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Fix incorrect batching audio index calculation for Phi-4-Multimodal (#38103)
* fix Signed-off-by: Isotr0py <2037008807@qq.com> * add tests Signed-off-by: Isotr0py <2037008807@qq.com> * code format Signed-off-by: Isotr0py <2037008807@qq.com> * Update src/transformers/models/phi4_multimodal/feature_extraction_phi4_multimodal.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> --------- Signed-off-by: Isotr0py <2037008807@qq.com> Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
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@ -300,7 +300,7 @@ class Phi4MultimodalFeatureExtractor(SequenceFeatureExtractor):
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to_mask_batch_idxs = torch.arange(batch_size)[audio_lengths != audio_lengths.max()]
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if to_mask_batch_idxs.numel() > 0:
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batch_idxs_down = (audio_lengths[to_mask_batch_idxs] - self.win_length) // self.hop_length + 1
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batch_idxs_up = audio_lengths[to_mask_batch_idxs] // self.hop_length + 1
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batch_idxs_up = (audio_lengths[to_mask_batch_idxs] // self.hop_length) - 1
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offset_idx = batch_idxs_down.min()
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max_idx = batch_idxs_up.max()
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@ -0,0 +1,288 @@
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# Copyright 2025 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 os
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import random
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import tempfile
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import unittest
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import numpy as np
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from datasets import load_dataset
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from transformers import Phi4MultimodalFeatureExtractor
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from transformers.testing_utils import check_json_file_has_correct_format, require_torch
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from transformers.utils.import_utils import is_torch_available
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from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
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if is_torch_available():
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import torch
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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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class Phi4MultimodalFeatureExtractionTester:
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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=80,
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hop_length=160,
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win_length=400,
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padding_value=0.0,
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sampling_rate=16_000,
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return_attention_mask=False,
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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.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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self.feature_size = feature_size
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self.win_length = win_length
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self.hop_length = hop_length
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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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"hop_length": self.hop_length,
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"win_length": self.win_length,
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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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class Phi4MultimodalFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.TestCase):
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feature_extraction_class = Phi4MultimodalFeatureExtractor
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def setUp(self):
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self.feat_extract_tester = Phi4MultimodalFeatureExtractionTester(self)
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def test_feat_extract_from_and_save_pretrained(self):
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feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict)
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with tempfile.TemporaryDirectory() as tmpdirname:
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saved_file = feat_extract_first.save_pretrained(tmpdirname)[0]
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check_json_file_has_correct_format(saved_file)
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feat_extract_second = self.feature_extraction_class.from_pretrained(tmpdirname)
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dict_first = feat_extract_first.to_dict()
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dict_second = feat_extract_second.to_dict()
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mel_1 = feat_extract_first.mel_filters
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mel_2 = feat_extract_second.mel_filters
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self.assertTrue(np.allclose(mel_1, mel_2))
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self.assertEqual(dict_first, dict_second)
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def test_feat_extract_to_json_file(self):
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feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict)
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with tempfile.TemporaryDirectory() as tmpdirname:
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json_file_path = os.path.join(tmpdirname, "feat_extract.json")
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feat_extract_first.to_json_file(json_file_path)
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feat_extract_second = self.feature_extraction_class.from_json_file(json_file_path)
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dict_first = feat_extract_first.to_dict()
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dict_second = feat_extract_second.to_dict()
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mel_1 = feat_extract_first.mel_filters
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mel_2 = feat_extract_second.mel_filters
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self.assertTrue(np.allclose(mel_1, mel_2))
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self.assertEqual(dict_first, dict_second)
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def test_feat_extract_from_pretrained_kwargs(self):
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feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict)
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with tempfile.TemporaryDirectory() as tmpdirname:
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saved_file = feat_extract_first.save_pretrained(tmpdirname)[0]
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check_json_file_has_correct_format(saved_file)
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feat_extract_second = self.feature_extraction_class.from_pretrained(
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tmpdirname, feature_size=2 * self.feat_extract_dict["feature_size"]
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)
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mel_1 = feat_extract_first.mel_filters
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mel_2 = feat_extract_second.mel_filters
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self.assertTrue(2 * mel_1.shape[1] == mel_2.shape[1])
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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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pt_speech_inputs = [torch.tensor(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, return_tensors="np").audio_input_features
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max_audio_len = (1200 - feature_extractor.win_length) // feature_extractor.hop_length + 1
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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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self.assertTrue(input_features.shape[-2] == max_audio_len)
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# Test not batched input
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encoded_sequences_1 = feature_extractor(pt_speech_inputs[0], return_tensors="np").audio_input_features
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encoded_sequences_2 = feature_extractor(np_speech_inputs[0], return_tensors="np").audio_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(pt_speech_inputs, return_tensors="np").audio_input_features
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encoded_sequences_2 = feature_extractor(np_speech_inputs, return_tensors="np").audio_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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# Test 2-D numpy arrays are batched.
