||||
"""
|
||||
Computes the output length of the convolutional layers
|
||||
"""
|
||||
|
||||
for i in range(self.num_conv_layers):
|
||||
input_lengths = (input_lengths - 1) // 2 + 1
|
||||
|
||||
return input_lengths
|
||||
|
||||
def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict):
|
||||
model = Speech2TextModel(config=config).get_decoder().to(torch_device).eval()
|
||||
input_ids = inputs_dict["decoder_input_ids"]
|
||||
attention_mask = inputs_dict["decoder_attention_mask"]
|
||||
|
||||
# first forward pass
|
||||
outputs = model(input_ids, attention_mask=attention_mask, use_cache=True)
|
||||
|
||||
output, past_key_values = outputs.to_tuple()
|
||||
|
||||
# create hypothetical multiple next token and extent to next_input_ids
|
||||
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size).clamp(2)
|
||||
next_attn_mask = ids_tensor((self.batch_size, 3), 2)
|
||||
|
||||
# append to next input_ids and
|
||||
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
|
||||
next_attention_mask = torch.cat([attention_mask, next_attn_mask], dim=-1)
|
||||
|
||||
output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["last_hidden_state"]
|
||||
output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[
|
||||
"last_hidden_state"
|
||||
]
|
||||
|
||||
# select random slice
|
||||
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
|
||||
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
|
||||
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
|
||||
|
||||
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
|
||||
|
||||
# test that outputs are equal for slice
|
||||
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-2))
|
||||
|
||||
def check_encoder_decoder_model_standalone(self, config, inputs_dict):
|
||||
model = Speech2TextModel(config=config).to(torch_device).eval()
|
||||
outputs = model(**inputs_dict)
|
||||
|
||||
encoder_last_hidden_state = outputs.encoder_last_hidden_state
|
||||
last_hidden_state = outputs.last_hidden_state
|
||||
|
||||
with tempfile.TemporaryDirectory() as tmpdirname:
|
||||
encoder = model.get_encoder()
|
||||
encoder.save_pretrained(tmpdirname)
|
||||
encoder = Speech2TextEncoder.from_pretrained(tmpdirname).to(torch_device)
|
||||
|
||||
encoder_last_hidden_state_2 = encoder(
|
||||
inputs_dict["input_features"], attention_mask=inputs_dict["attention_mask"]
|
||||
)[0]
|
||||
|
||||
self.parent.assertTrue((encoder_last_hidden_state_2 - encoder_last_hidden_state).abs().max().item() < 1e-3)
|
||||
|
||||
with tempfile.TemporaryDirectory() as tmpdirname:
|
||||
decoder = model.get_decoder()
|
||||
decoder.save_pretrained(tmpdirname)
|
||||
decoder = Speech2TextDecoder.from_pretrained(tmpdirname).to(torch_device)
|
||||
|
||||
encoder_attention_mask = encoder._get_feature_vector_attention_mask(
|
||||
encoder_last_hidden_state.shape[1], inputs_dict["attention_mask"]
|
||||
)
|
||||
|
||||
last_hidden_state_2 = decoder(
|
||||
input_ids=inputs_dict["decoder_input_ids"],
|
||||
attention_mask=inputs_dict["decoder_attention_mask"],
|
||||
encoder_hidden_states=encoder_last_hidden_state,
|
||||
encoder_attention_mask=encoder_attention_mask,
|
||||
)[0]
|
||||
|
||||
self.parent.assertTrue((last_hidden_state_2 - last_hidden_state).abs().max().item() < 1e-3)
|
||||
|
||||
|
||||
@require_torch
|
||||
class Speech2TextModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
|
||||
all_model_classes = (Speech2TextModel, Speech2TextForConditionalGeneration) if is_torch_available() else ()
|
||||
all_generative_model_classes = (Speech2TextForConditionalGeneration,) if is_torch_available() else ()
|
||||
is_encoder_decoder = True
|
||||
test_pruning = False
|
||||
test_missing_keys = False
|
||||
test_torchscript = True
|
||||
|
||||
input_name = "input_features"
|
||||
|
||||
def setUp(self):
|
||||
self.model_tester = Speech2TextModelTester(self)
|
||||
self.config_tester = ConfigTester(self, config_class=Speech2TextConfig)
|
||||
self.maxDiff = 3000
|
||||
|
||||
def test_config(self):
|
||||
self.config_tester.run_common_tests()
|
||||
|
||||
def test_save_load_strict(self):
|
||||
config, inputs_dict = self.model_tester.prepare_config_and_inputs()
|
||||
for model_class in self.all_model_classes:
|
||||
model = model_class(config)
|
||||
|
||||
with tempfile.TemporaryDirectory() as tmpdirname:
|
||||
model.save_pretrained(tmpdirname)
|
||||
model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True)
|
||||
self.assertEqual(info["missing_keys"], [])
|
||||
|
||||
def test_decoder_model_past_with_large_inputs(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
|
||||
|
||||
def test_encoder_decoder_model_standalone(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
self.model_tester.check_encoder_decoder_model_standalone(*config_and_inputs)
|
||||
|
||||
# not implemented currently
|
||||
def test_inputs_embeds(self):
|
||||
pass
|
||||
|
||||
# training is not supported yet
|
||||
def test_training(self):
|
||||
pass
|
||||
|
||||
def test_training_gradient_checkpointing(self):
|
||||
pass
|
||||
|
||||
def test_generate_fp16(self):
|
||||
config, input_dict = self.model_tester.prepare_config_and_inputs()
|
||||
input_features = input_dict["input_features"]
|
||||
attention_mask = input_dict["attention_mask"]
|
||||
model = Speech2TextForConditionalGeneration(config).eval().to(torch_device)
|
||||
if torch_device == "cuda":
|
||||
input_features = input_features.half()
|
||||
model.half()
|
||||
model.generate(input_features, attention_mask=attention_mask)
|
||||
model.generate(input_features, num_beams=4, do_sample=True, early_stopping=False, num_return_sequences=3)
|
||||
|
||||
def test_forward_signature(self):
|
||||
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
|
||||
for model_class in self.all_model_classes:
|
||||
model = model_class(config)
|
||||
signature = inspect.signature(model.forward)
|
||||
# signature.parameters is an OrderedDict => so arg_names order is deterministic
|
||||
arg_names = [*signature.parameters.keys()]
|
||||
|
||||
expected_arg_names = [
|
||||
"input_features",
|
||||
"attention_mask",
|
||||
"decoder_input_ids",
|
||||
"decoder_attention_mask",
|
||||
]
|
||||
expected_arg_names.extend(
|
||||
["head_mask", "decoder_head_mask", "cross_attn_head_mask", "encoder_outputs"]
|
||||
if "head_mask" and "decoder_head_mask" and "cross_attn_head_mask" in arg_names
|
||||
else ["encoder_outputs"]
|
||||
)
|
||||
self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)
|
||||
|
||||
def test_hidden_states_output(self):
|
||||
def check_hidden_states_output(inputs_dict, config, model_class):
|
||||
model = model_class(config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
|
||||
with torch.no_grad():
|
||||
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
|
||||
|
||||
hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
|
||||
|
||||
expected_num_layers = getattr(
|
||||
self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
|
||||
)
|
||||
self.assertEqual(len(hidden_states), expected_num_layers)
|
||||
|
||||
if hasattr(self.model_tester, "encoder_seq_length"):
|
||||
seq_length = self.model_tester.encoder_seq_length
|
||||
else:
|
||||
seq_length = self.model_tester.seq_length
|
||||
|
||||
subsampled_seq_length = model._get_feat_extract_output_lengths(seq_length)
|
||||
|
||||
self.assertListEqual(
|
||||
list(hidden_states[0].shape[-2:]),
|
||||
[subsampled_seq_length, self.model_tester.hidden_size],
|
||||
)
|
||||
|
||||
if config.is_encoder_decoder:
|
||||
hidden_states = outputs.decoder_hidden_states
|
||||
|
||||
self.assertIsInstance(hidden_states, (list, tuple))
|
