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PaddleSpeech/paddlespeech/s2t/training/cli.py

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6.1 KiB

# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import distutils
from yacs.config import CfgNode
class ExtendAction(argparse.Action):
"""
[Since Python 3.8, the "extend" is available directly in stdlib]
(https://docs.python.org/3.8/library/argparse.html#action).
If you only have to support 3.8+ then defining it yourself is no longer required.
Usage of stdlib "extend" action is exactly the same way as this answer originally described:
"""
def __call__(self, parser, namespace, values, option_string=None):
items = getattr(namespace, self.dest) or []
items.extend(values)
setattr(namespace, self.dest, items)
class LoadFromFile(argparse.Action):
def __call__(self, parser, namespace, values, option_string=None):
with values as f:
# parse arguments in the file and store them in the target namespace
parser.parse_args(f.read().split(), namespace)
def default_argument_parser(parser=None):
r"""A simple yet genral argument parser for experiments with t2s.
This is used in examples with t2s. And it is intended to be used by
other experiments with t2s. It requires a minimal set of command line
arguments to start a training script.
The ``--config`` and ``--opts`` are used for overwrite the deault
configuration.
The ``--data`` and ``--output`` specifies the data path and output path.
Resuming training from existing progress at the output directory is the
intended default behavior.
The ``--checkpoint_path`` specifies the checkpoint to load from.
The ``--ngpu`` specifies how to run the training.
See Also
--------
paddlespeech.t2s.training.experiment
Returns
-------
argparse.ArgumentParser
the parser
"""
if parser is None:
parser = argparse.ArgumentParser()
parser.register('action', 'extend', ExtendAction)
parser.add_argument(
'--conf', type=open, action=LoadFromFile, help="config file.")
parser.add_argument(
"--debug",
type=distutils.util.strtobool,
default=False,
help="logging with debug mode.")
parser.add_argument(
"--dump_path", type=str, default=None, help="path to dump config file.")
# train group
train_group = parser.add_argument_group(
title='Train Options', description=None)
train_group.add_argument(
"--seed",
type=int,
default=None,
help="seed to use for paddle, np and random. None or 0 for random, else set seed."
)
train_group.add_argument(
"--ngpu",
type=int,
default=1,
help="number of parallel processes. 0 for cpu.")
train_group.add_argument(
'--nxpu',
type=int,
default=0,
choices=[0, 1],
help="if nxpu == 0 and ngpu == 0, use cpu.")
train_group.add_argument(
"--config", metavar="CONFIG_FILE", help="config file.")
train_group.add_argument(
"--output", metavar="CKPT_DIR", help="path to save checkpoint.")
train_group.add_argument(
"--checkpoint_path", type=str, help="path to load checkpoint")
train_group.add_argument(
"--opts",
action='extend',
nargs=2,
metavar=('key', 'val'),
help="overwrite --config field, passing (KEY VALUE) pairs")
train_group.add_argument(
"--dump-config", metavar="FILE", help="dump config to `this` file.")
# test group
test_group = parser.add_argument_group(
title='Test Options', description=None)
test_group.add_argument(
"--decode_cfg",
metavar="DECODE_CONFIG_FILE",
help="decode config file.")
test_group.add_argument(
"--result_file", type=str, help="path of save the asr result")
test_group.add_argument(
"--audio_file", type=str, help="path of the input audio file")
# quant & export
quant_group = parser.add_argument_group(
title='Quant Options', description=None)
quant_group.add_argument(
"--audio_scp", type=str, help="path of the input audio scp file")
quant_group.add_argument(
"--num_utts",
type=int,
default=200,
help="num utts for quant calibrition.")
quant_group.add_argument(
"--export_path",
type=str,
default='export.jit.quant',
help="path of the jit model to save")
# profile group
profile_group = parser.add_argument_group(
title='Benchmark Options', description=None)
profile_group.add_argument(
'--profiler-options',
type=str,
default=None,
help='The option of profiler, which should be in format \"key1=value1;key2=value2;key3=value3\".'
)
profile_group.add_argument(
'--benchmark-batch-size',
type=int,
default=None,
help='batch size for benchmark.')
profile_group.add_argument(
'--benchmark-max-step',
type=int,
default=None,
help='max iteration for benchmark.')
return parser
def config_from_args(args):
# https://yaml.org/type/float.html
config = CfgNode(new_allowed=True)
if args.config:
config.merge_from_file(args.config)
if args.decode_cfg:
decode_confs = CfgNode(new_allowed=True)
decode_confs.merge_from_file(args.decode_cfg)
config.decode = decode_confs
if args.opts:
config.merge_from_list(args.opts)
config.freeze()
return config
def maybe_dump_config(dump_path, config):
if dump_path:
with open(dump_path, 'w') as f:
print(config, file=f)
print(f"save config to {dump_path}")