repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
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espnet | espnet-master/test/test_scheduler.py | import chainer
import numpy
import pytest
import torch
from espnet.scheduler import scheduler
from espnet.scheduler.chainer import ChainerScheduler
from espnet.scheduler.pytorch import PyTorchScheduler
@pytest.mark.parametrize("name", scheduler.SCHEDULER_DICT.keys())
def test_scheduler(name):
s = scheduler.dynam... | 1,247 | 26.130435 | 65 | py |
espnet | espnet-master/test/test_e2e_st_conformer.py | # coding: utf-8
# Copyright 2019 Hirofumi Inaguma
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
import argparse
import pytest
import torch
from espnet.nets.pytorch_backend.e2e_st_conformer import E2E
from espnet.nets.pytorch_backend.transformer import plot
def make_arg(**kwargs):
defaults = dict(... | 4,216 | 26.383117 | 85 | py |
espnet | espnet-master/test/espnet2/svs/test_naive_rnn_dp.py | import pytest
import torch
from espnet2.svs.naive_rnn.naive_rnn_dp import NaiveRNNDP
@pytest.mark.parametrize("eprenet_conv_layers", [0, 1])
@pytest.mark.parametrize("midi_embed_integration_type", ["add", "cat"])
@pytest.mark.parametrize("postnet_layers", [0, 1])
@pytest.mark.parametrize("reduction_factor", [1, 3])
... | 5,665 | 29.627027 | 87 | py |
espnet | espnet-master/test/espnet2/svs/test_singing_tacotron.py | import pytest
import torch
from espnet2.svs.singing_tacotron.singing_tacotron import singing_tacotron
@pytest.mark.parametrize("prenet_layers", [0, 1])
@pytest.mark.parametrize("postnet_layers", [0, 1])
@pytest.mark.parametrize("reduction_factor", [1, 3])
@pytest.mark.parametrize("atype", ["location", "forward", "fo... | 8,282 | 29.12 | 87 | py |
espnet | espnet-master/test/espnet2/gan_tts/jets/test_jets.py | # Copyright 2022 Dan Lim
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Test JETS related modules."""
import pytest
import torch
from espnet2.gan_tts.jets import JETS
def make_jets_generator_args(**kwargs):
defaults = dict(
generator_type="jets_generator",
generator_params={
... | 32,573 | 33.469841 | 86 | py |
espnet | espnet-master/test/espnet2/gan_tts/melgan/test_melgan.py | # Copyright 2021 Tomoki Hayashi
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Test code for MelGAN modules."""
import numpy as np
import pytest
import torch
from espnet2.gan_tts.hifigan.loss import (
DiscriminatorAdversarialLoss,
FeatureMatchLoss,
GeneratorAdversarialLoss,
)
from espnet2... | 4,503 | 28.246753 | 82 | py |
espnet | espnet-master/test/espnet2/gan_tts/wavenet/test_wavenet.py | # Copyright 2021 Tomoki Hayashi
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Test code for WaveNet modules."""
import pytest
import torch
from espnet2.gan_tts.wavenet import WaveNet
def make_wavenet_args(**kwargs):
defaults = dict(
in_channels=1,
out_channels=1,
kernel... | 1,768 | 25.014706 | 76 | py |
espnet | espnet-master/test/espnet2/gan_tts/joint/test_joint_text2wav.py | # Copyright 2021 Tomoki Hayashi
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Test VITS related modules."""
import pytest
import torch
from packaging.version import parse as V
from espnet2.gan_tts.joint import JointText2Wav
def make_text2mel_args(**kwargs):
defaults = dict(
text2mel_ty... | 18,259 | 32.504587 | 88 | py |
espnet | espnet-master/test/espnet2/gan_tts/style_melgan/test_style_melgan.py | # Copyright 2021 Tomoki Hayashi
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Test code for StyleMelGAN modules."""
import numpy as np
import pytest
import torch
from espnet2.gan_tts.hifigan.loss import (
DiscriminatorAdversarialLoss,
GeneratorAdversarialLoss,
)
from espnet2.gan_tts.style_me... | 4,247 | 28.09589 | 87 | py |
espnet | espnet-master/test/espnet2/gan_tts/hifigan/test_hifigan.py | # Copyright 2021 Tomoki Hayashi
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Test code for HiFi-GAN modules."""
import numpy as np
import pytest
import torch
from espnet2.gan_tts.hifigan import (
HiFiGANGenerator,
HiFiGANMultiScaleMultiPeriodDiscriminator,
)
from espnet2.gan_tts.hifigan.los... | 6,163 | 29.068293 | 82 | py |
espnet | espnet-master/test/espnet2/gan_tts/vits/test_generator.py | # Copyright 2021 Tomoki Hayashi
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Test VITS generator modules."""
import pytest
import torch
from espnet2.gan_tts.vits.generator import VITSGenerator
def make_generator_args(**kwargs):
defaults = dict(
vocabs=10,
aux_channels=5,
... | 11,068 | 32.24024 | 80 | py |
espnet | espnet-master/test/espnet2/gan_tts/vits/test_vits.py | # Copyright 2021 Tomoki Hayashi
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Test VITS related modules."""
import pytest
import torch
from espnet2.gan_tts.vits import VITS
def get_test_data():
test_data = [
({}, {}, {}),
({}, {}, {"cache_generator_outputs": True}),
(
... | 22,280 | 32.6571 | 88 | py |
espnet | espnet-master/test/espnet2/gan_tts/parallel_wavegan/test_parallel_wavegan.py | # Copyright 2021 Tomoki Hayashi
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Test code for ParallelWaveGAN modules."""
import numpy as np
import pytest
import torch
from espnet2.gan_tts.hifigan.loss import (
DiscriminatorAdversarialLoss,
GeneratorAdversarialLoss,
)
from espnet2.gan_tts.para... | 4,501 | 26.790123 | 86 | py |
espnet | espnet-master/test/espnet2/gan_svs/visinger/test_visinger.py | # Copyright 2021 Tomoki Hayashi
# Copyright 2023 Yifeng Yu
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Test VISinger related modules."""