||||
self.assertEqual(len(hidden_states), expected_num_layers)
|
||||
seq_len = getattr(self.model_tester, "seq_length", None)
|
||||
decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len)
|
||||
|
||||
self.assertListEqual(
|
||||
list(hidden_states[0].shape[-2:]),
|
||||
[decoder_seq_length, self.model_tester.hidden_size],
|
||||
)
|
||||
|
||||
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
|
||||
for model_class in self.all_model_classes:
|
||||
inputs_dict["output_hidden_states"] = True
|
||||
check_hidden_states_output(inputs_dict, config, model_class)
|
||||
|
||||
# check that output_hidden_states also work using config
|
||||
del inputs_dict["output_hidden_states"]
|
||||
config.output_hidden_states = True
|
||||
|
||||
check_hidden_states_output(inputs_dict, config, model_class)
|
||||
|
||||
def test_attention_outputs(self):
|
||||
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
config.return_dict = True
|
||||
|
||||
seq_len = getattr(self.model_tester, "seq_length", None)
|
||||
decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len)
|
||||
encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len)
|
||||
decoder_key_length = getattr(self.model_tester, "decoder_key_length", decoder_seq_length)
|
||||
encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
|
||||
|
||||
for model_class in self.all_model_classes:
|
||||
inputs_dict["output_attentions"] = True
|
||||
inputs_dict["output_hidden_states"] = False
|
||||
config.return_dict = True
|
||||
model = model_class(config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
|
||||
subsampled_encoder_seq_length = model._get_feat_extract_output_lengths(encoder_seq_length)
|
||||
subsampled_encoder_key_length = model._get_feat_extract_output_lengths(encoder_key_length)
|
||||
|
||||
with torch.no_grad():
|
||||
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
|
||||
attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
|
||||
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
|
||||
|
||||
# check that output_attentions also work using config
|
||||
del inputs_dict["output_attentions"]
|
||||
config.output_attentions = True
|
||||
model = model_class(config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
with torch.no_grad():
|
||||
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
|
||||
attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
|
||||
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
|
||||
|
||||
self.assertListEqual(
|
||||
list(attentions[0].shape[-3:]),
|
||||
[self.model_tester.num_attention_heads, subsampled_encoder_seq_length, subsampled_encoder_key_length],
|
||||
)
|
||||
out_len = len(outputs)
|
||||
|
||||
correct_outlen = 5
|
||||
|
||||
# loss is at first position
|
||||
if "labels" in inputs_dict:
|
||||
correct_outlen += 1 # loss is added to beginning
|
||||
if "past_key_values" in outputs:
|
||||
correct_outlen += 1 # past_key_values have been returned
|
||||
|
||||
self.assertEqual(out_len, correct_outlen)
|
||||
|
||||
# decoder attentions
|
||||
decoder_attentions = outputs.decoder_attentions
|
||||
self.assertIsInstance(decoder_attentions, (list, tuple))
|
||||
self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
|
||||
self.assertListEqual(
|
||||
list(decoder_attentions[0].shape[-3:]),
|
||||
[self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length],
|
||||
)
|
||||
|
||||
# cross attentions
|
||||
cross_attentions = outputs.cross_attentions
|
||||
self.assertIsInstance(cross_attentions, (list, tuple))
|
||||
self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers)
|
||||
self.assertListEqual(
|
||||
list(cross_attentions[0].shape[-3:]),
|
||||
[
|
||||
self.model_tester.num_attention_heads,
|
||||
decoder_seq_length,
|
||||
subsampled_encoder_key_length,
|
||||
],
|
||||
)
|
||||
|
||||
# Check attention is always last and order is fine
|
||||
inputs_dict["output_attentions"] = True
|
||||
inputs_dict["output_hidden_states"] = True
|
||||
model = model_class(config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
with torch.no_grad():
|
||||
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
|
||||
|
||||
added_hidden_states = 2
|
||||
self.assertEqual(out_len + added_hidden_states, len(outputs))
|
||||
|
||||
self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
|
||||
|
||||
self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
|
||||
self.assertListEqual(
|
||||
list(self_attentions[0].shape[-3:]),
|
||||
[self.model_tester.num_attention_heads, subsampled_encoder_seq_length, subsampled_encoder_key_length],
|
||||
)
|
||||
|
||||
def test_resize_tokens_embeddings(self):
|
||||
(
|
||||
original_config,
|
||||
inputs_dict,
|
||||
) = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
if not self.test_resize_embeddings:
|
||||
return
|
||||
|
||||
for model_class in self.all_model_classes:
|
||||
config = copy.deepcopy(original_config)
|
||||
model = model_class(config)
|
||||
model.to(torch_device)
|
||||
|
||||
if self.model_tester.is_training is False:
|
||||
model.eval()
|
||||
|
||||
model_vocab_size = config.vocab_size
|
||||
# Retrieve the embeddings and clone theme
|
||||
model_embed = model.resize_token_embeddings(model_vocab_size)
|
||||
cloned_embeddings = model_embed.weight.clone()
|
||||
|
||||
# Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
|
||||
model_embed = model.resize_token_embeddings(model_vocab_size + 10)
|
||||
self.assertEqual(model.config.vocab_size, model_vocab_size + 10)
|
||||
# Check that it actually resizes the embeddings matrix
|
||||
self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] + 10)
|
||||
# Check that the model can still do a forward pass successfully (every parameter should be resized)
|
||||
model(**self._prepare_for_class(inputs_dict, model_class))
|
||||
|
||||
# Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size
|
||||
model_embed = model.resize_token_embeddings(model_vocab_size - 15)
|
||||
self.assertEqual(model.config.vocab_size, model_vocab_size - 15)
|
||||
# Check that it actually resizes the embeddings matrix
|
||||
self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] - 15)
|
||||
|
||||
# make sure that decoder_input_ids are resized
|
||||
if "decoder_input_ids" in inputs_dict:
|
||||
inputs_dict["decoder_input_ids"].clamp_(max=model_vocab_size - 15 - 1)
|
||||
model(**self._prepare_for_class(inputs_dict, model_class))
|
||||
|
||||
# Check that adding and removing tokens has not modified the first part of the embedding matrix.
|
||||
models_equal = True
|
||||
for p1, p2 in zip(cloned_embeddings, model_embed.weight):
|
||||
if p1.data.ne(p2.data).sum() > 0:
|
||||
models_equal = False
|
||||
|
||||
self.assertTrue(models_equal)
|
||||
|
||||
def test_resize_embeddings_untied(self):
|
||||
(
|
||||
original_config,
|
||||
inputs_dict,
|
||||
) = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
if not self.test_resize_embeddings:
|
||||
return
|
||||
|
||||
original_config.tie_word_embeddings = False
|
||||
|
||||
# if model cannot untied embeddings -> leave test
|
||||
if original_config.tie_word_embeddings:
|
||||
return
|
||||
|
||||
for model_class in self.all_model_classes:
|
||||
config = copy.deepcopy(original_config)
|
||||
model = model_class(config).to(torch_device)
|
||||
|
||||
# if no output embeddings -> leave test
|
||||
if model.get_output_embeddings() is None:
|
||||
continue
|
||||
|
||||
# Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
|
||||
model_vocab_size = config.vocab_size
|
||||
model.resize_token_embeddings(model_vocab_size + 10)
|
||||
self.assertEqual(model.config.vocab_size, model_vocab_size + 10)
