python_code stringlengths 0 229k |
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from parameterized import parameterized
from torchaudio._internal.module_utils import is_module_available
from torchaudio_unittest.common_utils import TorchaudioTestCase, skipIfNoModule
if is_module_available("unidecode") and is_module_available("inflect"):
from pipeline_tacotron2.text.text_preprocessing import t... |
from torchaudio import sox_effects
from parameterized import parameterized
from torchaudio_unittest.common_utils import (
TempDirMixin,
TorchaudioTestCase,
skipIfNoSox,
get_wav_data,
get_sinusoid,
save_wav,
)
from .common import (
load_params,
)
@skipIfNoSox
class SmokeTest(TempDirMixin, ... |
import io
import itertools
from pathlib import Path
import tarfile
from parameterized import parameterized
from torchaudio import sox_effects
from torchaudio._internal import module_utils as _mod_utils
from torchaudio_unittest.common_utils import (
TempDirMixin,
HttpServerMixin,
PytorchTestCase,
skipI... |
import sys
import platform
from unittest import skipIf
from typing import List, Tuple
from concurrent.futures import ProcessPoolExecutor
import numpy as np
import torch
import torchaudio
from torchaudio_unittest.common_utils import (
TempDirMixin,
PytorchTestCase,
skipIfNoSox,
get_whitenoise,
save... |
from typing import List
import torch
from torchaudio import sox_effects
from parameterized import parameterized
from torchaudio_unittest.common_utils import (
TempDirMixin,
TorchaudioTestCase,
skipIfNoSox,
get_sinusoid,
save_wav,
torch_script,
)
from .common import (
load_params,
)
class... |
import json
from parameterized import param
from torchaudio_unittest.common_utils import get_asset_path
def name_func(func, _, params):
if isinstance(params.args[0], str):
args = "_".join([str(arg) for arg in params.args])
else:
args = "_".join([str(arg) for arg in params.args[0]])
retur... |
import os
from pathlib import Path
from torchaudio.datasets import vctk
from torchaudio_unittest.common_utils import (
TempDirMixin,
TorchaudioTestCase,
get_whitenoise,
save_wav,
normalize_wav,
)
# Used to generate a unique transcript for each dummy audio file
_TRANSCRIPT = [
'Please call Ste... |
import os
from pathlib import Path
from torchaudio_unittest.common_utils import (
TempDirMixin,
TorchaudioTestCase,
get_whitenoise,
normalize_wav,
save_wav,
)
from torchaudio.datasets import speechcommands
_LABELS = [
"bed",
"bird",
"cat",
"dog",
"down",
"eight",
"five... |
import os
from pathlib import Path
from torchaudio.datasets import cmuarctic
from torchaudio_unittest.common_utils import (
TempDirMixin,
TorchaudioTestCase,
get_whitenoise,
save_wav,
normalize_wav,
)
def get_mock_dataset(root_dir):
"""
root_dir: directory to the mocked dataset
"""
... |
import csv
import os
from pathlib import Path
from torchaudio_unittest.common_utils import (
TempDirMixin,
TorchaudioTestCase,
get_whitenoise,
normalize_wav,
save_wav,
)
from torchaudio.datasets import ljspeech
_TRANSCRIPTS = [
"Test transcript 1",
"Test transcript 2",
"Test transcrip... |
import os
from pathlib import Path
from torchaudio_unittest.common_utils import (
TempDirMixin,
TorchaudioTestCase,
get_whitenoise,
save_wav,
normalize_wav,
)
from torchaudio.datasets.libritts import LIBRITTS
_UTTERANCE_IDS = [
[19, 198, '000000', '000000'],
[26, 495, '000004', '000000'],... |
import csv
import os
from pathlib import Path
from typing import Tuple, Dict
from torch import Tensor
from torchaudio_unittest.common_utils import (
TempDirMixin,
TorchaudioTestCase,
get_whitenoise,
save_wav,
normalize_wav,
)
from torchaudio.datasets import COMMONVOICE
_ORIGINAL_EXT_AUDIO = COMMO... |
import os
from pathlib import Path
from torchaudio_unittest.common_utils import (
TempDirMixin,
TorchaudioTestCase,
get_whitenoise,
save_wav,
normalize_wav,
)
from torchaudio.datasets import librispeech
