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import os
import platform
import sys
from concurrent.futures import ProcessPoolExecutor
from typing import List, Tuple
from unittest import skipIf
import numpy as np
import torch
import torchaudio
from torchaudio_unittest.common_utils import get_whitenoise, PytorchTestCase, save_wav, skipIfNoSox, TempDirMixin
class RandomPerturbationFile(torch.utils.data.Dataset):
"""Given flist, apply random speed perturbation"""
def __init__(self, flist: List[str], sample_rate: int):
super().__init__()
self.flist = flist
self.sample_rate = sample_rate
self.rng = None
def __getitem__(self, index):
speed = self.rng.uniform(0.5, 2.0)
effects = [
["gain", "-n", "-10"],
["speed", f"{speed:.5f}"], # duration of data is 0.5 ~ 2.0 seconds.
["rate", f"{self.sample_rate}"],
["pad", "0", "1.5"], # add 1.5 seconds silence at the end
["trim", "0", "2"], # get the first 2 seconds
]
data, _ = torchaudio.sox_effects.apply_effects_file(self.flist[index], effects)
return data
def __len__(self):
return len(self.flist)
class RandomPerturbationTensor(torch.utils.data.Dataset):
"""Apply speed purturbation to (synthetic) Tensor data"""
def __init__(self, signals: List[Tuple[torch.Tensor, int]], sample_rate: int):
super().__init__()
self.signals = signals
self.sample_rate = sample_rate
self.rng = None
def __getitem__(self, index):
speed = self.rng.uniform(0.5, 2.0)
effects = [
["gain", "-n", "-10"],
["speed", f"{speed:.5f}"], # duration of data is 0.5 ~ 2.0 seconds.
["rate", f"{self.sample_rate}"],
["pad", "0", "1.5"], # add 1.5 seconds silence at the end
["trim", "0", "2"], # get the first 2 seconds
]
tensor, sample_rate = self.signals[index]
data, _ = torchaudio.sox_effects.apply_effects_tensor(tensor, sample_rate, effects)
return data
def __len__(self):
return len(self.signals)
def init_random_seed(worker_id):
dataset = torch.utils.data.get_worker_info().dataset
dataset.rng = np.random.RandomState(worker_id)
@skipIfNoSox
@skipIf(
platform.system() == "Darwin" and sys.version_info.major == 3 and sys.version_info.minor in [6, 7],
"This test is known to get stuck for macOS with Python < 3.8. "
"See https://github.com/pytorch/pytorch/issues/46409",
)
class TestSoxEffectsDataset(TempDirMixin, PytorchTestCase):
"""Test `apply_effects_file` in multi-process dataloader setting"""
def _generate_dataset(self, num_samples=128):
flist = []
for i in range(num_samples):
sample_rate = np.random.choice([8000, 16000, 44100])
dtype = np.random.choice(["float32", "int32", "int16", "uint8"])
data = get_whitenoise(n_channels=2, sample_rate=sample_rate, duration=1, dtype=dtype)
path = self.get_temp_path(f"{i:03d}_{dtype}_{sample_rate}.wav")
save_wav(path, data, sample_rate)
flist.append(path)
return flist
def test_apply_effects_file(self):
sample_rate = 12000
flist = self._generate_dataset()
dataset = RandomPerturbationFile(flist, sample_rate)
loader = torch.utils.data.DataLoader(
dataset,
batch_size=32,
num_workers=16,
worker_init_fn=init_random_seed,
)
for batch in loader:
assert batch.shape == (32, 2, 2 * sample_rate)
def _generate_signals(self, num_samples=128):
signals = []
for _ in range(num_samples):
sample_rate = np.random.choice([8000, 16000, 44100])
data = get_whitenoise(n_channels=2, sample_rate=sample_rate, duration=1, dtype="float32")
signals.append((data, sample_rate))
return signals
def test_apply_effects_tensor(self):
sample_rate = 12000
signals = self._generate_signals()
dataset = RandomPerturbationTensor(signals, sample_rate)
loader = torch.utils.data.DataLoader(
dataset,
batch_size=32,
num_workers=16,
worker_init_fn=init_random_seed,
)
for batch in loader:
assert batch.shape == (32, 2, 2 * sample_rate)
def speed(path):
wav, sample_rate = torchaudio.backend.sox_io_backend.load(path)
effects = [
["speed", "1.03756523535464655"],
["rate", f"{sample_rate}"],
]
return torchaudio.sox_effects.apply_effects_tensor(wav, sample_rate, effects)[0]
@skipIfNoSox
class TestProcessPoolExecutor(TempDirMixin, PytorchTestCase):
backend = "sox_io"
def setUp(self):
sample_rate = 16000
self.flist = []
for i in range(10):
path = self.get_temp_path(f"{i}.wav")
data = get_whitenoise(n_channels=1, sample_rate=sample_rate, duration=1, dtype="float")
save_wav(path, data, sample_rate)
self.flist.append(path)
@skipIf(os.environ.get("CI") == "true", "This test now hangs in CI")
def test_executor(self):
"""Test that apply_effects_tensor with speed + rate does not crush
https://github.com/pytorch/audio/issues/1021
"""
executor = ProcessPoolExecutor(1)
futures = [executor.submit(speed, path) for path in self.flist]
for future in futures:
future.result()
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