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import os
import shutil
from typing import Sequence
import gin
import numpy as np
from sklearn.model_selection import train_test_split
from .preprocess_audio import preprocess_audio
from ...utils import seed_all
def create_directory(path):
if not os.path.isdir(path):
try:
os.mkdir(path)
except OSError:
print("Failed to create directory %s" % path)
else:
print("Created directory %s..." % path)
else:
print("Directory %s already exists. Skipping..." % path)
def create_directories(target_root, names):
create_directory(target_root)
for name in names:
create_directory(os.path.join(target_root, name))
def make_splits(
audio_list: Sequence[str],
control_list: Sequence[str],
splits: Sequence[str],
split_proportions: Sequence[float],
):
assert len(splits) == len(
split_proportions
), "Length of splits and split_proportions must be equal"
train_size = split_proportions[0] / np.sum(split_proportions)
audio_0, audio_1, control_0, control_1 = train_test_split(
audio_list, control_list, train_size=train_size
)
if len(splits) == 2:
return {
splits[0]: {
"audio": audio_0,
"control": control_0,
},
splits[1]: {
"audio": audio_1,
"control": control_1,
},
}
elif len(splits) > 2:
return {
splits[0]: {
"audio": audio_0,
"control": control_0,
},
**make_splits(audio_1, control_1, splits[1:], split_proportions[1:]),
}
elif len(splits) == 1:
return {
splits[0]: {
"audio": audio_list,
"control": control_list,
}
}
def lazy_create_dataset(
files: Sequence[str],
output_directory: str,
splits: Sequence[str],
split_proportions: Sequence[float],
):
audio_files = []
control_files = []
audio_max = 1e-5
means = []
stds = []
lengths = []
control_mean = 0
control_std = 1
for i, (all_audio, all_f0, all_confidence, all_loudness, all_mfcc) in enumerate(
preprocess_audio(files)
):
file = os.path.split(files[i])[-1].replace(".wav", "")
for j, (audio, f0, confidence, loudness, mfcc) in enumerate(
zip(all_audio, all_f0, all_confidence, all_loudness, all_mfcc)
):
audio_file_name = "audio_%s_%d.npy" % (file, j)
control_file_name = "control_%s_%d.npy" % (file, j)
max_sample = np.abs(audio).max()
if max_sample > audio_max:
audio_max = max_sample
np.save(
os.path.join(output_directory, "temp", "audio", audio_file_name),
audio,
)
control = np.stack((f0, loudness, confidence), axis=0)
control = np.concatenate((control, mfcc), axis=0)
np.save(
os.path.join(output_directory, "temp", "control", control_file_name),
control,
)
audio_files.append(audio_file_name)
control_files.append(control_file_name)
means.append(control.mean(axis=-1))
stds.append(control.std(axis=-1))
lengths.append(control.shape[-1])
if len(audio_files) == 0:
print("No datapoints to split. Skipping...")
return
data_mean = np.mean(np.stack(means, axis=-1), axis=-1)[:, np.newaxis]
lengths = np.stack(lengths)[np.newaxis, :]
stds = np.stack(stds, axis=-1)
data_std = np.sqrt(np.sum(lengths * stds ** 2, axis=-1) / np.sum(lengths))[
:, np.newaxis
]
print("Saving dataset stats...")
np.save(os.path.join(output_directory, "data_mean.npy"), data_mean)
np.save(os.path.join(output_directory, "data_std.npy"), data_std)
splits = make_splits(audio_files, control_files, splits, split_proportions)
for split in splits:
for audio_file in splits[split]["audio"]:
audio = np.load(os.path.join(output_directory, "temp", "audio", audio_file))
audio = audio / audio_max
np.save(os.path.join(output_directory, split, "audio", audio_file), audio)
for control_file in splits[split]["control"]:
control = np.load(
os.path.join(output_directory, "temp", "control", control_file)
)
control = (control - data_mean) / data_std
np.save(
os.path.join(output_directory, split, "control", control_file), control
)
@gin.configurable
def create_dataset(
files: Sequence[str],
output_directory: str,
splits: Sequence[str] = ("train", "val", "test"),
split_proportions: Sequence[float] = (0.8, 0.1, 0.1),
lazy: bool = True,
):
create_directories(output_directory, (*splits, "temp"))
for split in (*splits, "temp"):
create_directories(os.path.join(output_directory, split), ("audio", "control"))
if lazy:
lazy_create_dataset(files, output_directory, splits, split_proportions)
shutil.rmtree(os.path.join(output_directory, "temp")) |