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inference_SAT.py
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import os
import sys
import argparse
from glob import glob
from ruamel.yaml import YAML
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
import torchaudio
import torch.distributed as dist
from tqdm.auto import tqdm
# from datasets import load_dataset
from datasets import create_dataset, PrefetchLoader_split
import wandb
from model import SAT
from torcheval.metrics import FrechetAudioDistance
from torchmetrics.audio import ScaleInvariantSignalDistortionRatio, ScaleInvariantSignalNoiseRatio, ShortTimeObjectiveIntelligibility
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
def parse_args():
parser = argparse.ArgumentParser()
# config file
parser.add_argument("--config", type=str, default=None, help="config file used to specify parameters")
# data
parser.add_argument("--data", type=str, default=None, help="data")
parser.add_argument("--train_dir", type=str, default='/voyager/AudioSet/audioset_unbalanced_train_mp3', help="data folder")
parser.add_argument("--test_dir", type=str, default='')
parser.add_argument("--train_csv", type=str, default='/voyager/AudioSet/unbalanced_train_segments.csv')
parser.add_argument("--dataset_name", type=str, default="audioset", help="dataset name")
parser.add_argument("--batch_size", type=int, default=1, help="per gpu batch size")
parser.add_argument("--tensor_cut", type=int, default=24000)
parser.add_argument("--fixed_length", type=int, default=0)
parser.add_argument("--num_workers", type=int, default=8, help="batch size")
parser.add_argument("--use_prefetcher", type=bool, default=False)
parser.add_argument("--gradient_accumulation_steps", type=int, default=1, help='gradient accumulation steps')
# training
parser.add_argument("--train_disc", type=bool, default=False)
parser.add_argument("--warmup_epoch", type=int, default=0)
parser.add_argument("--debug", type=bool, default=False)
parser.add_argument("--split_run", type=bool, default=False)
parser.add_argument("--node", type=int, default=0)
parser.add_argument("--gpus", type=int, default=8)
parser.add_argument("--run_name", type=str, default=None, help="run_name")
parser.add_argument("--output_dir", type=str, default="experiments", help="output folder")
parser.add_argument("--num_epochs", type=int, default=100)
parser.add_argument("--optimizer", type=str, default="adamw", help="optimizer")
parser.add_argument("--learning_rate", type=float, default=3e-4, help="learning rate")
parser.add_argument("--lr_scheduler", type=str, default='cosine', help='lr scheduler')
parser.add_argument("--lr_warmup_steps", type=float, default=0.03, help="warmup steps")
parser.add_argument("--log_interval", type=int, default=500, help='log interval for steps')
parser.add_argument("--val_interval", type=int, default=1, help='validation interval for epochs')
parser.add_argument("--save_interval", type=str, default='epoch', help='save interval')
parser.add_argument("--mixed_precision", type=str, default='bf16', help='mixed precision', choices=['no', 'fp16', 'bf16', 'fp8'])
parser.add_argument("--clip", type=float, default=1, help='gradient clip, set to -1 if not used')
parser.add_argument("--resume", type=str, default=False, help='resume')
# audio-vqvae
parser.add_argument('--sample_rate', type=int, default=24000)
parser.add_argument('--window', type=float, default=1)
parser.add_argument('--channels', type=int, default=1)
parser.add_argument('--model_norm', type=str, default='weight_norm')
parser.add_argument('--audio_normalize', type=bool, default=False)
parser.add_argument('--ratios', nargs='+', type=int, default=[8, 5, 4, 2])
parser.add_argument('--multi_scale', nargs='+', type=int, default=None)
parser.add_argument('--phi_kernel', nargs='+', type=int, default=None)
parser.add_argument('--dimension', type=int, default=128)
parser.add_argument('--latent_dim', type=int, default=32)
# discriminator
parser.add_argument("--filters", type=int, default=32, help="filter for disc")
parser.add_argument('--disc_win_lengths', nargs='+', type=int, default=[1024, 2048, 512])
parser.add_argument('--disc_hop_lengths', nargs='+', type=int, default=[256, 512, 128])
parser.add_argument('--disc_n_ffts', nargs='+', type=int, default=[1024, 2048, 512])
parser.add_argument('--clap_process', type=bool, default=False)
parser.add_argument('--gen_dir', type=str, default='Audio_gen')
parser.add_argument('--scale', type=bool, default=False)
# fFirst parse of command-line args to check for config file
args = parser.parse_args()
# If a config file is specified, load it and set defaults
if args.config is not None:
with open(args.config, 'r', encoding='utf-8') as f:
yaml = YAML(typ='safe')
with open(args.config, 'r', encoding='utf-8') as file:
config_args = yaml.load(file)
parser.set_defaults(**config_args)
# re-parse command-line args to overwrite with any command-line inputs
args = parser.parse_args()
return args
def process(args):
audiovae = SAT(
args.sample_rate,
args.channels,
causal=False, model_norm=args.model_norm,
audio_normalize=args.audio_normalize,
ratios=args.ratios,
multi_scale=args.multi_scale,
phi_kernel=args.phi_kernel,
dimension=args.dimension,
latent_dim=args.latent_dim
).to(device)
state_dict = torch.load(args.resume, map_location=torch.device("cpu"))
if "generator_state_dict" in state_dict.keys():
generator_state_dict = state_dict['generator_state_dict']
new_state_dict = {}
for k, v in generator_state_dict.items():
if k.startswith('module.'):
new_state_dict[k[7:]] = v
else:
new_state_dict[k] = v
audiovae.load_state_dict(new_state_dict, strict=True)
print(f"resume from checkpoint: {args.resume}")
print("create dataset for inference")
dataset = create_dataset("audioset", args, split="test")
dataloader = DataLoader(dataset, shuffle=False, batch_size=args.batch_size, num_workers=args.num_workers, pin_memory=True, drop_last=True)
dataloader = PrefetchLoader_split(dataloader, device=device, tensor_cut=args.tensor_cut)
audiovae.eval()
for batch_idx, input_wav in enumerate(tqdm(dataloader, desc="Process reconstruction")):
with torch.no_grad():
output_wav, _, _ = audiovae(input_wav)
for idx in range(0, input_wav.shape[0], 240000 // args.tensor_cut):
output_wavs = torch.zeros((1, 240000))
for i in range(240000 // args.tensor_cut):
output_wavs[0, i*args.tensor_cut:(i+1)*args.tensor_cut] = output_wav[idx+i].flatten()
torchaudio.save(os.path.join(args.gen_dir, f'{batch_idx*args.batch_size+idx//(240000 // args.tensor_cut)}.wav'), output_wavs, sample_rate=args.sample_rate)
if __name__ == '__main__':
args = parse_args()
process(args)