|
||||
output_embeds = model.get_output_embeddings()
|
||||
self.assertEqual(output_embeds.weight.shape[0], model_vocab_size + 10)
|
||||
# Check bias if present
|
||||
if output_embeds.bias is not None:
|
||||
self.assertEqual(output_embeds.bias.shape[0], model_vocab_size + 10)
|
||||
# Check that the model can still do a forward pass successfully (every parameter should be resized)
|
||||
model(**self._prepare_for_class(inputs_dict, model_class))
|
||||
|
||||
# Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size
|
||||
model.resize_token_embeddings(model_vocab_size - 15)
|
||||
self.assertEqual(model.config.vocab_size, model_vocab_size - 15)
|
||||
# Check that it actually resizes the embeddings matrix
|
||||
output_embeds = model.get_output_embeddings()
|
||||
self.assertEqual(output_embeds.weight.shape[0], model_vocab_size - 15)
|
||||
# Check bias if present
|
||||
if output_embeds.bias is not None:
|
||||
self.assertEqual(output_embeds.bias.shape[0], model_vocab_size - 15)
|
||||
# Check that the model can still do a forward pass successfully (every parameter should be resized)
|
||||
if "decoder_input_ids" in inputs_dict:
|
||||
inputs_dict["decoder_input_ids"].clamp_(max=model_vocab_size - 15 - 1)
|
||||
# Check that the model can still do a forward pass successfully (every parameter should be resized)
|
||||
model(**self._prepare_for_class(inputs_dict, model_class))
|
||||
|
||||
def test_generate_without_input_ids(self):
|
||||
pass
|
||||
|
||||
@staticmethod
|
||||
def _get_encoder_outputs(
|
||||
model, input_ids, attention_mask, output_attentions=None, output_hidden_states=None, num_interleave=1
|
||||
):
|
||||
encoder = model.get_encoder()
|
||||
encoder_outputs = encoder(
|
||||
input_ids,
|
||||
attention_mask=attention_mask,
|
||||
output_attentions=output_attentions,
|
||||
output_hidden_states=output_hidden_states,
|
||||
)
|
||||
encoder_outputs["last_hidden_state"] = encoder_outputs.last_hidden_state.repeat_interleave(
|
||||
num_interleave, dim=0
|
||||
)
|
||||
input_ids = input_ids[:, :, 0]
|
||||
input_ids = torch.zeros_like(input_ids[:, :1], dtype=torch.long) + model._get_decoder_start_token_id()
|
||||
attention_mask = None
|
||||
return encoder_outputs, input_ids, attention_mask
|
||||
|
||||
def _check_outputs(self, output, input_ids, config, use_cache=False, num_return_sequences=1):
|
||||
batch_size, seq_length = input_ids.shape[:2]
|
||||
subsampled_seq_length = self.model_tester.get_subsampled_output_lengths(seq_length)
|
||||
num_sequences_in_output = batch_size * num_return_sequences
|
||||
gen_len = (
|
||||
output.sequences.shape[-1] - 1 if config.is_encoder_decoder else output.sequences.shape[-1] - seq_length
|
||||
)
|
||||
|
||||
# scores
|
||||
self._check_scores(num_sequences_in_output, output.scores, length=gen_len, config=config)
|
||||
|
||||
# Attentions
|
||||
# encoder
|
||||
self._check_encoder_attention_for_generate(
|
||||
output.encoder_attentions, batch_size, config, subsampled_seq_length
|
||||
)
|
||||
# decoder
|
||||
self._check_attentions_for_generate(
|
||||
num_sequences_in_output,
|
||||
output.decoder_attentions,
|
||||
min_length=1,
|
||||
max_length=output.sequences.shape[-1],
|
||||
config=config,
|
||||
use_cache=use_cache,
|
||||
)
|
||||
|
||||
# Hidden States
|
||||
# encoder
|
||||
self._check_encoder_hidden_states_for_generate(
|
||||
output.encoder_hidden_states, batch_size, config, subsampled_seq_length
|
||||
)
|
||||
|
||||
# decoder
|
||||
self._check_hidden_states_for_generate(
|
||||
num_sequences_in_output,
|
||||
output.decoder_hidden_states,
|
||||
min_length=1,
|
||||
max_length=output.sequences.shape[-1],
|
||||
config=config,
|
||||
use_cache=use_cache,
|
||||
)
|
||||
|
||||
def _create_and_check_torchscript(self, config, inputs_dict):
|
||||
if not self.test_torchscript:
|
||||
return
|
||||
|
||||
configs_no_init = _config_zero_init(config) # To be sure we have no Nan
|
||||
configs_no_init.torchscript = True
|
||||
for model_class in self.all_model_classes:
|
||||
model = model_class(config=configs_no_init)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
inputs = self._prepare_for_class(inputs_dict, model_class)
|
||||
|
||||
try:
|
||||
model.config.use_cache = False # FSTM still requires this hack -> FSTM should probably be refactored similar to BART afterward
|
||||
input_features = inputs["input_features"]
|
||||
attention_mask = inputs["attention_mask"]
|
||||
decoder_input_ids = inputs["decoder_input_ids"]
|
||||
decoder_attention_mask = inputs["decoder_attention_mask"]
|
||||
traced_model = torch.jit.trace(
|
||||
model, (input_features, attention_mask, decoder_input_ids, decoder_attention_mask)
|
||||
)
|
||||
except RuntimeError:
|
||||
self.fail("Couldn't trace module.")
|
||||
|
||||
with tempfile.TemporaryDirectory() as tmp_dir_name:
|
||||
pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt")
|
||||
|
||||
try:
|
||||
torch.jit.save(traced_model, pt_file_name)
|
||||
except Exception:
|
||||
self.fail("Couldn't save module.")
|
||||
|
||||
try:
|
||||
loaded_model = torch.jit.load(pt_file_name)
|
||||
except Exception:
|
||||
self.fail("Couldn't load module.")
|
||||
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
|
||||
loaded_model.to(torch_device)
|
||||
loaded_model.eval()
|
||||
|
||||
model_state_dict = model.state_dict()
|
||||
loaded_model_state_dict = loaded_model.state_dict()
|
||||
|
||||
self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys()))
|
||||
|
||||
models_equal = True
|
||||
for layer_name, p1 in model_state_dict.items():
|
||||
p2 = loaded_model_state_dict[layer_name]
|
||||
if p1.data.ne(p2.data).sum() > 0:
|
||||
models_equal = False
|
||||
|
||||
self.assertTrue(models_equal)
|
||||
|
||||
|
||||
@require_torch
|
||||
@require_torchaudio
|
||||
@require_sentencepiece
|
||||
@require_tokenizers
|
||||
@slow
|
||||
class Speech2TextModelIntegrationTests(unittest.TestCase):
|
||||
@cached_property
|
||||
def default_processor(self):
|
||||
return Speech2TextProcessor.from_pretrained("facebook/s2t-small-librispeech-asr")
|
||||
|
||||
def _load_datasamples(self, num_samples):
|
||||
from datasets import load_dataset
|
||||
|
||||
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_generation_librispeech(self):
|
||||
model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-librispeech-asr")
|
||||
model.to(torch_device)
|
||||
processor = self.default_processor
|
||||
|
||||
input_speech = self._load_datasamples(1)
|
||||
|
||||
input_features = processor(input_speech, return_tensors="pt").input_features.to(torch_device)
|
||||
|
||||
generated_ids = model.generate(input_features)
|
||||
generated_transcript = processor.batch_decode(generated_ids, skip_special_tokens=True)
|
||||
|
||||
EXPECTED_TRANSCRIPTIONS = [
|
||||
"mister quilter is the apostle of the middle classes and we are glad to welcome his gospel"
|
||||
]
|
||||
self.assertListEqual(generated_transcript, EXPECTED_TRANSCRIPTIONS)
|
||||
|
||||
def test_generation_librispeech_batched(self):
|
||||
model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-librispeech-asr")
|
||||
model.to(torch_device)
|
||||
processor = self.default_processor
|
||||
|
||||
input_speech = self._load_datasamples(4)
|
||||
|
||||
inputs = processor(input_speech, return_tensors="pt", padding=True)
|
||||
|
||||
input_features = inputs.input_features.to(torch_device)
|
||||
attention_mask = inputs.attention_mask.to(torch_device)
|
||||
|
||||
generated_ids = model.generate(input_features, attention_mask=attention_mask)
|
||||
generated_transcripts = processor.batch_decode(generated_ids, skip_special_tokens=True)