# Used to generate a unique transcript for each dummy audio file
_NUMBERS = [
'ZERO',
... |
import os
from pathlib import Path
from torchaudio.datasets import yesno
from torchaudio_unittest.common_utils import (
TempDirMixin,
TorchaudioTestCase,
get_whitenoise,
save_wav,
normalize_wav,
)
def get_mock_data(root_dir, labels):
"""
root_dir: path
labels: list of labels
"""
... |
from pathlib import Path
import pytest
from torchaudio.datasets import dr_vctk
from torchaudio_unittest.common_utils import (
TempDirMixin,
TorchaudioTestCase,
get_whitenoise,
save_wav,
)
_SUBSETS = ["train", "test"]
_CONDITIONS = ["clean", "device-recorded"]
_SOURCES = ["DR-VCTK_Office1_ClosedWind... |
import os
import platform
from pathlib import Path
from torchaudio_unittest.common_utils import (
TempDirMixin,
TorchaudioTestCase,
get_whitenoise,
save_wav,
skipIfNoSox
)
from torchaudio.datasets import tedlium
# Used to generate a unique utterance for each dummy audio file
_UTTERANCES = [
"... |
import os
from pathlib import Path
from torchaudio.datasets import CMUDict
from torchaudio_unittest.common_utils import (
TempDirMixin,
TorchaudioTestCase,
)
def get_mock_dataset(root_dir, return_punc=False):
"""
root_dir: directory to the mocked dataset
"""
header = [
";;; # CMUdict... |
import os
from pathlib import Path
from torchaudio.datasets import gtzan
from torchaudio_unittest.common_utils import (
TempDirMixin,
TorchaudioTestCase,
get_whitenoise,
save_wav,
normalize_wav,
)
def get_mock_dataset(root_dir):
"""
root_dir: directory to the mocked dataset
"""
m... |
from torchaudio.utils import sox_utils
from torchaudio_unittest.common_utils import (
PytorchTestCase,
skipIfNoSox,
)
@skipIfNoSox
class TestSoxUtils(PytorchTestCase):
"""Smoke tests for sox_util module"""
def test_set_seed(self):
"""`set_seed` does not crush"""
sox_utils.set_seed(0)
... |
import torchaudio
from torchaudio_unittest import common_utils
class BackendSwitchMixin:
"""Test set/get_audio_backend works"""
backend = None
backend_module = None
def test_switch(self):
torchaudio.set_audio_backend(self.backend)
if self.backend is None:
assert torchaudi... |
from torchaudio_unittest.common_utils import sox_utils
def get_encoding(ext, dtype):
exts = {
'mp3',
'flac',
'vorbis',
}
encodings = {
'float32': 'PCM_F',
'int32': 'PCM_S',
'int16': 'PCM_S',
'uint8': 'PCM_U',
}
return ext.upper() if ext in ex... |
from unittest.mock import patch
import warnings
import tarfile
import torch
from torchaudio.backend import soundfile_backend
from torchaudio._internal import module_utils as _mod_utils
from torchaudio_unittest.common_utils import (
TempDirMixin,
PytorchTestCase,
skipIfNoModule,
get_wav_data,
save_... |
import os
import tarfile
from unittest.mock import patch
import torch
from torchaudio._internal import module_utils as _mod_utils
from torchaudio.backend import soundfile_backend
from parameterized import parameterized
from torchaudio_unittest.common_utils import (
TempDirMixin,
PytorchTestCase,
skipIfNoM... |
import io
from unittest.mock import patch
from torchaudio._internal import module_utils as _mod_utils
from torchaudio.backend import soundfile_backend
from torchaudio_unittest.common_utils import (
TempDirMixin,
PytorchTestCase,
skipIfNoModule,
get_wav_data,
load_wav,
nested_params,
)
from .co... |
import itertools
from unittest import skipIf
from parameterized import parameterized
from torchaudio._internal.module_utils import is_module_available
def name_func(func, _, params):
return f'{func.__name__}_{"_".join(str(arg) for arg in params.args)}'
def dtype2subtype(dtype):
return {
"float64": ... |
import io
import itertools
import unittest
from torchaudio.utils import sox_utils
from torchaudio.backend import sox_io_backend
from torchaudio._internal.module_utils import is_sox_available
from parameterized import parameterized
