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Commit 5859923a authored by Joao Gomes's avatar Joao Gomes Committed by Facebook GitHub Bot
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Apply arc lint to pytorch audio (#2096) 

Summary:
Pull Request resolved: https://github.com/pytorch/audio/pull/2096

run: `arc lint --apply-patches --paths-cmd 'hg files -I "./**/*.py"'`

Reviewed By: mthrok

Differential Revision: D33297351

fbshipit-source-id: 7bf5956edf0717c5ca90219f72414ff4eeaf5aa8
parent 0e5913d5
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Showing with 522 additions and 487 deletions
test/torchaudio_unittest/datasets/speechcommands_test.py
View file @ 5859923a
import os
from pathlib import Path
from torchaudio.datasets import speechcommands
from torchaudio_unittest.common_utils import (
TempDirMixin,
TorchaudioTestCase,
......@@ -9,8 +10,6 @@ from torchaudio_unittest.common_utils import (
save_wav,
)
from torchaudio.datasets import speechcommands
_LABELS = [
"bed",
"bird",
......@@ -93,10 +92,10 @@ def get_mock_dataset(dataset_dir):
if j < 2:
mocked_train_samples.append(sample)
elif j < 4:
valid.write(f'{label}/{filename}\n')
valid.write(f"{label}/{filename}\n")
mocked_valid_samples.append(sample)
elif j < 6:
test.write(f'{label}/{filename}\n')
test.write(f"{label}/{filename}\n")
mocked_test_samples.append(sample)
return mocked_samples, mocked_train_samples, mocked_valid_samples, mocked_test_samples
......@@ -113,16 +112,12 @@ class TestSpeechCommands(TempDirMixin, TorchaudioTestCase):
@classmethod
def setUpClass(cls):
cls.root_dir = cls.get_base_temp_dir()
dataset_dir = os.path.join(
cls.root_dir, speechcommands.FOLDER_IN_ARCHIVE, speechcommands.URL
)
dataset_dir = os.path.join(cls.root_dir, speechcommands.FOLDER_IN_ARCHIVE, speechcommands.URL)
cls.samples, cls.train_samples, cls.valid_samples, cls.test_samples = get_mock_dataset(dataset_dir)
def _testSpeechCommands(self, dataset, data_samples):
num_samples = 0
for i, (data, sample_rate, label, speaker_id, utterance_number) in enumerate(
dataset
):
for i, (data, sample_rate, label, speaker_id, utterance_number) in enumerate(dataset):
self.assertEqual(data, data_samples[i][0], atol=5e-5, rtol=1e-8)
assert sample_rate == data_samples[i][1]
assert label == data_samples[i][2]
......
test/torchaudio_unittest/datasets/tedlium_test.py
View file @ 5859923a
......@@ -2,15 +2,8 @@ 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
from torchaudio_unittest.common_utils import TempDirMixin, TorchaudioTestCase, get_whitenoise, save_wav, skipIfNoSox
# Used to generate a unique utterance for each dummy audio file
_UTTERANCES = [
......@@ -145,6 +138,7 @@ class TestTedliumSoundfile(Tedlium, TorchaudioTestCase):
if platform.system() != "Windows":
@skipIfNoSox
class TestTedliumSoxIO(Tedlium, TorchaudioTestCase):
backend = "sox_io"
test/torchaudio_unittest/datasets/vctk_test.py
View file @ 5859923a
......@@ -2,7 +2,6 @@ import os
from pathlib import Path
from torchaudio.datasets import vctk
from torchaudio_unittest.common_utils import (
TempDirMixin,
TorchaudioTestCase,
......@@ -13,17 +12,17 @@ from torchaudio_unittest.common_utils import (
# Used to generate a unique transcript for each dummy audio file
_TRANSCRIPT = [
'Please call Stella',
'Ask her to bring these things',
'with her from the store',
'Six spoons of fresh snow peas, five thick slabs of blue cheese, and maybe a snack for her brother Bob',
'We also need a small plastic snake and a big toy frog for the kids',
'She can scoop these things into three red bags, and we will go meet her Wednesday at the train station',
'When the sunlight strikes raindrops in the air, they act as a prism and form a rainbow',
'The rainbow is a division of white light into many beautiful colors',
'These take the shape of a long round arch, with its path high above, and its two ends \
apparently beyond the horizon',
'There is, according to legend, a boiling pot of gold at one end'
"Please call Stella",
"Ask her to bring these things",
"with her from the store",
"Six spoons of fresh snow peas, five thick slabs of blue cheese, and maybe a snack for her brother Bob",
"We also need a small plastic snake and a big toy frog for the kids",
"She can scoop these things into three red bags, and we will go meet her Wednesday at the train station",
"When the sunlight strikes raindrops in the air, they act as a prism and form a rainbow",
"The rainbow is a division of white light into many beautiful colors",
"These take the shape of a long round arch, with its path high above, and its two ends \
apparently beyond the horizon",
"There is, according to legend, a boiling pot of gold at one end",
]
......@@ -32,51 +31,39 @@ def get_mock_dataset(root_dir):
root_dir: root directory of the mocked data
"""
mocked_samples = []
dataset_dir = os.path.join(root_dir, 'VCTK-Corpus-0.92')
dataset_dir = os.path.join(root_dir, "VCTK-Corpus-0.92")
os.makedirs(dataset_dir, exist_ok=True)
sample_rate = 48000
seed = 0
for speaker in range(225, 230):
speaker_id = 'p' + str(speaker)
audio_dir = os.path.join(dataset_dir, 'wav48_silence_trimmed', speaker_id)
speaker_id = "p" + str(speaker)
audio_dir = os.path.join(dataset_dir, "wav48_silence_trimmed", speaker_id)
os.makedirs(audio_dir, exist_ok=True)
file_dir = os.path.join(dataset_dir, 'txt', speaker_id)
file_dir = os.path.join(dataset_dir, "txt", speaker_id)
os.makedirs(file_dir, exist_ok=True)
for utterance_id in range(1, 11):
filename = f'{speaker_id}_{utterance_id:03d}_mic2'
audio_file_path = os.path.join(audio_dir, filename + '.wav')
data = get_whitenoise(
sample_rate=sample_rate,
duration=0.01,
n_channels=1,
dtype='float32',
seed=seed
)
filename = f"{speaker_id}_{utterance_id:03d}_mic2"
audio_file_path = os.path.join(audio_dir, filename + ".wav")
data = get_whitenoise(sample_rate=sample_rate, duration=0.01, n_channels=1, dtype="float32", seed=seed)
save_wav(audio_file_path, data, sample_rate)
txt_file_path = os.path.join(file_dir, filename[:-5] + '.txt')
txt_file_path = os.path.join(file_dir, filename[:-5] + ".txt")
transcript = _TRANSCRIPT[utterance_id - 1]
with open(txt_file_path, 'w') as f:
with open(txt_file_path, "w") as f:
f.write(transcript)
sample = (
normalize_wav(data),
sample_rate,
transcript,
speaker_id,
utterance_id
)
sample = (normalize_wav(data), sample_rate, transcript, speaker_id, utterance_id)
mocked_samples.append(sample)
seed += 1
return mocked_samples
class TestVCTK(TempDirMixin, TorchaudioTestCase):
backend = 'default'
backend = "default"
root_dir = None
samples = []
......
test/torchaudio_unittest/datasets/yesno_test.py
View file @ 5859923a
......@@ -2,7 +2,6 @@ import os
from pathlib import Path
from torchaudio.datasets import yesno
from torchaudio_unittest.common_utils import (
TempDirMixin,
TorchaudioTestCase,
......@@ -18,19 +17,19 @@ def get_mock_data(root_dir, labels):
labels: list of labels
"""
mocked_data = []
base_dir = os.path.join(root_dir, 'waves_yesno')
base_dir = os.path.join(root_dir, "waves_yesno")
os.makedirs(base_dir, exist_ok=True)
for i, label in enumerate(labels):
filename = f'{"_".join(str(l) for l in label)}.wav'
path = os.path.join(base_dir, filename)
data = get_whitenoise(sample_rate=8000, duration=6, n_channels=1, dtype='int16', seed=i)
data = get_whitenoise(sample_rate=8000, duration=6, n_channels=1, dtype="int16", seed=i)
save_wav(path, data, 8000)
mocked_data.append(normalize_wav(data))
return mocked_data
class TestYesNo(TempDirMixin, TorchaudioTestCase):
backend = 'default'
backend = "default"
root_dir = None
data = []
......
test/torchaudio_unittest/example/__init__.py
View file @ 5859923a
......@@ -2,7 +2,4 @@ import os
import sys
sys.path.append(
os.path.join(
os.path.dirname(__file__),
'..', '..', '..', 'examples'))
sys.path.append(os.path.join(os.path.dirname(__file__), "..", "..", "..", "examples"))
test/torchaudio_unittest/example/souce_sepration/metrics_test.py
View file @ 5859923a
from itertools import product
import torch
from torch.testing._internal.common_utils import TestCase
from parameterized import parameterized
from source_separation.utils import metrics
from torch.testing._internal.common_utils import TestCase
from . import sdr_reference
from source_separation.utils import metrics
class TestSDR(TestCase):
@parameterized.expand([(1, ), (2, ), (32, )])
@parameterized.expand([(1,), (2,), (32,)])
def test_sdr(self, batch_size):
"""sdr produces the same result as the reference implementation"""
num_frames = 256
......
test/torchaudio_unittest/example/souce_sepration/sdr_reference.py
View file @ 5859923a
"""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-ShareAlike 3.0 United States License.