|
||||
|
||||
EXPECTED_TRANSCRIPTIONS = [
|
||||
"mister quilter is the apostle of the middle classes and we are glad to welcome his gospel",
|
||||
"nor is mister cultar's manner less interesting than his matter",
|
||||
"he tells us that at this festive season of the year with christmas and roast beef looming before us similes drawn from eating and its results occur most readily to the mind",
|
||||
"he has grave doubts whether sir frederick leyton's work is really greek after all and can discover in it but little of rocky ithaca",
|
||||
]
|
||||
|
||||
self.assertListEqual(generated_transcripts, EXPECTED_TRANSCRIPTIONS)
|
||||
605
tests/speech_to_text/test_modeling_tf_speech_to_text.py
Normal file
605
tests/speech_to_text/test_modeling_tf_speech_to_text.py
Normal file
@@ -0,0 +1,605 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# 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 TensorFlow Speech2Text model. """
|
||||
|
||||
import inspect
|
||||
import unittest
|
||||
|
||||
from transformers import Speech2TextConfig
|
||||
from transformers.file_utils import cached_property, is_tf_available
|
||||
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
|
||||
|
||||
from ..test_configuration_common import ConfigTester
|
||||
from ..test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
|
||||
|
||||
|
||||
if is_tf_available():
|
||||
import tensorflow as tf
|
||||
|
||||
from transformers import Speech2TextProcessor, TFSpeech2TextForConditionalGeneration, TFSpeech2TextModel
|
||||
|
||||
|
||||
def prepare_speech_to_text_inputs_dict(
|
||||
config,
|
||||
input_features,
|
||||
decoder_input_ids,
|
||||
attention_mask=None,
|
||||
decoder_attention_mask=None,
|
||||
head_mask=None,
|
||||
decoder_head_mask=None,
|
||||
cross_attn_head_mask=None,
|
||||
):
|
||||
if attention_mask is None:
|
||||
attention_mask = tf.math.not_equal(input_features, 0)
|
||||
if decoder_attention_mask is None:
|
||||
decoder_attention_mask = tf.math.not_equal(decoder_input_ids, config.pad_token_id)
|
||||
if head_mask is None:
|
||||
head_mask = tf.ones((config.encoder_layers, config.encoder_attention_heads))
|
||||
if decoder_head_mask is None:
|
||||
decoder_head_mask = tf.ones((config.decoder_layers, config.decoder_attention_heads))
|
||||
if cross_attn_head_mask is None:
|
||||
cross_attn_head_mask = tf.ones((config.decoder_layers, config.decoder_attention_heads))
|
||||
return {
|
||||
"input_features": input_features,
|
||||
"decoder_input_ids": decoder_input_ids,
|
||||
"attention_mask": attention_mask,
|
||||
"decoder_attention_mask": attention_mask,
|
||||
"head_mask": head_mask,
|
||||
"decoder_head_mask": decoder_head_mask,
|
||||
"cross_attn_head_mask": cross_attn_head_mask,
|
||||
}
|
||||
|
||||
|
||||
@require_tf
|
||||
class TFSpeech2TextModelTester:
|
||||
def __init__(
|
||||
self,
|
||||
parent,
|
||||
batch_size=13,
|
||||
seq_length=7,
|
||||
is_training=True,
|
||||
use_labels=False,
|
||||
vocab_size=99,
|
||||
hidden_size=16,
|
||||
num_hidden_layers=2,
|
||||
num_attention_heads=4,
|
||||
intermediate_size=4,
|
||||
num_conv_layers=2,
|
||||
conv_kernel_sizes=(5, 5),
|
||||
conv_channels=32,
|
||||
input_feat_per_channel=24,
|
||||
input_channels=1,
|
||||
hidden_act="relu",
|
||||
hidden_dropout_prob=0.1,
|
||||
attention_probs_dropout_prob=0.1,
|
||||
max_position_embeddings=20,
|
||||
max_source_positions=20,
|
||||
max_target_positions=20,
|
||||
eos_token_id=2,
|
||||
pad_token_id=1,
|
||||
bos_token_id=0,
|
||||
):
|
||||
self.parent = parent
|
||||
self.batch_size = batch_size
|
||||
self.seq_length = seq_length
|
||||
self.is_training = is_training
|
||||
self.use_labels = use_labels
|
||||
self.vocab_size = vocab_size
|
||||
self.hidden_size = hidden_size
|
||||
self.num_hidden_layers = num_hidden_layers
|
||||
self.num_attention_heads = num_attention_heads
|
||||
self.intermediate_size = intermediate_size
|
||||
self.num_conv_layers = num_conv_layers
|
||||
self.conv_kernel_sizes = conv_kernel_sizes
|
||||
self.conv_channels = conv_channels
|
||||
self.input_feat_per_channel = input_feat_per_channel
|
||||
self.input_channels = input_channels
|
||||
self.hidden_act = hidden_act
|
||||
self.hidden_dropout_prob = hidden_dropout_prob
|
||||
self.attention_probs_dropout_prob = attention_probs_dropout_prob
|
||||
self.max_position_embeddings = max_position_embeddings
|
||||
self.max_source_positions = max_source_positions
|
||||
self.max_target_positions = max_target_positions
|
||||
self.eos_token_id = eos_token_id
|
||||
self.pad_token_id = pad_token_id
|
||||
self.bos_token_id = bos_token_id
|
||||
|
||||
def prepare_config_and_inputs(self):
|
||||
input_features = floats_tensor(
|
||||
[self.batch_size, self.seq_length, self.input_feat_per_channel], self.vocab_size
|
||||
)
|
||||
attention_mask = tf.ones([self.batch_size, self.seq_length], dtype=tf.int64)
|
||||
decoder_input_ids = tf.math.maximum(ids_tensor([self.batch_size, self.seq_length], self.vocab_size), 2)
|
||||
|
||||
config = self.get_config()
|
||||
inputs_dict = prepare_speech_to_text_inputs_dict(
|
||||
config,
|
||||
input_features=input_features,
|
||||
decoder_input_ids=decoder_input_ids,
|
||||
attention_mask=attention_mask,
|
||||
)
|
||||
return config, inputs_dict
|
||||
|
||||
def get_config(self):
|
||||
return Speech2TextConfig(
|
||||
vocab_size=self.vocab_size,
|
||||
d_model=self.hidden_size,
|
||||
encoder_layers=self.num_hidden_layers,
|
||||
decoder_layers=self.num_hidden_layers,
|
||||
encoder_attention_heads=self.num_attention_heads,
|
||||
decoder_attention_heads=self.num_attention_heads,
|
||||
encoder_ffn_dim=self.intermediate_size,
|
||||
decoder_ffn_dim=self.intermediate_size,
|
||||
num_conv_layers=self.num_conv_layers,
|
||||
conv_kernel_sizes=self.conv_kernel_sizes,
|
||||
conv_channels=self.conv_channels,
|
||||
input_feat_per_channel=self.input_feat_per_channel,
|
||||
input_channels=self.input_channels,
|
||||
dropout=self.hidden_dropout_prob,
|
||||
attention_dropout=self.attention_probs_dropout_prob,
|
||||
max_position_embeddings=self.max_position_embeddings,
|
||||
max_source_positions=self.max_source_positions,
|
||||
max_target_positions=self.max_target_positions,
|
||||
eos_token_id=self.eos_token_id,
|
||||
bos_token_id=self.bos_token_id,
|
||||
pad_token_id=self.pad_token_id,
|
||||
)
|
||||
|
||||
def prepare_config_and_inputs_for_common(self):
|
||||
config, inputs_dict = self.prepare_config_and_inputs()
|
||||
return config, inputs_dict
|
||||
|
||||
def get_subsampled_output_lengths(self, input_lengths):
|
||||
"""
|
||||
Computes the output length of the convolutional layers
|
||||
"""
|
||||
|
||||
for _ in range(self.num_conv_layers):
|
||||
input_lengths = (input_lengths - 1) // 2 + 1
|
||||
|
||||
return input_lengths
|
||||
|
||||
def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict):
|
||||
model = TFSpeech2TextModel(config=config).get_decoder()
|
||||
input_ids = inputs_dict["decoder_input_ids"]
|
||||
attention_mask = inputs_dict["decoder_attention_mask"]
|
||||
|
||||
# first forward pass
|
||||
outputs = model(input_ids, attention_mask=attention_mask, use_cache=True)
|
||||
|
||||
_, (_, past_key_values) = outputs.to_tuple()
|
||||
|
||||
# create hypothetical multiple next token and extent to next_input_ids
|
||||
next_tokens = tf.math.maximum(ids_tensor((self.batch_size, 3), config.vocab_size), 2)
|
||||
next_attn_mask = ids_tensor((self.batch_size, 3), 2, dtype=tf.int64)
|
||||
|
||||
# append to next input_ids and
|
||||
next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
|
||||