from torchaudio_unittest.common_utils import (
TempDirMixin,
TorchaudioTestCase... |
from contextlib import contextmanager
import io
import os
import itertools
import tarfile
from parameterized import parameterized
from torchaudio.backend import sox_io_backend
from torchaudio.utils.sox_utils import get_buffer_size, set_buffer_size
from torchaudio._internal import module_utils as _mod_utils
from torch... |
import io
import itertools
import tarfile
from parameterized import parameterized
from torchaudio.backend import sox_io_backend
from torchaudio._internal import module_utils as _mod_utils
from torchaudio_unittest.common_utils import (
TempDirMixin,
HttpServerMixin,
PytorchTestCase,
skipIfNoExec,
s... |
import itertools
from torchaudio.backend import sox_io_backend
from parameterized import parameterized
from torchaudio_unittest.common_utils import (
TempDirMixin,
PytorchTestCase,
skipIfNoExec,
skipIfNoSox,
get_wav_data,
)
from .common import (
name_func,
get_enc_params,
)
@skipIfNoExec... |
import io
import os
import unittest
import torch
from torchaudio.backend import sox_io_backend
from parameterized import parameterized
from torchaudio_unittest.common_utils import (
TempDirMixin,
TorchaudioTestCase,
PytorchTestCase,
skipIfNoExec,
skipIfNoSox,
get_wav_data,
load_wav,
sa... |
import itertools
from typing import Optional
import torch
import torchaudio
from parameterized import parameterized
from torchaudio_unittest.common_utils import (
TempDirMixin,
TorchaudioTestCase,
skipIfNoExec,
skipIfNoSox,
get_wav_data,
save_wav,
load_wav,
sox_utils,
torch_script,... |
def name_func(func, _, params):
return f'{func.__name__}_{"_".join(str(arg) for arg in params.args)}'
def get_enc_params(dtype):
if dtype == 'float32':
return 'PCM_F', 32
if dtype == 'int32':
return 'PCM_S', 32
if dtype == 'int16':
return 'PCM_S', 16
if dtype == 'uint8':
... |
import itertools
from collections import namedtuple
import torch
from parameterized import parameterized
from torchaudio.models import ConvTasNet, DeepSpeech, Wav2Letter, WaveRNN
from torchaudio.models.wavernn import MelResNet, UpsampleNetwork
from torchaudio_unittest import common_utils
from torchaudio_unittest.commo... |
import os
import torch
import torch.nn.functional as F
from typing import Tuple
from torchaudio.models.wav2vec2 import (
wav2vec2_base,
wav2vec2_large,
wav2vec2_large_lv60k,
hubert_base,
hubert_large,
hubert_xlarge,
)
from torchaudio_unittest.common_utils import (
TorchaudioTestCase,
s... |
import json
import torch
from torchaudio.models.wav2vec2 import (
wav2vec2_base,
wav2vec2_large,
wav2vec2_large_lv60k,
)
from torchaudio.models.wav2vec2.utils import import_huggingface_model
from parameterized import parameterized
from torchaudio_unittest.common_utils import (
get_asset_path,
skip... |
import json
import torch
from torchaudio.models.wav2vec2 import (
wav2vec2_base,
wav2vec2_large,
wav2vec2_large_lv60k,
hubert_base,
hubert_large,
hubert_xlarge,
)
from torchaudio.models.wav2vec2.utils import (
import_fairseq_model,
)
from parameterized import parameterized
from torchaudio_... |
import torch
from torchaudio_unittest.common_utils import PytorchTestCase
from .model_test_impl import (
Tacotron2EncoderTests,
Tacotron2DecoderTests,
Tacotron2Tests,
)
class TestTacotron2EncoderFloat32CPU(Tacotron2EncoderTests, PytorchTestCase):
dtype = torch.float32
device = torch.device("cpu")... |
from typing import Tuple
import torch
from torch import Tensor
from torchaudio.models import Tacotron2
from torchaudio.models.tacotron2 import _Encoder, _Decoder
from torchaudio_unittest.common_utils import TestBaseMixin, torch_script
class Tacotron2InferenceWrapper(torch.nn.Module):
def __init__(self, model):
... |
import torch
from torchaudio_unittest.common_utils import skipIfNoCuda, PytorchTestCase
from .model_test_impl import (
Tacotron2EncoderTests,