The module was modified in the following manner;
- Remove the functions other than `calc_sdr_torch` and `batch_SDR_torch`,
- Remove the import statements required only for the removed functions.
- Add `# flake8: noqa` so as not to report any format issue on this module.
The implementation of the retained functions and their formats are kept as-is.
"""
# flake8: noqa
import numpy as np
from itertools import permutations
import torch
def calc_sdr_torch(estimation, origin, mask=None):
"""
batch-wise SDR caculation for one audio file on pytorch Variables.
estimation: (batch, nsample)
origin: (batch, nsample)
mask: optional, (batch, nsample), binary
"""
if mask is not None:
origin = origin * mask
estimation = estimation * mask
origin_power = torch.pow(origin, 2).sum(1, keepdim=True) + 1e-8 # (batch, 1)
scale = torch.sum(origin*estimation, 1, keepdim=True) / origin_power # (batch, 1)
est_true = scale * origin # (batch, nsample)
est_res = estimation - est_true # (batch, nsample)
true_power = torch.pow(est_true, 2).sum(1)
res_power = torch.pow(est_res, 2).sum(1)
return 10*torch.log10(true_power) - 10*torch.log10(res_power) # (batch, 1)
def batch_SDR_torch(estimation, origin, mask=None):
"""
batch-wise SDR caculation for multiple audio files.
estimation: (batch, nsource, nsample)
origin: (batch, nsource, nsample)
mask: optional, (batch, nsample), binary
"""
batch_size_est, nsource_est, nsample_est = estimation.size()
batch_size_ori, nsource_ori, nsample_ori = origin.size()
assert batch_size_est == batch_size_ori, "Estimation and original sources should have same shape."
assert nsource_est == nsource_ori, "Estimation and original sources should have same shape."
assert nsample_est == nsample_ori, "Estimation and original sources should have same shape."
assert nsource_est < nsample_est, "Axis 1 should be the number of sources, and axis 2 should be the signal."
batch_size = batch_size_est
nsource = nsource_est
nsample = nsample_est
# zero mean signals
estimation = estimation - torch.mean(estimation, 2, keepdim=True).expand_as(estimation)
origin = origin - torch.mean(origin, 2, keepdim=True).expand_as(estimation)
# possible permutations
perm = list(set(permutations(np.arange(nsource))))
# pair-wise SDR
SDR = torch.zeros((batch_size, nsource, nsource)).type(estimation.type())
for i in range(nsource):
for j in range(nsource):
SDR[:,i,j] = calc_sdr_torch(estimation[:,i], origin[:,j], mask)
# choose the best permutation
SDR_max = []
SDR_perm = []
for permute in perm:
sdr = []
for idx in range(len(permute)):
sdr.append(SDR[:,idx,permute[idx]].view(batch_size,-1))
sdr = torch.sum(torch.cat(sdr, 1), 1)
SDR_perm.append(sdr.view(batch_size, 1))
SDR_perm = torch.cat(SDR_perm, 1)
SDR_max, _ = torch.max(SDR_perm, dim=1)
return SDR_max / nsource
"""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-ShareAlike 3.0 United States License.
The module was modified in the following manner;
- Remove the functions other than `calc_sdr_torch` and `batch_SDR_torch`,
- Remove the import statements required only for the removed functions.
- Add `# flake8: noqa` so as not to report any format issue on this module.
The implementation of the retained functions and their formats are kept as-is.
"""
from itertools import permutations
# flake8: noqa
import numpy as np
import torch
def calc_sdr_torch(estimation, origin, mask=None):
"""
batch-wise SDR caculation for one audio file on pytorch Variables.
estimation: (batch, nsample)
origin: (batch, nsample)
mask: optional, (batch, nsample), binary
"""
if mask is not None:
origin = origin * mask
estimation = estimation * mask
origin_power = torch.pow(origin, 2).sum(1, keepdim=True) + 1e-8 # (batch, 1)
scale = torch.sum(origin * estimation, 1, keepdim=True) / origin_power # (batch, 1)
est_true = scale * origin # (batch, nsample)
est_res = estimation - est_true # (batch, nsample)
true_power = torch.pow(est_true, 2).sum(1)
res_power = torch.pow(est_res, 2).sum(1)
return 10 * torch.log10(true_power) - 10 * torch.log10(res_power) # (batch, 1)
def batch_SDR_torch(estimation, origin, mask=None):
"""
batch-wise SDR caculation for multiple audio files.
estimation: (batch, nsource, nsample)
origin: (batch, nsource, nsample)
mask: optional, (batch, nsample), binary
"""
batch_size_est, nsource_est, nsample_est = estimation.size()
batch_size_ori, nsource_ori, nsample_ori = origin.size()
assert batch_size_est == batch_size_ori, "Estimation and original sources should have same shape."
assert nsource_est == nsource_ori, "Estimation and original sources should have same shape."
assert nsample_est == nsample_ori, "Estimation and original sources should have same shape."
assert nsource_est < nsample_est, "Axis 1 should be the number of sources, and axis 2 should be the signal."
batch_size = batch_size_est
nsource = nsource_est
nsample = nsample_est
# zero mean signals
estimation = estimation - torch.mean(estimation, 2, keepdim=True).expand_as(estimation)
origin = origin - torch.mean(origin, 2, keepdim=True).expand_as(estimation)
# possible permutations
perm = list(set(permutations(np.arange(nsource))))
# pair-wise SDR
SDR = torch.zeros((batch_size, nsource, nsource)).type(estimation.type())
for i in range(nsource):
for j in range(nsource):
SDR[:, i, j] = calc_sdr_torch(estimation[:, i], origin[:, j], mask)
# choose the best permutation
SDR_max = []
SDR_perm = []
for permute in perm:
sdr = []
for idx in range(len(permute)):
sdr.append(SDR[:, idx, permute[idx]].view(batch_size, -1))
sdr = torch.sum(torch.cat(sdr, 1), 1)
SDR_perm.append(sdr.view(batch_size, 1))
SDR_perm = torch.cat(SDR_perm, 1)
SDR_max, _ = torch.max(SDR_perm, dim=1)
return SDR_max / nsource
test/torchaudio_unittest/example/souce_sepration/wsj0mix_test.py
View file @ 5859923a
import os
from source_separation.utils.dataset import wsj0mix
from torchaudio_unittest.common_utils import (
TempDirMixin,
TorchaudioTestCase,
......@@ -8,8 +9,6 @@ from torchaudio_unittest.common_utils import (
normalize_wav,
)
from source_separation.utils.dataset import wsj0mix
_FILENAMES = [
"012c0207_1.9952_01cc0202_-1.9952.wav",
......@@ -45,9 +44,7 @@ def _mock_dataset(root_dir, num_speaker):
mix = None
src = []
for dirname in dirnames:
waveform = get_whitenoise(
sample_rate=8000, duration=1, n_channels=1, dtype="int16", seed=seed
)
waveform = get_whitenoise(sample_rate=8000, duration=1, n_channels=1, dtype="int16", seed=seed)
seed += 1
path = os.path.join(root_dir, dirname, filename)
......