next_attention_mask = tf.concat([attention_mask, next_attn_mask], axis=-1)
|
||||
|
||||
output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["last_hidden_state"]
|
||||
output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[
|
||||
"last_hidden_state"
|
||||
]
|
||||
|
||||
# select random slice
|
||||
random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1]))
|
||||
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx]
|
||||
output_from_past_slice = output_from_past[:, :, random_slice_idx]
|
||||
|
||||
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
|
||||
|
||||
# test that outputs are equal for slice
|
||||
tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, atol=1e-2)
|
||||
|
||||
|
||||
@require_tf
|
||||
class TFSpeech2TextModelTest(TFModelTesterMixin, unittest.TestCase):
|
||||
all_model_classes = (TFSpeech2TextModel, TFSpeech2TextForConditionalGeneration) if is_tf_available() else ()
|
||||
all_generative_model_classes = (TFSpeech2TextForConditionalGeneration,) if is_tf_available() else ()
|
||||
is_encoder_decoder = True
|
||||
test_pruning = False
|
||||
test_missing_keys = False
|
||||
test_onnx = False
|
||||
|
||||
input_name = "input_ids"
|
||||
|
||||
def setUp(self):
|
||||
self.model_tester = TFSpeech2TextModelTester(self)
|
||||
self.config_tester = ConfigTester(self, config_class=Speech2TextConfig)
|
||||
self.maxDiff = 3000
|
||||
|
||||
def test_config(self):
|
||||
self.config_tester.run_common_tests()
|
||||
|
||||
def test_decoder_model_past_with_large_inputs(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
|
||||
|
||||
# not implemented currently
|
||||
def test_inputs_embeds(self):
|
||||
pass
|
||||
|
||||
# training is not supported yet
|
||||
def test_training(self):
|
||||
pass
|
||||
|
||||
def test_training_gradient_checkpointing(self):
|
||||
pass
|
||||
|
||||
def test_generate_fp16(self):
|
||||
pass
|
||||
|
||||
def test_hidden_states_output(self):
|
||||
def check_hidden_states_output(inputs_dict, config, model_class):
|
||||
model = model_class(config)
|
||||
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
|
||||
|
||||
hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
|
||||
|
||||
expected_num_layers = getattr(
|
||||
self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
|
||||
)
|
||||
self.assertEqual(len(hidden_states), expected_num_layers)
|
||||
|
||||
if hasattr(self.model_tester, "encoder_seq_length"):
|
||||
seq_length = self.model_tester.encoder_seq_length
|
||||
else:
|
||||
seq_length = self.model_tester.seq_length
|
||||
|
||||
subsampled_seq_length = model._get_feat_extract_output_lengths(seq_length)
|
||||
|
||||
self.assertListEqual(
|
||||
list(hidden_states[0].shape[-2:]),
|
||||
[subsampled_seq_length, self.model_tester.hidden_size],
|
||||
)
|
||||
|
||||
if config.is_encoder_decoder:
|
||||
hidden_states = outputs.decoder_hidden_states
|
||||
|
||||
self.assertIsInstance(hidden_states, (list, tuple))
|
||||
self.assertEqual(len(hidden_states), expected_num_layers)
|
||||
seq_len = getattr(self.model_tester, "seq_length", None)
|
||||
decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len)
|
||||
|
||||
self.assertListEqual(
|
||||
list(hidden_states[0].shape[-2:]),
|
||||
[decoder_seq_length, self.model_tester.hidden_size],
|
||||
)
|
||||
|
||||
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
|
||||
for model_class in self.all_model_classes:
|
||||
inputs_dict["output_hidden_states"] = True
|
||||
check_hidden_states_output(inputs_dict, config, model_class)
|
||||
|
||||
# check that output_hidden_states also work using config
|
||||
del inputs_dict["output_hidden_states"]
|
||||
config.output_hidden_states = True
|
||||
|
||||
check_hidden_states_output(inputs_dict, config, model_class)
|
||||
|
||||
def test_attention_outputs(self):
|
||||
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
config.return_dict = True
|
||||
|
||||
seq_len = getattr(self.model_tester, "seq_length", None)
|
||||
decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len)
|
||||
encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len)
|
||||
decoder_key_length = getattr(self.model_tester, "decoder_key_length", decoder_seq_length)
|
||||
encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
|
||||
|
||||
for model_class in self.all_model_classes:
|
||||
inputs_dict["output_attentions"] = True
|
||||
inputs_dict["output_hidden_states"] = False
|
||||
config.return_dict = True
|
||||
model = model_class(config)
|
||||
|
||||
subsampled_encoder_seq_length = model._get_feat_extract_output_lengths(encoder_seq_length)
|
||||
subsampled_encoder_key_length = model._get_feat_extract_output_lengths(encoder_key_length)
|
||||
|
||||
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
|
||||
attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
|
||||
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
|
||||
|
||||
# check that output_attentions also work using config
|
||||
del inputs_dict["output_attentions"]
|
||||
config.output_attentions = True
|
||||
model = model_class(config)
|
||||
|
||||
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
|
||||
attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
|
||||
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
|
||||
|
||||
self.assertListEqual(
|
||||
list(attentions[0].shape[-3:]),
|
||||
[self.model_tester.num_attention_heads, subsampled_encoder_seq_length, subsampled_encoder_key_length],
|
||||
)
|
||||
out_len = len(outputs)
|
||||
|
||||
correct_outlen = 5
|
||||
|
||||
# loss is at first position
|
||||
if "labels" in inputs_dict:
|
||||
correct_outlen += 1 # loss is added to beginning
|
||||
if "past_key_values" in outputs:
|
||||
correct_outlen += 1 # past_key_values have been returned
|
||||
|
||||
self.assertEqual(out_len, correct_outlen)
|
||||
|
||||
# decoder attentions
|
||||
decoder_attentions = outputs.decoder_attentions
|
||||
self.assertIsInstance(decoder_attentions, (list, tuple))
|
||||
self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
|
||||
self.assertListEqual(
|
||||
list(decoder_attentions[0].shape[-3:]),
|
||||
[self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length],
|
||||
)
|
||||
|
||||
# cross attentions
|
||||
cross_attentions = outputs.cross_attentions
|
||||
self.assertIsInstance(cross_attentions, (list, tuple))
|
||||
self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers)
|
||||
self.assertListEqual(
|
||||
list(cross_attentions[0].shape[-3:]),
|
||||
[
|
||||
self.model_tester.num_attention_heads,
|
||||
decoder_seq_length,
|
||||
subsampled_encoder_key_length,
|
||||
],
|
||||
)
|
||||
|
||||
# Check attention is always last and order is fine
|
||||
inputs_dict["output_attentions"] = True
|
||||
inputs_dict["output_hidden_states"] = True
|
||||
model = model_class(config)
|
||||
|
||||
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
|
||||
|
||||
added_hidden_states = 2
|
||||
self.assertEqual(out_len + added_hidden_states, len(outputs))
|
||||
|
||||
self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
|
||||
|
||||
self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
|
||||
self.assertListEqual(
|
||||
list(self_attentions[0].shape[-3:]),
|
||||
[self.model_tester.num_attention_heads, subsampled_encoder_seq_length, subsampled_encoder_key_length],
|
||||
)
|
||||
|
||||
def test_resize_token_embeddings(self):
|
||||
# Overwritten method from parent; see `test_resize_embeddings_untied`
|
||||
pass
|
||||
|
||||
def test_resize_tokens_embeddings(self):
|
||||
# see `test_resize_embeddings_untied`
|
||||
pass
|
||||
|
||||
def test_resize_embeddings_untied(self):