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speech_inputs = [floats_list((1, x))[0] for x in (800, 800, 800)]
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np_speech_inputs = np.asarray(speech_inputs)
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pt_speech_inputs = torch.tensor(speech_inputs)
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encoded_sequences_1 = feature_extractor(pt_speech_inputs, return_tensors="np").audio_input_features
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encoded_sequences_2 = feature_extractor(np_speech_inputs, return_tensors="np").audio_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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@require_torch
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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([{"audio_input_features": inputs}], return_tensors="np")
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self.assertTrue(np_processed.audio_input_features.dtype == np.float32)
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pt_processed = feature_extractor.pad([{"audio_input_features": inputs}], return_tensors="pt")
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self.assertTrue(pt_processed.audio_input_features.dtype == torch.float32)
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def _load_datasamples(self, num_samples):
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ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
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# automatic decoding with librispeech
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speech_samples = ds.sort("id").select(range(num_samples))[:num_samples]["audio"]
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return [x["array"] for x in speech_samples]
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@require_torch
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def test_torch_integration(self):
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# fmt: off
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EXPECTED_INPUT_FEATURES = torch.tensor(
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[
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6.5243, 7.2267, 8.0917, 8.0041, 6.8247, 6.3216, 5.9599, 5.6770,
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5.7441, 5.6138, 6.6793, 6.8597, 5.5375, 6.5330, 5.4880, 7.3280,
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9.0736, 9.7665, 9.8773, 10.0828, 10.0518, 10.1736, 10.0145, 9.2545,
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11.0495, 11.6518, 10.8654, 10.2293, 9.1045, 9.4819,
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]
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)
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# fmt: on
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input_speech = self._load_datasamples(1)
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feature_extractor = Phi4MultimodalFeatureExtractor()
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input_features = feature_extractor(input_speech, return_tensors="pt").audio_input_features
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self.assertEqual(input_features.shape, (1, 584, 80))
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torch.testing.assert_close(input_features[0, 0, :30], EXPECTED_INPUT_FEATURES, rtol=1e-4, atol=1e-4)
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@unittest.mock.patch(
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"transformers.models.phi4_multimodal.feature_extraction_phi4_multimodal.is_torch_available", lambda: False
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)
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def test_numpy_integration(self):
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# fmt: off
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EXPECTED_INPUT_FEATURES = np.array(
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[
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6.5242944, 7.226712, 8.091721, 8.004097, 6.824679, 6.3216243,
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5.959894, 5.676975, 5.744051, 5.61384, 6.6793485, 6.8597484,
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5.5374746, 6.532976, 5.4879804, 7.3279905, 9.073576, 9.766463,
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9.877262, 10.082759, 10.051792, 10.173581, 10.0144825, 9.254548,
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11.049487, 11.651841, 10.865354, 10.229329, 9.104464, 9.481946,
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]
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)
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# fmt: on
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input_speech = self._load_datasamples(1)
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feature_extractor = Phi4MultimodalFeatureExtractor()
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input_features = feature_extractor(input_speech, return_tensors="np").audio_input_features
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self.assertEqual(input_features.shape, (1, 584, 80))
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self.assertTrue(np.allclose(input_features[0, 0, :30], EXPECTED_INPUT_FEATURES, atol=1e-4))
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@require_torch
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def test_torch_integration_batch(self):
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# fmt: off
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EXPECTED_INPUT_FEATURES = torch.tensor(
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[
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[
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6.5243, 7.2267, 8.0917, 8.0041, 6.8247, 6.3216, 5.9599, 5.6770,
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5.7441, 5.6138, 6.6793, 6.8597, 5.5375, 6.5330, 5.4880, 7.3280,
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9.0736, 9.7665, 9.8773, 10.0828, 10.0518, 10.1736, 10.0145, 9.2545,
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11.0495, 11.6518, 10.8654, 10.2293, 9.1045, 9.4819
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],
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[
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7.5105, 7.9453, 8.6161, 7.7666, 7.2572, 6.8823, 6.3242, 6.1899,
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6.9706, 8.0810, 7.3227, 5.8580, 5.4990, 7.7373, 8.5447, 7.7203,
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6.3230, 7.1995, 7.1463, 7.3153, 7.4054, 7.2855, 6.9396, 7.0255,
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7.3285, 7.2748, 8.0742, 7.3998, 6.4813, 6.7509
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],
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[
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7.7932, 8.1604, 8.7653, 8.2080, 7.2630, 6.4537, 4.8394, 6.3153,
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8.0207, 8.3379, 6.0896, 5.7369, 5.8601, 4.7598, 4.8850, 6.2529,
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3.9354, 6.1577, 7.9921, 9.6577, 10.1449, 9.1414, 9.3361, 9.0022,
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9.2533, 10.0548, 10.4372, 8.8550, 9.1266, 9.9013
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]
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]
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)
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# fmt: on
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input_speech = self._load_datasamples(3)
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feature_extractor = Phi4MultimodalFeatureExtractor()
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input_features = feature_extractor(input_speech, return_tensors="pt").audio_input_features
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self.assertEqual(input_features.shape, (3, 1247, 80))
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print(input_features[:, 0, :30])
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torch.testing.assert_close(input_features[:, 0, :30], EXPECTED_INPUT_FEATURES, rtol=1e-4, atol=1e-4)
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