test/torchaudio_unittest/example/tacotron2/tacotron2_loss_cpu_test.py
View file @ 5859923a
import torch
from torchaudio_unittest.common_utils import PytorchTestCase
from .tacotron2_loss_impl import (
Tacotron2LossShapeTests,
Tacotron2LossTorchscriptTests,
Tacotron2LossGradcheckTests,
)
from torchaudio_unittest.common_utils import PytorchTestCase
class TestTacotron2LossShapeFloat32CPU(Tacotron2LossShapeTests, PytorchTestCase):
......@@ -19,5 +19,5 @@ class TestTacotron2TorchsciptFloat32CPU(Tacotron2LossTorchscriptTests, PytorchTe
class TestTacotron2GradcheckFloat64CPU(Tacotron2LossGradcheckTests, PytorchTestCase):
dtype = torch.float64 # gradcheck needs a higher numerical accuracy
dtype = torch.float64 # gradcheck needs a higher numerical accuracy
device = torch.device("cpu")
test/torchaudio_unittest/example/tacotron2/tacotron2_loss_gpu_test.py
View file @ 5859923a
import torch
from torchaudio_unittest.common_utils import skipIfNoCuda, PytorchTestCase
from .tacotron2_loss_impl import (
Tacotron2LossShapeTests,
Tacotron2LossTorchscriptTests,
Tacotron2LossGradcheckTests,
)
from torchaudio_unittest.common_utils import skipIfNoCuda, PytorchTestCase
@skipIfNoCuda
......@@ -22,5 +22,5 @@ class TestTacotron2TorchsciptFloat32CUDA(PytorchTestCase, Tacotron2LossTorchscri
@skipIfNoCuda
class TestTacotron2GradcheckFloat64CUDA(PytorchTestCase, Tacotron2LossGradcheckTests):
dtype = torch.float64 # gradcheck needs a higher numerical accuracy
dtype = torch.float64 # gradcheck needs a higher numerical accuracy
device = torch.device("cuda")
test/torchaudio_unittest/example/tacotron2/tacotron2_loss_impl.py
View file @ 5859923a
import torch
from torch.autograd import gradcheck, gradgradcheck
from pipeline_tacotron2.loss import Tacotron2Loss
from torch.autograd import gradcheck, gradgradcheck
from torchaudio_unittest.common_utils import (
TestBaseMixin,
torch_script,
......@@ -9,18 +8,11 @@ from torchaudio_unittest.common_utils import (
class Tacotron2LossInputMixin(TestBaseMixin):
def _get_inputs(self, n_mel=80, n_batch=16, max_mel_specgram_length=300):
mel_specgram = torch.rand(
n_batch, n_mel, max_mel_specgram_length, dtype=self.dtype, device=self.device
)
mel_specgram_postnet = torch.rand(
n_batch, n_mel, max_mel_specgram_length, dtype=self.dtype, device=self.device
)
mel_specgram = torch.rand(n_batch, n_mel, max_mel_specgram_length, dtype=self.dtype, device=self.device)
mel_specgram_postnet = torch.rand(n_batch, n_mel, max_mel_specgram_length, dtype=self.dtype, device=self.device)
gate_out = torch.rand(n_batch, dtype=self.dtype, device=self.device)
truth_mel_specgram = torch.rand(
n_batch, n_mel, max_mel_specgram_length, dtype=self.dtype, device=self.device
)
truth_mel_specgram = torch.rand(n_batch, n_mel, max_mel_specgram_length, dtype=self.dtype, device=self.device)
truth_gate_out = torch.rand(n_batch, dtype=self.dtype, device=self.device)
truth_mel_specgram.requires_grad = False
......@@ -36,7 +28,6 @@ class Tacotron2LossInputMixin(TestBaseMixin):
class Tacotron2LossShapeTests(Tacotron2LossInputMixin):
def test_tacotron2_loss_shape(self):
"""Validate the output shape of Tacotron2Loss."""
n_batch = 16
......@@ -50,8 +41,7 @@ class Tacotron2LossShapeTests(Tacotron2LossInputMixin):
) = self._get_inputs(n_batch=n_batch)
mel_loss, mel_postnet_loss, gate_loss = Tacotron2Loss()(
(mel_specgram, mel_specgram_postnet, gate_out),
(truth_mel_specgram, truth_gate_out)
(mel_specgram, mel_specgram_postnet, gate_out), (truth_mel_specgram, truth_gate_out)
)
self.assertEqual(mel_loss.size(), torch.Size([]))
......@@ -60,7 +50,6 @@ class Tacotron2LossShapeTests(Tacotron2LossInputMixin):
class Tacotron2LossTorchscriptTests(Tacotron2LossInputMixin):
def _assert_torchscript_consistency(self, fn, tensors):
ts_func = torch_script(fn)
......@@ -77,7 +66,6 @@ class Tacotron2LossTorchscriptTests(Tacotron2LossInputMixin):
class Tacotron2LossGradcheckTests(Tacotron2LossInputMixin):
def test_tacotron2_loss_gradcheck(self):
"""Performing gradient check on Tacotron2Loss."""
(
......
test/torchaudio_unittest/example/tacotron2/test_text_preprocessing.py
View file @ 5859923a
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 text_to_sequence
from pipeline_tacotron2.text.numbers import (
_remove_commas,
_expand_pounds,
......@@ -13,25 +11,62 @@ if is_module_available("unidecode") and is_module_available("inflect"):
_expand_ordinal,
_expand_number,
)
from pipeline_tacotron2.text.text_preprocessing import text_to_sequence
@skipIfNoModule("unidecode")
@skipIfNoModule("inflect")
class TestTextPreprocessor(TorchaudioTestCase):
@parameterized.expand(
[
["dr. Strange?", [15, 26, 14, 31, 26, 29, 11, 30, 31, 29, 12, 25, 18, 16, 10]],
["ML, is fun.", [24, 23, 6, 11, 20, 30, 11, 17, 32, 25, 7]],
["I love torchaudio!", [20, 11, 23, 26, 33, 16, 11, 31, 26, 29, 14, 19, 12, 32, 15, 20, 26, 2]],
# 'one thousand dollars, twenty cents'
["$1,000.20", [26, 25, 16, 11, 31, 19, 26, 32, 30, 12, 25, 15, 11, 15, 26, 23, 23,
12, 29, 30, 6, 11, 31, 34, 16, 25, 31, 36, 11, 14, 16, 25, 31, 30]],
[
"$1,000.20",
[
26,
25,
16,
11,
31,
19,
26,
32,
30,
12,
25,
15,
11,
15,
26,
23,
23,
12,
29,
30,
6,
11,
31,
34,
16,
25,
31,
36,
11,
14,
16,
25,
31,
30,
],
],
]
)
def test_text_to_sequence(self, sent, seq):
assert (text_to_sequence(sent) == seq)
assert text_to_sequence(sent) == seq
@parameterized.expand(
[
......@@ -40,7 +75,7 @@ class TestTextPreprocessor(TorchaudioTestCase):
)
def test_remove_commas(self, sent, truth):
assert (_remove_commas(sent) == truth)
assert _remove_commas(sent) == truth
@parameterized.expand(
[
......@@ -49,19 +84,21 @@ class TestTextPreprocessor(TorchaudioTestCase):
)
def test_expand_pounds(self, sent, truth):
assert (_expand_pounds(sent) == truth)
assert _expand_pounds(sent) == truth
@parameterized.expand(
[
["He, she, and I have $1000", "He, she, and I have 1000 dollars"],
["He, she, and I have $3000.01", "He, she, and I have 3000 dollars, 1 cent"],
["He has $500.20 and she has $1000.50.",
"He has 500 dollars, 20 cents and she has 1000 dollars, 50 cents."],
[
"He has $500.20 and she has $1000.50.",
"He has 500 dollars, 20 cents and she has 1000 dollars, 50 cents.",
],
]
)
def test_expand_dollars(self, sent, truth):
assert (_expand_dollars(sent) == truth)
assert _expand_dollars(sent) == truth
@parameterized.expand(
[
......@@ -71,7 +108,7 @@ class TestTextPreprocessor(TorchaudioTestCase):
)
def test_expand_decimal_point(self, sent, truth):
assert (_expand_decimal_point(sent) == truth)
assert _expand_decimal_point(sent) == truth
@parameterized.expand(
[
......@@ -82,16 +119,19 @@ class TestTextPreprocessor(TorchaudioTestCase):
)
def test_expand_ordinal(self, sent, truth):
assert (_expand_ordinal(sent) == truth)
assert _expand_ordinal(sent) == truth
_expand_ordinal,
@parameterized.expand(
[
["100020 dollars.", "one hundred thousand twenty dollars."],
["1234567890!", "one billion, two hundred thirty-four million, "
"five hundred sixty-seven thousand, eight hundred ninety!"],
[
"1234567890!",
"one billion, two hundred thirty-four million, "
