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train.py
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#!/usr/bin/env python
import os
import pprint
import argparse
from libs.trainer import SiSnrTrainer
from utils.dataset import make_dataloader
from utils.logger import get_logger
from nnet.spex_plus import SpEx_Plus
logger = get_logger(__name__)
def run(args):
gpuids = tuple(map(int, args.gpus.split(",")))
L1 = int(args.L1 * args.sample_rate)
L2 = int(args.L2 * args.sample_rate)
L3 = int(args.L3 * args.sample_rate)
nnet = SpEx_Plus(L1=L1,
L2=L2,
L3=L3,
N=args.N,
B=args.B,
O=args.O,
P=args.P,
Q=args.Q,
num_spks=args.num_spks,
spk_embed_dim=args.spk_embed_dim,
causal=args.causal)
trainer = SiSnrTrainer(nnet,
gpuid=gpuids,
checkpoint=args.checkpoint,
optimizer=args.optimizer,
lr=args.lr,
weight_decay=1e-5,
clip_norm=None,
min_lr=1e-8,
patience=2,
factor=0.5,
logging_period=200,
no_impr=6)
tr_mix_scp = os.path.join(args.train_dir, "mix.scp")
tr_ref_scp = os.path.join(args.train_dir, "ref.scp")
tr_aux_scp = os.path.join(args.train_dir, "aux.scp")
dev_mix_scp = os.path.join(args.dev_dir, "mix.scp")
dev_ref_scp = os.path.join(args.dev_dir, "ref.scp")
dev_aux_scp = os.path.join(args.dev_dir, "aux.scp")
chunk_size = args.chunk_size * args.sample_rate
train_loader = make_dataloader(train=True,
mix_scp=tr_mix_scp,
ref_scp=tr_ref_scp,
aux_scp=tr_aux_scp,
spk_list=args.spk_list,
sample_rate=args.sample_rate,
batch_size=args.batch_size,
chunk_size=chunk_size,
num_workers=args.num_workers)
dev_loader = make_dataloader(train=False,
mix_scp=dev_mix_scp,
ref_scp=dev_ref_scp,
aux_scp=dev_aux_scp,
spk_list=args.spk_list,
sample_rate=args.sample_rate,
batch_size=args.batch_size,
chunk_size=chunk_size,
num_workers=args.num_workers)
trainer.run(train_loader, dev_loader, num_epochs=args.epochs)
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description=
"Command to start SpEx_Plus training",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("--gpus",
type=str,
default="0,1",
help="Training on which GPUs "
"(one or more, egs: 0, \"0,1\")")
parser.add_argument("--epochs",
type=int,
default=50,
help="Number of training epochs")
parser.add_argument("--checkpoint",
type=str,
required=True,
help="Directory to dump models")
parser.add_argument("--batch-size",
type=int,
default=16,
help="Number of utterances in each batch")
parser.add_argument("--num-workers",
type=int,
default=4,
help="Number of workers used in data loader")
parser.add_argument("--L1",
type=float,
default=0.0025,
help="Short window length for high temporal resolution, default 2.5ms.")
parser.add_argument("--L2",
type=float,
default=0.01,
help="Middle window length for middle temporal resolution, default 10ms.")
parser.add_argument("--L3",
type=float,
default=0.02,
help="Long window length for low temporal resolution, default 20ms.")
parser.add_argument("--N",
type=int,
default=256,
help="Number of filters of convolution in speech encoder.")
parser.add_argument("--B",
type=int,
default=8,
help="Number of TCN blocks in each stack.")
parser.add_argument("--O",
type=int,
default=256,
help="Number of filters of 1x1 convolution.")
parser.add_argument("--P",
type=int,
default=512,
help="Number of filters of depthwise convolution.")
parser.add_argument("--Q",
type=int,
default=3,
help="Kernel size of depthwise convolution.")
parser.add_argument("--num_spks",
type=int,
default=101,
help="Number of speakers within the training data.")
parser.add_argument("--spk_embed_dim",
type=int,
default=256,
help="Speaker embedding dimension.")
parser.add_argument("--causal",
type=bool,
default=False,
help="causal for online or non-causal for offline process.")
parser.add_argument("--train_dir",
type=str,
default="data/wsj0_2mix/tr",
help="Data folder for training data.")
parser.add_argument("--dev_dir",
type=str,
default="data/wsj0_2mix/cv",
help="Data folder for development data.")
parser.add_argument("--spk_list",
type=str,
default="data/wsj0_2mix_extr_tr.spk",
help="List of speakers in the training data.")
parser.add_argument("--sample_rate",
type=int,
default=8000,
help="Sampling rate.")
parser.add_argument("--chunk_size",
type=int,
default=4,
help="Duration of a segment.")
parser.add_argument("--lr",
type=float,
default=1e-3,
help="Learning rate.")
parser.add_argument("--optimizer",
type=str,
default="adam",
help="Optimizer type.")
args = parser.parse_args()
logger.info("Arguments in command:\n{}".format(pprint.pformat(vars(args))))
run(args)