# Copyright 2025 HuggingFace Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import itertools import os import random import tempfile import unittest import numpy as np from datasets import load_dataset from transformers import Phi4MultimodalFeatureExtractor from transformers.testing_utils import check_json_file_has_correct_format, require_torch from transformers.utils.import_utils import is_torch_available from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin if is_torch_available(): import torch global_rng = random.Random() def floats_list(shape, scale=1.0, rng=None, name=None): """Creates a random float32 tensor""" if rng is None: rng = global_rng values = [] for batch_idx in range(shape[0]): values.append([]) for _ in range(shape[1]): values[-1].append(rng.random() * scale) return values class Phi4MultimodalFeatureExtractionTester: def __init__( self, parent, batch_size=7, min_seq_length=400, max_seq_length=2000, feature_size=80, hop_length=160, win_length=400, padding_value=0.0, sampling_rate=16_000, return_attention_mask=False, do_normalize=True, ): self.parent = parent self.batch_size = batch_size self.min_seq_length = min_seq_length self.max_seq_length = max_seq_length self.seq_length_diff = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1) self.padding_value = padding_value self.sampling_rate = sampling_rate self.return_attention_mask = return_attention_mask self.do_normalize = do_normalize self.feature_size = feature_size self.win_length = win_length self.hop_length = hop_length def prepare_feat_extract_dict(self): return { "feature_size": self.feature_size, "hop_length": self.hop_length, "win_length": self.win_length, "padding_value": self.padding_value, "sampling_rate": self.sampling_rate, "return_attention_mask": self.return_attention_mask, "do_normalize": self.do_normalize, } def prepare_inputs_for_common(self, equal_length=False, numpify=False): def _flatten(list_of_lists): return list(itertools.chain(*list_of_lists)) if equal_length: speech_inputs = [floats_list((self.max_seq_length, self.feature_size)) for _ in range(self.batch_size)] else: # make sure that inputs increase in size speech_inputs = [ floats_list((x, self.feature_size)) for x in range(self.min_seq_length, self.max_seq_length, self.seq_length_diff) ] if numpify: speech_inputs = [np.asarray(x) for x in speech_inputs] return speech_inputs class Phi4MultimodalFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.TestCase): feature_extraction_class = Phi4MultimodalFeatureExtractor def setUp(self): self.feat_extract_tester = Phi4MultimodalFeatureExtractionTester(self) def test_feat_extract_from_and_save_pretrained(self): feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict) with tempfile.TemporaryDirectory() as tmpdirname: saved_file = feat_extract_first.save_pretrained(tmpdirname)[0] check_json_file_has_correct_format(saved_file) feat_extract_second = self.feature_extraction_class.from_pretrained(tmpdirname) dict_first = feat_extract_first.to_dict() dict_second = feat_extract_second.to_dict() mel_1 = feat_extract_first.mel_filters mel_2 = feat_extract_second.mel_filters self.assertTrue(np.allclose(mel_1, mel_2)) self.assertEqual(dict_first, dict_second) def test_feat_extract_to_json_file(self): feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict) with tempfile.TemporaryDirectory() as tmpdirname: json_file_path = os.path.join(tmpdirname, "feat_extract.json") feat_extract_first.to_json_file(json_file_path) feat_extract_second = self.feature_extraction_class.from_json_file(json_file_path) dict_first = feat_extract_first.to_dict() dict_second = feat_extract_second.to_dict() mel_1 = feat_extract_first.mel_filters mel_2 = feat_extract_second.mel_filters self.assertTrue(np.allclose(mel_1, mel_2)) self.assertEqual(dict_first, dict_second) def test_feat_extract_from_pretrained_kwargs(self): feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict) with tempfile.TemporaryDirectory() as tmpdirname: saved_file = feat_extract_first.save_pretrained(tmpdirname)[0] check_json_file_has_correct_format(saved_file) feat_extract_second = self.feature_extraction_class.from_pretrained( tmpdirname, feature_size=2 * self.feat_extract_dict["feature_size"] ) mel_1 = feat_extract_first.mel_filters mel_2 = feat_extract_second.mel_filters self.assertTrue(2 * mel_1.shape[1] == mel_2.shape[1]) def test_call(self): # Tests that all call wrap to encode_plus and batch_encode_plus feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()) # create three inputs of length 800, 1000, and 1200 speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)] np_speech_inputs = [np.asarray(speech_input) for speech_input in speech_inputs] pt_speech_inputs = [torch.tensor(speech_input) for speech_input in speech_inputs] # Test feature size input_features = feature_extractor(np_speech_inputs, return_tensors="np").audio_input_features max_audio_len = (1200 - feature_extractor.win_length) // feature_extractor.hop_length + 1 