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63f0b06 | 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 | import argparse
import gc
import json
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from torch.nn import functional as F
from stable_audio_tools.data.dataset import create_dataloader_from_config
from stable_audio_tools.models.factory import create_model_from_config
from stable_audio_tools.models.pretrained import get_pretrained_model
from stable_audio_tools.models.utils import load_ckpt_state_dict, copy_state_dict
def load_model(model_config=None, model_ckpt_path=None, pretrained_name=None, model_half=False):
if pretrained_name is not None:
print(f"Loading pretrained model {pretrained_name}")
model, model_config = get_pretrained_model(pretrained_name)
elif model_config is not None and model_ckpt_path is not None:
print(f"Creating model from config")
model = create_model_from_config(model_config)
print(f"Loading model checkpoint from {model_ckpt_path}")
copy_state_dict(model, load_ckpt_state_dict(model_ckpt_path))
model.eval().requires_grad_(False)
if model_half:
model.to(torch.float16)
print("Done loading model")
return model, model_config
class PreEncodedLatentsInferenceWrapper(pl.LightningModule):
def __init__(
self,
model,
output_path,
is_discrete=False,
model_half=False,
model_config=None,
dataset_config=None,
sample_size=1920000,
args_dict=None
):
super().__init__()
self.save_hyperparameters(ignore=['model'])
self.model = model
self.output_path = Path(output_path)
def prepare_data(self):
# runs on rank 0
self.output_path.mkdir(parents=True, exist_ok=True)
details_path = self.output_path / "details.json"
if not details_path.exists(): # Only save if it doesn't exist
details = {
"model_config": self.hparams.model_config,
"dataset_config": self.hparams.dataset_config,
"sample_size": self.hparams.sample_size,
"args": self.hparams.args_dict
}
details_path.write_text(json.dumps(details))
def setup(self, stage=None):
# runs on each device
process_dir = self.output_path / str(self.global_rank)
process_dir.mkdir(parents=True, exist_ok=True)
def validation_step(self, batch, batch_idx):
audio, metadata = batch
if audio.ndim == 4 and audio.shape[0] == 1:
audio = audio[0]
if torch.cuda.is_available():
torch.cuda.empty_cache()
gc.collect()
if self.hparams.model_half:
audio = audio.to(torch.float16)
with torch.no_grad():
if not self.hparams.is_discrete:
latents = self.model.encode(audio)
else:
_, info = self.model.encode(audio, return_info=True)
latents = info[self.model.bottleneck.tokens_id]
latents = latents.cpu().numpy()
# Save each sample in the batch
for i, latent in enumerate(latents):
latent_id = f"{self.global_rank:03d}{batch_idx:06d}{i:04d}"
# Save latent as numpy file
latent_path = self.output_path / str(self.global_rank) / f"{latent_id}.npy"
with open(latent_path, "wb") as f:
np.save(f, latent)
md = metadata[i]
padding_mask = F.interpolate(
md["padding_mask"].unsqueeze(0).unsqueeze(1).float(),
size=latent.shape[1],
mode="nearest"
).squeeze().int()
md["padding_mask"] = padding_mask.cpu().numpy().tolist()
# Convert tensors in md to serializable types
for k, v in md.items():
if isinstance(v, torch.Tensor):
md[k] = v.cpu().numpy().tolist()
# Save metadata to json file
metadata_path = self.output_path / str(self.global_rank) / f"{latent_id}.json"
with open(metadata_path, "w") as f:
json.dump(md, f)
def configure_optimizers(self):
return None
def main(args):
with open(args.model_config) as f:
model_config = json.load(f)
with open(args.dataset_config) as f:
dataset_config = json.load(f)
model, model_config = load_model(
model_config=model_config,
model_ckpt_path=args.ckpt_path,
model_half=args.model_half
)
data_loader = create_dataloader_from_config(
dataset_config,
batch_size=args.batch_size,
num_workers=args.num_workers,
sample_rate=model_config["sample_rate"],
sample_size=args.sample_size,
audio_channels=model_config.get("audio_channels", 2),
shuffle=args.shuffle
)
pl_module = PreEncodedLatentsInferenceWrapper(
model=model,
output_path=args.output_path,
is_discrete=args.is_discrete,
model_half=args.model_half,
model_config=args.model_config,
dataset_config=args.dataset_config,
sample_size=args.sample_size,
args_dict=vars(args)
)
trainer = pl.Trainer(
accelerator="gpu",
devices="auto",
num_nodes = args.num_nodes,
strategy=args.strategy,
precision="16-true" if args.model_half else "32",
max_steps=args.limit_batches if args.limit_batches else -1,
logger=False, # Disable logging since we're just doing inference
enable_checkpointing=False,
)
trainer.validate(pl_module, data_loader)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Encode audio dataset to VAE latents using PyTorch Lightning')
parser.add_argument('--model-config', type=str, help='Path to model config', required=False)
parser.add_argument('--ckpt-path', type=str, help='Path to unwrapped autoencoder model checkpoint', required=False)
parser.add_argument('--model-half', action='store_true', help='Whether to use half precision')
parser.add_argument('--dataset-config', type=str, help='Path to dataset config file', required=True)
parser.add_argument('--output-path', type=str, help='Path to output folder', required=True)
parser.add_argument('--batch-size', type=int, help='Batch size', default=1)
parser.add_argument('--sample-size', type=int, help='Number of audio samples to pad/crop to', default=1320960)
parser.add_argument('--is-discrete', action='store_true', help='Whether the model is discrete')
parser.add_argument('--num-nodes', type=int, help='Number of GPU nodes', default=1)
parser.add_argument('--num-workers', type=int, help='Number of dataloader workers', default=4)
parser.add_argument('--strategy', type=str, help='PyTorch Lightning strategy', default='auto')
parser.add_argument('--limit-batches', type=int, help='Limit number of batches (optional)', default=None)
parser.add_argument('--shuffle', action='store_true', help='Shuffle dataset')
args = parser.parse_args()
main(args) |