From 98abc0e5ac1a1da0fe1802d9ffb623802fbf0b2f Mon Sep 17 00:00:00 2001
From: jmwang66 <wangjiaming.wjm@alibaba-inc.com>
Date: 星期四, 29 六月 2023 16:30:39 +0800
Subject: [PATCH] update setup (#686)
---
funasr/tasks/abs_task.py | 173 ++++++++++++++++++++++++++++++++++++++++++---------------
1 files changed, 126 insertions(+), 47 deletions(-)
diff --git a/funasr/tasks/abs_task.py b/funasr/tasks/abs_task.py
index 4e79c63..91d33c5 100644
--- a/funasr/tasks/abs_task.py
+++ b/funasr/tasks/abs_task.py
@@ -30,9 +30,8 @@
import torch.nn
import torch.optim
import yaml
+from funasr.models.base_model import FunASRModel
from torch.utils.data import DataLoader
-from typeguard import check_argument_types
-from typeguard import check_return_type
from funasr import __version__
from funasr.datasets.dataset import AbsDataset
@@ -43,17 +42,19 @@
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.fairseq_adam import FairseqAdam
from funasr.optimizers.sgd import SGD
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.tri_stage_scheduler import TriStageLR
from funasr.schedulers.warmup_lr import WarmupLR
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
from funasr.torch_utils.set_all_random_seed import set_all_random_seed
-from funasr.train.abs_espnet_model import AbsESPnetModel
from funasr.train.class_choices import ClassChoices
from funasr.train.distributed_utils import DistributedOption
from funasr.train.trainer import Trainer
@@ -68,7 +69,7 @@
from funasr.utils.types import str2triple_str
from funasr.utils.types import str_or_int
from funasr.utils.types import str_or_none
-from funasr.utils.wav_utils import calc_shape, generate_data_list
+from funasr.utils.wav_utils import calc_shape, generate_data_list, filter_wav_text
from funasr.utils.yaml_no_alias_safe_dump import yaml_no_alias_safe_dump
try:
@@ -83,6 +84,7 @@
optim_classes = dict(
adam=torch.optim.Adam,
+ fairseq_adam=FairseqAdam,
adamw=torch.optim.AdamW,
sgd=SGD,
adadelta=torch.optim.Adadelta,
@@ -149,6 +151,7 @@
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,
@@ -225,8 +228,8 @@
>>> cls.check_task_requirements()
If your model is defined as following,
- >>> from funasr.train.abs_espnet_model import AbsESPnetModel
- >>> class Model(AbsESPnetModel):
+ >>> from funasr.models.base_model import FunASRModel
+ >>> class Model(FunASRModel):
... def forward(self, input, output, opt=None): pass
then "required_data_names" should be as
@@ -246,8 +249,8 @@
>>> cls.check_task_requirements()
If your model is defined as follows,
- >>> from funasr.train.abs_espnet_model import AbsESPnetModel
- >>> class Model(AbsESPnetModel):
+ >>> from funasr.models.base_model import FunASRModel
+ >>> class Model(FunASRModel):
... def forward(self, input, output, opt=None): pass
then "optional_data_names" should be as
@@ -258,12 +261,12 @@
@classmethod
@abstractmethod
- def build_model(cls, args: argparse.Namespace) -> AbsESPnetModel:
+ def build_model(cls, args: argparse.Namespace) -> FunASRModel:
raise NotImplementedError
+
@classmethod
def get_parser(cls) -> config_argparse.ArgumentParser:
- assert check_argument_types()
class ArgumentDefaultsRawTextHelpFormatter(
argparse.RawTextHelpFormatter,
@@ -440,6 +443,12 @@
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,
@@ -458,6 +467,12 @@
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",
@@ -536,6 +551,12 @@
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",
@@ -624,8 +645,8 @@
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, "
@@ -634,12 +655,12 @@
"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",
@@ -651,7 +672,7 @@
"--freeze_param",
type=str,
default=[],
- nargs="*",
+ action="append",
help="Freeze parameters",
)
@@ -935,7 +956,6 @@
cls.trainer.add_arguments(parser)
cls.add_task_arguments(parser)
- assert check_return_type(parser)
return parser
@classmethod
@@ -983,7 +1003,6 @@
return _cls
# This method is used only for --print_config
- assert check_argument_types()
parser = cls.get_parser()
args, _ = parser.parse_known_args()
config = vars(args)
@@ -1023,7 +1042,6 @@
@classmethod
def check_required_command_args(cls, args: argparse.Namespace):
- assert check_argument_types()
if hasattr(args, "required"):
for k in vars(args):
if "-" in k:
@@ -1053,7 +1071,6 @@
inference: bool = False,
) -> None:
"""Check if the dataset satisfy the requirement of current Task"""
- assert check_argument_types()
mes = (
f"If you intend to use an additional input, modify "
f'"{cls.__name__}.required_data_names()" or '
@@ -1080,14 +1097,12 @@
@classmethod
def print_config(cls, file=sys.stdout) -> None:
- assert check_argument_types()
# Shows the config: e.g. python train.py asr --print_config
config = cls.get_default_config()
file.write(yaml_no_alias_safe_dump(config, indent=4, sort_keys=False))
@classmethod
def main(cls, args: argparse.Namespace = None, cmd: Sequence[str] = None):
- assert check_argument_types()
print(get_commandline_args(), file=sys.stderr)
if args is None:
parser = cls.get_parser()
@@ -1124,7 +1139,6 @@
@classmethod
def main_worker(cls, args: argparse.Namespace):
- assert check_argument_types()
# 0. Init distributed process
distributed_option = build_dataclass(DistributedOption, args)
@@ -1142,16 +1156,27 @@
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
+ if (
+ args.train_shape_file is None and args.dataset_type == "small") or args.train_data_file is None and args.dataset_type == "large":
+ if not args.simple_ddp or distributed_option.dist_rank == 0:
+ filter_wav_text(args.data_dir, args.train_set)
+ filter_wav_text(args.data_dir, args.dev_set)
+ if args.simple_ddp:
+ dist.barrier()
if args.train_shape_file is None and args.dataset_type == "small":
if not args.simple_ddp or distributed_option.dist_rank == 0:
- calc_shape(args.data_dir, args.train_set, args.frontend_conf, args.speech_length_min, args.speech_length_max)
- calc_shape(args.data_dir, args.dev_set, args.frontend_conf, args.speech_length_min, args.speech_length_max)
+ calc_shape(args.data_dir, args.train_set, args.frontend_conf, args.speech_length_min,
+ args.speech_length_max)
+ calc_shape(args.data_dir, args.dev_set, args.frontend_conf, args.speech_length_min,
+ args.speech_length_max)
if args.simple_ddp:
dist.barrier()
args.train_shape_file = [os.path.join(args.data_dir, args.train_set, "speech_shape")]
@@ -1180,12 +1205,18 @@
# 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",
@@ -1207,9 +1238,9 @@
# 2. Build model
model = cls.build_model(args=args)
- if not isinstance(model, AbsESPnetModel):
+ if not isinstance(model, FunASRModel):
raise RuntimeError(
- f"model must inherit {AbsESPnetModel.__name__}, but got {type(model)}"
+ f"model must inherit {FunASRModel.__name__}, but got {type(model)}"
)
model = model.to(
dtype=getattr(torch, args.train_dtype),
@@ -1268,6 +1299,54 @@
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,
+ mc=args.mc,
+ 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,
+ mc=args.mc,
+ 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
@@ -1287,15 +1366,10 @@
# 7. Build iterator factories
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,
- 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,
- mode="eval")
+ from funasr.datasets.large_datasets.build_dataloader import LargeDataLoader
+ train_iter_factory = LargeDataLoader(args, mode="train")
+ valid_iter_factory = LargeDataLoader(args, mode="eval")
+
elif args.dataset_type == "small":
train_iter_factory = cls.build_iter_factory(
args=args,
@@ -1472,7 +1546,6 @@
- 4 epoch with "--num_iters_per_epoch" == 4
"""
- assert check_argument_types()
iter_options = cls.build_iter_options(args, distributed_option, mode)
# Overwrite iter_options if any kwargs is given
@@ -1505,7 +1578,14 @@
def build_sequence_iter_factory(
cls, args: argparse.Namespace, iter_options: IteratorOptions, mode: str
) -> 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,
@@ -1513,6 +1593,7 @@
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
@@ -1590,7 +1671,6 @@
iter_options: IteratorOptions,
mode: str,
) -> AbsIterFactory:
- assert check_argument_types()
dataset = ESPnetDataset(
iter_options.data_path_and_name_and_type,
@@ -1695,7 +1775,6 @@
def build_multiple_iter_factory(
cls, args: argparse.Namespace, distributed_option: DistributedOption, mode: str
):
- assert check_argument_types()
iter_options = cls.build_iter_options(args, distributed_option, mode)
assert len(iter_options.data_path_and_name_and_type) > 0, len(
iter_options.data_path_and_name_and_type
@@ -1784,6 +1863,7 @@
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,
@@ -1791,7 +1871,6 @@
inference: bool = False,
) -> DataLoader:
"""Build DataLoader using iterable dataset"""
- assert check_argument_types()
# For backward compatibility for pytorch DataLoader
if collate_fn is not None:
kwargs = dict(collate_fn=collate_fn)
@@ -1802,6 +1881,7 @@
data_path_and_name_and_type,
float_dtype=dtype,
fs=fs,
+ mc=mc,
preprocess=preprocess_fn,
key_file=key_file,
)
@@ -1829,7 +1909,7 @@
model_file: Union[Path, str] = None,
cmvn_file: Union[Path, str] = None,
device: str = "cpu",
- ) -> Tuple[AbsESPnetModel, argparse.Namespace]:
+ ) -> Tuple[FunASRModel, argparse.Namespace]:
"""Build model from the files.
This method is used for inference or fine-tuning.
@@ -1840,7 +1920,6 @@
device: Device type, "cpu", "cuda", or "cuda:N".
"""
- assert check_argument_types()
if config_file is None:
assert model_file is not None, (
"The argument 'model_file' must be provided "
@@ -1856,9 +1935,9 @@
args["cmvn_file"] = cmvn_file
args = argparse.Namespace(**args)
model = cls.build_model(args)
- if not isinstance(model, AbsESPnetModel):
+ if not isinstance(model, FunASRModel):
raise RuntimeError(
- f"model must inherit {AbsESPnetModel.__name__}, but got {type(model)}"
+ f"model must inherit {FunASRModel.__name__}, but got {type(model)}"
)
model.to(device)
if model_file is not None:
--
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