"five hundred sixty-seven thousand, eight hundred ninety!",
],
]
)
def test_expand_number(self, sent, truth):
assert (_expand_number(sent) == truth)
assert _expand_number(sent) == truth
test/torchaudio_unittest/functional/autograd_cpu_test.py
View file @ 5859923a
import torch
from .autograd_impl import Autograd, AutogradFloat32
from torchaudio_unittest import common_utils
from .autograd_impl import Autograd, AutogradFloat32
class TestAutogradLfilterCPU(Autograd, common_utils.PytorchTestCase):
dtype = torch.float64
device = torch.device('cpu')
device = torch.device("cpu")
class TestAutogradRNNTCPU(AutogradFloat32, common_utils.PytorchTestCase):
dtype = torch.float32
device = torch.device('cpu')
device = torch.device("cpu")
test/torchaudio_unittest/functional/autograd_cuda_test.py
View file @ 5859923a
import torch
from .autograd_impl import Autograd, AutogradFloat32
from torchaudio_unittest import common_utils
from .autograd_impl import Autograd, AutogradFloat32
@common_utils.skipIfNoCuda
class TestAutogradLfilterCUDA(Autograd, common_utils.PytorchTestCase):
dtype = torch.float64
device = torch.device('cuda')
device = torch.device("cuda")
@common_utils.skipIfNoCuda
class TestAutogradRNNTCUDA(AutogradFloat32, common_utils.PytorchTestCase):
dtype = torch.float32
device = torch.device('cuda')
device = torch.device("cuda")
test/torchaudio_unittest/functional/autograd_impl.py
View file @ 5859923a
from typing import Callable, Tuple
from functools import partial
from typing import Callable, Tuple
import torch
import torchaudio.functional as F
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,
......@@ -14,11 +15,11 @@ from torchaudio_unittest.common_utils import (
class Autograd(TestBaseMixin):
def assert_grad(
self,
transform: Callable[..., Tensor],
inputs: Tuple[torch.Tensor],
*,
enable_all_grad: bool = True,
self,
transform: Callable[..., Tensor],
inputs: Tuple[torch.Tensor],
*,
enable_all_grad: bool = True,
):
inputs_ = []
for i in inputs:
......@@ -64,19 +65,15 @@ class Autograd(TestBaseMixin):
def test_lfilter_filterbanks(self):
torch.random.manual_seed(2434)
x = get_whitenoise(sample_rate=22050, duration=0.01, n_channels=3)
a = torch.tensor([[0.7, 0.2, 0.6],
[0.8, 0.2, 0.9]])
b = torch.tensor([[0.4, 0.2, 0.9],
[0.7, 0.2, 0.6]])
a = torch.tensor([[0.7, 0.2, 0.6], [0.8, 0.2, 0.9]])
b = torch.tensor([[0.4, 0.2, 0.9], [0.7, 0.2, 0.6]])
self.assert_grad(partial(F.lfilter, batching=False), (x, a, b))
def test_lfilter_batching(self):
torch.random.manual_seed(2434)
x = get_whitenoise(sample_rate=22050, duration=0.01, n_channels=2)
a = torch.tensor([[0.7, 0.2, 0.6],
[0.8, 0.2, 0.9]])
b = torch.tensor([[0.4, 0.2, 0.9],
[0.7, 0.2, 0.6]])
a = torch.tensor([[0.7, 0.2, 0.6], [0.8, 0.2, 0.9]])
b = torch.tensor([[0.4, 0.2, 0.9], [0.7, 0.2, 0.6]])
self.assert_grad(F.lfilter, (x, a, b))
def test_filtfilt_a(self):
......@@ -105,10 +102,8 @@ class Autograd(TestBaseMixin):
def test_filtfilt_batching(self):
torch.random.manual_seed(2434)
x = get_whitenoise(sample_rate=22050, duration=0.01, n_channels=2)
a = torch.tensor([[0.7, 0.2, 0.6],
[0.8, 0.2, 0.9]])
b = torch.tensor([[0.4, 0.2, 0.9],
[0.7, 0.2, 0.6]])
a = torch.tensor([[0.7, 0.2, 0.6], [0.8, 0.2, 0.9]])
b = torch.tensor([[0.4, 0.2, 0.9], [0.7, 0.2, 0.6]])
self.assert_grad(F.filtfilt, (x, a, b))
def test_biquad(self):
......@@ -118,10 +113,12 @@ class Autograd(TestBaseMixin):
b = torch.tensor([0.4, 0.2, 0.9])
self.assert_grad(F.biquad, (x, b[0], b[1], b[2], a[0], a[1], a[2]))
@parameterized.expand([
(800, 0.7, True),
(800, 0.7, False),
])
@parameterized.expand(
[
(800, 0.7, True),
(800, 0.7, False),
]
)
def test_band_biquad(self, central_freq, Q, noise):
torch.random.manual_seed(2434)
sr = 22050
......@@ -130,10 +127,12 @@ class Autograd(TestBaseMixin):
Q = torch.tensor(Q)
self.assert_grad(F.band_biquad, (x, sr, central_freq, Q, noise))
@parameterized.expand([
(800, 0.7, 10),
(800, 0.7, -10),
])
@parameterized.expand(
[
(800, 0.7, 10),
(800, 0.7, -10),
]
)
def test_bass_biquad(self, central_freq, Q, gain):
torch.random.manual_seed(2434)
sr = 22050
......@@ -143,11 +142,12 @@ class Autograd(TestBaseMixin):
gain = torch.tensor(gain)
self.assert_grad(F.bass_biquad, (x, sr, gain, central_freq, Q))
@parameterized.expand([
(3000, 0.7, 10),
(3000, 0.7, -10),
])
@parameterized.expand(
[
(3000, 0.7, 10),
(3000, 0.7, -10),
]
)
def test_treble_biquad(self, central_freq, Q, gain):
torch.random.manual_seed(2434)
sr = 22050
......@@ -157,9 +157,14 @@ class Autograd(TestBaseMixin):
gain = torch.tensor(gain)
self.assert_grad(F.treble_biquad, (x, sr, gain, central_freq, Q))
@parameterized.expand([
(800, 0.7, ),
])
@parameterized.expand(
[
(
800,
0.7,
),
]
)
def test_allpass_biquad(self, central_freq, Q):
torch.random.manual_seed(2434)
sr = 22050
......@@ -168,9 +173,14 @@ class Autograd(TestBaseMixin):
Q = torch.tensor(Q)
self.assert_grad(F.allpass_biquad, (x, sr, central_freq, Q))
@parameterized.expand([
(800, 0.7, ),
])
@parameterized.expand(
[
(
800,
0.7,
),
]
)
def test_lowpass_biquad(self, cutoff_freq, Q):
torch.random.manual_seed(2434)
sr = 22050
......@@ -179,9 +189,14 @@ class Autograd(TestBaseMixin):
Q = torch.tensor(Q)
self.assert_grad(F.lowpass_biquad, (x, sr, cutoff_freq, Q))
@parameterized.expand([
(800, 0.7, ),
])
@parameterized.expand(
[
(
800,
0.7,
),
]
)
def test_highpass_biquad(self, cutoff_freq, Q):
torch.random.manual_seed(2434)
sr = 22050
......@@ -190,10 +205,12 @@ class Autograd(TestBaseMixin):
Q = torch.tensor(Q)
self.assert_grad(F.highpass_biquad, (x, sr, cutoff_freq, Q))
@parameterized.expand([
(800, 0.7, True),
(800, 0.7, False),
])
@parameterized.expand(
[
(800, 0.7, True),
(800, 0.7, False),
]
)
def test_bandpass_biquad(self, central_freq, Q, const_skirt_gain):
torch.random.manual_seed(2434)
sr = 22050
......@@ -202,10 +219,12 @@ class Autograd(TestBaseMixin):
Q = torch.tensor(Q)
self.assert_grad(F.bandpass_biquad, (x, sr, central_freq, Q, const_skirt_gain))
@parameterized.expand([
(800, 0.7, 10),
(800, 0.7, -10),
])
@parameterized.expand(
[
(800, 0.7, 10),
(800, 0.7, -10),
]
)
def test_equalizer_biquad(self, central_freq, Q, gain):
torch.random.manual_seed(2434)
sr = 22050
......@@ -215,9 +234,14 @@ class Autograd(TestBaseMixin):
gain = torch.tensor(gain)
self.assert_grad(F.equalizer_biquad, (x, sr, central_freq, gain, Q))
@parameterized.expand([
(800, 0.7, ),
])
@parameterized.expand(
[
(
800,
0.7,
),
]
)
def test_bandreject_biquad(self, central_freq, Q):
torch.random.manual_seed(2434)
sr = 22050
......@@ -229,10 +253,10 @@ class Autograd(TestBaseMixin):
class AutogradFloat32(TestBaseMixin):
def assert_grad(
self,
transform: Callable[..., Tensor],
inputs: Tuple[torch.Tensor],
enable_all_grad: bool = True,
self,
transform: Callable[..., Tensor],
inputs: Tuple[torch.Tensor],
enable_all_grad: bool = True,
):
inputs_ = []
for i in inputs:
......@@ -242,13 +266,15 @@ class AutogradFloat32(TestBaseMixin):
i.requires_grad = True
inputs_.append(i)
# gradcheck with float32 requires higher atol and epsilon
assert gradcheck(transform, inputs, eps=1e-3, atol=1e-3, nondet_tol=0.)
