Spaces:
Build error
Build error
File size: 11,228 Bytes
51da11a |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 |
import torch
import julius
import torchopenl3
import torchmetrics
import pytorch_lightning as pl
from typing import Tuple, List, Dict
from argparse import ArgumentParser
from deepafx_st.probes.cdpam_encoder import CDPAMEncoder
from deepafx_st.probes.random_mel import RandomMelProjection
import deepafx_st.utils as utils
from deepafx_st.utils import DSPMode
from deepafx_st.system import System
from deepafx_st.data.style import StyleDataset
class ProbeSystem(pl.LightningModule):
def __init__(
self,
audio_dir=None,
num_classes=5,
task="style",
encoder_type="deepafx_st_autodiff",
deepafx_st_autodiff_ckpt=None,
deepafx_st_spsa_ckpt=None,
deepafx_st_proxy0_ckpt=None,
probe_type="linear",
batch_size=32,
lr=3e-4,
lr_patience=20,
patience=10,
preload=False,
sample_rate=24000,
shuffle=True,
num_workers=16,
**kwargs,
):
super().__init__()
self.save_hyperparameters()
if "deepafx_st" in self.hparams.encoder_type:
if "autodiff" in self.hparams.encoder_type:
self.hparams.deepafx_st_ckpt = self.hparams.deepafx_st_autodiff_ckpt
elif "spsa" in self.hparams.encoder_type:
self.hparams.deepafx_st_ckpt = self.hparams.deepafx_st_spsa_ckpt
elif "proxy0" in self.hparams.encoder_type:
self.hparams.deepafx_st_ckpt = self.hparams.deepafx_st_proxy0_ckpt
else:
raise RuntimeError(f"Invalid encoder_type: {self.hparams.encoder_type}")
if self.hparams.deepafx_st_ckpt is None:
raise RuntimeError(
f"Must supply {self.hparams.encoder_type}_ckpt checkpoint."
)
use_dsp = DSPMode.NONE
system = System.load_from_checkpoint(
self.hparams.deepafx_st_ckpt,
use_dsp=use_dsp,
batch_size=self.hparams.batch_size,
spsa_parallel=False,
proxy_ckpts=[],
strict=False,
)
system.eval()
self.encoder = system.encoder
self.hparams.embed_dim = self.encoder.embed_dim
# freeze weights
for name, param in self.encoder.named_parameters():
param.requires_grad = False
elif self.hparams.encoder_type == "openl3":
self.encoder = torchopenl3.models.load_audio_embedding_model(
input_repr=self.hparams.openl3_input_repr,
embedding_size=self.hparams.openl3_embedding_size,
content_type=self.hparams.openl3_content_type,
)
self.hparams.embed_dim = 6144
elif self.hparams.encoder_type == "random_mel":
self.encoder = RandomMelProjection(
self.hparams.sample_rate,
self.hparams.random_mel_embedding_size,
self.hparams.random_mel_n_mels,
self.hparams.random_mel_n_fft,
self.hparams.random_mel_hop_size,
)
self.hparams.embed_dim = self.hparams.random_mel_embedding_size
elif self.hparams.encoder_type == "cdpam":
self.encoder = CDPAMEncoder(self.hparams.cdpam_ckpt)
self.encoder.eval()
self.hparams.embed_dim = self.encoder.embed_dim
else:
raise ValueError(f"Invalid encoder_type: {self.hparams.encoder_type}")
if self.hparams.probe_type == "linear":
if self.hparams.task == "style":
self.probe = torch.nn.Sequential(
torch.nn.Linear(self.hparams.embed_dim, self.hparams.num_classes),
# torch.nn.Softmax(-1),
)
elif self.hparams.probe_type == "mlp":
if self.hparams.task == "style":
self.probe = torch.nn.Sequential(
torch.nn.Linear(self.hparams.embed_dim, 512),
torch.nn.ReLU(),
torch.nn.Linear(512, 512),
torch.nn.ReLU(),
torch.nn.Linear(512, self.hparams.num_classes),
)
self.accuracy = torchmetrics.Accuracy()
self.f1_score = torchmetrics.F1Score(self.hparams.num_classes)
def forward(self, x):
bs, chs, samp = x.size()
with torch.no_grad():
if "deepafx_st" in self.hparams.encoder_type:
x /= x.abs().max()
x *= 10 ** (-12.0 / 20) # with min 12 dBFS headroom
e = self.encoder(x)
norm = torch.norm(e, p=2, dim=-1, keepdim=True)
e = e / norm
elif self.hparams.encoder_type == "openl3":
# x = julius.resample_frac(x, self.hparams.sample_rate, 48000)
e, ts = torchopenl3.get_audio_embedding(
x,
48000,
model=self.encoder,
input_repr="mel128",
content_type="music",
)
e = e.permute(0, 2, 1)
e = e.mean(dim=-1)
# normalize by L2 norm
norm = torch.norm(e, p=2, dim=-1, keepdim=True)
e = e / norm
elif self.hparams.encoder_type == "random_mel":
e = self.encoder(x)
norm = torch.norm(e, p=2, dim=-1, keepdim=True)
e = e / norm
elif self.hparams.encoder_type == "cdpam":
# x = julius.resample_frac(x, self.hparams.sample_rate, 22050)
x = torch.round(x * 32768)
e = self.encoder(x)
