From 24f73665e2d8ea8e4de2fe4f900bc539d7f7b989 Mon Sep 17 00:00:00 2001
From: hnluo <haoneng.lhn@alibaba-inc.com>
Date: 星期一, 17 四月 2023 15:49:45 +0800
Subject: [PATCH] Merge pull request #367 from alibaba-damo-academy/dev_lhn2
---
funasr/tasks/abs_task.py | 96 +++++++++++++++++++++++++++++++++++++++++++----
1 files changed, 87 insertions(+), 9 deletions(-)
diff --git a/funasr/tasks/abs_task.py b/funasr/tasks/abs_task.py
index c4566a8..777513e 100644
--- a/funasr/tasks/abs_task.py
+++ b/funasr/tasks/abs_task.py
@@ -43,12 +43,15 @@
from funasr.iterators.chunk_iter_factory import ChunkIterFactory
from funasr.iterators.multiple_iter_factory import MultipleIterFactory
from funasr.iterators.sequence_iter_factory import SequenceIterFactory
+from funasr.main_funcs.collect_stats import collect_stats
from funasr.optimizers.sgd import SGD
+from funasr.optimizers.fairseq_adam import FairseqAdam
from funasr.samplers.build_batch_sampler import BATCH_TYPES
from funasr.samplers.build_batch_sampler import build_batch_sampler
from funasr.samplers.unsorted_batch_sampler import UnsortedBatchSampler
from funasr.schedulers.noam_lr import NoamLR
from funasr.schedulers.warmup_lr import WarmupLR
+from funasr.schedulers.tri_stage_scheduler import TriStageLR
from funasr.torch_utils.load_pretrained_model import load_pretrained_model
from funasr.torch_utils.model_summary import model_summary
from funasr.torch_utils.pytorch_version import pytorch_cudnn_version
@@ -83,6 +86,7 @@
optim_classes = dict(
adam=torch.optim.Adam,
+ fairseq_adam=FairseqAdam,
adamw=torch.optim.AdamW,
sgd=SGD,
adadelta=torch.optim.Adadelta,
@@ -149,6 +153,7 @@
CosineAnnealingLR=torch.optim.lr_scheduler.CosineAnnealingLR,
noamlr=NoamLR,
warmuplr=WarmupLR,
+ tri_stage=TriStageLR,
cycliclr=torch.optim.lr_scheduler.CyclicLR,
onecyclelr=torch.optim.lr_scheduler.OneCycleLR,
CosineAnnealingWarmRestarts=torch.optim.lr_scheduler.CosineAnnealingWarmRestarts,
@@ -459,6 +464,12 @@
default=sys.maxsize,
help="The maximum number update step to train",
)
+ parser.add_argument(
+ "--batch_interval",
+ type=int,
+ default=10000,
+ help="The batch interval for saving model.",
+ )
group.add_argument(
"--patience",
type=int_or_none,
@@ -634,12 +645,12 @@
"and exclude_keys excludes keys of model states for the initialization."
"e.g.\n"
" # Load all parameters"
- " --init_param some/where/model.pth\n"
+ " --init_param some/where/model.pb\n"
" # Load only decoder parameters"
- " --init_param some/where/model.pth:decoder:decoder\n"
+ " --init_param some/where/model.pb:decoder:decoder\n"
" # Load only decoder parameters excluding decoder.embed"
- " --init_param some/where/model.pth:decoder:decoder:decoder.embed\n"
- " --init_param some/where/model.pth:decoder:decoder:decoder.embed\n",
+ " --init_param some/where/model.pb:decoder:decoder:decoder.embed\n"
+ " --init_param some/where/model.pb:decoder:decoder:decoder.embed\n",
)
group.add_argument(
"--ignore_init_mismatch",
@@ -1188,12 +1199,18 @@
# 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",
@@ -1276,6 +1293,52 @@
if args.dry_run:
pass
+ elif args.collect_stats:
+ # Perform on collect_stats mode. This mode has two roles
+ # - Derive the length and dimension of all input data
+ # - Accumulate feats, square values, and the length for whitening
+
+ if args.valid_batch_size is None:
+ args.valid_batch_size = args.batch_size
+
+ if len(args.train_shape_file) != 0:
+ train_key_file = args.train_shape_file[0]
+ else:
+ train_key_file = None
+ if len(args.valid_shape_file) != 0:
+ valid_key_file = args.valid_shape_file[0]
+ else:
+ valid_key_file = None
+
+ collect_stats(
+ model=model,
+ train_iter=cls.build_streaming_iterator(
+ data_path_and_name_and_type=args.train_data_path_and_name_and_type,
+ key_file=train_key_file,
+ batch_size=args.batch_size,
+ dtype=args.train_dtype,
+ num_workers=args.num_workers,
+ allow_variable_data_keys=args.allow_variable_data_keys,
+ ngpu=args.ngpu,
+ preprocess_fn=cls.build_preprocess_fn(args, train=False),
+ collate_fn=cls.build_collate_fn(args, train=False),
+ ),
+ valid_iter=cls.build_streaming_iterator(
+ 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,
+ dtype=args.train_dtype,
+ num_workers=args.num_workers,
+ allow_variable_data_keys=args.allow_variable_data_keys,
+ ngpu=args.ngpu,
+ preprocess_fn=cls.build_preprocess_fn(args, train=False),
+ collate_fn=cls.build_collate_fn(args, train=False),
+ ),
+ output_dir=output_dir,
+ ngpu=args.ngpu,
+ log_interval=args.log_interval,
+ write_collected_feats=args.write_collected_feats,
+ )
else:
logging.info("Training args: {}".format(args))
# 6. Loads pre-trained model
@@ -1297,12 +1360,16 @@
if args.dataset_type == "large":
from funasr.datasets.large_datasets.build_dataloader import ArkDataLoader
train_iter_factory = ArkDataLoader(args.train_data_file, args.token_list, args.dataset_conf,
- seg_dict_file=args.seg_dict_file if hasattr(args,
- "seg_dict_file") else None,
+ 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,
+ 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,
- seg_dict_file=args.seg_dict_file if hasattr(args,
- "seg_dict_file") else None,
+ 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,
+ 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(
@@ -1515,12 +1582,21 @@
) -> 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=dest_sample_rate,
)
cls.check_task_requirements(
dataset, args.allow_variable_data_keys, train=iter_options.train
@@ -1792,6 +1868,7 @@
key_file: str = None,
batch_size: int = 1,
fs: dict = None,
+ mc: bool = False,
dtype: str = np.float32,
num_workers: int = 1,
allow_variable_data_keys: bool = False,
@@ -1810,6 +1887,7 @@
data_path_and_name_and_type,
float_dtype=dtype,
fs=fs,
+ mc=mc,
preprocess=preprocess_fn,
key_file=key_file,
)
--
Gitblit v1.9.1