codingutf8 20212023 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 tests for the speecht5 feature extractors import itertools import random import unittest import numpy as np from transformers import batchfeature speecht5featureextractor from transformers testingutils import requiretorch from transformers utils importutils import istorchavailable from testsequencefeatureextractioncommon import sequencefeatureextractiontestmixin if istorchavailable import torch globalrng random random copied from tests models whisper testfeatureextractionwhisper floatslist def floatslistshape scale1 0 rngnone namenone make sure that inputs increase in size make sure that inputs increase in size tests that all call wrap to encodeplus and batchencodeplus create three inputs of length 800 1000 and 1200 test not batched input test batched make sure that if maxlength longest then pad to maxlength make sure that if maxlength longest then pad to longest tests that all call wrap to encodeplus and batchencodeplus create three inputs of length 800 1000 and 1200 test feature size test not batched input test batched test 2d numpy arrays are batched automatic decoding with librispeech fmt off fmt on fmt off fmt on coding utf 8 2021 2023 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 tests for the speecht5 feature extractors copied from tests models whisper test_feature_extraction_whisper floats_list creates a random float32 tensor make sure that inputs increase in size 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 make sure that if max_length longest then pad to max_length make sure that if max_length longest then pad to longest tests that all call wrap to encode_plus and batch_encode_plus create three inputs of length 800 1000 and 1200 test feature size test not batched input test batched test 2 d numpy arrays are batched hack hack hack automatic decoding with librispeech fmt off fmt on fmt off fmt on
import itertools
import random
import unittest

import numpy as np

from transformers import BatchFeature, SpeechT5FeatureExtractor
from transformers.testing_utils import 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):
    
    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


@require_torch
class SpeechT5FeatureExtractionTester(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,
        do_normalize=True,
        num_mel_bins=80,
        hop_length=16,
        win_length=64,
        win_function="hann_window",
        fmin=80,
        fmax=7600,
        mel_floor=1e-10,
        return_attention_mask=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.do_normalize = do_normalize
        self.num_mel_bins = num_mel_bins
        self.hop_length = hop_length
        self.win_length = win_length
        self.win_function = win_function
        self.fmin = fmin
        self.fmax = fmax
        self.mel_floor = mel_floor
        self.return_attention_mask = return_attention_mask

    def prepare_feat_extract_dict(self):
        return {
            "feature_size": self.feature_size,
            "padding_value": self.padding_value,
            "sampling_rate": self.sampling_rate,
            "do_normalize": self.do_normalize,
            "num_mel_bins": self.num_mel_bins,
            "hop_length": self.hop_length,
            "win_length": self.win_length,
            "win_function": self.win_function,
            "fmin": self.fmin,
            "fmax": self.fmax,
            "mel_floor": self.mel_floor,
            "return_attention_mask": self.return_attention_mask,
        }

    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

    def prepare_inputs_for_target(self, equal_length=False, numpify=False):
        if equal_length:
            speech_inputs = [floats_list((self.max_seq_length, self.num_mel_bins)) for _ in range(self.batch_size)]
        else:
            
            speech_inputs = [
                floats_list((x, self.num_mel_bins))
                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
class SpeechT5FeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.TestCase):
    feature_extraction_class = SpeechT5FeatureExtractor

    def setUp(self):
        self.feat_extract_tester = SpeechT5FeatureExtractionTester(self)

    def _check_zero_mean_unit_variance(self, input_vector):
        self.assertTrue(np.all(np.mean(input_vector, axis=0) < 1e-3))
        self.assertTrue(np.all(np.abs(np.var(input_vector, axis=0) - 1) < 1e-3))

    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, 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))

    def test_zero_mean_unit_variance_normalization_np(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)]

        paddings = ["longest", "max_length", "do_not_pad"]
        max_lengths = [None, 1600, None]
        for max_length, padding in zip(max_lengths, paddings):
            processed = feat_extract(speech_inputs, padding=padding, max_length=max_length, return_tensors="np")
            input_values = processed.input_values

            self._check_zero_mean_unit_variance(input_values[0][:800])
            self.assertTrue(input_values[0][800:].sum() < 1e-6)
            self._check_zero_mean_unit_variance(input_values[1][:1000])
            self.assertTrue(input_values[0][1000:].sum() < 1e-6)
            self._check_zero_mean_unit_variance(input_values[2][:1200])

    def test_zero_mean_unit_variance_normalization(self):
        feat_extract = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
        lengths = range(800, 1400, 200)
        speech_inputs = [floats_list((1, x))[0] for x in lengths]