assert gradcheck(transform, inputs, eps=1e-3, atol=1e-3, nondet_tol=0.0)
@parameterized.expand([
(rnnt_utils.get_B1_T10_U3_D4_data, ),
(rnnt_utils.get_B2_T4_U3_D3_data, ),
(rnnt_utils.get_B1_T2_U3_D5_data, ),
])
@parameterized.expand(
[
(rnnt_utils.get_B1_T10_U3_D4_data,),
(rnnt_utils.get_B2_T4_U3_D3_data,),
(rnnt_utils.get_B1_T2_U3_D5_data,),
]
)
def test_rnnt_loss(self, data_func):
def get_data(data_func, device):
data = data_func()
......@@ -259,11 +285,11 @@ class AutogradFloat32(TestBaseMixin):
data = get_data(data_func, self.device)
inputs = (
data["logits"].to(torch.float32), # logits
data["targets"], # targets
data["logit_lengths"], # logit_lengths
data["target_lengths"], # target_lengths
data["blank"], # blank
-1, # clamp
data["targets"], # targets
data["logit_lengths"], # logit_lengths
data["target_lengths"], # target_lengths
data["blank"], # blank
-1, # clamp
)
self.assert_grad(F.rnnt_loss, inputs, enable_all_grad=False)
test/torchaudio_unittest/functional/batch_consistency_test.py
View file @ 5859923a
......@@ -2,41 +2,37 @@
import itertools
import math
from parameterized import parameterized, parameterized_class
import torch
import torchaudio.functional as F
from parameterized import parameterized, parameterized_class
from torchaudio_unittest import common_utils
def _name_from_args(func, _, params):
"""Return a parameterized test name, based on parameter values."""
return "{}_{}".format(
func.__name__,
"_".join(str(arg) for arg in params.args))
return "{}_{}".format(func.__name__, "_".join(str(arg) for arg in params.args))
@parameterized_class([
# Single-item batch isolates problems that come purely from adding a
# dimension (rather than processing multiple items)
{"batch_size": 1},
{"batch_size": 3},
])
@parameterized_class(
[
# Single-item batch isolates problems that come purely from adding a
# dimension (rather than processing multiple items)
{"batch_size": 1},
{"batch_size": 3},
]
)
class TestFunctional(common_utils.TorchaudioTestCase):
"""Test functions defined in `functional` module"""
backend = 'default'
def assert_batch_consistency(
self, functional, batch, *args, atol=1e-8, rtol=1e-5, seed=42,
**kwargs):
backend = "default"
def assert_batch_consistency(self, functional, batch, *args, atol=1e-8, rtol=1e-5, seed=42, **kwargs):
n = batch.size(0)
# Compute items separately, then batch the result
torch.random.manual_seed(seed)
items_input = batch.clone()
items_result = torch.stack([
functional(items_input[i], *args, **kwargs) for i in range(n)
])
items_result = torch.stack([functional(items_input[i], *args, **kwargs) for i in range(n)])
# Batch the input and run
torch.random.manual_seed(seed)
......@@ -58,43 +54,45 @@ class TestFunctional(common_utils.TorchaudioTestCase):
torch.random.manual_seed(0)
batch = torch.rand(self.batch_size, 1, 201, 6)
self.assert_batch_consistency(
F.griffinlim, batch, window, n_fft, hop, ws, power,
n_iter, momentum, length, 0, atol=5e-5)
@parameterized.expand(list(itertools.product(
[8000, 16000, 44100],
[1, 2],
)), name_func=_name_from_args)
F.griffinlim, batch, window, n_fft, hop, ws, power, n_iter, momentum, length, 0, atol=5e-5
)
@parameterized.expand(
list(
itertools.product(
[8000, 16000, 44100],
[1, 2],
)
),
name_func=_name_from_args,
)
def test_detect_pitch_frequency(self, sample_rate, n_channels):
# Use different frequencies to ensure each item in the batch returns a
# different answer.
torch.manual_seed(0)
frequencies = torch.randint(100, 1000, [self.batch_size])
waveforms = torch.stack([
common_utils.get_sinusoid(
frequency=frequency, sample_rate=sample_rate,
n_channels=n_channels, duration=5)
for frequency in frequencies
])
self.assert_batch_consistency(
F.detect_pitch_frequency, waveforms, sample_rate)
waveforms = torch.stack(
[
common_utils.get_sinusoid(
frequency=frequency, sample_rate=sample_rate, n_channels=n_channels, duration=5
)
for frequency in frequencies
]
)
self.assert_batch_consistency(F.detect_pitch_frequency, waveforms, sample_rate)
def test_amplitude_to_DB(self):
torch.manual_seed(0)
spec = torch.rand(self.batch_size, 2, 100, 100) * 200
amplitude_mult = 20.
amplitude_mult = 20.0
amin = 1e-10
ref = 1.0
db_mult = math.log10(max(amin, ref))
# Test with & without a `top_db` clamp
self.assert_batch_consistency(
F.amplitude_to_DB, spec, amplitude_mult,
amin, db_mult, top_db=None)
self.assert_batch_consistency(
F.amplitude_to_DB, spec, amplitude_mult,
amin, db_mult, top_db=40.)
self.assert_batch_consistency(F.amplitude_to_DB, spec, amplitude_mult, amin, db_mult, top_db=None)
self.assert_batch_consistency(F.amplitude_to_DB, spec, amplitude_mult, amin, db_mult, top_db=40.0)
def test_amplitude_to_DB_itemwise_clamps(self):
"""Ensure that the clamps are separate for each spectrogram in a batch.
......@@ -106,11 +104,11 @@ class TestFunctional(common_utils.TorchaudioTestCase):
https://github.com/pytorch/audio/issues/994
"""
amplitude_mult = 20.
amplitude_mult = 20.0
amin = 1e-10
ref = 1.0
db_mult = math.log10(max(amin, ref))
top_db = 20.
top_db = 20.0
# Make a batch of noise
torch.manual_seed(0)
......@@ -118,36 +116,30 @@ class TestFunctional(common_utils.TorchaudioTestCase):
# Make one item blow out the other
spec[0] += 50
batchwise_dbs = F.amplitude_to_DB(spec, amplitude_mult, amin,
db_mult, top_db=top_db)
itemwise_dbs = torch.stack([
F.amplitude_to_DB(item, amplitude_mult, amin,
db_mult, top_db=top_db)
for item in spec
])
batchwise_dbs = F.amplitude_to_DB(spec, amplitude_mult, amin, db_mult, top_db=top_db)
itemwise_dbs = torch.stack(
[F.amplitude_to_DB(item, amplitude_mult, amin, db_mult, top_db=top_db) for item in spec]
)
self.assertEqual(batchwise_dbs, itemwise_dbs)
def test_amplitude_to_DB_not_channelwise_clamps(self):
"""Check that clamps are applied per-item, not per channel."""
amplitude_mult = 20.
amplitude_mult = 20.0
amin = 1e-10
ref = 1.0
db_mult = math.log10(max(amin, ref))
top_db = 40.
top_db = 40.0
torch.manual_seed(0)
spec = torch.rand([1, 2, 100, 100]) * 200
# Make one channel blow out the other
spec[:, 0] += 50
specwise_dbs = F.amplitude_to_DB(spec, amplitude_mult, amin,
db_mult, top_db=top_db)
channelwise_dbs = torch.stack([
F.amplitude_to_DB(spec[:, i], amplitude_mult, amin,
db_mult, top_db=top_db)
for i in range(spec.size(-3))
])
specwise_dbs = F.amplitude_to_DB(spec, amplitude_mult, amin, db_mult, top_db=top_db)
channelwise_dbs = torch.stack(
[F.amplitude_to_DB(spec[:, i], amplitude_mult, amin, db_mult, top_db=top_db) for i in range(spec.size(-3))]
)
# Just check channelwise gives a different answer.
difference = (specwise_dbs - channelwise_dbs).abs()
......@@ -156,27 +148,24 @@ class TestFunctional(common_utils.TorchaudioTestCase):
def test_contrast(self):
torch.random.manual_seed(0)
waveforms = torch.rand(self.batch_size, 2, 100) - 0.5
self.assert_batch_consistency(
F.contrast, waveforms, enhancement_amount=80.)
