Spaces:
Runtime error
Runtime error
File size: 7,270 Bytes
abc4e5e |
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 |
import time
import librosa
import numpy as np
import torch
from torch import autocast
from torch.cuda.amp import GradScaler
from diffusion.logger import utils
from diffusion.logger.saver import Saver
def test(args, model, vocoder, loader_test, saver):
print(' [*] testing...')
model.eval()
# losses
test_loss = 0.
# intialization
num_batches = len(loader_test)
rtf_all = []
# run
with torch.no_grad():
for bidx, data in enumerate(loader_test):
fn = data['name'][0].split("/")[-1]
speaker = data['name'][0].split("/")[-2]
print('--------')
print('{}/{} - {}'.format(bidx, num_batches, fn))
# unpack data
for k in data.keys():
if not k.startswith('name'):
data[k] = data[k].to(args.device)
print('>>', data['name'][0])
# forward
st_time = time.time()
mel = model(
data['units'],
data['f0'],
data['volume'],
data['spk_id'],
gt_spec=None if model.k_step_max == model.timesteps else data['mel'],
infer=True,
infer_speedup=args.infer.speedup,
method=args.infer.method,
k_step=model.k_step_max
)
signal = vocoder.infer(mel, data['f0'])
ed_time = time.time()
# RTF
run_time = ed_time - st_time
song_time = signal.shape[-1] / args.data.sampling_rate
rtf = run_time / song_time
print('RTF: {} | {} / {}'.format(rtf, run_time, song_time))
rtf_all.append(rtf)
# loss
for i in range(args.train.batch_size):
loss = model(
data['units'],
data['f0'],
data['volume'],
data['spk_id'],
gt_spec=data['mel'],
infer=False,
k_step=model.k_step_max)
test_loss += loss.item()
# log mel
saver.log_spec(f"{speaker}_{fn}.wav", data['mel'], mel)
# log audi
path_audio = data['name_ext'][0]
audio, sr = librosa.load(path_audio, sr=args.data.sampling_rate)
if len(audio.shape) > 1:
audio = librosa.to_mono(audio)
audio = torch.from_numpy(audio).unsqueeze(0).to(signal)
saver.log_audio({f"{speaker}_{fn}_gt.wav": audio,f"{speaker}_{fn}_pred.wav": signal})
# report
test_loss /= args.train.batch_size
test_loss /= num_batches
# check
print(' [test_loss] test_loss:', test_loss)
print(' Real Time Factor', np.mean(rtf_all))
return test_loss
def train(args, initial_global_step, model, optimizer, scheduler, vocoder, loader_train, loader_test):
# saver
saver = Saver(args, initial_global_step=initial_global_step)
# model size
params_count = utils.get_network_paras_amount({'model': model})
saver.log_info('--- model size ---')
saver.log_info(params_count)
# run
num_batches = len(loader_train)
model.train()
saver.log_info('======= start training =======')
scaler = GradScaler()
if args.train.amp_dtype == 'fp32':
dtype = torch.float32
elif args.train.amp_dtype == 'fp16':
dtype = torch.float16
elif args.train.amp_dtype == 'bf16':
dtype = torch.bfloat16
else:
raise ValueError(' [x] Unknown amp_dtype: ' + args.train.amp_dtype)
saver.log_info("epoch|batch_idx/num_batches|output_dir|batch/s|lr|time|step")
for epoch in range(args.train.epochs):
for batch_idx, data in enumerate(loader_train):
saver.global_step_increment()
optimizer.zero_grad()
# unpack data
for k in data.keys():
if not k.startswith('name'):
data[k] = data[k].to(args.device)
# forward
if dtype == torch.float32:
loss = model(data['units'].float(), data['f0'], data['volume'], data['spk_id'],
aug_shift = data['aug_shift'], gt_spec=data['mel'].float(), infer=False, k_step=model.k_step_max)
else:
with autocast(device_type=args.device, dtype=dtype):
loss = model(data['units'], data['f0'], data['volume'], data['spk_id'],
aug_shift = data['aug_shift'], gt_spec=data['mel'], infer=False, k_step=model.k_step_max)
# handle nan loss
if torch.isnan(loss):
raise ValueError(' [x] nan loss ')
else:
# backpropagate
if dtype == torch.float32:
loss.backward()
optimizer.step()
else:
scaler.scale(loss).backward()
scaler.step(optimizer)
scaler.update()
scheduler.step()
# log loss
if saver.global_step % args.train.interval_log == 0:
current_lr = optimizer.param_groups[0]['lr']
saver.log_info(
'epoch: {} | {:3d}/{:3d} | {} | batch/s: {:.2f} | lr: {:.6} | loss: {:.3f} | time: {} | step: {}'.format(
epoch,
batch_idx,
num_batches,
args.env.expdir,
args.train.interval_log/saver.get_interval_time(),
current_lr,
loss.item(),
saver.get_total_time(),
saver.global_step
)
)
saver.log_value({
'train/loss': loss.item()
})
saver.log_value({
'train/lr': current_lr
})
# validation
if saver.global_step % args.train.interval_val == 0:
optimizer_save = optimizer if args.train.save_opt else None
# save latest
saver.save_model(model, optimizer_save, postfix=f'{saver.global_step}')
last_val_step = saver.global_step - args.train.interval_val
if last_val_step % args.train.interval_force_save != 0:
saver.delete_model(postfix=f'{last_val_step}')
# run testing set
test_loss = test(args, model, vocoder, loader_test, saver)
# log loss
saver.log_info(
' --- <validation> --- \nloss: {:.3f}. '.format(
test_loss,
)
)
saver.log_value({
'validation/loss': test_loss
})
model.train()
|