codingutf8 2022 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 orglicenseslicense2 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 copied from tests models whisper testfeatureextractionwhisper floatslist creates a random float32 tensor if rng is none rng globalrng values for batchidx in rangeshape0 values append for in rangeshape1 values1 appendrng random scale return values class astfeatureextractiontesterunittest testcase def init self parent batchsize7 minseqlength400 maxseqlength2000 featuresize1 paddingvalue0 0 samplingrate16000 returnattentionmasktrue donormalizetrue self parent parent self batchsize batchsize self minseqlength minseqlength self maxseqlength maxseqlength self seqlengthdiff self maxseqlength self minseqlength self batchsize 1 self featuresize featuresize self paddingvalue paddingvalue self samplingrate samplingrate self returnattentionmask returnattentionmask self donormalize donormalize def preparefeatextractdictself return featuresize self featuresize paddingvalue self paddingvalue samplingrate self samplingrate returnattentionmask self returnattentionmask donormalize self donormalize def prepareinputsforcommonself equallengthfalse numpifyfalse def flattenlistoflists return listitertools chainlistoflists if equallength speechinputs floatslistself batchsize self maxseqlength else make sure that inputs increase in size speechinputs flattenfloatslistx self featuresize for x in rangeself minseqlength self maxseqlength self seqlengthdiff if numpify speechinputs np asarrayx for x in speechinputs return speechinputs requiretorch requiretorchaudio class astfeatureextractiontestsequencefeatureextractiontestmixin unittest testcase featureextractionclass astfeatureextractor def setupself self featextracttester astfeatureextractiontesterself def testcallself tests that all call wrap to encodeplus and batchencodeplus featextract self featureextractionclassself featextracttester preparefeatextractdict create three inputs of length 800 1000 and 1200 speechinputs floatslist1 x0 for x in range800 1400 200 npspeechinputs np asarrayspeechinput for speechinput in speechinputs test not batched input encodedsequences1 featextractspeechinputs0 returntensorsnp inputvalues encodedsequences2 featextractnpspeechinputs0 returntensorsnp inputvalues self asserttruenp allcloseencodedsequences1 encodedsequences2 atol1e3 test batched encodedsequences1 featextractspeechinputs paddingtrue returntensorsnp inputvalues encodedsequences2 featextractnpspeechinputs paddingtrue returntensorsnp inputvalues for encseq1 encseq2 in zipencodedsequences1 encodedsequences2 self asserttruenp allcloseencseq1 encseq2 atol1e3 test 2d numpy arrays are batched speechinputs floatslist1 x0 for x in 800 800 800 npspeechinputs np asarrayspeechinputs encodedsequences1 featextractspeechinputs returntensorsnp inputvalues encodedsequences2 featextractnpspeechinputs returntensorsnp inputvalues for encseq1 encseq2 in zipencodedsequences1 encodedsequences2 self asserttruenp allcloseencseq1 encseq2 atol1e3 requiretorch def testdoubleprecisionpadself import torch featureextractor self featureextractionclassself featextracttester preparefeatextractdict npspeechinputs np random rand100 astypenp float64 pyspeechinputs npspeechinputs tolist for inputs in pyspeechinputs npspeechinputs npprocessed featureextractor padinputvalues inputs returntensorsnp self asserttruenpprocessed inputvalues dtype np float32 ptprocessed featureextractor padinputvalues inputs returntensorspt self asserttrueptprocessed inputvalues dtype torch float32 def loaddatasamplesself numsamples from datasets import loaddataset ds loaddatasethfinternaltestinglibrispeechasrdummy clean splitvalidation automatic decoding with librispeech speechsamples ds sortid selectrangenumsamples numsamplesaudio return xarray for x in speechsamples requiretorch def testintegrationself fmt off expectedinputvalues torch tensor 0 9894 1 2776 0 9066 1 2776 0 9349 1 2609 1 0386 1 2776 1 1561 1 2776 1 2052 1 2723 1 2190 1 2132 1 2776 1 1133 1 1953 1 1343 1 1584 1 2203 1 1770 1 2474 1 2381 1 1936 0 9270 0 8317 0 8049 0 7706 0 7565 0 7869 fmt on inputspeech self loaddatasamples1 featureextractor astfeatureextractor inputvalues featureextractorinputspeech returntensorspt inputvalues self assertequalsinputvalues shape 1 1024 128 self asserttruetorch allcloseinputvalues0 0 30 expectedinputvalues atol1e4 def testfeatextractfromandsavepretrainedself featextractfirst self featureextractionclassself featextractdict with tempfile temporarydirectory as tmpdirname savedfile featextractfirst savepretrainedtmpdirname0 checkjsonfilehascorrectformatsavedfile featextractsecond self featureextractionclass frompretrainedtmpdirname dictfirst featextractfirst todict dictsecond featextractsecond todict self assertdictequaldictfirst dictsecond def testfeatextracttojsonfileself featextractfirst self featureextractionclassself featextractdict with tempfile temporarydirectory as tmpdirname jsonfilepath os path jointmpdirname featextract json featextractfirst tojsonfilejsonfilepath featextractsecond self featureextractionclass fromjsonfilejsonfilepath dictfirst featextractfirst todict dictsecond featextractsecond todict self assertequaldictfirst dictsecond exact same tests than before except that we simulate that torchaudio is not available requiretorch unittest mock patch transformers models audiospectrogramtransformer featureextractionaudiospectrogramtransformer isspeechavailable lambda false class astfeatureextractionwithouttorchaudiotestastfeatureextractiontest def testusingaudioutilsself tests that it uses audioutils instead of torchaudio featextract self featureextractionclassself featextracttester preparefeatextractdict self asserttruehasattrfeatextract window self asserttruehasattrfeatextract melfilters from transformers models audiospectrogramtransformer featureextractionaudiospectrogramtransformer import isspeechavailable self assertfalseisspeechavailable coding utf 8 2022 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 copied from tests models whisper test_feature_extraction_whisper floats_list creates a random float32 tensor make sure that inputs increase in size tests that all call wrap to encode_plus and batch_encode_plus create three inputs of length 800 1000 and 1200 test not batched input test batched test 2 d numpy arrays are batched automatic decoding with librispeech fmt off fmt on exact same tests than before except that we simulate that torchaudio is not available tests that it uses audio_utils instead of torchaudio
import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