Tacotron2DecoderTests,
Tacotron2Tests,
)
@skipIfNoCuda
class TestTacotron2EncoderFloat32CUDA(Tacotron2EncoderTests, PytorchTestCase):
dtype = torch.float32
... |
import torch
from torchaudio_unittest.common_utils import TestBaseMixin, torch_script
from torchaudio.prototype import Emformer
class EmformerTestImpl(TestBaseMixin):
def _gen_model(self, input_dim, right_context_length):
emformer = Emformer(
input_dim,
8,
256,
... |
import torch
from torchaudio_unittest.prototype.emformer_test_impl import EmformerTestImpl
from torchaudio_unittest.common_utils import skipIfNoCuda, PytorchTestCase
@skipIfNoCuda
class EmformerFloat32GPUTest(EmformerTestImpl, PytorchTestCase):
dtype = torch.float32
device = torch.device("cuda")
@skipIfNoCu... |
import torch
from torchaudio_unittest.prototype.emformer_test_impl import EmformerTestImpl
from torchaudio_unittest.common_utils import PytorchTestCase
class EmformerFloat32CPUTest(EmformerTestImpl, PytorchTestCase):
dtype = torch.float32
device = torch.device("cpu")
class EmformerFloat64CPUTest(EmformerTes... |
import torch
import torchaudio.transforms as T
from parameterized import parameterized, param
from torchaudio_unittest.common_utils import (
TestBaseMixin,
get_whitenoise,
get_spectrogram,
nested_params,
)
from torchaudio_unittest.common_utils.psd_utils import psd_numpy
def _get_ratio(mat):
return... |
from typing import List
import unittest
from parameterized import parameterized
import torch
from torch.autograd import gradcheck, gradgradcheck
import torchaudio.transforms as T
from torchaudio_unittest.common_utils import (
TestBaseMixin,
get_whitenoise,
get_spectrogram,
nested_params,
rnnt_util... |
import warnings
import torch
import torchaudio.transforms as T
from parameterized import parameterized
from torchaudio_unittest.common_utils import (
skipIfNoSox,
skipIfNoExec,
TempDirMixin,
TorchaudioTestCase,
get_asset_path,
sox_utils,
load_wav,
save_wav,
get_whitenoise,
)
@ski... |
import math
import torch
import torchaudio
import torchaudio.transforms as transforms
import torchaudio.functional as F
from torchaudio_unittest import common_utils
class Tester(common_utils.TorchaudioTestCase):
backend = 'default'
# create a sinewave signal for testing
sample_rate = 16000
freq = 4... |
import torch
from torchaudio_unittest.common_utils import (
PytorchTestCase,
skipIfNoCuda,
)
from . transforms_test_impl import TransformsTestBase
@skipIfNoCuda
class TransformsCUDAFloat32Test(TransformsTestBase, PytorchTestCase):
device = 'cuda'
dtype = torch.float32
@skipIfNoCuda
class Transforms... |
import torch
from torchaudio_unittest.common_utils import PytorchTestCase
from . transforms_test_impl import TransformsTestBase
class TransformsCPUFloat32Test(TransformsTestBase, PytorchTestCase):
device = 'cpu'
dtype = torch.float32
class TransformsCPUFloat64Test(TransformsTestBase, PytorchTestCase):
... |
from torchaudio_unittest.common_utils import (
PytorchTestCase,
skipIfNoCuda,
)
from .autograd_test_impl import AutogradTestMixin, AutogradTestFloat32
@skipIfNoCuda
class AutogradCUDATest(AutogradTestMixin, PytorchTestCase):
device = 'cuda'
@skipIfNoCuda
class AutogradRNNTCUDATest(AutogradTestFloat32, P... |
"""Test suites for jit-ability and its numerical compatibility"""
import torch
import torchaudio.transforms as T
from parameterized import parameterized
from torchaudio_unittest import common_utils
from torchaudio_unittest.common_utils import (
skipIfRocm,
TestBaseMixin,
torch_script,
)
class Transforms... |
from torchaudio_unittest.common_utils import PytorchTestCase
from .autograd_test_impl import AutogradTestMixin, AutogradTestFloat32
class AutogradCPUTest(AutogradTestMixin, PytorchTestCase):
device = 'cpu'
class AutogradRNNTCPUTest(AutogradTestFloat32, PytorchTestCase):
device = 'cpu'
|
"""Test numerical consistency among single input and batched input."""
