def run(args):
# set random seed
seed = set_seed(args.seed)
if seed is not None:
print(f"Set random seed as {seed}")
conf, vocab = load_am_conf(args.conf, args.dict)
print(f"Arguments in args:\n{pprint.pformat(vars(args))}", flush=True)
print(f"Arguments in yaml:\n{pprint.pformat(conf)}", flush=True)
asr_cls = aps_asr_nnet(conf["nnet"])
asr_transform = None
enh_transform = None
if "asr_transform" in conf:
asr_transform = aps_transform("asr")(**conf["asr_transform"])
if "enh_transform" in conf:
enh_transform = aps_transform("enh")(**conf["enh_transform"])
if enh_transform:
nnet = asr_cls(enh_transform=enh_transform,
asr_transform=asr_transform,
**conf["nnet_conf"])
elif asr_transform:
nnet = asr_cls(asr_transform=asr_transform, **conf["nnet_conf"])
else:
nnet = asr_cls(**conf["nnet_conf"])
task = aps_task(conf["task"], nnet, **conf["task_conf"])
train_worker(task, conf, vocab, args)
def run(args):
_ = set_seed(args.seed)
conf, vocab = load_am_conf(args.conf, args.dict)
print(f"Arguments in args:\n{pprint.pformat(vars(args))}", flush=True)
print(f"Arguments in yaml:\n{pprint.pformat(conf)}", flush=True)
asr_cls = aps_asr_nnet(conf["nnet"])
asr_transform = None
enh_transform = None
if "asr_transform" in conf:
asr_transform = aps_transform("asr")(**conf["asr_transform"])
if "enh_transform" in conf:
enh_transform = aps_transform("enh")(**conf["enh_transform"])
if enh_transform:
nnet = asr_cls(enh_transform=enh_transform,
asr_transform=asr_transform,
**conf["nnet_conf"])
elif asr_transform:
nnet = asr_cls(asr_transform=asr_transform, **conf["nnet_conf"])
else:
nnet = asr_cls(**conf["nnet_conf"])
other_loader_conf = {
"vocab_dict": vocab,
}
start_trainer(args.trainer,
conf,
nnet,
args,
reduction_tag="#tok",
other_loader_conf=other_loader_conf)
dump_dict(f"{args.checkpoint}/dict", vocab, reverse=False)
def run(args):
# set random seed
seed = set_seed(args.seed)
if seed is not None:
print(f"Set random seed as {seed}")
conf, vocab_dict = load_am_conf(args.conf, args.dict)
print(f"Arguments in args:\n{pprint.pformat(vars(args))}", flush=True)
print(f"Arguments in yaml:\n{pprint.pformat(conf)}", flush=True)
data_conf = conf["data_conf"]
load_conf = {
"fmt": data_conf["fmt"],
"vocab_dict": vocab_dict,
"num_workers": args.num_workers,
"max_batch_size": args.batch_size
}
load_conf.update(data_conf["loader"])
trn_loader = aps_dataloader(train=True, **data_conf["train"], **load_conf)
dev_loader = aps_dataloader(train=False, **data_conf["valid"], **load_conf)
asr_cls = aps_asr_nnet(conf["nnet"])
asr_transform = None
enh_transform = None
if "asr_transform" in conf:
asr_transform = aps_transform("asr")(**conf["asr_transform"])
if "enh_transform" in conf:
enh_transform = aps_transform("enh")(**conf["enh_transform"])
if enh_transform:
nnet = asr_cls(enh_transform=enh_transform,
asr_transform=asr_transform,
**conf["nnet_conf"])
elif asr_transform:
nnet = asr_cls(asr_transform=asr_transform, **conf["nnet_conf"])
else:
nnet = asr_cls(**conf["nnet_conf"])
task = aps_task(conf["task"], nnet, **conf["task_conf"])
Trainer = aps_trainer(args.trainer, distributed=False)
trainer = Trainer(task,
device_ids=args.device_id,
checkpoint=args.checkpoint,
resume=args.resume,
init=args.init,
save_interval=args.save_interval,
prog_interval=args.prog_interval,
tensorboard=args.tensorboard,
reduction_tag="#tok",
**conf["trainer_conf"])
# dump yaml configurations
conf["cmd_args"] = vars(args)
with open(f"{args.checkpoint}/train.yaml", "w") as f:
yaml.dump(conf, f)
# dump dict
dump_dict(f"{args.checkpoint}/dict", vocab_dict, reverse=False)
trainer.run(trn_loader,
dev_loader,
num_epochs=args.epochs,
eval_interval=args.eval_interval)
def run(args):
_ = set_seed(args.seed)
conf = load_ss_conf(args.conf)
_ = set_seed(args.seed)
print(f"Arguments in args:\n{pprint.pformat(vars(args))}", flush=True)
print(f"Arguments in yaml:\n{pprint.pformat(conf)}", flush=True)
sse_cls = aps_sse_nnet(conf["nnet"])
# with or without enh_tranform
if "enh_transform" in conf:
enh_transform = aps_transform("enh")(**conf["enh_transform"])
nnet = sse_cls(enh_transform=enh_transform, **conf["nnet_conf"])
else:
nnet = sse_cls(**conf["nnet_conf"])
is_distributed = args.distributed != "none"
if is_distributed and args.eval_interval <= 0:
raise RuntimeError("For distributed training of SE/SS model, "
"--eval-interval must be larger than 0")
start_trainer(args.trainer, conf, nnet, args, reduction_tag="none")