import pytest
import torch
from espnet2.gan_svs.vits import VITS
def get_test_data():
test_data = [
({}, {}, {}),
({}, {}, {"cache_generato... | 62,568 | 35.740458 | 87 | py |
espnet | espnet-master/test/espnet2/gan_svs/visinger/test_visinger_generator.py | # Copyright 2021 Tomoki Hayashi
# Copyright 2023 Yifeng Yu
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Test VISinger generator modules."""
import pytest
import torch
from espnet2.gan_svs.vits.generator import VISingerGenerator
def make_generator_args(**kwargs):
defaults = dict(
vocabs... | 12,584 | 27.997696 | 83 | py |
espnet | espnet-master/test/espnet2/enh/test_espnet_enh_s2t_model.py | import pytest
import torch
from espnet2.asr.ctc import CTC
from espnet2.asr.decoder.transformer_decoder import TransformerDecoder
from espnet2.asr.encoder.transformer_encoder import TransformerEncoder
from espnet2.asr.espnet_model import ESPnetASRModel
from espnet2.asr.frontend.default import DefaultFrontend
from espn... | 8,614 | 25.921875 | 86 | py |
espnet | espnet-master/test/espnet2/enh/test_espnet_model_tse.py | import pytest
import torch
from packaging.version import parse as V
from espnet2.enh.decoder.conv_decoder import ConvDecoder
from espnet2.enh.decoder.stft_decoder import STFTDecoder
from espnet2.enh.encoder.conv_encoder import ConvEncoder
from espnet2.enh.encoder.stft_encoder import STFTEncoder
from espnet2.enh.espnet... | 3,910 | 33.307018 | 86 | py |
espnet | espnet-master/test/espnet2/enh/test_espnet_model.py | import pytest
import torch
from packaging.version import parse as V
from espnet2.enh.decoder.conv_decoder import ConvDecoder
from espnet2.enh.decoder.null_decoder import NullDecoder
from espnet2.enh.decoder.stft_decoder import STFTDecoder
from espnet2.enh.encoder.conv_encoder import ConvEncoder
from espnet2.enh.encode... | 18,543 | 32.055258 | 87 | py |
espnet | espnet-master/test/espnet2/enh/separator/test_beamformer.py | import numpy as np
import pytest
import torch
from packaging.version import parse as V
from espnet2.enh.encoder.stft_encoder import STFTEncoder
from espnet2.enh.layers.complex_utils import is_torch_complex_tensor
from espnet2.enh.layers.dnn_beamformer import BEAMFORMER_TYPES
from espnet2.enh.separator.neural_beamforme... | 12,458 | 34.095775 | 88 | py |
espnet | espnet-master/test/espnet2/enh/separator/test_dptnet_separator.py | import pytest
import torch
from torch import Tensor
from torch_complex import ComplexTensor
from espnet2.enh.separator.dptnet_separator import DPTNetSeparator
@pytest.mark.parametrize("input_dim", [8])
@pytest.mark.parametrize("post_enc_relu", [True, False])
@pytest.mark.parametrize("rnn_type", ["lstm", "gru"])
@pyt... | 4,741 | 26.569767 | 77 | py |
espnet | espnet-master/test/espnet2/enh/separator/test_dpcl_e2e_separator.py | import pytest
import torch
from torch import Tensor
from torch_complex import ComplexTensor
from espnet2.enh.separator.dpcl_e2e_separator import DPCLE2ESeparator
@pytest.mark.parametrize("input_dim", [5])
@pytest.mark.parametrize("rnn_type", ["blstm"])
@pytest.mark.parametrize("layer", [1, 3])
@pytest.mark.parametri... | 4,049 | 25.821192 | 77 | py |
espnet | espnet-master/test/espnet2/enh/separator/test_dc_crn_separator.py | import pytest
import torch
from packaging.version import parse as V
from torch_complex import ComplexTensor
from espnet2.enh.layers.complex_utils import is_complex
from espnet2.enh.separator.dc_crn_separator import DC_CRNSeparator
is_torch_1_9_plus = V(torch.__version__) >= V("1.9.0")
@pytest.mark.parametrize("inpu... | 4,997 | 29.662577 | 85 | py |
espnet | espnet-master/test/espnet2/enh/separator/test_svoice_separator.py | import pytest
import torch
from torch import Tensor
from espnet2.enh.separator.svoice_separator import SVoiceSeparator
@pytest.mark.parametrize("input_dim", [1])
@pytest.mark.parametrize("enc_dim", [4])
@pytest.mark.parametrize("kernel_size", [4])
@pytest.mark.parametrize("hidden_size", [4])
@pytest.mark.parametrize... | 2,605 | 26.431579 | 66 | py |
espnet | espnet-master/test/espnet2/enh/separator/test_tcn_separator.py | import pytest
import torch
from torch import Tensor
from torch_complex import ComplexTensor
from espnet2.enh.separator.tcn_separator import TCNSeparator
@pytest.mark.parametrize("input_dim", [5])
@pytest.mark.parametrize("bottleneck_dim", [5])
@pytest.mark.parametrize("hidden_dim", [5])
@pytest.mark.parametrize("ker... | 4,667 | 26.139535 | 77 | py |
espnet | espnet-master/test/espnet2/enh/separator/test_fasnet_separator.py | import pytest
import torch
from torch import Tensor
from espnet2.enh.separator.fasnet_separator import FaSNetSeparator
@pytest.mark.parametrize("input_dim", [1])
@pytest.mark.parametrize("enc_dim", [4])
@pytest.mark.parametrize("feature_dim", [4])
@pytest.mark.parametrize("hidden_dim", [4])
@pytest.mark.parametrize(... | 2,209 | 25.95122 | 66 | py |
espnet | espnet-master/test/espnet2/enh/separator/test_dccrn_separator.py | import pytest
import torch
from packaging.version import parse as V
from torch_complex import ComplexTensor