|
||||
# TODO: copy test from PT. Not working at the moment because the test relies on `model.resize_token_embeddings`,
|
||||
# whose TF implementation assumes the use of `TFWrappedEmbeddings`. But with a `TFWrappedEmbeddings` we can't
|
||||
# load the weights from PT (also, it induces TF1 behavior, so we might want to rework how
|
||||
# `model.resize_token_embeddings` operates).
|
||||
pass
|
||||
|
||||
def test_generate_without_input_ids(self):
|
||||
pass
|
||||
|
||||
@staticmethod
|
||||
def _get_encoder_outputs(
|
||||
model, input_ids, attention_mask, output_attentions=None, output_hidden_states=None, num_interleave=1
|
||||
):
|
||||
encoder = model.get_encoder()
|
||||
encoder_outputs = encoder(
|
||||
input_ids,
|
||||
attention_mask=attention_mask,
|
||||
output_attentions=output_attentions,
|
||||
output_hidden_states=output_hidden_states,
|
||||
)
|
||||
encoder_outputs["last_hidden_state"] = tf.repeat(encoder_outputs.last_hidden_state, num_interleave, axis=0)
|
||||
|
||||
input_ids = input_ids[:, :, 0]
|
||||
input_ids = tf.zeros_like(input_ids[:, :1], dtype=tf.int64) + model._get_decoder_start_token_id()
|
||||
attention_mask = None
|
||||
return encoder_outputs, input_ids, attention_mask
|
||||
|
||||
def _check_outputs(self, output, input_ids, config, use_cache=False, num_return_sequences=1):
|
||||
batch_size, seq_length = input_ids.shape[:2]
|
||||
subsampled_seq_length = self.model_tester.get_subsampled_output_lengths(seq_length)
|
||||
num_sequences_in_output = batch_size * num_return_sequences
|
||||
gen_len = (
|
||||
output.sequences.shape[-1] - 1 if config.is_encoder_decoder else output.sequences.shape[-1] - seq_length
|
||||
)
|
||||
|
||||
# scores
|
||||
self._check_scores(num_sequences_in_output, output.scores, length=gen_len, config=config)
|
||||
|
||||
# Attentions
|
||||
# encoder
|
||||
self._check_encoder_attention_for_generate(
|
||||
output.encoder_attentions, batch_size, config, subsampled_seq_length
|
||||
)
|
||||
# decoder
|
||||
self._check_attentions_for_generate(
|
||||
num_sequences_in_output,
|
||||
output.decoder_attentions,
|
||||
min_length=1,
|
||||
max_length=output.sequences.shape[-1],
|
||||
config=config,
|
||||
use_cache=use_cache,
|
||||
)
|
||||
|
||||
# Hidden States
|
||||
# encoder
|
||||
self._check_encoder_hidden_states_for_generate(
|
||||
output.encoder_hidden_states, batch_size, config, subsampled_seq_length
|
||||
)
|
||||
|
||||
# decoder
|
||||
self._check_hidden_states_for_generate(
|
||||
num_sequences_in_output,
|
||||
output.decoder_hidden_states,
|
||||
min_length=1,
|
||||
max_length=output.sequences.shape[-1],
|
||||
config=config,
|
||||
use_cache=use_cache,
|
||||
)
|
||||
|
||||
# overwritten from parent due to the inability to work when non-text inputs are not passed AND because the input is
|
||||
# `input_features`
|
||||
def test_lm_head_model_random_no_beam_search_generate(self):
|
||||
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
input_features = inputs_dict.get("input_features", None)
|
||||
|
||||
# iterate over all generative models
|
||||
for model_class in self.all_generative_model_classes:
|
||||
model = model_class(config)
|
||||
|
||||
if config.bos_token_id is None:
|
||||
# if bos token id is not defined model needs input_features
|
||||
with self.assertRaises(AssertionError):
|
||||
model.generate(do_sample=True, max_length=5)
|
||||
# num_return_sequences = 1
|
||||
self._check_generated_ids(model.generate(input_features, do_sample=True))
|
||||
|
||||
with self.assertRaises(ValueError):
|
||||
# generating multiple sequences when no beam search generation
|
||||
# is not allowed as it would always generate the same sequences
|
||||
model.generate(input_features, do_sample=False, num_return_sequences=2)
|
||||
|
||||
# num_return_sequences > 1, sample
|
||||
self._check_generated_ids(model.generate(input_features, do_sample=True, num_return_sequences=2))
|
||||
|
||||
# check bad words tokens language generation
|
||||
# create list of 1-seq bad token and list of 2-seq of bad tokens
|
||||
bad_words_ids = [self._generate_random_bad_tokens(1, model), self._generate_random_bad_tokens(2, model)]
|
||||
output_tokens = model.generate(
|
||||
input_features, do_sample=True, bad_words_ids=bad_words_ids, num_return_sequences=2
|
||||
)
|
||||
# only count generated tokens
|
||||
generated_ids = output_tokens[:, input_features.shape[-1] :]
|
||||
self.assertFalse(self._check_match_tokens(generated_ids.numpy().tolist(), bad_words_ids))
|
||||
|
||||
# overwritten from parent due to the inability to work when non-text inputs are not passed AND because the input is
|
||||
# `input_features`
|
||||
def test_lm_head_model_random_beam_search_generate(self):
|
||||
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
input_features = inputs_dict.get("input_features", None)
|
||||
|
||||
for model_class in self.all_generative_model_classes:
|
||||
model = model_class(config)
|
||||
|
||||
if config.bos_token_id is None:
|
||||
# if bos token id is not defined model needs input_ids, num_return_sequences = 1
|
||||
self._check_generated_ids(model.generate(input_features, do_sample=True, num_beams=2))
|
||||
|
||||
with self.assertRaises(AssertionError):
|
||||
# generating more sequences than having beams leads is not possible
|
||||
model.generate(input_features, do_sample=False, num_return_sequences=3, num_beams=2)
|
||||
|
||||
# num_return_sequences > 1, sample
|
||||
self._check_generated_ids(
|
||||
model.generate(
|
||||
input_features,
|
||||
do_sample=True,
|
||||
num_beams=2,
|
||||
num_return_sequences=2,
|
||||
)
|
||||
)
|
||||
# num_return_sequences > 1, greedy
|
||||
self._check_generated_ids(
|
||||
model.generate(input_features, do_sample=False, num_beams=2, num_return_sequences=2)
|
||||
)
|
||||
|
||||
# check bad words tokens language generation
|
||||
# create list of 1-seq bad token and list of 2-seq of bad tokens
|
||||
bad_words_ids = [self._generate_random_bad_tokens(1, model), self._generate_random_bad_tokens(2, model)]
|
||||
output_tokens = model.generate(
|
||||
input_features, do_sample=False, bad_words_ids=bad_words_ids, num_beams=2, num_return_sequences=2
|
||||
)
|
||||
# only count generated tokens
|
||||
generated_ids = output_tokens[:, input_features.shape[-1] :]
|
||||
self.assertFalse(self._check_match_tokens(generated_ids.numpy().tolist(), bad_words_ids))
|
||||
|
||||
# overwritten from parent -- the input is `input_features`, not `input_ids`
|
||||
def test_forward_signature(self):
|
||||
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
|
||||
for model_class in self.all_model_classes:
|
||||
model = model_class(config)
|
||||
signature = inspect.signature(model.call)
|
||||
# signature.parameters is an OrderedDict => so arg_names order is deterministic
|
||||
arg_names = [*signature.parameters.keys()]
|
||||
|
||||
expected_arg_names = [
|
||||
"input_features",
|
||||
"attention_mask",
|
||||
"decoder_input_ids",
|
||||
"decoder_attention_mask",
|
||||
]
|
||||
self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)
|
||||
|
||||
|
||||
@require_tf
|
||||
@require_sentencepiece
|
||||
@require_tokenizers
|
||||
@slow
|
||||
class TFSpeech2TextModelIntegrationTests(unittest.TestCase):
|
||||
@cached_property
|
||||
def default_processor(self):
|
||||
return Speech2TextProcessor.from_pretrained("facebook/s2t-small-librispeech-asr")
|
||||
|
||||
def _load_datasamples(self, num_samples):
|
||||
from datasets import load_dataset
|
||||
|
||||
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_generation_librispeech(self):
|
||||
model = TFSpeech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-librispeech-asr")
|
||||
processor = self.default_processor
|
||||
|
||||
input_speech = self._load_datasamples(1)
|
||||
|
||||
input_features = processor(input_speech, return_tensors="tf").input_features
|
||||
|
||||
generated_ids = model.generate(input_features)
|
||||
generated_transcript = processor.batch_decode(generated_ids, skip_special_tokens=True)
|
||||
|
||||
EXPECTED_TRANSCRIPTIONS = [
|
||||
"mister quilter is the apostle of the middle classes and we are glad to welcome his gospel"
|
||||
]
|
||||
self.assertListEqual(generated_transcript, EXPECTED_TRANSCRIPTIONS)
|
||||
|
||||
def test_generation_librispeech_batched(self):
|
||||
model = TFSpeech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-librispeech-asr")
|
||||
processor = self.default_processor
|
||||
|
||||
input_speech = self._load_datasamples(4)
|
||||
|
||||
inputs = processor(input_speech, return_tensors="tf", padding=True)
|
||||
generated_ids = model.generate(inputs.input_features, attention_mask=inputs.attention_mask)
|
||||
generated_transcripts = processor.batch_decode(generated_ids, skip_special_tokens=True)
|
||||
|
||||
EXPECTED_TRANSCRIPTIONS = [
|
||||
"mister quilter is the apostle of the middle classes and we are glad to welcome his gospel",
|
||||
"nor is mister cultar's manner less interesting than his matter",
|
||||
"he tells us that at this festive season of the year with christmas and roast beef looming before us similes drawn from eating and its results occur most readily to the mind",
|
||||
"he has grave doubts whether sir frederick leyton's work is really greek after all and can discover in it but little of rocky ithaca",
|
||||
]
|
||||
self.assertListEqual(generated_transcripts, EXPECTED_TRANSCRIPTIONS)