import torch
from parameterized import parameterized
from torchaudio import transforms as T
from torchaudio_unittest import common_utils
class TestTransforms(common_utils.TorchaudioTestCase):
"""Test suite for classes defined in `transforms` ... |
import unittest
import torch
import torchaudio.transforms as T
from torchaudio._internal.module_utils import is_module_available
from parameterized import param, parameterized
from torchaudio_unittest.common_utils import (
TestBaseMixin,
get_whitenoise,
get_sinusoid,
get_spectrogram,
nested_params... |
import torch
from torchaudio_unittest.common_utils import skipIfNoCuda, PytorchTestCase
from .torchscript_consistency_impl import Transforms, TransformsFloat32Only, TransformsFloat64Only
@skipIfNoCuda
class TestTransformsFloat32(Transforms, TransformsFloat32Only, PytorchTestCase):
dtype = torch.float32
devic... |
import torch
from torchaudio_unittest.common_utils import PytorchTestCase
from .torchscript_consistency_impl import Transforms, TransformsFloat32Only, TransformsFloat64Only
class TestTransformsFloat32(Transforms, TransformsFloat32Only, PytorchTestCase):
dtype = torch.float32
device = torch.device('cpu')
cl... |
import torch
from torchaudio_unittest import common_utils
from .kaldi_compatibility_impl import Kaldi
@common_utils.skipIfNoCuda
class TestKaldiFloat32(Kaldi, common_utils.PytorchTestCase):
dtype = torch.float32
device = torch.device('cuda')
@common_utils.skipIfNoCuda
class TestKaldiFloat64(Kaldi, common_u... |
"""Test suites for checking numerical compatibility against Kaldi"""
import torchaudio.compliance.kaldi
from parameterized import parameterized
from torchaudio_unittest.common_utils import (
TestBaseMixin,
TempDirMixin,
load_params,
skipIfNoExec,
get_asset_path,
load_wav,
)
from torchaudio_unit... |
import torch
from torchaudio_unittest import common_utils
from .kaldi_compatibility_impl import Kaldi
class TestKaldiFloat32(Kaldi, common_utils.PytorchTestCase):
dtype = torch.float32
device = torch.device('cpu')
class TestKaldiFloat64(Kaldi, common_utils.PytorchTestCase):
dtype = torch.float64
de... |
import torch
from torchaudio_unittest.common_utils import PytorchTestCase, skipIfNoCuda
from .librosa_compatibility_test_impl import TransformsTestBase
@skipIfNoCuda
class TestTransforms(TransformsTestBase, PytorchTestCase):
dtype = torch.float64
device = torch.device('cuda')
|
import torch
from torchaudio_unittest.common_utils import PytorchTestCase
from .librosa_compatibility_test_impl import TransformsTestBase
class TestTransforms(TransformsTestBase, PytorchTestCase):
dtype = torch.float64
device = torch.device('cpu')
|
"""Generate opus file for testing load functions"""
import argparse
import subprocess
import scipy.io.wavfile
import torch
def _parse_args():
parser = argparse.ArgumentParser(
description='Generate opus files for test'
)
parser.add_argument('--num-channels', required=True, type=int)
parser.a... |
#!/usr/bin/env python3
"""Generate the conf JSON from fairseq pretrained weight file, that is consumed by unit tests
Usage:
1. Download pretrained parameters from https://github.com/pytorch/fairseq/tree/main/examples/wav2vec