from espnet2.enh.separator.dccrn_separator import DCCRNSeparator
is_torch_1_9_plus = V(torch.__version__) >= V("1.9.0")
@pytest.mark.parametrize("input_dim", [9])
@pytest.mark.parametrize("num_spk", [1, 2])
@... | 2,790 | 28.378947 | 77 | py |
espnet | espnet-master/test/espnet2/enh/separator/test_dprnn_separator.py | import pytest
import torch
from torch import Tensor
from torch_complex import ComplexTensor
from espnet2.enh.separator.dprnn_separator import DPRNNSeparator
@pytest.mark.parametrize("input_dim", [5])
@pytest.mark.parametrize("rnn_type", ["lstm", "gru"])
@pytest.mark.parametrize("layer", [1, 3])
@pytest.mark.parametr... | 3,668 | 25.977941 | 77 | py |
espnet | espnet-master/test/espnet2/enh/separator/test_dan_separator.py | import pytest
import torch
from torch import Tensor
from torch_complex import ComplexTensor
from espnet2.enh.separator.dan_separator import DANSeparator
@pytest.mark.parametrize("input_dim", [5])
@pytest.mark.parametrize("rnn_type", ["blstm"])
@pytest.mark.parametrize("layer", [1, 3])
@pytest.mark.parametrize("unit"... | 3,559 | 26.596899 | 77 | py |
espnet | espnet-master/test/espnet2/enh/separator/test_skim_separator.py | import pytest
import torch
from torch import Tensor
from torch_complex import ComplexTensor
from espnet2.enh.separator.skim_separator import SkiMSeparator
@pytest.mark.parametrize("input_dim", [5])
@pytest.mark.parametrize("layer", [1, 3])
@pytest.mark.parametrize("causal", [True, False])
@pytest.mark.parametrize("u... | 4,718 | 26.436047 | 77 | py |
espnet | espnet-master/test/espnet2/enh/separator/test_transformer_separator.py | import pytest
import torch
from torch import Tensor
from torch_complex import ComplexTensor
from espnet2.enh.separator.transformer_separator import TransformerSeparator
@pytest.mark.parametrize("input_dim", [5])
@pytest.mark.parametrize("num_spk", [1, 2])
@pytest.mark.parametrize("adim", [8])
@pytest.mark.parametriz... | 4,849 | 29.696203 | 77 | py |
espnet | espnet-master/test/espnet2/enh/separator/test_dpcl_separator.py | import pytest
import torch
from torch_complex import ComplexTensor
from espnet2.enh.separator.dpcl_separator import DPCLSeparator
@pytest.mark.parametrize("input_dim", [5])
@pytest.mark.parametrize("rnn_type", ["blstm"])
@pytest.mark.parametrize("layer", [1, 3])
@pytest.mark.parametrize("unit", [8])
@pytest.mark.par... | 3,127 | 26.928571 | 72 | py |
espnet | espnet-master/test/espnet2/enh/separator/test_conformer_separator.py | import pytest
import torch
from torch import Tensor
from torch_complex.tensor import ComplexTensor
from espnet2.enh.separator.conformer_separator import ConformerSeparator
@pytest.mark.parametrize("input_dim", [15])
@pytest.mark.parametrize("num_spk", [1, 2])
@pytest.mark.parametrize("adim", [8])
@pytest.mark.parame... | 7,306 | 34.470874 | 77 | py |
espnet | espnet-master/test/espnet2/enh/separator/test_rnn_separator.py | import pytest
import torch
from torch import Tensor
from torch_complex import ComplexTensor
from espnet2.enh.separator.rnn_separator import RNNSeparator
@pytest.mark.parametrize("input_dim", [5])
@pytest.mark.parametrize("rnn_type", ["blstm"])
@pytest.mark.parametrize("layer", [1, 3])
@pytest.mark.parametrize("unit"... | 3,896 | 28.300752 | 77 | py |
espnet | espnet-master/test/espnet2/enh/layers/test_complex_utils.py | import numpy as np
import pytest
import torch
import torch_complex.functional as FC
from packaging.version import parse as V
from torch_complex.tensor import ComplexTensor
from espnet2.enh.layers.complex_utils import (
cat,
complex_norm,
einsum,
inverse,
matmul,
solve,
stack,
trace,
)
... | 7,177 | 33.344498 | 88 | py |
espnet | espnet-master/test/espnet2/enh/layers/test_conv_utils.py | import pytest
import torch
from espnet2.enh.layers.conv_utils import conv2d_output_shape, convtransp2d_output_shape
@pytest.mark.parametrize("input_dim", [(10, 17), (10, 33)])
@pytest.mark.parametrize("kernel_size", [(1, 3), (3, 5)])
@pytest.mark.parametrize("stride", [(1, 1), (1, 2)])
@pytest.mark.parametrize("padd... | 1,984 | 30.507937 | 88 | py |
espnet | espnet-master/test/espnet2/enh/layers/test_enh_layers.py | import numpy as np
import pytest
import torch
import torch_complex.functional as FC
from packaging.version import parse as V
from torch_complex.tensor import ComplexTensor
from espnet2.enh.layers.beamformer import (
generalized_eigenvalue_decomposition,
get_rtf,
gev_phase_correction,
signal_framing,
)
... | 7,408 | 38.833333 | 83 | py |
espnet | espnet-master/test/espnet2/enh/encoder/test_stft_encoder.py | import pytest
import torch
from packaging.version import parse as V
from espnet2.enh.encoder.stft_encoder import STFTEncoder
is_torch_1_12_1_plus = V(torch.__version__) >= V("1.12.1")
@pytest.mark.parametrize("n_fft", [512])
@pytest.mark.parametrize("win_length", [512])
@pytest.mark.parametrize("hop_length", [128])... | 2,290 | 28 | 79 | py |
espnet | espnet-master/test/espnet2/enh/encoder/test_conv_encoder.py | import pytest
import torch
from espnet2.enh.encoder.conv_encoder import ConvEncoder
@pytest.mark.parametrize("channel", [64])
