def main():
args = parse_args()
assert (torch.cuda.is_available())
assert args.prediction_frequency % args.log_frequency == 0
torch.backends.cudnn.benchmark = args.cudnn_benchmark
# set up distributed training
multi_gpu = int(os.environ.get('WORLD_SIZE', 1)) > 1
if multi_gpu:
torch.cuda.set_device(args.local_rank)
dist.init_process_group(backend='nccl', init_method='env://')
world_size = dist.get_world_size()
print_once(f'Distributed training with {world_size} GPUs\n')
else:
world_size = 1
torch.manual_seed(args.seed + args.local_rank)
np.random.seed(args.seed + args.local_rank)
random.seed(args.seed + args.local_rank)
init_log(args)
cfg = config.load(args.model_config)
config.apply_config_overrides(cfg, args)
symbols = helpers.add_ctc_blank(cfg['labels'])
assert args.grad_accumulation_steps >= 1
assert args.batch_size % args.grad_accumulation_steps == 0
batch_size = args.batch_size // args.grad_accumulation_steps
print_once('Setting up datasets...')
train_dataset_kw, train_features_kw = config.input(cfg, 'train')
val_dataset_kw, val_features_kw = config.input(cfg, 'val')
use_dali = args.dali_device in ('cpu', 'gpu')
if use_dali:
assert train_dataset_kw['ignore_offline_speed_perturbation'], \
"DALI doesn't support offline speed perturbation"
# pad_to_max_duration is not supported by DALI - have simple padders
if train_features_kw['pad_to_max_duration']:
train_feat_proc = BaseFeatures(
pad_align=train_features_kw['pad_align'],
pad_to_max_duration=True,
max_duration=train_features_kw['max_duration'],
sample_rate=train_features_kw['sample_rate'],
window_size=train_features_kw['window_size'],
window_stride=train_features_kw['window_stride'])
train_features_kw['pad_to_max_duration'] = False
else:
train_feat_proc = None
if val_features_kw['pad_to_max_duration']:
val_feat_proc = BaseFeatures(
pad_align=val_features_kw['pad_align'],
pad_to_max_duration=True,
max_duration=val_features_kw['max_duration'],
sample_rate=val_features_kw['sample_rate'],
window_size=val_features_kw['window_size'],
window_stride=val_features_kw['window_stride'])
val_features_kw['pad_to_max_duration'] = False
else:
val_feat_proc = None
train_loader = DaliDataLoader(
gpu_id=args.local_rank,
dataset_path=args.dataset_dir,
config_data=train_dataset_kw,
config_features=train_features_kw,
json_names=args.train_manifests,
batch_size=batch_size,
grad_accumulation_steps=args.grad_accumulation_steps,
pipeline_type="train",
device_type=args.dali_device,
symbols=symbols)
val_loader = DaliDataLoader(gpu_id=args.local_rank,
dataset_path=args.dataset_dir,
config_data=val_dataset_kw,
config_features=val_features_kw,
json_names=args.val_manifests,
batch_size=batch_size,
pipeline_type="val",
device_type=args.dali_device,
symbols=symbols)
else:
train_dataset_kw, train_features_kw = config.input(cfg, 'train')
train_dataset = AudioDataset(args.dataset_dir, args.train_manifests,