2. Download the dict from https://dl.fbaipublicfiles.com/fairseq/wav2vec/dict.ltr.txt
and p... |
#!/usr/bin/env python3
"""Generate the conf JSONs from fairseq pretrained weight file, consumed by unit tests
Note:
The current configuration files were generated on fairseq e47a4c84
Usage:
1. Download pretrained parameters from https://github.com/pytorch/fairseq/tree/main/examples/hubert
2. Run this script and s... |
import os
import json
from transformers import Wav2Vec2Model
_THIS_DIR = os.path.dirname(os.path.abspath(__file__))
def _main():
keys = [
# pretrained
"facebook/wav2vec2-base",
"facebook/wav2vec2-large",
"facebook/wav2vec2-large-lv60",
"facebook/wav2vec2-base-10k-voxpopul... |
from typing import Callable, Tuple
from functools import partial
import torch
from parameterized import parameterized
from torch import Tensor
import torchaudio.functional as F
from torch.autograd import gradcheck, gradgradcheck
from torchaudio_unittest.common_utils import (
TestBaseMixin,
get_whitenoise,
r... |
import torch
import unittest
from torchaudio_unittest.common_utils import PytorchTestCase, skipIfNoCuda
from .functional_impl import Functional
@skipIfNoCuda
class TestFunctionalFloat32(Functional, PytorchTestCase):
dtype = torch.float32
device = torch.device('cuda')
@unittest.expectedFailure
def te... |
import torch
import torchaudio.functional as F
from torchaudio_unittest.common_utils import (
skipIfNoSox,
skipIfNoExec,
TempDirMixin,
TorchaudioTestCase,
get_asset_path,
sox_utils,
load_wav,
save_wav,
get_whitenoise,
)
@skipIfNoSox
@skipIfNoExec('sox')
class TestFunctionalFilteri... |
import torch
import torchaudio.functional as F
import unittest
from parameterized import parameterized
from torchaudio_unittest.common_utils import PytorchTestCase, TorchaudioTestCase, skipIfNoSox
from .functional_impl import Functional, FunctionalCPUOnly
class TestFunctionalFloat32(Functional, FunctionalCPUOnly, Py... |
import torch
from .autograd_impl import Autograd, AutogradFloat32
from torchaudio_unittest import common_utils
@common_utils.skipIfNoCuda
class TestAutogradLfilterCUDA(Autograd, common_utils.PytorchTestCase):
dtype = torch.float64
device = torch.device('cuda')
@common_utils.skipIfNoCuda
class TestAutogradRN... |
"""Test suites for jit-ability and its numerical compatibility"""
import unittest
import torch
import torchaudio.functional as F
from torchaudio_unittest import common_utils
from torchaudio_unittest.common_utils import (
TempDirMixin,
TestBaseMixin,
skipIfRocm,
torch_script,
)
class Functional(TempD... |
from parameterized import parameterized
import torch
import torchaudio.functional as F
from torchaudio_unittest.common_utils import (
get_sinusoid,
load_params,
save_wav,
skipIfNoExec,
TempDirMixin,
TestBaseMixin,
)
from torchaudio_unittest.common_utils.kaldi_utils import (
convert_args,
... |
import torch
from .autograd_impl import Autograd, AutogradFloat32
from torchaudio_unittest import common_utils
class TestAutogradLfilterCPU(Autograd, common_utils.PytorchTestCase):
dtype = torch.float64
device = torch.device('cpu')
class TestAutogradRNNTCPU(AutogradFloat32, common_utils.PytorchTestCase):
... |
"""Test numerical consistency among single input and batched input."""