@pytest.mark.parametrize("kernel_size", [10, 20])
@pytest.mark.parametrize("stride", [5, 10])
def test_ConvEncoder_backward(channel, kernel_size, stride):
encoder = ConvEncoder(
c... | 545 | 25 | 60 | py |
espnet | espnet-master/test/espnet2/enh/loss/criterions/test_time_domain.py | import pytest
import torch
from packaging.version import parse as V
from espnet2.enh.loss.criterions.time_domain import (
CISDRLoss,
MultiResL1SpecLoss,
SDRLoss,
SISNRLoss,
SNRLoss,
TimeDomainL1,
TimeDomainMSE,
)
is_torch_1_12_1_plus = V(torch.__version__) >= V("1.12.1")
@pytest.mark.par... | 2,175 | 30.536232 | 88 | py |
espnet | espnet-master/test/espnet2/enh/loss/criterions/test_tf_domain.py | import pytest
import torch
from packaging.version import parse as V
from torch_complex import ComplexTensor
from espnet2.enh.loss.criterions.tf_domain import (
FrequencyDomainAbsCoherence,
FrequencyDomainCrossEntropy,
FrequencyDomainDPCL,
FrequencyDomainL1,
FrequencyDomainMSE,
)
is_torch_1_9_plus ... | 4,064 | 35.621622 | 85 | py |
espnet | espnet-master/test/espnet2/enh/loss/wrappers/test_fixed_order_solver.py | import pytest
import torch
from espnet2.enh.loss.criterions.tf_domain import FrequencyDomainL1
from espnet2.enh.loss.wrappers.fixed_order import FixedOrderSolver
@pytest.mark.parametrize("num_spk", [1, 2, 3])
def test_PITSolver_forward(num_spk):
batch = 2
inf = [torch.rand(batch, 10, 100) for spk in range(nu... | 506 | 30.6875 | 82 | py |
espnet | espnet-master/test/espnet2/enh/loss/wrappers/test_multilayer_pit_solver.py | import pytest
import torch
from espnet2.enh.loss.criterions.tf_domain import FrequencyDomainL1
from espnet2.enh.loss.wrappers.multilayer_pit_solver import MultiLayerPITSolver
@pytest.mark.parametrize("num_spk", [1, 2, 3])
@pytest.mark.parametrize("layer_weights", [[1, 1], [1, 2]])
def test_MultiLayerPITSolver_forwar... | 2,099 | 34 | 87 | py |
espnet | espnet-master/test/espnet2/enh/loss/wrappers/test_dpcl_solver.py | import pytest
import torch
from espnet2.enh.loss.criterions.tf_domain import FrequencyDomainDPCL
from espnet2.enh.loss.wrappers.dpcl_solver import DPCLSolver
@pytest.mark.parametrize("num_spk", [1, 2, 3])
def test_DPCLSolver_forward(num_spk):
batch = 2
o = {"tf_embedding": torch.rand(batch, 10 * 200, 40)}
... | 560 | 32 | 82 | py |
espnet | espnet-master/test/espnet2/enh/loss/wrappers/test_pit_solver.py | import pytest
import torch
import torch.nn.functional as F
from espnet2.enh.loss.criterions.tf_domain import (
FrequencyDomainCrossEntropy,
FrequencyDomainL1,
)
from espnet2.enh.loss.wrappers.pit_solver import PITSolver
@pytest.mark.parametrize("num_spk", [1, 2, 3])
@pytest.mark.parametrize("flexible_numspk"... | 2,092 | 31.703125 | 84 | py |
espnet | espnet-master/test/espnet2/enh/loss/wrappers/test_mixit_solver.py | import pytest
import torch
import torch.nn.functional as F
from packaging.version import parse as V
from torch_complex.tensor import ComplexTensor
from espnet2.enh.loss.criterions.tf_domain import FrequencyDomainL1
from espnet2.enh.loss.criterions.time_domain import TimeDomainL1
from espnet2.enh.loss.wrappers.mixit_so... | 3,730 | 29.581967 | 80 | py |
espnet | espnet-master/test/espnet2/enh/extractor/test_td_speakerbeam_extractor.py | import pytest
import torch
from espnet2.enh.extractor.td_speakerbeam_extractor import TDSpeakerBeamExtractor
@pytest.mark.parametrize("input_dim", [5])
@pytest.mark.parametrize("layer", [4])
@pytest.mark.parametrize("stack", [2])
@pytest.mark.parametrize("bottleneck_dim", [5])
@pytest.mark.parametrize("hidden_dim", ... | 2,465 | 28.710843 | 84 | py |
espnet | espnet-master/test/espnet2/enh/decoder/test_conv_decoder.py | import pytest
import torch
from espnet2.enh.decoder.conv_decoder import ConvDecoder
from espnet2.enh.encoder.conv_encoder import ConvEncoder
@pytest.mark.parametrize("channel", [64])
@pytest.mark.parametrize("kernel_size", [10, 20])
@pytest.mark.parametrize("stride", [5, 10])
def test_ConvEncoder_backward(channel, k... | 1,559 | 31.5 | 82 | py |
espnet | espnet-master/test/espnet2/enh/decoder/test_stft_decoder.py | import pytest
import torch
import torch_complex
from packaging.version import parse as V
from torch_complex import ComplexTensor
from espnet2.enh.decoder.stft_decoder import STFTDecoder
from espnet2.enh.encoder.stft_encoder import STFTEncoder
is_torch_1_12_1_plus = V(torch.__version__) >= V("1.12.1")
is_torch_1_9_plu... | 4,652 | 33.984962 | 88 | py |
espnet | espnet-master/test/espnet2/torch_utils/test_pytorch_version.py | from espnet2.torch_utils.pytorch_version import pytorch_cudnn_version
def test_pytorch_cudnn_version():
print(pytorch_cudnn_version())
| 141 | 22.666667 | 69 | py |
espnet | espnet-master/test/espnet2/torch_utils/test_set_all_random_seed.py | from espnet2.torch_utils.set_all_random_seed import set_all_random_seed
def test_set_all_random_seed():
set_all_random_seed(0)
| 133 | 21.333333 | 71 | py |
espnet | espnet-master/test/espnet2/torch_utils/test_add_gradient_noise.py | import torch
from espnet2.torch_utils.add_gradient_noise import add_gradient_noise
