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2e1dc7f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 | import lightning as L
from pathlib import Path
from typing import Set, Optional
from config import ConfigurationError
from data.stem import Stem
from data.stemmed_dataset import StemmedDataset
from data.dataset_mixed import MixDataset
import hyperparameters as hp
import torch.utils.data
from torch import Tensor
import random
class StemmedDatamodule(L.LightningDataModule):
def __init__(self, params: hp.StemmedDatasetParams):
super().__init__()
# self.stems: Set[Stem] = params.stems
# self.single_stem: bool = params.single_stem
# self.root_dir: Path = Path(params.root_dir)
# self.clip_length_in_seconds: int = params.clip_length_in_seconds
# self.sample_rate: int = params.sample_rate
# self.batch_size_train: int = params.batch_size_train
# self.batch_size_test: int = params.batch_size_test
# self.num_workers: int = params.num_workers
# self.speed_transform_p: float = params.speed_transform_p
# self.pitch_transform_p: float = params.pitch_transform_p
# self.n_samples_per_epoch: int = params.n_samples_per_epoch
# self.target_stem: Stem = params.target_stem
# self.add_click: bool = params.add_click
# self.sync_chunks: bool = params.sync_chunks
# self.bpm_in_caption: bool = params.bpm_in_caption
self.params = params
if isinstance(params, hp.MixDatasetParams):
self.dataset_class = MixDataset
else:
self.dataset_class = StemmedDataset
self.setup(None)
self.lengths = {
"train": len(self.train_dataloader()),
"valid": len(self.val_dataloader())
}
def _collate_fn(self, batch):
if self.params.min_context_seconds > self.params.clip_length_in_seconds:
raise ConfigurationError(
"Context has to be smaller than clip length")
if (self.params.min_context_seconds ==
self.params.clip_length_in_seconds):
inputs = {
k:
torch.stack([s[k] for s in batch]) if isinstance(
batch[0][k], Tensor) else [s[k] for s in batch]
for k in batch[0].keys()
# if k != "name"
}
else:
inputs = {
k:
torch.stack([s[k] for s in batch]) if
(isinstance(batch[0][k], Tensor) and
k != "context") else [s[k] for s in batch]
for k in batch[0].keys()
# if k != "name"
}
# inputs = {
# "target": torch.stack([s["target"] for s in batch]),
# "context": torch.stack([s["context"] for s in batch]),
# "description": [s["description"] for s in batch],
# "style": torch.stack([s["style"] for s in batch])
# }
return inputs
def setup(self, stage: Optional[str]):
self.train_dataset = self.dataset_class(
Path(self.params.root_dir),
self.params.stems,
train=True,
target_stem=self.params.target_stem,
single_stem=self.params.single_stem,
min_context_seconds=self.params.min_context_seconds,
use_style_conditioning=self.params.use_style_conditioning,
use_beat_conditioning=self.params.use_beat_conditioning,
add_click=self.params.add_click,
sync_chunks=self.params.sync_chunks,
bpm_in_caption=self.params.bpm_in_caption,
sample_rate=self.params.sample_rate,
type_of_context=self.params.type_of_context,
chunk_size_samples=self.params.clip_length_in_seconds *
self.params.sample_rate,
speed_transform_p=self.params.speed_transform_p,
pitch_transform_p=self.params.pitch_transform_p,
n_samples_per_epoch=self.params.n_samples_per_epoch,
stereo=False,
max_genres_in_description=3,
max_moods_in_description=3,
)
self.val_dataset = self.dataset_class(
Path(self.params.root_dir),
self.params.stems,
train=False,
target_stem=self.params.target_stem,
single_stem=self.params.single_stem,
min_context_seconds=self.params.min_context_seconds,
use_style_conditioning=self.params.use_style_conditioning,
use_beat_conditioning=self.params.use_beat_conditioning,
add_click=self.params.add_click,
sync_chunks=self.params.sync_chunks,
bpm_in_caption=self.params.bpm_in_caption,
sample_rate=self.params.sample_rate,
type_of_context=self.params.type_of_context,
chunk_size_samples=self.params.clip_length_in_seconds *
self.params.sample_rate,
speed_transform_p=self.params.speed_transform_p,
pitch_transform_p=self.params.pitch_transform_p,
n_samples_per_epoch=None,
stereo=False,
max_genres_in_description=3,
max_moods_in_description=3,
)
def train_dataloader(self):
return torch.utils.data.DataLoader(
dataset=self.train_dataset,
batch_size=self.params.batch_size_train,
shuffle=True,
num_workers=self.params.num_workers,
collate_fn=self._collate_fn,
pin_memory=True,
worker_init_fn=lambda id: random.seed(id),
# prefetch_factor=1,
)
def val_dataloader(self):
return torch.utils.data.DataLoader(
self.val_dataset,
self.params.batch_size_test,
shuffle=False,
num_workers=self.params.num_workers,
collate_fn=self._collate_fn,
pin_memory=True,
worker_init_fn=lambda id: random.seed(id),
# prefetch_factor=1,
)
# def main():
if __name__ == "__main__":
from tqdm import tqdm
import config as cfg
# root_dir = cfg.mixdata_path()
root_dir = cfg.moises_path()
stems = {
Stem.DRUMS, Stem.GUITAR, Stem.BASS, Stem.PIANO, Stem.KEYBOARD,
Stem.STRINGS, Stem.OTHER
}
dataset_params = hp.MixDatasetParams(
root_dir=root_dir,
stems=stems,
single_stem=True,
min_context_seconds=5,
use_style_conditioning=True,
use_beat_conditioning=True,
target_stem=Stem.DRUMS,
add_click=False,
sync_chunks=False,
bpm_in_caption=False,
batch_size_train=4,
batch_size_test=4,
num_workers=8,
clip_length_in_seconds=10,
sample_rate=32_000,
speed_transform_p=1,
pitch_transform_p=0.5,
n_samples_per_epoch=2000,
)
d = dataset_params.instantiate()
vd = d.val_dataloader()
vbatch = next(iter(vd))
td = d.train_dataloader()
tbatch = next(iter(td))
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