self.assert_batch_consistency(F.contrast, waveforms, enhancement_amount=80.0)
def test_dcshift(self):
torch.random.manual_seed(0)
waveforms = torch.rand(self.batch_size, 2, 100) - 0.5
self.assert_batch_consistency(
F.dcshift, waveforms, shift=0.5, limiter_gain=0.05)
self.assert_batch_consistency(F.dcshift, waveforms, shift=0.5, limiter_gain=0.05)
def test_overdrive(self):
torch.random.manual_seed(0)
waveforms = torch.rand(self.batch_size, 2, 100) - 0.5
self.assert_batch_consistency(
F.overdrive, waveforms, gain=45, colour=30)
self.assert_batch_consistency(F.overdrive, waveforms, gain=45, colour=30)
def test_phaser(self):
sample_rate = 44100
n_channels = 2
waveform = common_utils.get_whitenoise(
sample_rate=sample_rate, n_channels=self.batch_size * n_channels,
duration=1)
sample_rate=sample_rate, n_channels=self.batch_size * n_channels, duration=1
)
batch = waveform.view(self.batch_size, n_channels, waveform.size(-1))
self.assert_batch_consistency(F.phaser, batch, sample_rate)
......@@ -186,37 +175,48 @@ class TestFunctional(common_utils.TorchaudioTestCase):
sample_rate = 44100
self.assert_batch_consistency(F.flanger, waveforms, sample_rate)
@parameterized.expand(list(itertools.product(
[True, False], # center
[True, False], # norm_vars
)), name_func=_name_from_args)
@parameterized.expand(
list(
itertools.product(
[True, False], # center
[True, False], # norm_vars
)
),
name_func=_name_from_args,
)
def test_sliding_window_cmn(self, center, norm_vars):
torch.manual_seed(0)
spectrogram = torch.rand(self.batch_size, 2, 1024, 1024) * 200
self.assert_batch_consistency(
F.sliding_window_cmn, spectrogram, center=center,
norm_vars=norm_vars)
self.assert_batch_consistency(F.sliding_window_cmn, spectrogram, center=center, norm_vars=norm_vars)
@parameterized.expand([("sinc_interpolation"), ("kaiser_window")])
def test_resample_waveform(self, resampling_method):
num_channels = 3
sr = 16000
new_sr = sr // 2
multi_sound = common_utils.get_whitenoise(sample_rate=sr, n_channels=num_channels, duration=0.5,)
multi_sound = common_utils.get_whitenoise(
sample_rate=sr,
n_channels=num_channels,
duration=0.5,
)
self.assert_batch_consistency(
F.resample, multi_sound, orig_freq=sr, new_freq=new_sr,
resampling_method=resampling_method, rtol=1e-4, atol=1e-7)
F.resample,
multi_sound,
orig_freq=sr,
new_freq=new_sr,
resampling_method=resampling_method,
rtol=1e-4,
atol=1e-7,
)
@common_utils.skipIfNoKaldi
def test_compute_kaldi_pitch(self):
sample_rate = 44100
n_channels = 2
waveform = common_utils.get_whitenoise(
sample_rate=sample_rate, n_channels=self.batch_size * n_channels)
waveform = common_utils.get_whitenoise(sample_rate=sample_rate, n_channels=self.batch_size * n_channels)
batch = waveform.view(self.batch_size, n_channels, waveform.size(-1))
self.assert_batch_consistency(
F.compute_kaldi_pitch, batch, sample_rate=sample_rate)
self.assert_batch_consistency(F.compute_kaldi_pitch, batch, sample_rate=sample_rate)
def test_lfilter(self):
signal_length = 2048
......@@ -226,10 +226,7 @@ class TestFunctional(common_utils.TorchaudioTestCase):
b = torch.rand(self.batch_size, 3)
batchwise_output = F.lfilter(x, a, b, batching=True)
itemwise_output = torch.stack([
F.lfilter(x[i], a[i], b[i])
for i in range(self.batch_size)
])
itemwise_output = torch.stack([F.lfilter(x[i], a[i], b[i]) for i in range(self.batch_size)])
self.assertEqual(batchwise_output, itemwise_output)
......@@ -241,9 +238,6 @@ class TestFunctional(common_utils.TorchaudioTestCase):
b = torch.rand(self.batch_size, 3)
batchwise_output = F.filtfilt(x, a, b)
itemwise_output = torch.stack([
F.filtfilt(x[i], a[i], b[i])
for i in range(self.batch_size)
])
itemwise_output = torch.stack([F.filtfilt(x[i], a[i], b[i]) for i in range(self.batch_size)])
self.assertEqual(batchwise_output, itemwise_output)
test/torchaudio_unittest/functional/functional_cpu_test.py
View file @ 5859923a
import unittest
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, PytorchTestCase):
dtype = torch.float32
device = torch.device('cpu')
device = torch.device("cpu")
@unittest.expectedFailure
def test_lfilter_9th_order_filter_stability(self):
......@@ -18,7 +19,7 @@ class TestFunctionalFloat32(Functional, FunctionalCPUOnly, PytorchTestCase):
class TestFunctionalFloat64(Functional, PytorchTestCase):
dtype = torch.float64
device = torch.device('cpu')
device = torch.device("cpu")
@skipIfNoSox
......@@ -36,12 +37,7 @@ class TestApplyCodec(TorchaudioTestCase):
num_channels = 2
waveform = torch.rand(num_channels, num_frames)
augmented = F.apply_codec(waveform,
sample_rate,
format,
True,
compression
)
augmented = F.apply_codec(waveform, sample_rate, format, True, compression)
assert augmented.dtype == waveform.dtype
assert augmented.shape[0] == num_channels
if check_num_frames:
......
test/torchaudio_unittest/functional/functional_cuda_test.py
View file @ 5859923a
import torch
import unittest
import torch
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')
device = torch.device("cuda")
@unittest.expectedFailure
def test_lfilter_9th_order_filter_stability(self):
......@@ -18,4 +19,4 @@ class TestFunctionalFloat32(Functional, PytorchTestCase):
@skipIfNoCuda
class TestLFilterFloat64(Functional, PytorchTestCase):
dtype = torch.float64
device = torch.device('cuda')
device = torch.device("cuda")
test/torchaudio_unittest/functional/functional_impl.py
View file @ 5859923a
"""Test definition common to CPU and CUDA"""
import math
import itertools
import math
import warnings
import numpy as np
......@@ -8,7 +8,6 @@ import torch
import torchaudio.functional as F
from parameterized import parameterized
from scipy import signal
from torchaudio_unittest.common_utils import (
TestBaseMixin,
get_sinusoid,
......@@ -19,8 +18,9 @@ from torchaudio_unittest.common_utils import (
class Functional(TestBaseMixin):
def _test_resample_waveform_accuracy(self, up_scale_factor=None, down_scale_factor=None,
resampling_method="sinc_interpolation", atol=1e-1, rtol=1e-4):
def _test_resample_waveform_accuracy(
self, up_scale_factor=None, down_scale_factor=None, resampling_method="sinc_interpolation", atol=1e-1, rtol=1e-4
):
# resample the signal and compare it to the ground truth
n_to_trim = 20
sample_rate = 1000
......@@ -36,10 +36,9 @@ class Functional(TestBaseMixin):
original_timestamps = torch.arange(0, duration, 1.0 / sample_rate)
sound = 123 * torch.cos(2 * math.pi * 3 * original_timestamps).unsqueeze(0)
estimate = F.resample(sound, sample_rate, new_sample_rate,
resampling_method=resampling_method).squeeze()
estimate = F.resample(sound, sample_rate, new_sample_rate, resampling_method=resampling_method).squeeze()
new_timestamps = torch.arange(0, duration, 1.0 / new_sample_rate)[:estimate.size(0)]
new_timestamps = torch.arange(0, duration, 1.0 / new_sample_rate)[: estimate.size(0)]
ground_truth = 123 * torch.cos(2 * math.pi * 3 * new_timestamps)
# trim the first/last n samples as these points have boundary effects
......@@ -48,9 +47,7 @@ class Functional(TestBaseMixin):
self.assertEqual(estimate, ground_truth, atol=atol, rtol=rtol)
def _test_costs_and_gradients(
self, data, ref_costs, ref_gradients, atol=1e-6, rtol=1e-2
):
def _test_costs_and_gradients(self, data, ref_costs, ref_gradients, atol=1e-6, rtol=1e-2):
logits_shape = data["logits"].shape
costs, gradients = rnnt_utils.compute_with_pytorch_transducer(data=data)
self.assertEqual(costs, ref_costs, atol=atol, rtol=rtol)
......@@ -81,15 +78,41 @@ class Functional(TestBaseMixin):
output_signal = F.lfilter(input_signal, a_coeffs, b_coeffs, clamp=False)
assert output_signal.max() > 1
@parameterized.expand([
((44100,), (4,), (44100,)),
((3, 44100), (4,), (3, 44100,)),
((2, 3, 44100), (4,), (2, 3, 44100,)),
((1, 2, 3, 44100), (4,), (1, 2, 3, 44100,)),
((44100,), (2, 4), (2, 44100)),
((3, 44100), (1, 4), (3, 1, 44100)),
((1, 2, 44100), (3, 4), (1, 2, 3, 44100))
])
@parameterized.expand(
[
((44100,), (4,), (44100,)),
(
(3, 44100),
(4,),
(
3,
44100,
),
),
(
(2, 3, 44100),
(4,),
(
2,
3,
44100,
),
),
(
(1, 2, 3, 44100),
(4,),
(
1,
2,
3,
44100,
),
),
((44100,), (2, 4), (2, 44100)),
((3, 44100), (1, 4), (3, 1, 44100)),
((1, 2, 44100), (3, 4), (1, 2, 3, 44100)),
]
)
def test_lfilter_shape(self, input_shape, coeff_shape, target_shape):
torch.random.manual_seed(42)
waveform = torch.rand(*input_shape, dtype=self.dtype, device=self.device)
......@@ -109,13 +132,12 @@ class Functional(TestBaseMixin):
x[0] = 1
# get target impulse response
sos = signal.butter(9, 850, 'hp', fs=22050, output='sos')
sos = signal.butter(9, 850, "hp", fs=22050, output="sos")
y = torch.from_numpy(signal.sosfilt(sos, x.cpu().numpy())).to(self.dtype).to(self.device)
# get lfilter coefficients
b, a = signal.butter(9, 850, 'hp', fs=22050, output='ba')
b, a = torch.from_numpy(b).to(self.dtype).to(self.device), torch.from_numpy(
a).to(self.dtype).to(self.device)
b, a = signal.butter(9, 850, "hp", fs=22050, output="ba")
b, a = torch.from_numpy(b).to(self.dtype).to(self.device), torch.from_numpy(a).to(self.dtype).to(self.device)
# predict impulse response
yhat = F.lfilter(x, a, b, False)
......@@ -126,14 +148,10 @@ class Functional(TestBaseMixin):
Check that, for an arbitrary signal, applying filtfilt with filter coefficients
corresponding to a pure delay filter imparts no time delay.
