366 lines
16 KiB
Python
366 lines
16 KiB
Python
# Copyright 2023 The HuggingFace 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 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 Audio, load_dataset
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from transformers import UnivNetFeatureExtractor
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from transformers.testing_utils import check_json_file_has_correct_format, require_torch, slow
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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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class UnivNetFeatureExtractionTester(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=1,
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sampling_rate=24000,
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padding_value=0.0,
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do_normalize=True,
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num_mel_bins=100,
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hop_length=256,
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win_length=1024,
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win_function="hann_window",
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filter_length=1024,
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max_length_s=10,
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fmin=0.0,
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fmax=12000,
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mel_floor=1e-9,
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center=False,
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compression_factor=1.0,
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compression_clip_val=1e-5,
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normalize_min=-11.512925148010254,
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normalize_max=2.3143386840820312,
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model_in_channels=64,
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pad_end_length=10,
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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.sampling_rate = sampling_rate
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self.padding_value = padding_value
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self.do_normalize = do_normalize
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self.num_mel_bins = num_mel_bins
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self.hop_length = hop_length
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self.win_length = win_length
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self.win_function = win_function
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self.filter_length = filter_length
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self.max_length_s = max_length_s
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self.fmin = fmin
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self.fmax = fmax
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self.mel_floor = mel_floor
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self.center = center
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self.compression_factor = compression_factor
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self.compression_clip_val = compression_clip_val
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self.normalize_min = normalize_min
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self.normalize_max = normalize_max
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self.model_in_channels = model_in_channels
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self.pad_end_length = pad_end_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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"sampling_rate": self.sampling_rate,
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"padding_value": self.padding_value,
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"do_normalize": self.do_normalize,
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"num_mel_bins": self.num_mel_bins,
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"hop_length": self.hop_length,
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"win_length": self.win_length,
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"win_function": self.win_function,
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"filter_length": self.filter_length,
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"max_length_s": self.max_length_s,
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"fmin": self.fmin,
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"fmax": self.fmax,
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"mel_floor": self.mel_floor,
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"center": self.center,
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"compression_factor": self.compression_factor,
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"compression_clip_val": self.compression_clip_val,
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"normalize_min": self.normalize_min,
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"normalize_max": self.normalize_max,
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"model_in_channels": self.model_in_channels,
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"pad_end_length": self.pad_end_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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speech_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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speech_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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speech_inputs = [np.asarray(x) for x in speech_inputs]
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return speech_inputs
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class UnivNetFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.TestCase):
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feature_extraction_class = UnivNetFeatureExtractor
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def setUp(self):
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self.feat_extract_tester = UnivNetFeatureExtractionTester(self)
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# Copied from tests.models.whisper.test_feature_extraction_whisper.WhisperFeatureExtractionTest.test_feat_extract_from_and_save_pretrained
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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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# Copied from tests.models.whisper.test_feature_extraction_whisper.WhisperFeatureExtractionTest.test_feat_extract_to_json_file
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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_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(
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np_speech_inputs, padding="max_length", max_length=1600, return_tensors="np"
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).input_features
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self.assertTrue(input_features.ndim == 3)
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# Note: for some reason I get a weird padding error when feature_size > 1
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# self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size)
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# Note: we use the shape convention (batch_size, seq_len, num_mel_bins)
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self.assertTrue(input_features.shape[-1] == feature_extractor.num_mel_bins)
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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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# 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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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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# Test truncation required
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speech_inputs = [
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floats_list((1, x))[0]
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for x in range((feature_extractor.num_max_samples - 100), (feature_extractor.num_max_samples + 500), 200)
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]
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np_speech_inputs = [np.asarray(speech_input) for speech_input in speech_inputs]
