From 8dab6d184a034ca86eafa644ea0d2100aadfe27d Mon Sep 17 00:00:00 2001
From: jmwang66 <wangjiaming.wjm@alibaba-inc.com>
Date: 星期二, 09 五月 2023 10:58:33 +0800
Subject: [PATCH] Merge pull request #473 from alibaba-damo-academy/dev_smohan
---
funasr/tasks/abs_task.py | 52 +++++++++++++++++++++++++++++++++++++++++++---------
1 files changed, 43 insertions(+), 9 deletions(-)
diff --git a/funasr/tasks/abs_task.py b/funasr/tasks/abs_task.py
index 3f20b4f..55a5d79 100644
--- a/funasr/tasks/abs_task.py
+++ b/funasr/tasks/abs_task.py
@@ -445,6 +445,12 @@
help='Perform on "collect stats" mode',
)
group.add_argument(
+ "--mc",
+ type=bool,
+ default=False,
+ help="MultiChannel input",
+ )
+ group.add_argument(
"--write_collected_feats",
type=str2bool,
default=False,
@@ -463,6 +469,12 @@
type=int,
default=sys.maxsize,
help="The maximum number update step to train",
+ )
+ parser.add_argument(
+ "--batch_interval",
+ type=int,
+ default=-1,
+ help="The batch interval for saving model.",
)
group.add_argument(
"--patience",
@@ -541,6 +553,12 @@
type=int,
default=1,
help="The number of gradient accumulation",
+ )
+ group.add_argument(
+ "--bias_grad_times",
+ type=float,
+ default=1.0,
+ help="To scale the gradient of contextual related params",
)
group.add_argument(
"--no_forward_run",
@@ -629,8 +647,8 @@
group.add_argument(
"--init_param",
type=str,
+ action="append",
default=[],
- nargs="*",
help="Specify the file path used for initialization of parameters. "
"The format is '<file_path>:<src_key>:<dst_key>:<exclude_keys>', "
"where file_path is the model file path, "
@@ -656,7 +674,7 @@
"--freeze_param",
type=str,
default=[],
- nargs="*",
+ action="append",
help="Freeze parameters",
)
@@ -1147,10 +1165,10 @@
elif args.distributed and args.simple_ddp:
distributed_option.init_torch_distributed_pai(args)
args.ngpu = dist.get_world_size()
- if args.dataset_type == "small":
+ if args.dataset_type == "small" and args.ngpu > 0:
if args.batch_size is not None:
args.batch_size = args.batch_size * args.ngpu
- if args.batch_bins is not None:
+ if args.batch_bins is not None and args.ngpu > 0:
args.batch_bins = args.batch_bins * args.ngpu
# filter samples if wav.scp and text are mismatch
@@ -1193,12 +1211,18 @@
# logging.basicConfig() is invoked in main_worker() instead of main()
# because it can be invoked only once in a process.
# FIXME(kamo): Should we use logging.getLogger()?
+ # BUGFIX: Remove previous handlers and reset log level
+ for handler in logging.root.handlers[:]:
+ logging.root.removeHandler(handler)
logging.basicConfig(
level=args.log_level,
format=f"[{os.uname()[1].split('.')[0]}]"
f" %(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
)
else:
+ # BUGFIX: Remove previous handlers and reset log level
+ for handler in logging.root.handlers[:]:
+ logging.root.removeHandler(handler)
# Suppress logging if RANK != 0
logging.basicConfig(
level="ERROR",
@@ -1304,6 +1328,7 @@
data_path_and_name_and_type=args.train_data_path_and_name_and_type,
key_file=train_key_file,
batch_size=args.batch_size,
+ mc=args.mc,
dtype=args.train_dtype,
num_workers=args.num_workers,
allow_variable_data_keys=args.allow_variable_data_keys,
@@ -1315,6 +1340,7 @@
data_path_and_name_and_type=args.valid_data_path_and_name_and_type,
key_file=valid_key_file,
batch_size=args.valid_batch_size,
+ mc=args.mc,
dtype=args.train_dtype,
num_workers=args.num_workers,
allow_variable_data_keys=args.allow_variable_data_keys,
@@ -1349,15 +1375,15 @@
from funasr.datasets.large_datasets.build_dataloader import ArkDataLoader
train_iter_factory = ArkDataLoader(args.train_data_file, args.token_list, args.dataset_conf,
frontend_conf=args.frontend_conf if hasattr(args, "frontend_conf") else None,
- seg_dict_file=args.seg_dict_file if hasattr(args,
- "seg_dict_file") else None,
+ seg_dict_file=args.seg_dict_file if hasattr(args, "seg_dict_file") else None,
punc_dict_file=args.punc_list if hasattr(args, "punc_list") else None,
+ bpemodel_file=args.bpemodel if hasattr(args, "bpemodel") else None,
mode="train")
valid_iter_factory = ArkDataLoader(args.valid_data_file, args.token_list, args.dataset_conf,
frontend_conf=args.frontend_conf if hasattr(args, "frontend_conf") else None,
- seg_dict_file=args.seg_dict_file if hasattr(args,
- "seg_dict_file") else None,
+ seg_dict_file=args.seg_dict_file if hasattr(args, "seg_dict_file") else None,
punc_dict_file=args.punc_list if hasattr(args, "punc_list") else None,
+ bpemodel_file=args.bpemodel if hasattr(args, "bpemodel") else None,
mode="eval")
elif args.dataset_type == "small":
train_iter_factory = cls.build_iter_factory(
@@ -1570,13 +1596,21 @@
) -> AbsIterFactory:
assert check_argument_types()
+ if hasattr(args, "frontend_conf"):
+ if args.frontend_conf is not None and "fs" in args.frontend_conf:
+ dest_sample_rate = args.frontend_conf["fs"]
+ else:
+ dest_sample_rate = 16000
+ else:
+ dest_sample_rate = 16000
+
dataset = ESPnetDataset(
iter_options.data_path_and_name_and_type,
float_dtype=args.train_dtype,
preprocess=iter_options.preprocess_fn,
max_cache_size=iter_options.max_cache_size,
max_cache_fd=iter_options.max_cache_fd,
- dest_sample_rate=args.frontend_conf["fs"],
+ dest_sample_rate=dest_sample_rate,
)
cls.check_task_requirements(
dataset, args.allow_variable_data_keys, train=iter_options.train
--
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