def run(args):
# set random seed
seed = set_seed(args.seed)
if seed is not None:
print(f"Set random seed as {seed}")
conf, vocab = load_lm_conf(args.conf, args.dict)
print(f"Arguments in args:\n{pprint.pformat(vars(args))}", flush=True)
print(f"Arguments in yaml:\n{pprint.pformat(conf)}", flush=True)
nnet = aps_asr_nnet(conf["nnet"])(**conf["nnet_conf"])
task = aps_task(conf["task"], nnet, **conf["task_conf"])
train_worker(task, conf, vocab, args)
def run(args):
# set random seed
seed = set_seed(args.seed)
if seed is not None:
print(f"Set random seed as {seed}")
conf = load_ss_conf(args.conf)
print(f"Arguments in args:\n{pprint.pformat(vars(args))}", flush=True)
print(f"Arguments in yaml:\n{pprint.pformat(conf)}", flush=True)
data_conf = conf["data_conf"]
load_conf = {
"fmt": data_conf["fmt"],
"batch_size": args.batch_size,
"num_workers": args.num_workers,
}
load_conf.update(data_conf["loader"])
trn_loader = aps_dataloader(train=True, **load_conf, **data_conf["train"])
dev_loader = aps_dataloader(train=False, **load_conf, **data_conf["valid"])
sse_cls = aps_sse_nnet(conf["nnet"])
if "enh_transform" in conf:
enh_transform = aps_transform("enh")(**conf["enh_transform"])
nnet = sse_cls(enh_transform=enh_transform, **conf["nnet_conf"])
else:
nnet = sse_cls(**conf["nnet_conf"])
task = aps_task(conf["task"], nnet, **conf["task_conf"])
Trainer = aps_trainer(args.trainer, distributed=False)
trainer = Trainer(task,
device_ids=args.device_id,
checkpoint=args.checkpoint,
resume=args.resume,
init=args.init,
save_interval=args.save_interval,
prog_interval=args.prog_interval,
tensorboard=args.tensorboard,
**conf["trainer_conf"])
# dump configurations
conf["cmd_args"] = vars(args)
with open(f"{args.checkpoint}/train.yaml", "w") as f:
yaml.dump(conf, f)
trainer.run(trn_loader,
dev_loader,
num_epochs=args.epochs,
eval_interval=args.eval_interval)
def run(args):
# set random seed
seed = set_seed(args.seed)
if seed is not None:
print(f"Set random seed as {seed}")
conf, vocab = load_lm_conf(args.conf, args.dict)
print(f"Arguments in args:\n{pprint.pformat(vars(args))}", flush=True)
print(f"Arguments in yaml:\n{pprint.pformat(conf)}", flush=True)
data_conf = conf["data_conf"]
load_conf = {
"vocab_dict": vocab,
"num_workers": args.num_workers,
"sos": vocab["<sos>"],
"eos": vocab["<eos>"],
"fmt": data_conf["fmt"],
"max_batch_size": args.batch_size
}
load_conf.update(data_conf["loader"])
trn_loader = aps_dataloader(train=True, **data_conf["train"], **load_conf)
dev_loader = aps_dataloader(train=False, **data_conf["valid"], **load_conf)
nnet = aps_asr_nnet(conf["nnet"])(**conf["nnet_conf"])
task = aps_task(conf["task"], nnet, **conf["task_conf"])
Trainer = aps_trainer(args.trainer, distributed=False)
trainer = Trainer(task,
device_ids=args.device_id,
checkpoint=args.checkpoint,
resume=args.resume,
save_interval=args.save_interval,
prog_interval=args.prog_interval,
tensorboard=args.tensorboard,
reduction_tag="#tok",
**conf["trainer_conf"])
# dump configurations
conf["cmd_args"] = vars(args)
with open(f"{args.checkpoint}/train.yaml", "w") as f:
yaml.dump(conf, f)
trainer.run(trn_loader,
dev_loader,
num_epochs=args.epochs,
eval_interval=args.eval_interval)
def run(args):
# set random seed
seed = set_seed(args.seed)
if seed is not None:
print(f"Set random seed as {seed}")
conf = load_ss_conf(args.conf)
print(f"Arguments in args:\n{pprint.pformat(vars(args))}", flush=True)
print(f"Arguments in yaml:\n{pprint.pformat(conf)}", flush=True)
sse_cls = aps_sse_nnet(conf["nnet"])
# with or without enh_tranform
if "enh_transform" in conf:
enh_transform = aps_transform("enh")(**conf["enh_transform"])
nnet = sse_cls(enh_transform=enh_transform, **conf["nnet_conf"])
else:
nnet = sse_cls(**conf["nnet_conf"])
task = aps_task(conf["task"], nnet, **conf["task_conf"])
train_worker(task, conf, args)
def run(args):
_ = set_seed(args.seed)
conf, vocab = load_lm_conf(args.conf, args.dict)
print(f"Arguments in args:\n{pprint.pformat(vars(args))}", flush=True)
print(f"Arguments in yaml:\n{pprint.pformat(conf)}", flush=True)
nnet = aps_asr_nnet(conf["nnet"])(**conf["nnet_conf"])
other_loader_conf = {
"vocab_dict": vocab,
"sos": vocab["<sos>"],
"eos": vocab["<eos>"],
}
start_trainer(args.trainer,
conf,
nnet,
args,
reduction_tag="#tok",
other_loader_conf=other_loader_conf)
dump_dict(f"{args.checkpoint}/dict", vocab, reverse=False)