symbols, **train_dataset_kw)
train_loader = get_data_loader(train_dataset,
batch_size,
multi_gpu=multi_gpu,
shuffle=True,
num_workers=4)
train_feat_proc = FilterbankFeatures(**train_features_kw)
val_dataset_kw, val_features_kw = config.input(cfg, 'val')
val_dataset = AudioDataset(args.dataset_dir, args.val_manifests,
symbols, **val_dataset_kw)
val_loader = get_data_loader(val_dataset,
batch_size,
multi_gpu=multi_gpu,
shuffle=False,
num_workers=4,
drop_last=False)
val_feat_proc = FilterbankFeatures(**val_features_kw)
dur = train_dataset.duration / 3600
dur_f = train_dataset.duration_filtered / 3600
nsampl = len(train_dataset)
print_once(f'Training samples: {nsampl} ({dur:.1f}h, '
f'filtered {dur_f:.1f}h)')
if train_feat_proc is not None:
train_feat_proc.cuda()
if val_feat_proc is not None:
val_feat_proc.cuda()
steps_per_epoch = len(train_loader) // args.grad_accumulation_steps
# set up the model
model = Jasper(encoder_kw=config.encoder(cfg),
decoder_kw=config.decoder(cfg, n_classes=len(symbols)))
model.cuda()
ctc_loss = CTCLossNM(n_classes=len(symbols))
greedy_decoder = GreedyCTCDecoder()
print_once(f'Model size: {num_weights(model) / 10**6:.1f}M params\n')
# optimization
kw = {'lr': args.lr, 'weight_decay': args.weight_decay}
if args.optimizer == "novograd":
optimizer = Novograd(model.parameters(), **kw)
elif args.optimizer == "adamw":
optimizer = AdamW(model.parameters(), **kw)
else:
raise ValueError(f'Invalid optimizer "{args.optimizer}"')
scaler = torch.cuda.amp.GradScaler(enabled=args.amp)
adjust_lr = lambda step, epoch, optimizer: lr_policy(
step,
epoch,
args.lr,
optimizer,
steps_per_epoch=steps_per_epoch,
warmup_epochs=args.warmup_epochs,
hold_epochs=args.hold_epochs,
num_epochs=args.epochs,
policy=args.lr_policy,
min_lr=args.min_lr,
exp_gamma=args.lr_exp_gamma)
if args.ema > 0:
ema_model = copy.deepcopy(model)
else:
ema_model = None
if multi_gpu:
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[args.local_rank], output_device=args.local_rank)
if args.pyprof:
pyprof.init(enable_function_stack=True)
# load checkpoint
meta = {'best_wer': 10**6, 'start_epoch': 0}
checkpointer = Checkpointer(args.output_dir, 'Jasper',
args.keep_milestones)
if args.resume:
args.ckpt = checkpointer.last_checkpoint() or args.ckpt
if args.ckpt is not None:
checkpointer.load(args.ckpt, model, ema_model, optimizer, scaler, meta)
start_epoch = meta['start_epoch']
best_wer = meta['best_wer']
epoch = 1
step = start_epoch * steps_per_epoch + 1
if args.pyprof:
torch.autograd.profiler.emit_nvtx().__enter__()
profiler.start()
# training loop
model.train()
# pre-allocate
if args.pre_allocate_range is not None:
n_feats = train_features_kw['n_filt']
pad_align = train_features_kw['pad_align']
a, b = args.pre_allocate_range
for n_frames in range(a, b + pad_align, pad_align):
print_once(
f'Pre-allocation ({batch_size}x{n_feats}x{n_frames})...')