global_rng = random.Random()

if is_torch_available():
    import torch



def floats_list(shape, scale=1.0, rng=None, name=None):
    
    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 ASTFeatureExtractionTester(unittest.TestCase):
    def __init__(
        self,
        parent,
        batch_size=7,
        min_seq_length=400,
        max_seq_length=2000,
        feature_size=1,
        padding_value=0.0,
        sampling_rate=16000,
        return_attention_mask=True,
        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.feature_size = feature_size
        self.padding_value = padding_value
        self.sampling_rate = sampling_rate
        self.return_attention_mask = return_attention_mask
        self.do_normalize = do_normalize

    def prepare_feat_extract_dict(self):
        return {
            "feature_size": self.feature_size,
            "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.batch_size, self.max_seq_length))
        else:
            
            speech_inputs = [
                _flatten(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


@require_torch
@require_torchaudio
class ASTFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.TestCase):
    feature_extraction_class = ASTFeatureExtractor

    def setUp(self):
        self.feat_extract_tester = ASTFeatureExtractionTester(self)

    def test_call(self):
        
        feat_extract = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
        
        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]

        
        encoded_sequences_1 = feat_extract(speech_inputs[0], return_tensors="np").input_values
        encoded_sequences_2 = feat_extract(np_speech_inputs[0], return_tensors="np").input_values
        self.assertTrue(np.allclose(encoded_sequences_1, encoded_sequences_2, atol=1e-3))

        
        encoded_sequences_1 = feat_extract(speech_inputs, padding=True, return_tensors="np").input_values
        encoded_sequences_2 = feat_extract(np_speech_inputs, padding=True, return_tensors="np").input_values
        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))

        
        speech_inputs = [floats_list((1, x))[0] for x in (800, 800, 800)]
        np_speech_inputs = np.asarray(speech_inputs)
        encoded_sequences_1 = feat_extract(speech_inputs, return_tensors="np").input_values
        encoded_sequences_2 = feat_extract(np_speech_inputs, return_tensors="np").input_values
        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).astype(np.float64)
        py_speech_inputs = np_speech_inputs.tolist()

        for inputs in [py_speech_inputs, np_speech_inputs]:
            np_processed = feature_extractor.pad([{"input_values": inputs}], return_tensors="np")
            self.assertTrue(np_processed.input_values.dtype == np.float32)
            pt_processed = feature_extractor.pad([{"input_values": inputs}], return_tensors="pt")
            self.assertTrue(pt_processed.input_values.dtype == torch.float32)

    def _load_datasamples(self, num_samples):
        from datasets import load_dataset

        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
        
        speech_samples = ds.sort("id").select(range(num_samples))[:num_samples]["audio"]

        return [x["array"] for x in speech_samples]

    @require_torch
    def test_integration(self):
        
        EXPECTED_INPUT_VALUES = torch.tensor(
            [-0.9894, -1.2776, -0.9066, -1.2776, -0.9349, -1.2609, -1.0386, -1.2776,
             -1.1561, -1.2776, -1.2052, -1.2723, -1.2190, -1.2132, -1.2776, -1.1133,
             -1.1953, -1.1343, -1.1584, -1.2203, -1.1770, -1.2474, -1.2381, -1.1936,
             -0.9270, -0.8317, -0.8049, -0.7706, -0.7565, -0.7869]
        )
        

        input_speech = self._load_datasamples(1)
        feature_extractor = ASTFeatureExtractor()
        input_values = feature_extractor(input_speech, return_tensors="pt").input_values
        self.assertEquals(input_values.shape, (1, 1024, 128))
        self.assertTrue(torch.allclose(input_values[0, 0, :30], EXPECTED_INPUT_VALUES, atol=1e-4))

    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()
        self.assertDictEqual(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()
        self.assertEqual(dict_first, dict_second)



@require_torch
@unittest.mock.patch(
    "transformers.models.audio_spectrogram_transformer.feature_extraction_audio_spectrogram_transformer.is_speech_available",
    lambda: False,
)
class ASTFeatureExtractionWithoutTorchaudioTest(ASTFeatureExtractionTest):
    def test_using_audio_utils(self):
        
        feat_extract = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())

        self.assertTrue(hasattr(feat_extract, "window"))
        self.assertTrue(hasattr(feat_extract, "mel_filters"))

        from transformers.models.audio_spectrogram_transformer.feature_extraction_audio_spectrogram_transformer import (
            is_speech_available,
        )

        self.assertFalse(is_speech_available())