#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""Argument parser for training scripts."""
import argparse
import glob
import os
import re
from TTS.tts.utils.generic_utils import check_config_tts
from TTS.tts.utils.text.symbols import parse_symbols
from TTS.utils.console_logger import ConsoleLogger
from TTS.utils.generic_utils import create_experiment_folder, get_git_branch
from TTS.utils.io import copy_model_files, load_config
from TTS.utils.tensorboard_logger import TensorboardLogger
def parse_arguments(argv):
"""Parse command line arguments of training scripts.
Parameters
----------
argv : list
This is a list of input arguments as given by sys.argv
Returns
-------
argparse.Namespace
Parsed arguments.
"""
parser = argparse.ArgumentParser()
parser.add_argument(
"--continue_path",
type=str,
help=("Training output folder to continue training. Used to continue "
"a training. If it is used, 'config_path' is ignored."),
default="",
required="--config_path" not in argv)
parser.add_argument(
"--restore_path",
type=str,
help="Model file to be restored. Use to finetune a model.",
default="")
parser.add_argument(
"--config_path",
type=str,
help="Path to config file for training.",
required="--continue_path" not in argv)
parser.add_argument(
"--debug",
type=bool,
default=False,
help="Do not verify commit integrity to run training.")
parser.add_argument(
"--rank",
type=int,
default=0,
help="DISTRIBUTED: process rank for distributed training.")
parser.add_argument(
"--group_id",
type=str,
default="",
help="DISTRIBUTED: process group id.")
return parser.parse_args()
def get_last_checkpoint(path):
"""Get latest checkpoint from a list of filenames.
It is based on globbing for `*.pth.tar` and the RegEx
`checkpoint_([0-9]+)`.
Parameters
----------
path : list
Path to files to be compared.
Raises
------
ValueError
If no checkpoint files are found.
Returns
-------
last_checkpoint : str
Last checkpoint filename.
"""
last_checkpoint_num = 0
last_checkpoint = None
filenames = glob.glob(
os.path.join(path, "/*.pth.tar"))
for filename in filenames:
try:
checkpoint_num = int(
re.search(r"checkpoint_([0-9]+)", filename).groups()[0])
if checkpoint_num > last_checkpoint_num:
last_checkpoint_num = checkpoint_num
last_checkpoint = filename
except AttributeError: # if there's no match in the filename
pass
if last_checkpoint is None:
raise ValueError(f"No checkpoints in {path}!")
return last_checkpoint
def process_args(args, model_type):
"""Process parsed comand line arguments.
Args:
args (argparse.Namespace or dict like): Parsed input arguments.
model_type (str): Model type used to check config parameters and setup the TensorBoard
logger. One of:
- tacotron
- glow_tts
- speedy_speech
- gan
- wavegrad
- wavernn
Raises:
ValueError
If `model_type` is not one of implemented choices.
Returns:
c (TTS.utils.io.AttrDict): Config paramaters.
out_path (str): Path to save models and logging.
audio_path (str): Path to save generated test audios.
c_logger (TTS.utils.console_logger.ConsoleLogger): Class that does logging to the console.
tb_logger (TTS.utils.tensorboard.TensorboardLogger): Class that does the TensorBoard loggind.
"""
if args.continue_path != "":
args.output_path = args.continue_path
args.config_path = os.path.join(args.continue_path, "config.json")
list_of_files = glob.glob(
os.path.join(args.continue_path, "*.pth.tar")
) # * means all if need specific format then *.csv
args.restore_path = max(list_of_files, key=os.path.getctime)
# checkpoint number based continuing
# args.restore_path = get_last_checkpoint(args.continue_path)
print(f" > Training continues for {args.restore_path}")
# setup output paths and read configs
c = load_config(args.config_path)
if model_type in "tacotron glow_tts speedy_speech":
model_class = "TTS"
elif model_type in "gan wavegrad wavernn":
model_class = "VOCODER"
else:
raise ValueError("model type {model_type} not recognized!")
if model_class == "TTS":
check_config_tts(c)
elif model_class == "VOCODER":
print("Vocoder config checker not implemented, "
"skipping ...")
else:
raise ValueError(f"model type {model_type} not recognized!")
_ = os.path.dirname(os.path.realpath(__file__))
if model_type in "tacotron wavegrad wavernn" and c.mixed_precision:
print(" > Mixed precision mode is ON")
out_path = args.continue_path
if args.continue_path == "":
out_path = create_experiment_folder(c.output_path, c.run_name,
args.debug)
audio_path = os.path.join(out_path, "test_audios")
c_logger = ConsoleLogger()
if args.rank == 0:
os.makedirs(audio_path, exist_ok=True)
new_fields = {}
if args.restore_path:
new_fields["restore_path"] = args.restore_path
new_fields["github_branch"] = get_git_branch()
# if model characters are not set in the config file
# save the default set to the config file for future
# compatibility.
if model_class == 'TTS' and not 'characters' in c.keys():
used_characters = parse_symbols()
new_fields['characters'] = used_characters
copy_model_files(c, args.config_path,
out_path, new_fields)
os.chmod(audio_path, 0o775)
os.chmod(out_path, 0o775)
log_path = out_path
tb_logger = TensorboardLogger(log_path, model_name=model_class)
# write model desc to tensorboard
tb_logger.tb_add_text("model-description", c["run_description"], 0)
return c, out_path, audio_path, c_logger, tb_logger