feat = torch.randn(batch_size, n_feats, n_frames, device='cuda')
feat_lens = torch.ones(batch_size, device='cuda').fill_(n_frames)
txt = torch.randint(high=len(symbols) - 1,
size=(batch_size, 100),
device='cuda')
txt_lens = torch.ones(batch_size, device='cuda').fill_(100)
with torch.cuda.amp.autocast(enabled=args.amp):
log_probs, enc_lens = model(feat, feat_lens)
del feat
loss = ctc_loss(log_probs, txt, enc_lens, txt_lens)
loss.backward()
model.zero_grad()
torch.cuda.empty_cache()
bmark_stats = BenchmarkStats()
for epoch in range(start_epoch + 1, args.epochs + 1):
if multi_gpu and not use_dali:
train_loader.sampler.set_epoch(epoch)
epoch_utts = 0
epoch_loss = 0
accumulated_batches = 0
epoch_start_time = time.time()
epoch_eval_time = 0
for batch in train_loader:
if accumulated_batches == 0:
step_loss = 0
step_utts = 0
step_start_time = time.time()
if use_dali:
# with DALI, the data is already on GPU
feat, feat_lens, txt, txt_lens = batch
if train_feat_proc is not None:
feat, feat_lens = train_feat_proc(feat, feat_lens)
else:
batch = [t.cuda(non_blocking=True) for t in batch]
audio, audio_lens, txt, txt_lens = batch
feat, feat_lens = train_feat_proc(audio, audio_lens)
# Use context manager to prevent redundant accumulation of gradients
if (multi_gpu and
accumulated_batches + 1 < args.grad_accumulation_steps):
ctx = model.no_sync()
else:
ctx = empty_context()
with ctx:
with torch.cuda.amp.autocast(enabled=args.amp):
log_probs, enc_lens = model(feat, feat_lens)
loss = ctc_loss(log_probs, txt, enc_lens, txt_lens)
loss /= args.grad_accumulation_steps
if multi_gpu:
reduced_loss = reduce_tensor(loss.data, world_size)
else:
reduced_loss = loss
if torch.isnan(reduced_loss).any():
print_once(f'WARNING: loss is NaN; skipping update')
continue
else:
step_loss += reduced_loss.item()
step_utts += batch[0].size(0) * world_size
epoch_utts += batch[0].size(0) * world_size
accumulated_batches += 1
scaler.scale(loss).backward()
if accumulated_batches % args.grad_accumulation_steps == 0:
epoch_loss += step_loss
scaler.step(optimizer)
scaler.update()
adjust_lr(step, epoch, optimizer)
optimizer.zero_grad()
apply_ema(model, ema_model, args.ema)
if step % args.log_frequency == 0:
preds = greedy_decoder(log_probs)
wer, pred_utt, ref = greedy_wer(preds, txt, txt_lens,
symbols)
if step % args.prediction_frequency == 0:
print_once(f' Decoded: {pred_utt[:90]}')
print_once(f' Reference: {ref[:90]}')
step_time = time.time() - step_start_time
log(
(epoch, step % steps_per_epoch
or steps_per_epoch, steps_per_epoch), step, 'train', {
'loss': step_loss,
'wer': 100.0 * wer,
'throughput': step_utts / step_time,
'took': step_time,
'lrate': optimizer.param_groups[0]['lr']
})
step_start_time = time.time()
if step % args.eval_frequency == 0:
tik = time.time()
wer = evaluate(epoch, step, val_loader, val_feat_proc,
symbols, model, ema_model, ctc_loss,
greedy_decoder, args.amp, use_dali)
if wer < best_wer and epoch >= args.save_best_from:
checkpointer.save(model,
ema_model,
optimizer,
scaler,
epoch,
step,
best_wer,
is_best=True)
best_wer = wer
epoch_eval_time += time.time() - tik
step += 1
accumulated_batches = 0
# end of step
# DALI iterator need to be exhausted;
# if not using DALI, simulate drop_last=True with grad accumulation
if not use_dali and step > steps_per_epoch * epoch:
break
epoch_time = time.time() - epoch_start_time
epoch_loss /= steps_per_epoch
log(
(epoch, ), None, 'train_avg', {
'throughput': epoch_utts / epoch_time,
'took': epoch_time,
'loss': epoch_loss
})
bmark_stats.update(epoch_utts, epoch_time, epoch_loss)
if epoch % args.save_frequency == 0 or epoch in args.keep_milestones:
checkpointer.save(model, ema_model, optimizer, scaler, epoch, step,
best_wer)
if 0 < args.epochs_this_job <= epoch - start_epoch:
print_once(f'Finished after {args.epochs_this_job} epochs.')