|
||||
149
tests/speech_to_text/test_processor_speech_to_text.py
Normal file
149
tests/speech_to_text/test_processor_speech_to_text.py
Normal file
@@ -0,0 +1,149 @@
|
||||
# Copyright 2021 The HuggingFace Team. All rights reserved.
|
||||
#
|
||||
# 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 os
|
||||
import shutil
|
||||
import tempfile
|
||||
import unittest
|
||||
from os.path import dirname
|
||||
from pathlib import Path
|
||||
from shutil import copyfile
|
||||
|
||||
from transformers import Speech2TextTokenizer, is_speech_available
|
||||
from transformers.file_utils import FEATURE_EXTRACTOR_NAME
|
||||
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
|
||||
from transformers.testing_utils import require_sentencepiece, require_torch, require_torchaudio
|
||||
|
||||
from .test_feature_extraction_speech_to_text import floats_list
|
||||
|
||||
|
||||
if is_speech_available():
|
||||
from transformers import Speech2TextFeatureExtractor, Speech2TextProcessor
|
||||
|
||||
|
||||
SAMPLE_SP = os.path.join(dirname(dirname(os.path.abspath(__file__))), "fixtures/test_sentencepiece.model")
|
||||
|
||||
|
||||
@require_torch
|
||||
@require_torchaudio
|
||||
@require_sentencepiece
|
||||
class Speech2TextProcessorTest(unittest.TestCase):
|
||||
def setUp(self):
|
||||
self.tmpdirname = tempfile.mkdtemp()
|
||||
|
||||
vocab = ["<s>", "<pad>", "</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est"]
|
||||
vocab_tokens = dict(zip(vocab, range(len(vocab))))
|
||||
save_dir = Path(self.tmpdirname)
|
||||
save_json(vocab_tokens, save_dir / VOCAB_FILES_NAMES["vocab_file"])
|
||||
if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
|
||||
copyfile(SAMPLE_SP, save_dir / VOCAB_FILES_NAMES["spm_file"])
|
||||
|
||||
tokenizer = Speech2TextTokenizer.from_pretrained(self.tmpdirname)
|
||||
tokenizer.save_pretrained(self.tmpdirname)
|
||||
|
||||
feature_extractor_map = {
|
||||
"feature_size": 24,
|
||||
"num_mel_bins": 24,
|
||||
"padding_value": 0.0,
|
||||
"sampling_rate": 16000,
|
||||
"return_attention_mask": False,
|
||||
"do_normalize": True,
|
||||
}
|
||||
save_json(feature_extractor_map, save_dir / FEATURE_EXTRACTOR_NAME)
|
||||
|
||||
def get_tokenizer(self, **kwargs):
|
||||
return Speech2TextTokenizer.from_pretrained(self.tmpdirname, **kwargs)
|
||||
|
||||
def get_feature_extractor(self, **kwargs):
|
||||
return Speech2TextFeatureExtractor.from_pretrained(self.tmpdirname, **kwargs)
|
||||
|
||||
def tearDown(self):
|
||||
shutil.rmtree(self.tmpdirname)
|
||||
|
||||
def test_save_load_pretrained_default(self):
|
||||
tokenizer = self.get_tokenizer()
|
||||
feature_extractor = self.get_feature_extractor()
|
||||
|
||||
processor = Speech2TextProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
|
||||
|
||||
processor.save_pretrained(self.tmpdirname)
|
||||
processor = Speech2TextProcessor.from_pretrained(self.tmpdirname)
|
||||
|
||||
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer.get_vocab())
|
||||
self.assertIsInstance(processor.tokenizer, Speech2TextTokenizer)
|
||||
|
||||
self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor.to_json_string())
|
||||
self.assertIsInstance(processor.feature_extractor, Speech2TextFeatureExtractor)
|
||||
|
||||
def test_save_load_pretrained_additional_features(self):
|
||||
processor = Speech2TextProcessor(
|
||||
tokenizer=self.get_tokenizer(), feature_extractor=self.get_feature_extractor()
|
||||
)
|
||||
processor.save_pretrained(self.tmpdirname)
|
||||
|
||||
tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)")
|
||||
feature_extractor_add_kwargs = self.get_feature_extractor(do_normalize=False, padding_value=1.0)
|
||||
|
||||
processor = Speech2TextProcessor.from_pretrained(
|
||||
self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False, padding_value=1.0
|
||||
)
|
||||
|
||||
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
|
||||
self.assertIsInstance(processor.tokenizer, Speech2TextTokenizer)
|
||||
|
||||
self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor_add_kwargs.to_json_string())
|
||||
self.assertIsInstance(processor.feature_extractor, Speech2TextFeatureExtractor)
|
||||
|
||||
def test_feature_extractor(self):
|
||||
feature_extractor = self.get_feature_extractor()
|
||||
tokenizer = self.get_tokenizer()
|
||||
|
||||
processor = Speech2TextProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
|
||||
|
||||
raw_speech = floats_list((3, 1000))
|
||||
|
||||
input_feat_extract = feature_extractor(raw_speech, return_tensors="np")
|
||||
input_processor = processor(raw_speech, return_tensors="np")
|
||||
|
||||
for key in input_feat_extract.keys():
|
||||
self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2)
|
||||
|
||||
def test_tokenizer(self):
|
||||
feature_extractor = self.get_feature_extractor()
|
||||
tokenizer = self.get_tokenizer()
|
||||
|
||||
processor = Speech2TextProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
|
||||
|
||||
input_str = "This is a test string"
|
||||
|
||||
with processor.as_target_processor():
|
||||
encoded_processor = processor(input_str)
|
||||
|
||||
encoded_tok = tokenizer(input_str)
|
||||
|
||||
for key in encoded_tok.keys():
|
||||
self.assertListEqual(encoded_tok[key], encoded_processor[key])
|
||||
|
||||
def test_tokenizer_decode(self):
|
||||
feature_extractor = self.get_feature_extractor()
|
||||
tokenizer = self.get_tokenizer()
|
||||
|
||||
processor = Speech2TextProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
|
||||
|
||||
predicted_ids = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
|
||||
|
||||
decoded_processor = processor.batch_decode(predicted_ids)
|
||||
decoded_tok = tokenizer.batch_decode(predicted_ids)
|
||||
|
||||
self.assertListEqual(decoded_tok, decoded_processor)
|
||||
165
tests/speech_to_text/test_tokenization_speech_to_text.py
Normal file
165
tests/speech_to_text/test_tokenization_speech_to_text.py
Normal file
@@ -0,0 +1,165 @@
|
||||
# Copyright 2021 The HuggingFace Team. All rights reserved.
|
||||
#
|
||||
# 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 os
|
||||
import unittest
|
||||
from os.path import dirname
|
||||
from pathlib import Path
|
||||
from shutil import copyfile
|
||||
|
||||
from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
|
||||
from transformers.models.speech_to_text import Speech2TextTokenizer
|
||||
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
|
||||
from transformers.testing_utils import require_sentencepiece, require_tokenizers, slow
|
||||
|
||||
from ..test_tokenization_common import TokenizerTesterMixin
|
||||
|
||||
|
||||
SAMPLE_SP = os.path.join(dirname(dirname(os.path.abspath(__file__))), "fixtures/test_sentencepiece.model")
|
||||
|
||||
if is_sentencepiece_available():
|
||||
import sentencepiece as sp
|
||||
|
||||
|
||||
FR_CODE = 5
|
||||
ES_CODE = 10
|
||||
|
||||
|
||||
@require_sentencepiece
|
||||
@require_tokenizers
|
||||
class SpeechToTextTokenizerTest(TokenizerTesterMixin, unittest.TestCase):
|
||||
tokenizer_class = Speech2TextTokenizer
|
||||
test_rust_tokenizer = False
|
||||
test_sentencepiece = True
|
||||
|
||||
def setUp(self):
|
||||
super().setUp()
|
||||
|
||||
spm_model = sp.SentencePieceProcessor()
|
||||
spm_model.Load(SAMPLE_SP)
|
||||
vocab = ["<s>", "<pad>", "</s>", "<unk>"]
|
||||
|
||||
vocab += [spm_model.IdToPiece(id_) for id_ in range(len(spm_model))]
|
||||
vocab_tokens = dict(zip(vocab, range(len(vocab))))
|
||||
|
||||
save_dir = Path(self.tmpdirname)
|
||||
save_json(vocab_tokens, save_dir / VOCAB_FILES_NAMES["vocab_file"])
|
||||
if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
|
||||
copyfile(SAMPLE_SP, save_dir / VOCAB_FILES_NAMES["spm_file"])
|
||||
|
||||
tokenizer = Speech2TextTokenizer.from_pretrained(self.tmpdirname)
|
||||
tokenizer.save_pretrained(self.tmpdirname)
|
||||
|
||||
def test_convert_token_and_id(self):
|
||||
"""Test ``_convert_token_to_id`` and ``_convert_id_to_token``."""
