wehub-resource-sync
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177 lines
8.3 KiB
Python
177 lines
8.3 KiB
Python
# Copyright 2026 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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import itertools
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import unittest
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import numpy as np
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from transformers import Xcodec2FeatureExtractor
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from transformers.testing_utils import require_torch, require_torch_gpu, slow
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from transformers.utils.import_utils import is_torch_available
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from ...test_processing_common import floats_list
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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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@require_torch
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class Xcodec2FeatureExtractionTester:
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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, # number of mel bins
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sampling_rate=16000,
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spec_hop_length=160,
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hop_length=320,
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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.spec_hop_length = spec_hop_length
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self.hop_length = hop_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.sampling_rate = sampling_rate
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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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"sampling_rate": self.sampling_rate,
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"hop_length": self.hop_length,
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"spec_hop_length": self.spec_hop_length,
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"padding_value": 0.0,
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}
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# Copied from transformers.tests.whisper.test_feature_extraction_whisper.WhisperFeatureExtractionTester.prepare_inputs_for_common
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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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class Xcodec2FeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.TestCase):
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feature_extraction_class = Xcodec2FeatureExtractor
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def setUp(self):
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self.feat_extract_tester = Xcodec2FeatureExtractionTester(self)
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def test_call(self):
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TOL = 1e-6
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# Tests that all call wrap to encode_plus and batch_encode_plus
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feat_extract = 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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audio_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
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np_audio_inputs = [np.asarray(audio_input) for audio_input in audio_inputs]
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torch_audio_inputs = [torch.tensor(audio_input) for audio_input in audio_inputs]
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# Test not batched input
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sampling_rate = self.feat_extract_tester.sampling_rate
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encoded_sequences_1 = feat_extract(torch_audio_inputs[0], sampling_rate=sampling_rate, return_tensors="np")
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encoded_sequences_2 = feat_extract(np_audio_inputs[0], sampling_rate=sampling_rate, return_tensors="np")
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encoded_sequences_3 = feat_extract(
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torch_audio_inputs[0], sampling_rate=sampling_rate, return_tensors="np", device="cpu"
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)
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self.assertTrue(np.allclose(encoded_sequences_1.input_values, encoded_sequences_2.input_values, atol=TOL))
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self.assertTrue(np.allclose(encoded_sequences_1.input_features, encoded_sequences_2.input_features, atol=TOL))
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self.assertTrue(np.allclose(encoded_sequences_1.input_features, encoded_sequences_3.input_features, atol=TOL))
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# Test batched
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encoded_sequences_1 = feat_extract(
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torch_audio_inputs, sampling_rate=sampling_rate, padding=True, return_tensors="np"
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)
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encoded_sequences_2 = feat_extract(
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np_audio_inputs, sampling_rate=sampling_rate, padding=True, return_tensors="np"
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)
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encoded_sequences_3 = feat_extract(
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torch_audio_inputs,
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sampling_rate=sampling_rate,
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padding=True,
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return_tensors="np",
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device="cpu",
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)
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for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1.input_values, encoded_sequences_2.input_values):
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self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=TOL))
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for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1.input_features, encoded_sequences_2.input_features):
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self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=TOL))
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for enc_seq_1, enc_seq_3 in zip(encoded_sequences_1.input_features, encoded_sequences_3.input_features):
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self.assertTrue(np.allclose(enc_seq_1, enc_seq_3, atol=TOL))
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@slow
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@require_torch_gpu
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def test_call_gpu(self):
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feat_extract = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
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audio_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
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torch_audio_inputs = [torch.tensor(audio_input) for audio_input in audio_inputs]
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sampling_rate = self.feat_extract_tester.sampling_rate
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# Single input: CPU vs GPU output should have same shape and dtype
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encoded_sequences_cpu = feat_extract(
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torch_audio_inputs[0], sampling_rate=sampling_rate, return_tensors="np", device="cpu"
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)
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encoded_sequences_gpu = feat_extract(
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torch_audio_inputs[0], sampling_rate=sampling_rate, return_tensors="np", device="cuda"
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)
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self.assertEqual(encoded_sequences_cpu.input_values.shape, encoded_sequences_gpu.input_values.shape)
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self.assertEqual(encoded_sequences_cpu.input_features.shape, encoded_sequences_gpu.input_features.shape)
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self.assertEqual(encoded_sequences_cpu.input_values.dtype, encoded_sequences_gpu.input_values.dtype)
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self.assertEqual(encoded_sequences_cpu.input_features.dtype, encoded_sequences_gpu.input_features.dtype)
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# Batched input: CPU vs GPU output should have same shape and dtype
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encoded_sequences_cpu = feat_extract(
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torch_audio_inputs, sampling_rate=sampling_rate, padding=True, return_tensors="np", device="cpu"
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)
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encoded_sequences_gpu = feat_extract(
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torch_audio_inputs, sampling_rate=sampling_rate, padding=True, return_tensors="np", device="cuda"
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)
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self.assertEqual(encoded_sequences_cpu.input_values.shape, encoded_sequences_gpu.input_values.shape)
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self.assertEqual(encoded_sequences_cpu.input_features.shape, encoded_sequences_gpu.input_features.shape)
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self.assertEqual(encoded_sequences_cpu.input_values.dtype, encoded_sequences_gpu.input_values.dtype)
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self.assertEqual(encoded_sequences_cpu.input_features.dtype, encoded_sequences_gpu.input_features.dtype)
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def test_double_precision_pad(self):
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feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
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np_audio_inputs = np.random.rand(100, 32).astype(np.float64)
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py_audio_inputs = np_audio_inputs.tolist()
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for inputs in [py_audio_inputs, np_audio_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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@unittest.skip("Xcodec2 doesn't support stereo input")
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def test_integration_stereo(self):
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pass
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