@@ -14,6 +14,7 @@ from network_define import WithLossCell
from Loss_final1 import loss
from Loss_final1 import loss
from model_asteroid import Dual_RNN_model
from model_asteroid import Dual_RNN_model
from train_wrapper import TrainingWrapper
from train_wrapper import TrainingWrapper
import time
parser = argparse.ArgumentParser(
parser = argparse.ArgumentParser(
description='Parameters for training Dual-Path-RNN')
description='Parameters for training Dual-Path-RNN')
@@ -42,7 +43,7 @@ parser.add_argument('--sample_rate', default=8000, type=int,
help='Sample rate')
help='Sample rate')
parser.add_argument('--segment', default=4, type=float, # 取音频的长度,2s。#数据集语音长度要相同
parser.add_argument('--segment', default=4, type=float, # 取音频的长度,2s。#数据集语音长度要相同
help='Segment length (seconds)')
help='Segment length (seconds)')
parser.add_argument('--batch_size', default=2 , type=int, # 需要抛弃的音频长度
parser.add_argument('--batch_size', default=3 , type=int, # 需要抛弃的音频长度
help='Batch size')
help='Batch size')
# Network architecture
# Network architecture
@@ -62,7 +63,7 @@ parser.add_argument('--norm', default='gln', type=str,
help='gln = "Global Norm", cln = "Cumulative Norm", ln = "Layer Norm"')
help='gln = "Global Norm", cln = "Cumulative Norm", ln = "Layer Norm"')
parser.add_argument('--dropout', default=0.0, type=float,
parser.add_argument('--dropout', default=0.0, type=float,
help='dropout')
help='dropout')
parser.add_argument('--num_layers', default=6 , type=int,
parser.add_argument('--num_layers', default=4 , type=int,
help='Number of Dual-Path-Block')
help='Number of Dual-Path-Block')
parser.add_argument('--K', default=250, type=int,
parser.add_argument('--K', default=250, type=int,
help='The length of chunk')
help='The length of chunk')
@@ -70,7 +71,7 @@ parser.add_argument('--num_spks', default=2, type=int,
help='The number of speakers')
help='The number of speakers')
# optimizer
# optimizer
parser.add_argument('--lr', default=1e-3 , type=float,
parser.add_argument('--lr', default=0.00 1, type=float,
help='Init learning rate')
help='Init learning rate')
parser.add_argument('--l2', default=1e-5, type=float,
parser.add_argument('--l2', default=1e-5, type=float,
help='weight decay (L2 penalty)')
help='weight decay (L2 penalty)')
@@ -110,7 +111,8 @@ def preprocess(args):
print("preprocess done")
print("preprocess done")
def main(args):
def main(args):
context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, save_graphs=True)
# context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, save_graphs=True)
context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
if args.run_distribute:
if args.run_distribute:
print("distribute")
print("distribute")
@@ -156,7 +158,7 @@ def main(args):
tr_dataset = DatasetGenerator(args.train_dir, args.batch_size,
tr_dataset = DatasetGenerator(args.train_dir, args.batch_size,
sample_rate=args.sample_rate, segment=args.segment)
sample_rate=args.sample_rate, segment=args.segment)
tr_loader = ds.GeneratorDataset(tr_dataset, ["mixture", "lens", "sources"],
tr_loader = ds.GeneratorDataset(tr_dataset, ["mixture", "lens", "sources"],
shuffle=Tru e, num_shards=rank_size, shard_id=rank_id)
shuffle=Fals e, num_shards=rank_size, shard_id=rank_id)
tr_loader = tr_loader.batch(4)
tr_loader = tr_loader.batch(4)
num_steps = tr_loader.get_dataset_size()
num_steps = tr_loader.get_dataset_size()
@@ -178,7 +180,7 @@ def main(args):
loss_cb = LossMonitor(1)
loss_cb = LossMonitor(1)
cb = [time_cb, loss_cb]
cb = [time_cb, loss_cb]
config_ck = CheckpointConfig(save_checkpoint_steps=2 00, keep_checkpoint_max=5)
config_ck = CheckpointConfig(save_checkpoint_steps=1 00, keep_checkpoint_max=5)
ckpt_cb = ModelCheckpoint(prefix='model',
ckpt_cb = ModelCheckpoint(prefix='model',
directory=save_ckpt,
directory=save_ckpt,
config=config_ck)
config=config_ck)