def test_add_gradient_noise():
linear = torch.nn.Linear(1, 1)
linear(torch.rand(1, 1)).sum().backward()
add_gradient_noise(linear, 100)
| 234 | 22.5 | 69 | py |
espnet | espnet-master/test/espnet2/torch_utils/test_model_summary.py | import torch
from espnet2.torch_utils.model_summary import model_summary
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
self.l1 = torch.nn.Linear(1000, 1000)
self.l2 = torch.nn.Linear(1000, 1000)
self.l3 = torch.nn.Linear(1000, 1000)
def test_model_summary(... | 357 | 21.375 | 59 | py |
espnet | espnet-master/test/espnet2/torch_utils/test_initialize.py | import pytest
import torch
from espnet2.torch_utils.initialize import initialize
initialize_types = {}
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv1 = torch.nn.Conv2d(2, 2, 3)
self.l1 = torch.nn.Linear(2, 2)
self.rnn_cell = torch.nn.LSTMCell(2, 2... | 1,083 | 20.68 | 53 | py |
espnet | espnet-master/test/espnet2/torch_utils/test_load_pretrained_model.py | import numpy as np
import torch
from espnet2.torch_utils.load_pretrained_model import load_pretrained_model
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
self.layer1 = torch.nn.Linear(1, 1)
self.layer2 = torch.nn.Linear(2, 2)
def test_load_pretrained_model_all(tmp... | 2,070 | 29.910448 | 84 | py |
espnet | espnet-master/test/espnet2/torch_utils/test_forward_adaptor.py | import pytest
import torch
from espnet2.torch_utils.forward_adaptor import ForwardAdaptor
class Model(torch.nn.Module):
def func(self, x):
return x
def test_ForwardAdaptor():
model = Model()
x = torch.randn(2, 2)
assert (ForwardAdaptor(model, "func")(x) == x).all()
def test_ForwardAdaptor... | 424 | 18.318182 | 62 | py |
espnet | espnet-master/test/espnet2/torch_utils/test_device_funcs.py | import dataclasses
from typing import NamedTuple
import pytest
import torch
from espnet2.torch_utils.device_funcs import force_gatherable, to_device
x = torch.tensor(10)
@dataclasses.dataclass(frozen=True)
class Data:
x: torch.Tensor
class Named(NamedTuple):
x: torch.Tensor
@pytest.mark.parametrize(
... | 1,275 | 22.2 | 81 | py |
espnet | espnet-master/test/espnet2/main_funcs/test_average_nbest_models.py | import pytest
import torch
from espnet2.main_funcs.average_nbest_models import average_nbest_models
from espnet2.train.reporter import Reporter
@pytest.fixture
def reporter():
_reporter = Reporter()
_reporter.set_epoch(1)
with _reporter.observe("valid") as sub:
sub.register({"acc": 0.4})
... | 1,927 | 27.352941 | 72 | py |
espnet | espnet-master/test/espnet2/main_funcs/test_calculate_all_attentions.py | from collections import defaultdict
import numpy as np
import pytest
import torch
from espnet2.asr.decoder.rnn_decoder import RNNDecoder
from espnet2.main_funcs.calculate_all_attentions import calculate_all_attentions
from espnet2.train.abs_espnet_model import AbsESPnetModel
from espnet.nets.pytorch_backend.rnn.atten... | 2,743 | 28.505376 | 87 | py |
espnet | espnet-master/test/espnet2/slu/test_transcript_espnet_model.py | import pytest
import torch
from packaging.version import parse as V
from espnet2.asr.ctc import CTC
from espnet2.asr.decoder.transformer_decoder import TransformerDecoder
from espnet2.asr.encoder.conformer_encoder import ConformerEncoder
from espnet2.asr.encoder.transformer_encoder import TransformerEncoder
from espne... | 8,945 | 28.919732 | 84 | py |
espnet | espnet-master/test/espnet2/slu/postencoder/test_conformer_encoder.py | import pytest
import torch
from espnet2.slu.postencoder.conformer_postencoder import ConformerPostEncoder
@pytest.mark.parametrize("input_layer", ["linear"])
@pytest.mark.parametrize("positionwise_layer_type", ["conv1d", "conv1d-linear"])
@pytest.mark.parametrize(
"rel_pos_type, pos_enc_layer_type, selfattention... | 2,716 | 29.188889 | 80 | py |
espnet | espnet-master/test/espnet2/slu/postencoder/test_transformer_encoder.py | import pytest
import torch
from espnet2.slu.postencoder.transformer_postencoder import TransformerPostEncoder
@pytest.mark.parametrize("input_layer", ["linear", "None"])
@pytest.mark.parametrize("positionwise_layer_type", ["conv1d", "conv1d-linear"])
def test_Encoder_forward_backward(
input_layer,
positionwi... | 908 | 26.545455 | 82 | py |
espnet | espnet-master/test/espnet2/slu/postdecoder/test_hugging_face_transformers_postdecoder.py | import pytest
import torch
from packaging.version import parse as V
from espnet2.slu.postdecoder.hugging_face_transformers_postdecoder import (
HuggingFaceTransformersPostDecoder,
)
is_torch_1_8_plus = V(torch.__version__) >= V("1.8.0")
@pytest.mark.execution_timeout(50)
def test_transformers_forward():
if ... | 2,203 | 33.984127 | 81 | py |
espnet | espnet-master/test/espnet2/bin/test_slu_inference.py | import string
from argparse import ArgumentParser
from distutils.version import LooseVersion
from pathlib import Path
import numpy as np
import pytest
import torch
from espnet2.bin.slu_inference import Speech2Understand, get_parser, main
from espnet2.tasks.lm import LMTask
from espnet2.tasks.slu import SLUTask
from e... | 4,171 | 27.972222 | 88 | py |