        paddings = ["longest", "max_length", "do_not_pad"]
        max_lengths = [None, 1600, None]

        for max_length, padding in zip(max_lengths, paddings):
            processed = feat_extract(speech_inputs, max_length=max_length, padding=padding)
            input_values = processed.input_values

            self._check_zero_mean_unit_variance(input_values[0][:800])
            self._check_zero_mean_unit_variance(input_values[1][:1000])
            self._check_zero_mean_unit_variance(input_values[2][:1200])

    def test_zero_mean_unit_variance_normalization_trunc_np_max_length(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)]
        processed = feat_extract(
            speech_inputs, truncation=True, max_length=1000, padding="max_length", return_tensors="np"
        )
        input_values = processed.input_values

        self._check_zero_mean_unit_variance(input_values[0, :800])
        self._check_zero_mean_unit_variance(input_values[1])
        self._check_zero_mean_unit_variance(input_values[2])

    def test_zero_mean_unit_variance_normalization_trunc_np_longest(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)]
        processed = feat_extract(
            speech_inputs, truncation=True, max_length=1000, padding="longest", return_tensors="np"
        )
        input_values = processed.input_values

        self._check_zero_mean_unit_variance(input_values[0, :800])
        self._check_zero_mean_unit_variance(input_values[1, :1000])
        self._check_zero_mean_unit_variance(input_values[2])

        
        self.assertTrue(input_values.shape == (3, 1000))

        speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
        processed = feat_extract(
            speech_inputs, truncation=True, max_length=2000, padding="longest", return_tensors="np"
        )
        input_values = processed.input_values

        self._check_zero_mean_unit_variance(input_values[0, :800])
        self._check_zero_mean_unit_variance(input_values[1, :1000])
        self._check_zero_mean_unit_variance(input_values[2])

        
        self.assertTrue(input_values.shape == (3, 1200))

    def test_double_precision_pad(self):
        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 test_call_target(self):
        
        feature_extractor = 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]

        
        input_values = feature_extractor(audio_target=np_speech_inputs, padding=True, return_tensors="np").input_values
        self.assertTrue(input_values.ndim == 3)
        self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins)

        
        encoded_sequences_1 = feature_extractor(speech_inputs[0], return_tensors="np").input_values
        encoded_sequences_2 = feature_extractor(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 = feature_extractor(speech_inputs, return_tensors="np").input_values
        encoded_sequences_2 = feature_extractor(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))

        
        speech_inputs = [floats_list((1, x))[0] for x in (800, 800, 800)]
        np_speech_inputs = np.asarray(speech_inputs)
        encoded_sequences_1 = feature_extractor(speech_inputs, return_tensors="np").input_values
        encoded_sequences_2 = feature_extractor(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))

    def test_batch_feature_target(self):
        speech_inputs = self.feat_extract_tester.prepare_inputs_for_target()
        feat_extract = self.feature_extraction_class(**self.feat_extract_dict)
        input_name = feat_extract.model_input_names[0]

        processed_features = BatchFeature({input_name: speech_inputs})

        self.assertTrue(all(len(x) == len(y) for x, y in zip(speech_inputs, processed_features[input_name])))

        speech_inputs = self.feat_extract_tester.prepare_inputs_for_target(equal_length=True)
        processed_features = BatchFeature({input_name: speech_inputs}, tensor_type="np")

        batch_features_input = processed_features[input_name]

        if len(batch_features_input.shape) < 3:
            batch_features_input = batch_features_input[:, :, None]

        self.assertTrue(
            batch_features_input.shape
            == (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.num_mel_bins)
        )

    @require_torch
    def test_batch_feature_target_pt(self):
        speech_inputs = self.feat_extract_tester.prepare_inputs_for_target(equal_length=True)
        feat_extract = self.feature_extraction_class(**self.feat_extract_dict)
        input_name = feat_extract.model_input_names[0]

        processed_features = BatchFeature({input_name: speech_inputs}, tensor_type="pt")

        batch_features_input = processed_features[input_name]

        if len(batch_features_input.shape) < 3:
            batch_features_input = batch_features_input[:, :, None]

        self.assertTrue(
            batch_features_input.shape
            == (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.num_mel_bins)
        )