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speech_inputs_truncated = [x[: feature_extractor.num_max_samples] for x in speech_inputs]
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np_speech_inputs_truncated = [np.asarray(speech_input) for speech_input in speech_inputs_truncated]
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encoded_sequences_1 = feature_extractor(np_speech_inputs, return_tensors="np").input_features
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encoded_sequences_2 = feature_extractor(np_speech_inputs_truncated, 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_batched_unbatched_consistency(self):
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feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
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speech_inputs = floats_list((1, 800))[0]
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np_speech_inputs = np.asarray(speech_inputs)
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# Test unbatched vs batched list
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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([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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# Test np.ndarray vs List[np.ndarray]
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encoded_sequences_1 = feature_extractor(np_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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# Test unbatched np.ndarray vs batched np.ndarray
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encoded_sequences_1 = feature_extractor(np_speech_inputs, return_tensors="np").input_features
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encoded_sequences_2 = feature_extractor(
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np.expand_dims(np_speech_inputs, axis=0), return_tensors="np"
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).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_generate_noise(self):
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feature_extractor = self.feature_extraction_class(**self.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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features = feature_extractor(speech_inputs, return_noise=True)
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input_features = features.input_features
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noise_features = features.noise_sequence
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for spectrogram, noise in zip(input_features, noise_features):
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self.assertEqual(spectrogram.shape[0], noise.shape[0])
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def test_pad_end(self):
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feature_extractor = self.feature_extraction_class(**self.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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input_features1 = feature_extractor(speech_inputs, padding=False, pad_end=False).input_features
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input_features2 = feature_extractor(speech_inputs, padding=False, pad_end=True).input_features
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for spectrogram1, spectrogram2 in zip(input_features1, input_features2):
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self.assertEqual(spectrogram1.shape[0] + self.feat_extract_tester.pad_end_length, spectrogram2.shape[0])
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def test_generate_noise_and_pad_end(self):
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feature_extractor = self.feature_extraction_class(**self.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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features = feature_extractor(speech_inputs, padding=False, return_noise=True, pad_end=True)
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input_features = features.input_features
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noise_features = features.noise_sequence
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for spectrogram, noise in zip(input_features, noise_features):
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self.assertEqual(spectrogram.shape[0], noise.shape[0])
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@require_torch
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def test_batch_decode(self):
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import torch
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feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
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input_lengths = list(range(800, 1400, 200))
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pad_samples = feature_extractor.pad_end_length * feature_extractor.hop_length
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output_features = {
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"waveforms": torch.tensor(floats_list((3, max(input_lengths) + pad_samples))),
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"waveform_lengths": torch.tensor(input_lengths),
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}
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waveforms = feature_extractor.batch_decode(**output_features)
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for input_length, waveform in zip(input_lengths, waveforms):
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self.assertTrue(len(waveform.shape) == 1, msg="Individual output waveforms should be 1D")
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self.assertEqual(waveform.shape[0], input_length)
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@require_torch
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# Copied from tests.models.whisper.test_feature_extraction_whisper.WhisperFeatureExtractionTest.test_double_precision_pad
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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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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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ds = ds.cast_column("audio", Audio(sampling_rate=self.feat_extract_tester.sampling_rate))
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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], [x["sampling_rate"] for x in speech_samples]
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@slow
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@require_torch
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def test_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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-5.0229, -6.1358, -5.8346, -5.4447, -5.6707, -5.8577, -5.0464, -5.0058,
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-5.6015, -5.6410, -5.4325, -5.6116, -5.3700, -5.7956, -5.3196, -5.3274,
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-5.9655, -5.6057, -5.8382, -5.9602, -5.9005, -5.9123, -5.7669, -6.1441,
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-5.5168, -5.1405, -5.3927, -6.0032, -5.5784, -5.3728
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],
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)
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# fmt: on
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input_speech, sr = self._load_datasamples(1)
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feature_extractor = UnivNetFeatureExtractor()
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input_features = feature_extractor(input_speech, sampling_rate=sr[0], return_tensors="pt").input_features
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self.assertEqual(input_features.shape, (1, 548, 100))
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input_features_mean = torch.mean(input_features)
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input_features_stddev = torch.std(input_features)
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EXPECTED_MEAN = torch.tensor(-6.18862009)
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EXPECTED_STDDEV = torch.tensor(2.80845642)
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torch.testing.assert_close(input_features_mean, EXPECTED_MEAN, atol=5e-5, rtol=5e-6)
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torch.testing.assert_close(input_features_stddev, EXPECTED_STDDEV)
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torch.testing.assert_close(input_features[0, :30, 0], EXPECTED_INPUT_FEATURES, atol=1e-4, rtol=1e-5)
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