espnet | espnet-master/test/espnet2/bin/test_enh_inference.py | import string
from argparse import ArgumentParser
from pathlib import Path
import pytest
import torch
import yaml
from espnet2.bin.enh_inference import SeparateSpeech, get_parser, main
from espnet2.enh.encoder.stft_encoder import STFTEncoder
from espnet2.tasks.enh import EnhancementTask
from espnet2.tasks.enh_s2t imp... | 7,502 | 29.75 | 88 | py |
espnet | espnet-master/test/espnet2/bin/test_enh_inference_streaming.py | from argparse import ArgumentParser
from pathlib import Path
import pytest
import torch
import yaml
from espnet2.bin.enh_inference_streaming import (
SeparateSpeechStreaming,
get_parser,
main,
)
from espnet2.tasks.enh import EnhancementTask
from espnet2.utils.yaml_no_alias_safe_dump import yaml_no_alias_s... | 2,274 | 24.852273 | 78 | py |
espnet | espnet-master/test/espnet2/bin/test_diar_inference.py | from argparse import ArgumentParser
from pathlib import Path
import pytest
import torch
from espnet2.bin.diar_inference import DiarizeSpeech, get_parser, main
from espnet2.tasks.diar import DiarizationTask
from espnet2.tasks.enh_s2t import EnhS2TTask
def test_get_parser():
assert isinstance(get_parser(), Argume... | 4,311 | 23.781609 | 77 | py |
espnet | espnet-master/test/espnet2/bin/test_asr_transducer_inference.py | import string
from argparse import ArgumentParser
from distutils.version import LooseVersion
from pathlib import Path
from typing import List
import numpy as np
import pytest
import torch
from espnet2.asr_transducer.beam_search_transducer import Hypothesis
from espnet2.bin.asr_transducer_inference import Speech2Text,... | 8,279 | 27.453608 | 84 | py |
espnet | espnet-master/test/espnet2/bin/test_enh_tse_inference.py | from argparse import ArgumentParser
from pathlib import Path
import pytest
import torch
import yaml
from espnet2.bin.enh_tse_inference import SeparateSpeech, get_parser, main
from espnet2.enh.encoder.stft_encoder import STFTEncoder
from espnet2.tasks.enh_tse import TargetSpeakerExtractionTask
from espnet2.utils.get_d... | 3,691 | 31.104348 | 88 | py |
espnet | espnet-master/test/espnet2/hubert/test_hubert_loss.py | import pytest
import torch
from espnet2.asr.encoder.hubert_encoder import ( # noqa: H301
FairseqHubertPretrainEncoder,
)
from espnet2.hubert.hubert_loss import HubertPretrainLoss # noqa: H301
pytest.importorskip("fairseq")
@pytest.fixture
def hubert_args():
encoder = FairseqHubertPretrainEncoder(
... | 1,026 | 24.675 | 71 | py |
espnet | espnet-master/test/espnet2/hubert/test_hubert_espnet_model.py | import pytest
import torch
from packaging.version import parse as V
from espnet2.asr.encoder.hubert_encoder import TorchAudioHuBERTPretrainEncoder
from espnet2.hubert.espnet_model import TorchAudioHubertPretrainModel
is_torch_1_12_1_plus = V(torch.__version__) >= V("1.12.1")
@pytest.mark.parametrize("finetuning", [... | 1,366 | 27.479167 | 78 | py |
espnet | espnet-master/test/espnet2/diar/test_espnet_model.py | import pytest
import torch
from espnet2.asr.encoder.transformer_encoder import TransformerEncoder
from espnet2.asr.frontend.default import DefaultFrontend
from espnet2.diar.attractor.rnn_attractor import RnnAttractor
from espnet2.diar.decoder.linear_decoder import LinearDecoder
from espnet2.diar.espnet_model import ES... | 1,934 | 23.493671 | 80 | py |
espnet | espnet-master/test/espnet2/diar/attractor/test_rnn_attractor.py | import pytest
import torch
from espnet2.diar.attractor.rnn_attractor import RnnAttractor
@pytest.mark.parametrize("encoder_output_size", [10])
@pytest.mark.parametrize("layer", [1])
@pytest.mark.parametrize("unit", [10])
@pytest.mark.parametrize("dropout", [0.1])
def test_rnn_attractor(encoder_output_size, layer, un... | 862 | 29.821429 | 66 | py |
espnet | espnet-master/test/espnet2/diar/decoder/test_linear_decoder.py | import pytest
import torch
from espnet2.diar.decoder.linear_decoder import LinearDecoder
@pytest.mark.parametrize("encoder_output_size", [10])
@pytest.mark.parametrize("num_spk", [2])
def test_linear_decoder(encoder_output_size, num_spk):
linear_decoder = LinearDecoder(
encoder_output_size=encoder_output... | 560 | 32 | 64 | py |
espnet | espnet-master/test/espnet2/layers/test_global_mvn.py | from pathlib import Path
import numpy as np
import pytest
import torch
from espnet2.layers.global_mvn import GlobalMVN
@pytest.fixture()
def stats_file(tmp_path: Path):
"""Kaldi like style"""
p = tmp_path / "stats.npy"
count = 10
np.random.seed(0)
x = np.random.randn(count, 80)
s = x.sum(0)... | 3,455 | 27.8 | 79 | py |
espnet | espnet-master/test/espnet2/layers/test_stft.py | import torch
from espnet2.layers.stft import Stft
def test_repr():
print(Stft())
def test_forward():
layer = Stft(win_length=4, hop_length=2, n_fft=4)
x = torch.randn(2, 30)
y, _ = layer(x)
assert y.shape == (2, 16, 3, 2)
y, ylen = layer(x, torch.tensor([30, 15], dtype=torch.long))
asse... | 1,317 | 22.535714 | 66 | py |
espnet | espnet-master/test/espnet2/layers/test_utterance_mvn.py | import pytest
import torch
from espnet2.layers.utterance_mvn import UtteranceMVN