    @require_torch
    def test_padding_accepts_tensors_target_pt(self):
        feat_extract = self.feature_extraction_class(**self.feat_extract_dict)
        speech_inputs = self.feat_extract_tester.prepare_inputs_for_target()
        input_name = feat_extract.model_input_names[0]

        processed_features = BatchFeature({input_name: speech_inputs})

        feat_extract.feature_size = feat_extract.num_mel_bins  

        input_np = feat_extract.pad(processed_features, padding="longest", return_tensors="np")[input_name]
        input_pt = feat_extract.pad(processed_features, padding="longest", return_tensors="pt")[input_name]

        self.assertTrue(abs(input_np.astype(np.float32).sum() - input_pt.numpy().astype(np.float32).sum()) < 1e-2)

    def test_attention_mask_target(self):
        feat_dict = self.feat_extract_dict
        feat_dict["return_attention_mask"] = True
        feat_extract = self.feature_extraction_class(**feat_dict)
        speech_inputs = self.feat_extract_tester.prepare_inputs_for_target()
        input_lengths = [len(x) for x in speech_inputs]
        input_name = feat_extract.model_input_names[0]

        processed = BatchFeature({input_name: speech_inputs})

        feat_extract.feature_size = feat_extract.num_mel_bins  

        processed = feat_extract.pad(processed, padding="longest", return_tensors="np")
        self.assertIn("attention_mask", processed)
        self.assertListEqual(list(processed.attention_mask.shape), list(processed[input_name].shape[:2]))
        self.assertListEqual(processed.attention_mask.sum(-1).tolist(), input_lengths)

    def test_attention_mask_with_truncation_target(self):
        feat_dict = self.feat_extract_dict
        feat_dict["return_attention_mask"] = True
        feat_extract = self.feature_extraction_class(**feat_dict)
        speech_inputs = self.feat_extract_tester.prepare_inputs_for_target()
        input_lengths = [len(x) for x in speech_inputs]
        input_name = feat_extract.model_input_names[0]

        processed = BatchFeature({input_name: speech_inputs})
        max_length = min(input_lengths)

        feat_extract.feature_size = feat_extract.num_mel_bins  

        processed_pad = feat_extract.pad(
            processed, padding="max_length", max_length=max_length, truncation=True, return_tensors="np"
        )
        self.assertIn("attention_mask", processed_pad)
        self.assertListEqual(
            list(processed_pad.attention_mask.shape), [processed_pad[input_name].shape[0], max_length]
        )
        self.assertListEqual(
            processed_pad.attention_mask[:, :max_length].sum(-1).tolist(), [max_length for x in speech_inputs]
        )

    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]

    def test_integration(self):
        
        EXPECTED_INPUT_VALUES = torch.tensor(
            [2.3804e-03, 2.0752e-03, 1.9836e-03, 2.1057e-03, 1.6174e-03,
             3.0518e-04, 9.1553e-05, 3.3569e-04, 9.7656e-04, 1.8311e-03,
             2.0142e-03, 2.1057e-03, 1.7395e-03, 4.5776e-04, -3.9673e-04,
             4.5776e-04, 1.0071e-03, 9.1553e-05, 4.8828e-04, 1.1597e-03,
             7.3242e-04, 9.4604e-04, 1.8005e-03, 1.8311e-03, 8.8501e-04,
             4.2725e-04, 4.8828e-04, 7.3242e-04, 1.0986e-03, 2.1057e-03]
        )
        

        input_speech = self._load_datasamples(1)
        feature_extractor = SpeechT5FeatureExtractor()
        input_values = feature_extractor(input_speech, return_tensors="pt").input_values
        self.assertEquals(input_values.shape, (1, 93680))
        self.assertTrue(torch.allclose(input_values[0, :30], EXPECTED_INPUT_VALUES, atol=1e-6))

    def test_integration_target(self):
        
        EXPECTED_INPUT_VALUES = torch.tensor(
            [-2.6870, -3.0104, -3.1356, -3.5352, -3.0044, -3.0353, -3.4719, -3.6777,
             -3.1520, -2.9435, -2.6553, -2.8795, -2.9944, -2.5921, -3.0279, -3.0386,
             -3.0864, -3.1291, -3.2353, -2.7444, -2.6831, -2.7287, -3.1761, -3.1571,
             -3.2726, -3.0582, -3.1007, -3.4533, -3.4695, -3.0998]
        )
        

        input_speech = self._load_datasamples(1)
        feature_extractor = SpeechT5FeatureExtractor()
        input_values = feature_extractor(audio_target=input_speech, return_tensors="pt").input_values
        self.assertEquals(input_values.shape, (1, 366, 80))
        self.assertTrue(torch.allclose(input_values[0, 0, :30], EXPECTED_INPUT_VALUES, atol=1e-4))