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216 lines
8.7 KiB
Python
216 lines
8.7 KiB
Python
# Copyright 2024 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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"""Tests for the dac feature extractor."""
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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 DacFeatureExtractor
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from transformers.testing_utils import 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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# Copied from tests.models.whisper.test_feature_extraction_whisper.floats_list
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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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# Copied from transformers.tests.encodec.test_feature_extraction_dac.EncodecFeatureExtractionTester with Encodec->Dac
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class DacFeatureExtractionTester:
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# Ignore copy
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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=1,
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padding_value=0.0,
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sampling_rate=16000,
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hop_length=512,
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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.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.padding_value = padding_value
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self.sampling_rate = sampling_rate
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# Ignore copy
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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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"padding_value": self.padding_value,
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"sampling_rate": self.sampling_rate,
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"hop_length": self.hop_length,
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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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audio_inputs = floats_list((self.batch_size, self.max_seq_length))
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else:
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# make sure that inputs increase in size
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audio_inputs = [
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_flatten(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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audio_inputs = [np.asarray(x) for x in audio_inputs]
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return audio_inputs
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@require_torch
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# Copied from transformers.tests.encodec.test_feature_extraction_dac.EnCodecFeatureExtractionTest with Encodec->Dac
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class DacFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.TestCase):
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feature_extraction_class = DacFeatureExtractor
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def setUp(self):
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self.feat_extract_tester = DacFeatureExtractionTester(self)
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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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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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# Test not batched input
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encoded_sequences_1 = feat_extract(audio_inputs[0], return_tensors="np").input_values
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encoded_sequences_2 = feat_extract(np_audio_inputs[0], return_tensors="np").input_values
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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 = feat_extract(audio_inputs, padding=True, return_tensors="np").input_values
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encoded_sequences_2 = feat_extract(np_audio_inputs, padding=True, return_tensors="np").input_values
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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_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).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_values": inputs}], return_tensors="np")
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self.assertTrue(np_processed.input_values.dtype == np.float32)
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pt_processed = feature_extractor.pad([{"input_values": inputs}], return_tensors="pt")
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self.assertTrue(pt_processed.input_values.dtype == torch.float32)
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def _load_datasamples(self, num_samples):
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from datasets import load_dataset
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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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audio_samples = ds.sort("id")[:num_samples]["audio"]
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return [x["array"] for x in audio_samples]
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def test_integration(self):
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# fmt: off
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EXPECTED_INPUT_VALUES = torch.tensor(
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[ 2.3803711e-03, 2.0751953e-03, 1.9836426e-03, 2.1057129e-03,
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1.6174316e-03, 3.0517578e-04, 9.1552734e-05, 3.3569336e-04,
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9.7656250e-04, 1.8310547e-03, 2.0141602e-03, 2.1057129e-03,
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1.7395020e-03, 4.5776367e-04, -3.9672852e-04, 4.5776367e-04,
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1.0070801e-03, 9.1552734e-05, 4.8828125e-04, 1.1596680e-03,
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7.3242188e-04, 9.4604492e-04, 1.8005371e-03, 1.8310547e-03,
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8.8500977e-04, 4.2724609e-04, 4.8828125e-04, 7.3242188e-04,
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1.0986328e-03, 2.1057129e-03]
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)
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# fmt: on
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input_audio = self._load_datasamples(1)
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feature_extractor = DacFeatureExtractor()
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input_values = feature_extractor(input_audio, return_tensors="pt")["input_values"]
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self.assertEqual(input_values.shape, (1, 1, 93696))
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torch.testing.assert_close(input_values[0, 0, :30], EXPECTED_INPUT_VALUES, rtol=1e-4, atol=1e-4)
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audio_input_end = torch.tensor(input_audio[0][-30:], dtype=torch.float32)
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torch.testing.assert_close(input_values[0, 0, -46:-16], audio_input_end, rtol=1e-4, atol=1e-4)
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# Ignore copy
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@unittest.skip("The DAC model doesn't support stereo logic")
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def test_integration_stereo(self):
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pass
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# Ignore copy
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def test_truncation_and_padding(self):
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input_audio = self._load_datasamples(2)
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# would be easier if the stride was like
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feature_extractor = DacFeatureExtractor()
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# pad and trunc raise an error ?
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with self.assertRaisesRegex(
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ValueError,
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"^Both padding and truncation were set. Make sure you only set one.$",
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):
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truncated_outputs = feature_extractor(
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input_audio, padding="max_length", truncation=True, return_tensors="pt"
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).input_values
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# force truncate to max_length
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truncated_outputs = feature_extractor(
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input_audio, truncation=True, max_length=48000, return_tensors="pt"
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).input_values
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self.assertEqual(truncated_outputs.shape, (2, 1, 48128))
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# pad:
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padded_outputs = feature_extractor(input_audio, padding=True, return_tensors="pt").input_values
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self.assertEqual(padded_outputs.shape, (2, 1, 93696))
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# force pad to max length
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truncated_outputs = feature_extractor(
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input_audio, padding="max_length", max_length=100000, return_tensors="pt"
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).input_values
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self.assertEqual(truncated_outputs.shape, (2, 1, 100352))
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# force no pad
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with self.assertRaisesRegex(
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ValueError,
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"^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$",
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):
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truncated_outputs = feature_extractor(input_audio, padding=False, return_tensors="pt").input_values
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truncated_outputs = feature_extractor(input_audio[0], padding=False, return_tensors="pt").input_values
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self.assertEqual(truncated_outputs.shape, (1, 1, 93680))
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