return self.probe(e)
def common_step(
self,
batch: Tuple,
batch_idx: int,
optimizer_idx: int = 0,
train: bool = True,
):
loss = 0
x, y = batch
y_hat = self(x)
# compute CE
if self.hparams.task == "style":
loss = torch.nn.functional.cross_entropy(y_hat, y)
if not train:
# store audio data
data_dict = {"x": x.float().cpu()}
else:
data_dict = {}
self.log(
"train_loss" if train else "val_loss",
loss,
on_step=True,
on_epoch=True,
prog_bar=False,
logger=True,
sync_dist=True,
)
if not train and self.hparams.task == "style":
self.log("val_acc_step", self.accuracy(y_hat, y))
self.log("val_f1_step", self.f1_score(y_hat, y))
return loss, data_dict
def training_step(self, batch, batch_idx, optimizer_idx=0):
loss, _ = self.common_step(batch, batch_idx)
return loss
def validation_step(self, batch, batch_idx):
loss, data_dict = self.common_step(batch, batch_idx, train=False)
if batch_idx == 0:
return data_dict
def validation_epoch_end(self, outputs) -> None:
if self.hparams.task == "style":
self.log("val_acc_epoch", self.accuracy.compute())
self.log("val_f1_epoch", self.f1_score.compute())
return super().validation_epoch_end(outputs)
def configure_optimizers(self):
optimizer = torch.optim.AdamW(
self.probe.parameters(),
lr=self.hparams.lr,
betas=(0.9, 0.999),
)
ms1 = int(self.hparams.max_epochs * 0.8)
ms2 = int(self.hparams.max_epochs * 0.95)
print(
"Learning rate schedule:",
f"0 {self.hparams.lr:0.2e} -> ",
f"{ms1} {self.hparams.lr*0.1:0.2e} -> ",
f"{ms2} {self.hparams.lr*0.01:0.2e}",
)
scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer,
milestones=[ms1, ms2],
gamma=0.1,
)
return [optimizer], {"scheduler": scheduler, "monitor": "val_loss"}
def train_dataloader(self):
if self.hparams.task == "style":
train_dataset = StyleDataset(
self.hparams.audio_dir,
"train",
sample_rate=self.hparams.encoder_sample_rate,
)
g = torch.Generator()
g.manual_seed(0)
return torch.utils.data.DataLoader(
train_dataset,
num_workers=self.hparams.num_workers,
batch_size=self.hparams.batch_size,
shuffle=True,
worker_init_fn=utils.seed_worker,
generator=g,
pin_memory=True,
)
def val_dataloader(self):
if self.hparams.task == "style":
val_dataset = StyleDataset(
self.hparams.audio_dir,
subset="val",
sample_rate=self.hparams.encoder_sample_rate,
)
g = torch.Generator()
g.manual_seed(0)
return torch.utils.data.DataLoader(
val_dataset,
num_workers=self.hparams.num_workers,
batch_size=self.hparams.batch_size,
worker_init_fn=utils.seed_worker,
generator=g,
pin_memory=True,
)
# add any model hyperparameters here
@staticmethod
def add_model_specific_args(parent_parser):
parser = ArgumentParser(parents=[parent_parser], add_help=False)
# --- Model ---
parser.add_argument("--encoder_type", type=str, default="deeapfx2")
parser.add_argument("--probe_type", type=str, default="linear")
parser.add_argument("--task", type=str, default="style")
parser.add_argument("--encoder_sample_rate", type=int, default=24000)
# --- deeapfx2 ---
parser.add_argument("--deepafx_st_autodiff_ckpt", type=str)
parser.add_argument("--deepafx_st_spsa_ckpt", type=str)
parser.add_argument("--deepafx_st_proxy0_ckpt", type=str)
# --- cdpam ---
parser.add_argument("--cdpam_ckpt", type=str)
# --- openl3 ---
parser.add_argument("--openl3_input_repr", type=str, default="mel128")
parser.add_argument("--openl3_content_type", type=str, default="env")
parser.add_argument("--openl3_embedding_size", type=int, default=6144)
# --- random_mel ---
parser.add_argument("--random_mel_embedding_size", type=str, default=4096)
parser.add_argument("--random_mel_n_fft", type=str, default=4096)
parser.add_argument("--random_mel_hop_size", type=str, default=1024)
parser.add_argument("--random_mel_n_mels", type=str, default=128)
# --- Training ---
parser.add_argument("--audio_dir", type=str)
parser.add_argument("--num_classes", type=int, default=5)
parser.add_argument("--batch_size", type=int, default=32)
parser.add_argument("--lr", type=float, default=3e-4)
parser.add_argument("--lr_patience", type=int, default=20)
parser.add_argument("--patience", type=int, default=10)
parser.add_argument("--preload", action="store_true")
parser.add_argument("--sample_rate", type=int, default=24000)
parser.add_argument("--num_workers", type=int, default=8)
return parser
|