break
# end of epoch
if args.pyprof:
profiler.stop()
torch.autograd.profiler.emit_nvtx().__exit__(None, None, None)
log((), None, 'train_avg', bmark_stats.get(args.benchmark_epochs_num))
if epoch == args.epochs:
evaluate(epoch, step, val_loader, val_feat_proc, symbols, model,
ema_model, ctc_loss, greedy_decoder, args.amp, use_dali)
checkpointer.save(model, ema_model, optimizer, scaler, epoch, step,
best_wer)
flush_log()
def main():
logging.configure_logger('RNNT')
logging.log_start(logging.constants.INIT_START)
args = parse_args()
assert(torch.cuda.is_available())
assert args.prediction_frequency is None or args.prediction_frequency % args.log_frequency == 0
torch.backends.cudnn.benchmark = args.cudnn_benchmark
# set up distributed training
multi_gpu = int(os.environ.get('WORLD_SIZE', 1)) > 1
if multi_gpu:
torch.cuda.set_device(args.local_rank)
dist.init_process_group(backend='nccl', init_method='env://')
world_size = dist.get_world_size()
print_once(f'Distributed training with {world_size} GPUs\n')
else:
world_size = 1
if args.seed is not None:
logging.log_event(logging.constants.SEED, value=args.seed)
torch.manual_seed(args.seed + args.local_rank)
np.random.seed(args.seed + args.local_rank)
random.seed(args.seed + args.local_rank)
# np_rng is used for buckets generation, and needs the same seed on every worker
np_rng = np.random.default_rng(seed=args.seed)
init_log(args)
cfg = config.load(args.model_config)
config.apply_duration_flags(cfg, args.max_duration)
assert args.grad_accumulation_steps >= 1
assert args.batch_size % args.grad_accumulation_steps == 0, f'{args.batch_size} % {args.grad_accumulation_steps} != 0'
logging.log_event(logging.constants.GRADIENT_ACCUMULATION_STEPS, value=args.grad_accumulation_steps)
batch_size = args.batch_size // args.grad_accumulation_steps
logging.log_event(logging.constants.SUBMISSION_BENCHMARK, value=logging.constants.RNNT)
logging.log_event(logging.constants.SUBMISSION_ORG, value='my-organization')
logging.log_event(logging.constants.SUBMISSION_DIVISION, value=logging.constants.CLOSED) # closed or open
logging.log_event(logging.constants.SUBMISSION_STATUS, value=logging.constants.ONPREM) # on-prem/cloud/research
logging.log_event(logging.constants.SUBMISSION_PLATFORM, value='my platform')
logging.log_end(logging.constants.INIT_STOP)
if multi_gpu:
torch.distributed.barrier()
logging.log_start(logging.constants.RUN_START)
if multi_gpu:
torch.distributed.barrier()
print_once('Setting up datasets...')
(
train_dataset_kw,
train_features_kw,
train_splicing_kw,
train_specaugm_kw,
) = config.input(cfg, 'train')
(
val_dataset_kw,
val_features_kw,
val_splicing_kw,
val_specaugm_kw,
) = config.input(cfg, 'val')
logging.log_event(logging.constants.DATA_TRAIN_MAX_DURATION,
value=train_dataset_kw['max_duration'])
logging.log_event(logging.constants.DATA_SPEED_PERTURBATON_MAX,
value=train_dataset_kw['speed_perturbation']['max_rate'])
logging.log_event(logging.constants.DATA_SPEED_PERTURBATON_MIN,
value=train_dataset_kw['speed_perturbation']['min_rate'])
logging.log_event(logging.constants.DATA_SPEC_AUGMENT_FREQ_N,
value=train_specaugm_kw['freq_masks'])
logging.log_event(logging.constants.DATA_SPEC_AUGMENT_FREQ_MIN,