|
||||
token = "<pad>"
|
||||
token_id = 1
|
||||
|
||||
self.assertEqual(self.get_tokenizer()._convert_token_to_id(token), token_id)
|
||||
self.assertEqual(self.get_tokenizer()._convert_id_to_token(token_id), token)
|
||||
|
||||
def test_get_vocab(self):
|
||||
vocab_keys = list(self.get_tokenizer().get_vocab().keys())
|
||||
|
||||
self.assertEqual(vocab_keys[0], "<s>")
|
||||
self.assertEqual(vocab_keys[1], "<pad>")
|
||||
self.assertEqual(vocab_keys[-1], "j")
|
||||
self.assertEqual(len(vocab_keys), 1_001)
|
||||
|
||||
def test_vocab_size(self):
|
||||
self.assertEqual(self.get_tokenizer().vocab_size, 1_001)
|
||||
|
||||
def test_full_tokenizer(self):
|
||||
tokenizer = Speech2TextTokenizer.from_pretrained(self.tmpdirname)
|
||||
|
||||
tokens = tokenizer.tokenize("This is a test")
|
||||
self.assertListEqual(tokens, ["▁This", "▁is", "▁a", "▁t", "est"])
|
||||
|
||||
self.assertListEqual(
|
||||
tokenizer.convert_tokens_to_ids(tokens),
|
||||
[289, 50, 14, 174, 386],
|
||||
)
|
||||
|
||||
tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.")
|
||||
self.assertListEqual(
|
||||
tokens,
|
||||
# fmt: off
|
||||
[SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "9", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "é", "."],
|
||||
# fmt: on
|
||||
)
|
||||
ids = tokenizer.convert_tokens_to_ids(tokens)
|
||||
self.assertListEqual(ids, [12, 25, 88, 59, 28, 23, 11, 4, 606, 351, 351, 351, 7, 16, 70, 50, 76, 84, 10, 4, 8])
|
||||
|
||||
back_tokens = tokenizer.convert_ids_to_tokens(ids)
|
||||
self.assertListEqual(
|
||||
back_tokens,
|
||||
# fmt: off
|
||||
[SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "<unk>", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "<unk>", "."],
|
||||
# fmt: on
|
||||
)
|
||||
|
||||
@slow
|
||||
def test_tokenizer_integration(self):
|
||||
# fmt: off
|
||||
expected_encoding = {'input_ids': [[3791, 797, 31, 11, 64, 797, 31, 2429, 433, 12, 1176, 12, 20, 786, 915, 142, 2413, 240, 37, 3238, 797, 31, 11, 35, 93, 915, 142, 2413, 240, 37, 5540, 567, 1276, 93, 37, 610, 40, 62, 455, 657, 1042, 123, 780, 177, 37, 309, 241, 1298, 514, 20, 292, 2737, 114, 2469, 241, 85, 64, 302, 548, 528, 423, 4, 509, 406, 423, 37, 601, 4, 777, 302, 548, 528, 423, 284, 4, 3388, 511, 459, 4, 3555, 40, 321, 302, 705, 4, 3388, 511, 583, 326, 5, 5, 5, 62, 3310, 560, 177, 2680, 217, 1508, 32, 31, 853, 418, 64, 583, 511, 1605, 62, 35, 93, 560, 177, 2680, 217, 1508, 1521, 64, 583, 511, 519, 62, 20, 1515, 764, 20, 149, 261, 5625, 7972, 20, 5540, 567, 1276, 93, 3925, 1675, 11, 15, 802, 7972, 576, 217, 1508, 11, 35, 93, 1253, 2441, 15, 289, 652, 31, 416, 321, 3842, 115, 40, 911, 8, 476, 619, 4, 380, 142, 423, 335, 240, 35, 93, 264, 8, 11, 335, 569, 420, 163, 5, 2], [260, 548, 528, 423, 20, 451, 20, 2681, 1153, 3434, 20, 5540, 37, 567, 126, 1253, 2441, 3376, 449, 210, 431, 1563, 177, 767, 5540, 11, 1203, 472, 11, 2953, 685, 285, 364, 706, 1153, 20, 6799, 20, 2869, 20, 4464, 126, 40, 2429, 20, 1040, 866, 2664, 418, 20, 318, 20, 1726, 186, 20, 265, 522, 35, 93, 2191, 4634, 20, 1040, 12, 6799, 15, 228, 2356, 142, 31, 11, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [2575, 2666, 684, 1582, 1176, 12, 627, 149, 619, 20, 4902, 563, 11, 20, 149, 261, 3420, 2356, 174, 142, 4714, 131, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
|
||||
# fmt: on
|
||||
|
||||
self.tokenizer_integration_test_util(
|
||||
expected_encoding=expected_encoding,
|
||||
model_name="facebook/s2t-small-mustc-en-de-st",
|
||||
revision="a14f04cf0776c02f62a8cb800cf7909e15ea23ad",
|
||||
)
|
||||
|
||||
|
||||
@require_sentencepiece
|
||||
class SpeechToTextTokenizerMultilinguialTest(unittest.TestCase):
|
||||
checkpoint_name = "valhalla/s2t_mustc_multilinguial_medium"
|
||||
|
||||
french_text = "C'est trop cool"
|
||||
spanish_text = "Esto es genial"
|
||||
|
||||
@classmethod
|
||||
def setUpClass(cls):
|
||||
cls.tokenizer: Speech2TextTokenizer = Speech2TextTokenizer.from_pretrained(cls.checkpoint_name)
|
||||
return cls
|
||||
|
||||
def check_language_codes(self):
|
||||
self.assertEqual(self.tokenizer.lang_code_to_id["pt"], 4)
|
||||
self.assertEqual(self.tokenizer.lang_code_to_id["ru"], 6)
|
||||
self.assertEqual(self.tokenizer.lang_code_to_id["it"], 9)
|
||||
self.assertEqual(self.tokenizer.lang_code_to_id["de"], 11)
|
||||
|
||||
def test_vocab_size(self):
|
||||
self.assertEqual(self.tokenizer.vocab_size, 10_000)
|
||||
|
||||
def test_tokenizer_decode_ignores_language_codes(self):
|
||||
self.assertIn(ES_CODE, self.tokenizer.all_special_ids)
|
||||
generated_ids = [ES_CODE, 4, 1601, 47, 7647, 2]
|
||||
result = self.tokenizer.decode(generated_ids, skip_special_tokens=True)
|
||||
expected_spanish = self.tokenizer.decode(generated_ids[1:], skip_special_tokens=True)
|
||||
self.assertEqual(result, expected_spanish)
|
||||
self.assertNotIn(self.tokenizer.eos_token, result)
|
||||
|
||||
def test_tokenizer_adds_special_tokens(self):
|
||||
self.tokenizer.tgt_lang = "fr"
|
||||
encoded = self.tokenizer(self.french_text).input_ids
|
||||
self.assertEqual(encoded[0], FR_CODE)
|
||||
self.assertEqual(encoded[-1], self.tokenizer.eos_token_id)
|
||||
|
||||
def test_tgt_lang_setter(self):
|
||||
self.tokenizer.tgt_lang = "fr"
|
||||
self.assertListEqual(self.tokenizer.prefix_tokens, [FR_CODE])
|
||||
|
||||
self.tokenizer.tgt_lang = "es"
|
||||
self.assertListEqual(self.tokenizer.prefix_tokens, [ES_CODE])
|
||||
Reference in New Issue
Block a user