python_code stringlengths 0 229k |
|---|
"""Collect the PRs between two specified tags or commits and
output the commit titles, PR numbers, and labels in a json file.
Usage: python tools/release_notes/retrieve_prs.py tags/v0.10.0 \
18685a517ae68353b05b9a0ede5343df31525c76 --file data.json
"""
import argparse
import json
import re
import subprocess
fro... |
# In[1]:
import pandas as pd
# In[2]:
# from https://github.com/pytorch/audio/blob/main/.github/process_commit.py
primary_labels_mapping = {
"BC-breaking": "Backward-incompatible changes",
"deprecation": "Deprecations",
"bug fix": "Bug Fixes",
"new feature": "New Features",
"improvement": "Imp... |
from .extension import * # noqa
|
import os
import platform
import subprocess
from pathlib import Path
import distutils.sysconfig
from setuptools import Extension
from setuptools.command.build_ext import build_ext
import torch
__all__ = [
'get_ext_modules',
'CMakeBuild',
]
_THIS_DIR = Path(__file__).parent.resolve()
_ROOT_DIR = _THIS_DIR.par... |
import torch
from torchaudio._internal import download_url_to_file
import pytest
class GreedyCTCDecoder(torch.nn.Module):
def __init__(self, labels, blank: int = 0):
super().__init__()
self.blank = blank
self.labels = labels
def forward(self, logits: torch.Tensor) -> str:
"""G... |
from torchaudio.pipelines import (
TACOTRON2_GRIFFINLIM_CHAR_LJSPEECH,
TACOTRON2_GRIFFINLIM_PHONE_LJSPEECH,
TACOTRON2_WAVERNN_CHAR_LJSPEECH,
TACOTRON2_WAVERNN_PHONE_LJSPEECH,
)
import pytest
@pytest.mark.parametrize(
'bundle',
[
TACOTRON2_GRIFFINLIM_CHAR_LJSPEECH,
TACOTRON2_GRI... |
import torchaudio
from torchaudio.pipelines import (
WAV2VEC2_BASE,
WAV2VEC2_LARGE,
WAV2VEC2_LARGE_LV60K,
WAV2VEC2_ASR_BASE_10M,
WAV2VEC2_ASR_BASE_100H,
WAV2VEC2_ASR_BASE_960H,
WAV2VEC2_ASR_LARGE_10M,
WAV2VEC2_ASR_LARGE_100H,
WAV2VEC2_ASR_LARGE_960H,
WAV2VEC2_ASR_LARGE_LV60K_10M,... |
import torch
import torchaudio.kaldi_io as kio
from torchaudio_unittest import common_utils
class Test_KaldiIO(common_utils.TorchaudioTestCase):
data1 = [[1, 2, 3], [11, 12, 13], [21, 22, 23]]
data2 = [[31, 32, 33], [41, 42, 43], [51, 52, 53]]
def _test_helper(self, file_name, expected_data, fn, expecte... |
try:
from . import fb # noqa
except Exception:
pass
|
import torch
import torchaudio.compliance.kaldi as kaldi
from torchaudio_unittest import common_utils
def extract_window(window, wave, f, frame_length, frame_shift, snip_edges):
# just a copy of ExtractWindow from feature-window.cc in python
def first_sample_of_frame(frame, window_size, window_shift, snip_ed... |
from typing import Optional
import numpy as np
import torch
def psd_numpy(
X: np.array,
mask: Optional[np.array],
multi_mask: bool = False,
normalize: bool = True,
eps: float = 1e-15
) -> np.array:
X_conj = np.conj(X)
psd_X = np.einsum("...cft,...eft->...ftce", X, X_co... |
import io
import torch
def torch_script(obj):
"""TorchScript the given function or Module"""
buffer = io.BytesIO()
torch.jit.save(torch.jit.script(obj), buffer)
buffer.seek(0)
return torch.jit.load(buffer)
|
import json
from itertools import product
from parameterized import param, parameterized
from .data_utils import get_asset_path
def load_params(*paths):
with open(get_asset_path(*paths), 'r') as file:
return [param(json.loads(line)) for line in file]
def _name_func(func, _, params):
strs = []
... |
import unittest
import torchaudio
def set_audio_backend(backend):
"""Allow additional backend value, 'default'"""
backends = torchaudio.list_audio_backends()
if backend == 'soundfile':
be = 'soundfile'
elif backend == 'default':
if 'sox_io' in backends:
be = 'sox_io'
... |
from typing import Optional
import torch
import scipy.io.wavfile
def normalize_wav(tensor: torch.Tensor) -> torch.Tensor:
if tensor.dtype == torch.float32:
pass
elif tensor.dtype == torch.int32:
tensor = tensor.to(torch.float32)
tensor[tensor > 0] /= 2147483647.