value=train_specaugm_kw['min_freq'])
logging.log_event(logging.constants.DATA_SPEC_AUGMENT_FREQ_MAX,
value=train_specaugm_kw['max_freq'])
logging.log_event(logging.constants.DATA_SPEC_AUGMENT_TIME_N,
value=train_specaugm_kw['time_masks'])
logging.log_event(logging.constants.DATA_SPEC_AUGMENT_TIME_MIN,
value=train_specaugm_kw['min_time'])
logging.log_event(logging.constants.DATA_SPEC_AUGMENT_TIME_MAX,
value=train_specaugm_kw['max_time'])
logging.log_event(logging.constants.GLOBAL_BATCH_SIZE,
value=batch_size * world_size * args.grad_accumulation_steps)
tokenizer_kw = config.tokenizer(cfg)
tokenizer = Tokenizer(**tokenizer_kw)
class PermuteAudio(torch.nn.Module):
def forward(self, x):
return (x[0].permute(2, 0, 1), *x[1:])
train_augmentations = torch.nn.Sequential(
train_specaugm_kw and features.SpecAugment(optim_level=args.amp, **train_specaugm_kw) or torch.nn.Identity(),
features.FrameSplicing(optim_level=args.amp, **train_splicing_kw),
PermuteAudio(),
)
val_augmentations = torch.nn.Sequential(
val_specaugm_kw and features.SpecAugment(optim_level=args.amp, **val_specaugm_kw) or torch.nn.Identity(),
features.FrameSplicing(optim_level=args.amp, **val_splicing_kw),
PermuteAudio(),
)
logging.log_event(logging.constants.DATA_TRAIN_NUM_BUCKETS, value=args.num_buckets)
if args.num_buckets is not None:
sampler = dali_sampler.BucketingSampler(
args.num_buckets,
batch_size,
world_size,
args.epochs,
np_rng
)
else:
sampler = dali_sampler.SimpleSampler()
train_loader = DaliDataLoader(gpu_id=args.local_rank,
dataset_path=args.dataset_dir,
config_data=train_dataset_kw,
config_features=train_features_kw,
json_names=args.train_manifests,
batch_size=batch_size,
sampler=sampler,
grad_accumulation_steps=args.grad_accumulation_steps,
pipeline_type="train",
device_type=args.dali_device,
tokenizer=tokenizer)
val_loader = DaliDataLoader(gpu_id=args.local_rank,
dataset_path=args.dataset_dir,
config_data=val_dataset_kw,
config_features=val_features_kw,
json_names=args.val_manifests,
batch_size=args.val_batch_size,
sampler=dali_sampler.SimpleSampler(),
pipeline_type="val",
device_type=args.dali_device,
tokenizer=tokenizer)
train_feat_proc = train_augmentations
val_feat_proc = val_augmentations
train_feat_proc.cuda()
val_feat_proc.cuda()
steps_per_epoch = len(train_loader) // args.grad_accumulation_steps
logging.log_event(logging.constants.TRAIN_SAMPLES, value=train_loader.dataset_size)
logging.log_event(logging.constants.EVAL_SAMPLES, value=val_loader.dataset_size)
# set up the model
rnnt_config = config.rnnt(cfg)
logging.log_event(logging.constants.MODEL_WEIGHTS_INITIALIZATION_SCALE, value=args.weights_init_scale)
if args.weights_init_scale is not None:
rnnt_config['weights_init_scale'] = args.weights_init_scale
if args.hidden_hidden_bias_scale is not None:
rnnt_config['hidden_hidden_bias_scale'] = args.hidden_hidden_bias_scale
model = RNNT(n_classes=tokenizer.num_labels + 1, **rnnt_config)
model.cuda()
blank_idx = tokenizer.num_labels
loss_fn = RNNTLoss(blank_idx=blank_idx)
logging.log_event(logging.constants.EVAL_MAX_PREDICTION_SYMBOLS, value=args.max_symbol_per_sample)
greedy_decoder = RNNTGreedyDecoder( blank_idx=blank_idx,
max_symbol_per_sample=args.max_symbol_per_sample)