import itertools
import math
from parameterized import parameterized, parameterized_class
import torch
import torchaudio.functional as F
from torchaudio_unittest import common_utils
def _name_from_args(func, _, params):
"""Return a parameter... |
import unittest
from distutils.version import StrictVersion
import torch
from parameterized import param
import torchaudio.functional as F
from torchaudio._internal.module_utils import is_module_available
LIBROSA_AVAILABLE = is_module_available('librosa')
if LIBROSA_AVAILABLE:
import numpy as np
import libr... |
import torch
from torchaudio_unittest.common_utils import skipIfNoCuda, PytorchTestCase
from .torchscript_consistency_impl import Functional, FunctionalFloat32Only
@skipIfNoCuda
class TestFunctionalFloat32(Functional, FunctionalFloat32Only, PytorchTestCase):
dtype = torch.float32
device = torch.device('cuda'... |
import torch
from torchaudio_unittest.common_utils import PytorchTestCase
from .torchscript_consistency_impl import Functional, FunctionalFloat32Only
class TestFunctionalFloat32(Functional, FunctionalFloat32Only, PytorchTestCase):
dtype = torch.float32
device = torch.device('cpu')
class TestFunctionalFloat... |
import torch
from torchaudio_unittest.common_utils import PytorchTestCase, skipIfNoCuda
from .kaldi_compatibility_test_impl import Kaldi
@skipIfNoCuda
class TestKaldiFloat32(Kaldi, PytorchTestCase):
dtype = torch.float32
device = torch.device('cuda')
@skipIfNoCuda
class TestKaldiFloat64(Kaldi, PytorchTestC... |
import torch
from torchaudio_unittest.common_utils import PytorchTestCase
from .kaldi_compatibility_test_impl import Kaldi, KaldiCPUOnly
class TestKaldiCPUOnly(KaldiCPUOnly, PytorchTestCase):
dtype = torch.float32
device = torch.device('cpu')
class TestKaldiFloat32(Kaldi, PytorchTestCase):
dtype = torc... |
from torchaudio_unittest.common_utils import PytorchTestCase, skipIfNoCuda
from .librosa_compatibility_test_impl import Functional, FunctionalComplex
@skipIfNoCuda
class TestFunctionalCUDA(Functional, PytorchTestCase):
device = 'cuda'
@skipIfNoCuda
class TestFunctionalComplexCUDA(FunctionalComplex, PytorchTestC... |
from torchaudio_unittest.common_utils import PytorchTestCase
from .librosa_compatibility_test_impl import Functional, FunctionalComplex
class TestFunctionalCPU(Functional, PytorchTestCase):
device = 'cpu'
class TestFunctionalComplexCPU(FunctionalComplex, PytorchTestCase):
device = 'cpu'
|
"""Test definition common to CPU and CUDA"""
import math
import itertools
import warnings
import numpy as np
import torch
import torchaudio.functional as F
from parameterized import parameterized
from scipy import signal
from torchaudio_unittest.common_utils import (
TestBaseMixin,
get_sinusoid,
nested_pa... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# PyTorch documentation build configuration file, created by
# sphinx-quickstart on Fri Dec 23 13:31:47 2016.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# au... |
#!/usr/bin/env python3
"""
Create a data preprocess pipeline that can be run with libtorchaudio
"""
import os
import argparse
import torch
import torchaudio
class Pipeline(torch.nn.Module):
"""Example audio process pipeline.
This example load waveform from a file then apply effects and save it to a file.
... |
#!/usr/bin/env python
"""Parse a directory contains VoxForge dataset.
Recursively search for "PROMPTS" file in the given directory and print out
`<ID>\\t<AUDIO_PATH>\\t<TRANSCRIPTION>`
example: python parse_voxforge.py voxforge/de/Helge-20150608-aku
de5-001\t/datasets/voxforge/de/guenter-20140214-afn/wav/de5-00... |
import torch
class Decoder(torch.nn.Module):
def __init__(self, labels):
super().__init__()
self.labels = labels
def forward(self, logits: torch.Tensor) -> str:
"""Given a sequence logits over labels, get the best path string
Args:
logits (Tensor): Logit tensors. ... |
#!/usr/bin/evn python3
"""Build Speech Recognition pipeline based on fairseq's wav2vec2.0 and dump it to TorchScript file.
To use this script, you need `fairseq`.
"""
import os
import argparse
import logging
from typing import Tuple
import torch
from torch.utils.mobile_optimizer import optimize_for_mobile
import torc... |
#!/usr/bin/env python3
"""Parse a directory contains Librispeech dataset.
Recursively search for "*.trans.txt" file in the given directory and print out
`<ID>\\t<AUDIO_PATH>\\t<TRANSCRIPTION>`
example: python parse_librispeech.py LibriSpeech/test-clean
1089-134691-0000\t/LibriSpeech/test-clean/1089/134691/1089-... |
#!/usr/bin/env python3
import argparse
import logging
import os
from typing import Tuple
import torch
import torchaudio
from torchaudio.models.wav2vec2.utils.import_huggingface import import_huggingface_model
from greedy_decoder import Decoder
TORCH_VERSION: Tuple[int, ...] = tuple(int(x) for x in torch.__version__.s... |
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