self.assertTrue(input_features.ndim == 3) self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size) self.assertTrue(input_features.shape[-2] == max_audio_len) # Test not batched input encoded_sequences_1 = feature_extractor(pt_speech_inputs[0], return_tensors="np").audio_input_features encoded_sequences_2 = feature_extractor(np_speech_inputs[0], return_tensors="np").audio_input_features self.assertTrue(np.allclose(encoded_sequences_1, encoded_sequences_2, atol=1e-3)) # Test batched encoded_sequences_1 = feature_extractor(pt_speech_inputs, return_tensors="np").audio_input_features encoded_sequences_2 = feature_extractor(np_speech_inputs, return_tensors="np").audio_input_features for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2): self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=1e-3)) # Test 2-D numpy arrays are batched. speech_inputs = [floats_list((1, x))[0] for x in (800, 800, 800)] np_speech_inputs = np.asarray(speech_inputs) pt_speech_inputs = torch.tensor(speech_inputs) encoded_sequences_1 = feature_extractor(pt_speech_inputs, return_tensors="np").audio_input_features encoded_sequences_2 = feature_extractor(np_speech_inputs, return_tensors="np").audio_input_features for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2): self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=1e-3)) @require_torch def test_double_precision_pad(self): import torch feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()) np_speech_inputs = np.random.rand(100, 32).astype(np.float64) py_speech_inputs = np_speech_inputs.tolist() for inputs in [py_speech_inputs, np_speech_inputs]: np_processed = feature_extractor.pad([{"audio_input_features": inputs}], return_tensors="np") self.assertTrue(np_processed.audio_input_features.dtype == np.float32) pt_processed = feature_extractor.pad([{"audio_input_features": inputs}], return_tensors="pt") self.assertTrue(pt_processed.audio_input_features.dtype == torch.float32) def _load_datasamples(self, num_samples): ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") # automatic decoding with librispeech speech_samples = ds.sort("id")[:num_samples]["audio"] return [x["array"] for x in speech_samples] @require_torch def test_torch_integration(self): # fmt: off EXPECTED_INPUT_FEATURES = torch.tensor( [ 6.5243, 7.2267, 8.0917, 8.0041, 6.8247, 6.3216, 5.9599, 5.6770, 5.7441, 5.6138, 6.6793, 6.8597, 5.5375, 6.5330, 5.4880, 7.3280, 9.0736, 9.7665, 9.8773, 10.0828, 10.0518, 10.1736, 10.0145, 9.2545, 11.0495, 11.6518, 10.8654, 10.2293, 9.1045, 9.4819, ] ) # fmt: on input_speech = self._load_datasamples(1) feature_extractor = Phi4MultimodalFeatureExtractor() input_features = feature_extractor(input_speech, return_tensors="pt").audio_input_features self.assertEqual(input_features.shape, (1, 584, 80)) torch.testing.assert_close(input_features[0, 0, :30], EXPECTED_INPUT_FEATURES, rtol=1e-4, atol=1e-4) @unittest.mock.patch( "transformers.models.phi4_multimodal.feature_extraction_phi4_multimodal.is_torch_available", lambda: False ) def test_numpy_integration(self): # fmt: off EXPECTED_INPUT_FEATURES = np.array( [ 6.5242944, 7.226712, 8.091721, 8.004097, 6.824679, 6.3216243, 5.959894, 5.676975, 5.744051, 5.61384, 6.6793485, 6.8597484, 5.5374746, 6.532976, 5.4879804, 7.3279905, 9.073576, 9.766463, 9.877262, 10.082759, 10.051792, 10.173581, 10.0144825, 9.254548, 11.049487, 11.651841, 10.865354, 10.229329, 9.104464, 9.481946, ] ) # fmt: on input_speech = self._load_datasamples(1) feature_extractor = Phi4MultimodalFeatureExtractor() input_features = feature_extractor(input_speech, return_tensors="np").audio_input_features self.assertEqual(input_features.shape, (1, 584, 80)) self.assertTrue(np.allclose(input_features[0, 0, :30], EXPECTED_INPUT_FEATURES, atol=1e-4)) @require_torch def test_torch_integration_batch(self): # fmt: off EXPECTED_INPUT_FEATURES = torch.tensor( [ [ 6.5243, 7.2267, 8.0917, 8.0041, 6.8247, 6.3216, 5.9599, 5.6770, 5.7441, 5.6138, 6.6793, 6.8597, 5.5375, 6.5330, 5.4880, 7.3280, 9.0736, 9.7665, 9.8773, 10.0828, 10.0518, 10.1736, 10.0145, 9.2545, 11.0495, 11.6518, 10.8654, 10.2293, 9.1045, 9.4819 ], [ 7.5105, 7.9453, 8.6161, 7.7666, 7.2572, 6.8823, 6.3242, 6.1899, 6.9706, 8.0810, 7.3227, 5.8580, 5.4990, 7.7373, 8.5447, 7.7203, 6.3230, 7.1995, 7.1463, 7.3153, 7.4054, 7.2855, 6.9396, 7.0255, 7.3285, 7.2748, 8.0742, 7.3998, 6.4813, 6.7509 ], [ 7.7932, 8.1604, 8.7653, 8.2080, 7.2630, 6.4537, 4.8394, 6.3153, 8.0207, 8.3379, 6.0896, 5.7369, 5.8601, 4.7598, 4.8850, 6.2529, 3.9354, 6.1577, 7.9921, 9.6577, 10.1449, 9.1414, 9.3361, 9.0022, 9.2533, 10.0548, 10.4372, 8.8550, 9.1266, 9.9013 ] ] ) # fmt: on input_speech = self._load_datasamples(3) feature_extractor = Phi4MultimodalFeatureExtractor() input_features = feature_extractor(input_speech, return_tensors="pt").audio_input_features self.assertEqual(input_features.shape, (3, 1247, 80)) print(input_features[:, 0, :30]) torch.testing.assert_close(input_features[:, 0, :30], EXPECTED_INPUT_FEATURES, rtol=1e-4, atol=1e-4)