print_once(f'Model size: {num_weights(model) / 10**6:.1f}M params\n')
opt_eps=1e-9
logging.log_event(logging.constants.OPT_NAME, value='lamb')
logging.log_event(logging.constants.OPT_BASE_LR, value=args.lr)
logging.log_event(logging.constants.OPT_LAMB_EPSILON, value=opt_eps)
logging.log_event(logging.constants.OPT_LAMB_LR_DECAY_POLY_POWER, value=args.lr_exp_gamma)
logging.log_event(logging.constants.OPT_LR_WARMUP_EPOCHS, value=args.warmup_epochs)
logging.log_event(logging.constants.OPT_LAMB_LR_HOLD_EPOCHS, value=args.hold_epochs)
logging.log_event(logging.constants.OPT_LAMB_BETA_1, value=args.beta1)
logging.log_event(logging.constants.OPT_LAMB_BETA_2, value=args.beta2)
logging.log_event(logging.constants.OPT_GRADIENT_CLIP_NORM, value=args.clip_norm)
logging.log_event(logging.constants.OPT_LR_ALT_DECAY_FUNC, value=True)
logging.log_event(logging.constants.OPT_LR_ALT_WARMUP_FUNC, value=True)
logging.log_event(logging.constants.OPT_LAMB_LR_MIN, value=args.min_lr)
logging.log_event(logging.constants.OPT_WEIGHT_DECAY, value=args.weight_decay)
# optimization
kw = {'params': model.param_groups(args.lr), 'lr': args.lr,
'weight_decay': args.weight_decay}
initial_lrs = [group['lr'] for group in kw['params']]
print_once(f'Starting with LRs: {initial_lrs}')
optimizer = FusedLAMB(betas=(args.beta1, args.beta2), eps=opt_eps, **kw)
adjust_lr = lambda step, epoch: lr_policy(
step, epoch, initial_lrs, optimizer, steps_per_epoch=steps_per_epoch,
warmup_epochs=args.warmup_epochs, hold_epochs=args.hold_epochs,
min_lr=args.min_lr, exp_gamma=args.lr_exp_gamma)
if args.amp:
model, optimizer = amp.initialize(
models=model,
optimizers=optimizer,
opt_level='O1',
max_loss_scale=512.0)
if args.ema > 0:
ema_model = copy.deepcopy(model).cuda()
else:
ema_model = None
logging.log_event(logging.constants.MODEL_EVAL_EMA_FACTOR, value=args.ema)
if multi_gpu:
model = DistributedDataParallel(model)
# load checkpoint
meta = {'best_wer': 10**6, 'start_epoch': 0}
checkpointer = Checkpointer(args.output_dir, 'RNN-T',
args.keep_milestones, args.amp)
if args.resume:
args.ckpt = checkpointer.last_checkpoint() or args.ckpt
if args.ckpt is not None:
checkpointer.load(args.ckpt, model, ema_model, optimizer, meta)
start_epoch = meta['start_epoch']
best_wer = meta['best_wer']
last_wer = meta['best_wer']
epoch = 1
step = start_epoch * steps_per_epoch + 1
# training loop
model.train()
for epoch in range(start_epoch + 1, args.epochs + 1):
logging.log_start(logging.constants.BLOCK_START,
metadata=dict(first_epoch_num=epoch,
epoch_count=1))
logging.log_start(logging.constants.EPOCH_START,
metadata=dict(epoch_num=epoch))
epoch_utts = 0
accumulated_batches = 0
epoch_start_time = time.time()
for batch in train_loader:
if accumulated_batches == 0:
adjust_lr(step, epoch)
optimizer.zero_grad()
step_utts = 0
step_start_time = time.time()
all_feat_lens = []
audio, audio_lens, txt, txt_lens = batch
feats, feat_lens = train_feat_proc([audio, audio_lens])
all_feat_lens += feat_lens
log_probs, log_prob_lens = model(feats, feat_lens, txt, txt_lens)
loss = loss_fn(log_probs[:, :log_prob_lens.max().item()],
log_prob_lens, txt, txt_lens)
loss /= args.grad_accumulation_steps
del log_probs, log_prob_lens
if torch.isnan(loss).any():