"""
waveform = get_whitenoise(sample_rate=8000, n_channels=2, dtype=self.dtype).to(
device=self.device
)
waveform = get_whitenoise(sample_rate=8000, n_channels=2, dtype=self.dtype).to(device=self.device)
b_coeffs = torch.tensor([0, 0, 0, 1], dtype=self.dtype, device=self.device)
a_coeffs = torch.tensor([1, 0, 0, 0], dtype=self.dtype, device=self.device)
padded_waveform = torch.cat(
(waveform, torch.zeros(2, 3, dtype=self.dtype, device=self.device)), axis=1
)
padded_waveform = torch.cat((waveform, torch.zeros(2, 3, dtype=self.dtype, device=self.device)), axis=1)
output_waveform = F.filtfilt(padded_waveform, a_coeffs, b_coeffs)
self.assertEqual(output_waveform, padded_waveform, atol=1e-5, rtol=1e-5)
......@@ -147,9 +165,9 @@ class Functional(TestBaseMixin):
T = 1.0
samples = 1000
waveform_k0 = get_sinusoid(
frequency=5, sample_rate=samples // T, dtype=self.dtype, device=self.device
).squeeze(0)
waveform_k0 = get_sinusoid(frequency=5, sample_rate=samples // T, dtype=self.dtype, device=self.device).squeeze(
0
)
waveform_k1 = get_sinusoid(
frequency=200,
sample_rate=samples // T,
......@@ -202,13 +220,13 @@ class Functional(TestBaseMixin):
# Remove padding from output waveform; confirm that result
# closely matches waveform_k0.
self.assertEqual(
output_waveform[samples - 1: 2 * samples - 1],
output_waveform[samples - 1 : 2 * samples - 1],
waveform_k0,
atol=1e-3,
rtol=1e-3,
)
@parameterized.expand([(0., ), (1., ), (2., ), (3., )])
@parameterized.expand([(0.0,), (1.0,), (2.0,), (3.0,)])
def test_spectrogram_grad_at_zero(self, power):
"""The gradient of power spectrogram should not be nan but zero near x=0
......@@ -235,19 +253,15 @@ class Functional(TestBaseMixin):
self.assertEqual(computed, expected)
def test_compute_deltas_two_channels(self):
specgram = torch.tensor([[[1.0, 2.0, 3.0, 4.0],
[1.0, 2.0, 3.0, 4.0]]], dtype=self.dtype, device=self.device)
expected = torch.tensor([[[0.5, 1.0, 1.0, 0.5],
[0.5, 1.0, 1.0, 0.5]]], dtype=self.dtype, device=self.device)
specgram = torch.tensor([[[1.0, 2.0, 3.0, 4.0], [1.0, 2.0, 3.0, 4.0]]], dtype=self.dtype, device=self.device)
expected = torch.tensor([[[0.5, 1.0, 1.0, 0.5], [0.5, 1.0, 1.0, 0.5]]], dtype=self.dtype, device=self.device)
computed = F.compute_deltas(specgram, win_length=3)
self.assertEqual(computed, expected)
@parameterized.expand([(100,), (440,)])
def test_detect_pitch_frequency_pitch(self, frequency):
sample_rate = 44100
test_sine_waveform = get_sinusoid(
frequency=frequency, sample_rate=sample_rate, duration=5
)
test_sine_waveform = get_sinusoid(frequency=frequency, sample_rate=sample_rate, duration=5)
freq = F.detect_pitch_frequency(test_sine_waveform, sample_rate)
......@@ -262,8 +276,8 @@ class Functional(TestBaseMixin):
This implicitly also tests `DB_to_amplitude`.
"""
amplitude_mult = 20.
power_mult = 10.
amplitude_mult = 20.0
power_mult = 10.0
amin = 1e-10
ref = 1.0
db_mult = math.log10(max(amin, ref))
......@@ -279,18 +293,18 @@ class Functional(TestBaseMixin):
# Spectrogram power -> DB -> power
db = F.amplitude_to_DB(spec, power_mult, amin, db_mult, top_db=None)
x2 = F.DB_to_amplitude(db, ref, 1.)
x2 = F.DB_to_amplitude(db, ref, 1.0)
self.assertEqual(x2, spec)
@parameterized.expand([([100, 100],), ([2, 100, 100],), ([2, 2, 100, 100],)])
def test_amplitude_to_DB_top_db_clamp(self, shape):
"""Ensure values are properly clamped when `top_db` is supplied."""
amplitude_mult = 20.
amplitude_mult = 20.0
amin = 1e-10
ref = 1.0
db_mult = math.log10(max(amin, ref))
top_db = 40.
top_db = 40.0
torch.manual_seed(0)
# A random tensor is used for increased entropy, but the max and min for
......@@ -304,24 +318,17 @@ class Functional(TestBaseMixin):