tensor[tensor ... |
from .data_utils import (
get_asset_path,
get_whitenoise,
get_sinusoid,
get_spectrogram,
)
from .backend_utils import (
set_audio_backend,
)
from .case_utils import (
TempDirMixin,
HttpServerMixin,
TestBaseMixin,
PytorchTestCase,
TorchaudioTestCase,
skipIfNoCuda,
skipIfNo... |
import unittest
import random
import torch
import numpy as np
from torchaudio.functional import rnnt_loss
CPU_DEVICE = torch.device("cpu")
class _NumpyTransducer(torch.autograd.Function):
@staticmethod
def forward(
ctx,
log_probs,
logit_lengths,
target_lengths,
target... |
import subprocess
import torch
def convert_args(**kwargs):
args = []
for key, value in kwargs.items():
if key == 'sample_rate':
key = 'sample_frequency'
key = '--' + key.replace('_', '-')
value = str(value).lower() if value in [True, False] else str(value)
args.app... |
import os.path
from typing import Union, Optional
import torch
_TEST_DIR_PATH = os.path.realpath(
os.path.join(os.path.dirname(__file__), '..'))
def get_asset_path(*paths):
"""Return full path of a test asset"""
return os.path.join(_TEST_DIR_PATH, 'assets', *paths)
def convert_tensor_encoding(
te... |
import sys
import subprocess
import warnings
def get_encoding(dtype):
encodings = {
'float32': 'floating-point',
'int32': 'signed-integer',
'int16': 'signed-integer',
'uint8': 'unsigned-integer',
}
return encodings[dtype]
def get_bit_depth(dtype):
bit_depths = {
... |
import shutil
import os.path
import subprocess
import tempfile
import time
import unittest
import torch
from torch.testing._internal.common_utils import TestCase as PytorchTestCase
from torchaudio._internal.module_utils import (
is_module_available,
is_sox_available,
is_kaldi_available
)
from .backend_uti... |
import os
import sys
sys.path.append(
os.path.join(
os.path.dirname(__file__),
'..', '..', '..', 'examples'))
|
import os
from torchaudio_unittest.common_utils import (
TempDirMixin,
TorchaudioTestCase,
get_whitenoise,
save_wav,
normalize_wav,
)
from source_separation.utils.dataset import wsj0mix
_FILENAMES = [
"012c0207_1.9952_01cc0202_-1.9952.wav",
"01co0302_1.63_014c020q_-1.63.wav",
"01do03... |
"""Reference Implementation of SDR and PIT SDR.
This module was taken from the following implementation
https://github.com/naplab/Conv-TasNet/blob/e66d82a8f956a69749ec8a4ae382217faa097c5c/utility/sdr.py
which was made available by Yi Luo under the following liscence,
Creative Commons Attribution-NonCommercial-Share... |
from itertools import product
import torch
from torch.testing._internal.common_utils import TestCase
from parameterized import parameterized
from . import sdr_reference
from source_separation.utils import metrics
class TestSDR(TestCase):
@parameterized.expand([(1, ), (2, ), (32, )])
def test_sdr(self, batch... |
import torch
from .tacotron2_loss_impl import (
Tacotron2LossShapeTests,
Tacotron2LossTorchscriptTests,
Tacotron2LossGradcheckTests,
)
from torchaudio_unittest.common_utils import PytorchTestCase
class TestTacotron2LossShapeFloat32CPU(Tacotron2LossShapeTests, PytorchTestCase):
dtype = torch.float32
... |
import torch
from torch.autograd import gradcheck, gradgradcheck
from pipeline_tacotron2.loss import Tacotron2Loss
from torchaudio_unittest.common_utils import (
TestBaseMixin,
torch_script,
)
class Tacotron2LossInputMixin(TestBaseMixin):
def _get_inputs(self, n_mel=80, n_batch=16, max_mel_specgram_leng... |
import torch
from .tacotron2_loss_impl import (
Tacotron2LossShapeTests,
Tacotron2LossTorchscriptTests,
Tacotron2LossGradcheckTests,
)
from torchaudio_unittest.common_utils import skipIfNoCuda, PytorchTestCase
@skipIfNoCuda
class TestTacotron2LossShapeFloat32CUDA(PytorchTestCase, Tacotron2LossShapeTests)... |
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... |
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