print_once(f'WARNING: loss is NaN; skipping update')
else:
if args.amp:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
loss_item = loss.item()
del loss
step_utts += batch[0].size(0) * world_size
epoch_utts += batch[0].size(0) * world_size
accumulated_batches += 1
if accumulated_batches % args.grad_accumulation_steps == 0:
if args.clip_norm is not None:
torch.nn.utils.clip_grad_norm_(
getattr(model, 'module', model).parameters(),
max_norm=args.clip_norm,
norm_type=2)
total_norm = 0.0
try:
if args.log_norm:
for p in getattr(model, 'module', model).parameters():
param_norm = p.grad.data.norm(2)
total_norm += param_norm.item() ** 2
total_norm = total_norm ** (1. / 2)
except AttributeError as e:
print_once(f'Exception happened: {e}')
total_norm = 0.0
optimizer.step()
apply_ema(model, ema_model, args.ema)
if step % args.log_frequency == 0:
if args.prediction_frequency is None or step % args.prediction_frequency == 0:
preds = greedy_decoder.decode(model, feats, feat_lens)
wer, pred_utt, ref = greedy_wer(
preds,
txt,
txt_lens,
tokenizer.detokenize)
print_once(f' Decoded: {pred_utt[:90]}')
print_once(f' Reference: {ref[:90]}')
wer = {'wer': 100 * wer}
else:
wer = {}
step_time = time.time() - step_start_time
log((epoch, step % steps_per_epoch or steps_per_epoch, steps_per_epoch),
step, 'train',
{'loss': loss_item,
**wer, # optional entry
'throughput': step_utts / step_time,
'took': step_time,
'grad-norm': total_norm,
'seq-len-min': min(all_feat_lens).item(),
'seq-len-max': max(all_feat_lens).item(),
'lrate': optimizer.param_groups[0]['lr']})
step_start_time = time.time()
step += 1
accumulated_batches = 0
# end of step
logging.log_end(logging.constants.EPOCH_STOP,
metadata=dict(epoch_num=epoch))
epoch_time = time.time() - epoch_start_time
log((epoch,), None, 'train_avg', {'throughput': epoch_utts / epoch_time,
'took': epoch_time})
if epoch % args.val_frequency == 0:
wer = evaluate(epoch, step, val_loader, val_feat_proc,
tokenizer.detokenize, ema_model, loss_fn,
greedy_decoder, args.amp)
last_wer = wer
if wer < best_wer and epoch >= args.save_best_from:
checkpointer.save(model, ema_model, optimizer, epoch,
step, best_wer, is_best=True)
best_wer = wer
save_this_epoch = (args.save_frequency is not None and epoch % args.save_frequency == 0) \
or (epoch in args.keep_milestones)
if save_this_epoch:
checkpointer.save(model, ema_model, optimizer, epoch, step, best_wer)
logging.log_end(logging.constants.BLOCK_STOP, metadata=dict(first_epoch_num=epoch))
if last_wer <= args.target:
logging.log_end(logging.constants.RUN_STOP, metadata={'status': 'success'})
print_once(f'Finished after {args.epochs_this_job} epochs.')
break
if 0 < args.epochs_this_job <= epoch - start_epoch:
print_once(f'Finished after {args.epochs_this_job} epochs.')
break
# end of epoch
log((), None, 'train_avg', {'throughput': epoch_utts / epoch_time})
if last_wer > args.target:
logging.log_end(logging.constants.RUN_STOP, metadata={'status': 'aborted'})
if epoch == args.epochs:
evaluate(epoch, step, val_loader, val_feat_proc, tokenizer.detokenize,
ema_model, loss_fn, greedy_decoder, args.amp)
flush_log()
if args.save_at_the_end:
checkpointer.save(model, ema_model, optimizer, epoch, step, best_wer)