# Expand the range to (0, 200) - wide enough to properly test clamping.
spec *= 200
decibels = F.amplitude_to_DB(spec, amplitude_mult, amin,
db_mult, top_db=top_db)
decibels = F.amplitude_to_DB(spec, amplitude_mult, amin, db_mult, top_db=top_db)
# Ensure the clamp was applied
below_limit = decibels < 6.0205
assert not below_limit.any(), (
"{} decibel values were below the expected cutoff:\n{}".format(
below_limit.sum().item(), decibels
)
assert not below_limit.any(), "{} decibel values were below the expected cutoff:\n{}".format(
below_limit.sum().item(), decibels
)
# Ensure it didn't over-clamp
close_to_limit = decibels < 6.0207
assert close_to_limit.any(), (
f"No values were close to the limit. Did it over-clamp?\n{decibels}"
)
assert close_to_limit.any(), f"No values were close to the limit. Did it over-clamp?\n{decibels}"
@parameterized.expand(
list(itertools.product([(2, 1025, 400), (1, 201, 100)], [100], [0., 30.], [1, 2]))
)
@parameterized.expand(list(itertools.product([(2, 1025, 400), (1, 201, 100)], [100], [0.0, 30.0], [1, 2])))
def test_mask_along_axis(self, shape, mask_param, mask_value, axis):
torch.random.manual_seed(42)
specgram = torch.randn(*shape, dtype=self.dtype, device=self.device)
......@@ -331,13 +338,12 @@ class Functional(TestBaseMixin):
masked_columns = (mask_specgram == mask_value).sum(other_axis)
num_masked_columns = (masked_columns == mask_specgram.size(other_axis)).sum()
num_masked_columns = torch.div(
num_masked_columns, mask_specgram.size(0), rounding_mode='floor')
num_masked_columns = torch.div(num_masked_columns, mask_specgram.size(0), rounding_mode="floor")
assert mask_specgram.size() == specgram.size()
assert num_masked_columns < mask_param
@parameterized.expand(list(itertools.product([100], [0., 30.], [2, 3])))
@parameterized.expand(list(itertools.product([100], [0.0, 30.0], [2, 3])))
def test_mask_along_axis_iid(self, mask_param, mask_value, axis):
torch.random.manual_seed(42)
specgrams = torch.randn(4, 2, 1025, 400, dtype=self.dtype, device=self.device)
......@@ -352,9 +358,7 @@ class Functional(TestBaseMixin):
assert mask_specgrams.size() == specgrams.size()
assert (num_masked_columns < mask_param).sum() == num_masked_columns.numel()
@parameterized.expand(
list(itertools.product([(2, 1025, 400), (1, 201, 100)], [100], [0., 30.], [1, 2]))
)
@parameterized.expand(list(itertools.product([(2, 1025, 400), (1, 201, 100)], [100], [0.0, 30.0], [1, 2])))
def test_mask_along_axis_preserve(self, shape, mask_param, mask_value, axis):
"""mask_along_axis should not alter original input Tensor
......@@ -369,7 +373,7 @@ class Functional(TestBaseMixin):
self.assertEqual(specgram, specgram_copy)
@parameterized.expand(list(itertools.product([100], [0., 30.], [2, 3])))
@parameterized.expand(list(itertools.product([100], [0.0, 30.0], [2, 3])))
def test_mask_along_axis_iid_preserve(self, mask_param, mask_value, axis):
"""mask_along_axis_iid should not alter original input Tensor
......@@ -384,10 +388,14 @@ class Functional(TestBaseMixin):
self.assertEqual(specgrams, specgrams_copy)
@parameterized.expand(list(itertools.product(
["sinc_interpolation", "kaiser_window"],
[16000, 44100],
)))
@parameterized.expand(
list(
itertools.product(
["sinc_interpolation", "kaiser_window"],
[16000, 44100],
)
)
)
def test_resample_identity(self, resampling_method, sample_rate):
waveform = get_whitenoise(sample_rate=sample_rate, duration=1)
......@@ -397,35 +405,52 @@ class Functional(TestBaseMixin):
@parameterized.expand([("sinc_interpolation"), ("kaiser_window")])
def test_resample_waveform_upsample_size(self, resampling_method):
sr = 16000
waveform = get_whitenoise(sample_rate=sr, duration=0.5,)
waveform = get_whitenoise(
sample_rate=sr,
duration=0.5,
)
upsampled = F.resample(waveform, sr, sr * 2, resampling_method=resampling_method)
assert upsampled.size(-1) == waveform.size(-1) * 2
@parameterized.expand([("sinc_interpolation"), ("kaiser_window")])
def test_resample_waveform_downsample_size(self, resampling_method):
sr = 16000
waveform = get_whitenoise(sample_rate=sr, duration=0.5,)
waveform = get_whitenoise(
sample_rate=sr,
duration=0.5,
)
downsampled = F.resample(waveform, sr, sr // 2, resampling_method=resampling_method)
assert downsampled.size(-1) == waveform.size(-1) // 2
@parameterized.expand([("sinc_interpolation"), ("kaiser_window")])
def test_resample_waveform_identity_size(self, resampling_method):
sr = 16000
waveform = get_whitenoise(sample_rate=sr, duration=0.5,)
waveform = get_whitenoise(
sample_rate=sr,
duration=0.5,
)
resampled = F.resample(waveform, sr, sr, resampling_method=resampling_method)
assert resampled.size(-1) == waveform.size(-1)
@parameterized.expand(list(itertools.product(
["sinc_interpolation", "kaiser_window"],
list(range(1, 20)),
)))
@parameterized.expand(
list(
itertools.product(
["sinc_interpolation", "kaiser_window"],
list(range(1, 20)),
)
)
)
def test_resample_waveform_downsample_accuracy(self, resampling_method, i):
self._test_resample_waveform_accuracy(down_scale_factor=i * 2, resampling_method=resampling_method)
@parameterized.expand(list(itertools.product(
["sinc_interpolation", "kaiser_window"],
list(range(1, 20)),
)))
@parameterized.expand(
list(
itertools.product(
["sinc_interpolation", "kaiser_window"],
list(range(1, 20)),
)
)
)
def test_resample_waveform_upsample_accuracy(self, resampling_method, i):
self._test_resample_waveform_accuracy(up_scale_factor=1.0 + i / 20.0, resampling_method=resampling_method)
......@@ -438,14 +463,9 @@ class Functional(TestBaseMixin):
batch_size = 2
torch.random.manual_seed(42)
spec = torch.randn(
batch_size, num_freq, num_frames, dtype=self.complex_dtype, device=self.device)
spec = torch.randn(batch_size, num_freq, num_frames, dtype=self.complex_dtype, device=self.device)
phase_advance = torch.linspace(
0,
np.pi * hop_length,
num_freq,
dtype=self.dtype, device=self.device)[..., None]
phase_advance = torch.linspace(0, np.pi * hop_length, num_freq, dtype=self.dtype, device=self.device)[..., None]
spec_stretch = F.phase_vocoder(spec, rate=rate, phase_advance=phase_advance)
......@@ -460,32 +480,31 @@ class Functional(TestBaseMixin):
["", "", 0], # equal
["abc", "abc", 0],
["ᑌᑎIᑕO", "ᑌᑎIᑕO", 0],
["abc", "", 3], # deletion
["aa", "aaa", 1],
["aaa", "aa", 1],
["ᑌᑎI", "ᑌᑎIᑕO", 2],
["aaa", "aba", 1], # substitution
["aba", "aaa", 1],
["aba", " ", 3],
["abc", "bcd", 2], # mix deletion and substitution
["0ᑌᑎI", "ᑌᑎIᑕO", 3],
# sentences
[["hello", "", "Tᕮ᙭T"], ["hello", "", "Tᕮ᙭T"], 0], # equal
[[], [], 0],
[["hello", "world"], ["hello", "world", "!"], 1], # deletion
[["hello", "world"], ["world"], 1],
[["hello", "world"], [], 2],
[["Tᕮ᙭T", ], ["world"], 1], # substitution
[
[
"Tᕮ᙭T",
],
["world"],
1,
], # substitution
[["Tᕮ᙭T", "XD"], ["world", "hello"], 2],
[["", "XD"], ["world", ""], 2],
["aba", " ", 3],
[["hello", "world"], ["world", "hello", "!"], 2], # mix deletion and substitution
[["Tᕮ᙭T", "world", "LOL", "XD"], ["world", "hello", "ʕ•́ᴥ•̀ʔっ"], 3],
]
......@@ -520,12 +539,14 @@ class Functional(TestBaseMixin):
logits.requires_grad_(False)
F.rnnt_loss(logits, targets, logit_lengths, target_lengths)
@parameterized.expand([
(rnnt_utils.get_B1_T2_U3_D5_data, torch.float32, 1e-6, 1e-2),
(rnnt_utils.get_B2_T4_U3_D3_data, torch.float32, 1e-6, 1e-2),
(rnnt_utils.get_B1_T2_U3_D5_data, torch.float16, 1e-3, 1e-2),
(rnnt_utils.get_B2_T4_U3_D3_data, torch.float16, 1e-3, 1e-2),
])
@parameterized.expand(
[
(rnnt_utils.get_B1_T2_U3_D5_data, torch.float32, 1e-6, 1e-2),
(rnnt_utils.get_B2_T4_U3_D3_data, torch.float32, 1e-6, 1e-2),
(rnnt_utils.get_B1_T2_U3_D5_data, torch.float16, 1e-3, 1e-2),
(rnnt_utils.get_B2_T4_U3_D3_data, torch.float16, 1e-3, 1e-2),
]
)
def test_rnnt_loss_costs_and_gradients(self, data_func, dtype, atol, rtol):
data, ref_costs, ref_gradients = data_func(
dtype=dtype,
......@@ -544,9 +565,7 @@ class Functional(TestBaseMixin):
for i in range(5):
data = rnnt_utils.get_random_data(dtype=torch.float32, device=self.device, seed=(seed + i))
ref_costs, ref_gradients = rnnt_utils.compute_with_numpy_transducer(data=data)
self._test_costs_and_gradients(
data=data, ref_costs=ref_costs, ref_gradients=ref_gradients
)
self._test_costs_and_gradients(data=data, ref_costs=ref_costs, ref_gradients=ref_gradients)
class FunctionalCPUOnly(TestBaseMixin):
......
test/torchaudio_unittest/functional/kaldi_compatibility_cpu_test.py
View file @ 5859923a
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')
device = torch.device("cpu")
class TestKaldiFloat32(Kaldi, PytorchTestCase):
dtype = torch.float32
device = torch.device('cpu')
device = torch.device("cpu")
class TestKaldiFloat64(Kaldi, PytorchTestCase):
dtype = torch.float64
device = torch.device('cpu')
device = torch.device("cpu")
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