def test_repr():
print(UtteranceMVN())
@pytest.mark.parametrize(
"norm_vars, norm_means",
[(True, True), (False, False), (True, False), (False, True)],
)
def test_forward(norm_vars, norm_means):
layer = UtteranceMVN(no... | 1,268 | 27.2 | 68 | py |
espnet | espnet-master/test/espnet2/layers/test_sinc_filters.py | import torch
from espnet2.layers.sinc_conv import BarkScale, LogCompression, MelScale, SincConv
def test_log_compression():
activation = LogCompression()
x = torch.randn([5, 20, 1, 40], requires_grad=True)
y = activation(x)
assert x.shape == y.shape
def test_sinc_filters():
filters = SincConv(
... | 1,536 | 26.446429 | 82 | py |
espnet | espnet-master/test/espnet2/layers/test_log_mel.py | import torch
from espnet2.layers.log_mel import LogMel
def test_repr():
print(LogMel())
def test_forward():
layer = LogMel(n_fft=16, n_mels=2)
x = torch.randn(2, 4, 9)
y, _ = layer(x)
assert y.shape == (2, 4, 2)
y, ylen = layer(x, torch.tensor([4, 2], dtype=torch.long))
assert (ylen == ... | 708 | 21.15625 | 65 | py |
espnet | espnet-master/test/espnet2/layers/test_label_aggregation.py | import pytest
import torch
from espnet2.layers.label_aggregation import LabelAggregate
@pytest.mark.parametrize(
("input_label", "expected_output"),
[
(torch.ones(10, 20000, 2), torch.ones(10, 157, 2)),
(torch.zeros(10, 20000, 2), torch.zeros(10, 157, 2)),
],
)
def test_LabelAggregate(inp... | 700 | 29.478261 | 81 | py |
espnet | espnet-master/test/espnet2/layers/test_mask_along_axis.py | import pytest
import torch
from espnet2.layers.mask_along_axis import MaskAlongAxis
@pytest.mark.parametrize("requires_grad", [False, True])
@pytest.mark.parametrize("replace_with_zero", [False, True])
@pytest.mark.parametrize("dim", ["freq", "time"])
def test_MaskAlongAxis(dim, replace_with_zero, requires_grad):
... | 1,043 | 28.828571 | 62 | py |
espnet | espnet-master/test/espnet2/layers/test_time_warp.py | import pytest
import torch
from espnet2.layers.time_warp import TimeWarp
@pytest.mark.parametrize("x_lens", [None, torch.tensor([80, 78])])
@pytest.mark.parametrize("requires_grad", [False, True])
def test_TimeWarp(x_lens, requires_grad):
time_warp = TimeWarp(window=10)
x = torch.randn(2, 100, 80, requires_g... | 600 | 26.318182 | 66 | py |
espnet | espnet-master/test/espnet2/train/test_distributed_utils.py | import argparse
import unittest.mock
from concurrent.futures.process import ProcessPoolExecutor
from concurrent.futures.thread import ThreadPoolExecutor
import pytest
from espnet2.tasks.abs_task import AbsTask
from espnet2.train.distributed_utils import (
DistributedOption,
free_port,
resolve_distributed_... | 11,600 | 27.021739 | 78 | py |
espnet | espnet-master/test/espnet2/train/test_reporter.py | import logging
import uuid
from pathlib import Path
import numpy as np
import pytest
import torch
from torch.utils.tensorboard import SummaryWriter
from espnet2.train.reporter import Average, ReportedValue, Reporter, aggregate
@pytest.mark.parametrize("weight1,weight2", [(None, None), (19, np.array(9))])
def test_r... | 12,124 | 25.416122 | 87 | py |
espnet | espnet-master/test/espnet2/asr/test_pit_espnet_model.py | import pytest
import torch
from espnet2.asr.ctc import CTC
from espnet2.asr.decoder.transformer_decoder import TransformerDecoder
from espnet2.asr.encoder.transformer_encoder_multispkr import TransformerEncoder
from espnet2.asr.pit_espnet_model import ESPnetASRModel
@pytest.mark.parametrize("encoder_arch", [Transfor... | 1,723 | 30.345455 | 83 | py |
espnet | espnet-master/test/espnet2/asr/test_ctc.py | import pytest
import torch
from espnet2.asr.ctc import CTC
@pytest.fixture
def ctc_args():
bs = 2
h = torch.randn(bs, 10, 10)
h_lens = torch.LongTensor([10, 8])
y = torch.randint(0, 4, [2, 5])
y_lens = torch.LongTensor([5, 2])
return h, h_lens, y, y_lens
@pytest.mark.parametrize("ctc_type",... | 1,096 | 27.128205 | 64 | py |
espnet | espnet-master/test/espnet2/asr/test_discrete_asr_espnet_model.py | import pytest
import torch
from espnet2.asr.ctc import CTC
from espnet2.asr.decoder.transformer_decoder import TransformerDecoder
from espnet2.asr.discrete_asr_espnet_model import ESPnetDiscreteASRModel
from espnet2.asr.encoder.e_branchformer_encoder import EBranchformerEncoder
from espnet2.mt.frontend.embedding impor... | 1,606 | 29.903846 | 83 | py |
espnet | espnet-master/test/espnet2/asr/test_maskctc_model.py | import pytest
import torch
from espnet2.asr.ctc import CTC
from espnet2.asr.decoder.mlm_decoder import MLMDecoder
from espnet2.asr.encoder.conformer_encoder import ConformerEncoder
from espnet2.asr.encoder.transformer_encoder import TransformerEncoder
from espnet2.asr.maskctc_model import MaskCTCInference, MaskCTCMode... | 2,076 | 25.974026 | 80 | py |
espnet | espnet-master/test/espnet2/asr/test_espnet_model.py | import pytest
import torch
from espnet2.asr.ctc import CTC
from espnet2.asr.decoder.transducer_decoder import TransducerDecoder
from espnet2.asr.decoder.transformer_decoder import TransformerDecoder
from espnet2.asr.encoder.conformer_encoder import ConformerEncoder
from espnet2.asr.encoder.transformer_encoder import T... | 3,305 | 36.146067 | 84 | py |
espnet | espnet-master/test/espnet2/asr/postencoder/test_hugging_face_transformers_postencoder.py | import pytest
import torch
from packaging.version import parse as V
from espnet2.asr.postencoder.hugging_face_transformers_postencoder import (
HuggingFaceTransformersPostEncoder,
)
is_torch_1_8_plus = V(torch.__version__) >= V("1.8.0")
@pytest.mark.parametrize(
"model_name_or_path, length_adaptor_n_layers,... | 2,631 | 31.9 | 88 | py |
espnet | espnet-master/test/espnet2/asr/transducer/test_transducer_beam_search.py | import pytest
import torch
from espnet2.asr.decoder.transducer_decoder import TransducerDecoder
from espnet2.asr.transducer.beam_search_transducer import BeamSearchTransducer
from espnet2.asr_transducer.joint_network import JointNetwork
from espnet2.lm.seq_rnn_lm import SequentialRNNLM
from espnet2.lm.transformer_lm i... | 2,181 | 30.623188 | 80 | py |
espnet | espnet-master/test/espnet2/asr/transducer/test_transducer_error_calculator.py | import pytest
import torch
from espnet2.asr.decoder.transducer_decoder import TransducerDecoder
from espnet2.asr.transducer.error_calculator import ErrorCalculatorTransducer
from espnet2.asr_transducer.joint_network import JointNetwork
@pytest.mark.parametrize(
"report_opts",
[
{"report_cer": False, ... | 1,178 | 25.2 | 80 | py |
espnet | espnet-master/test/espnet2/asr/specaug/test_specaug.py | import pytest
import torch
from espnet2.asr.specaug.specaug import SpecAug
@pytest.mark.parametrize("apply_time_warp", [False, True])
@pytest.mark.parametrize("apply_freq_mask", [False, True])
@pytest.mark.parametrize("apply_time_mask", [False, True])
@pytest.mark.parametrize("time_mask_width_range", [None, 100, (0,... | 2,869 | 32.372093 | 80 | py |
espnet | espnet-master/test/espnet2/asr/frontend/test_s3prl.py | import pytest
import torch
from packaging.version import parse as V
from espnet2.asr.frontend.s3prl import S3prlFrontend
is_torch_1_8_plus = V(torch.__version__) >= V("1.8.0")
def test_frontend_init():
if not is_torch_1_8_plus:
return
frontend = S3prlFrontend(
fs=16000,
frontend_con... | 1,627 | 24.84127 | 73 | py |
espnet | espnet-master/test/espnet2/asr/frontend/test_whisper.py | import sys
import pytest
import torch
from packaging.version import parse as V
from espnet2.asr.frontend.whisper import WhisperFrontend
pytest.importorskip("whisper")
# NOTE(Shih-Lun): required by `return_complex` in torch.stft()
is_torch_1_7_plus = V(torch.__version__) >= V("1.7.0")
is_python_3_8_plus = sys.versio... | 2,194 | 27.881579 | 82 | py |
espnet | espnet-master/test/espnet2/asr/frontend/test_windowing.py | import torch
from espnet2.asr.frontend.windowing import SlidingWindow
def test_frontend_output_size():
win_length = 400
frontend = SlidingWindow(win_length=win_length, hop_length=32, fs="16k")
assert frontend.output_size() == win_length
def test_frontend_forward():
frontend = SlidingWindow(fs=160, ... | 694 | 30.590909 | 77 | py |
espnet | espnet-master/test/espnet2/asr/frontend/test_fused.py | import torch
from espnet2.asr.frontend.fused import FusedFrontends
frontend1 = {"frontend_type": "default", "n_mels": 80, "n_fft": 512}
frontend2 = {"frontend_type": "default", "hop_length": 128}
list_frontends = [frontend1, frontend2]
def test_frontend_init():
frontend = FusedFrontends(
fs="16k",
... | 1,173 | 25.681818 | 68 | py |
espnet | espnet-master/test/espnet2/asr/frontend/test_frontend.py | import pytest
import torch
from espnet2.asr.frontend.default import DefaultFrontend
from espnet2.torch_utils.set_all_random_seed import set_all_random_seed
def test_frontend_repr():
frontend = DefaultFrontend(fs="16k")
print(frontend)
def test_frontend_output_size():
frontend = DefaultFrontend(fs="16k"... | 1,349 | 27.723404 | 77 | py |
espnet | espnet-master/test/espnet2/asr/preencoder/test_sinc.py | import torch
from espnet2.asr.preencoder.sinc import LightweightSincConvs, SpatialDropout
def test_spatial_dropout():
dropout = SpatialDropout()
x = torch.randn([5, 20, 40], requires_grad=True)
y = dropout(x)
assert x.shape == y.shape
def test_lightweight_sinc_convolutions_output_size():
fronte... | 1,003 | 30.375 | 76 | py |
espnet | espnet-master/test/espnet2/asr/preencoder/test_linear.py | import torch
from espnet2.asr.preencoder.linear import LinearProjection
def test_linear_projection_forward():
idim = 400
odim = 80
preencoder = LinearProjection(input_size=idim, output_size=odim)
x = torch.randn([2, 50, idim], requires_grad=True)
x_lengths = torch.LongTensor([30, 15])
y, y_le... | 469 | 28.375 | 68 | py |
espnet | espnet-master/test/espnet2/asr/encoder/test_rnn_encoder.py | import pytest
import torch
from espnet2.asr.encoder.rnn_encoder import RNNEncoder
@pytest.mark.parametrize("rnn_type", ["lstm", "gru"])
@pytest.mark.parametrize("bidirectional", [True, False])
@pytest.mark.parametrize("use_projection", [True, False])
@pytest.mark.parametrize("subsample", [None, (2, 2, 1, 1)])
def te... | 947 | 27.727273 | 86 | py |
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