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PaddleSpeech/examples/other/tts_finetune/tts3/local/prepare_env.py

63 lines
2.1 KiB

# Copyright (c) 2022 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 os
from pathlib import Path
def generate_finetune_env(output_dir: Path, pretrained_model_dir: Path):
output_dir = output_dir / "checkpoints/"
output_dir = output_dir.resolve()
output_dir.mkdir(parents=True, exist_ok=True)
model_path = sorted(list((pretrained_model_dir).rglob("*.pdz")))[0]
model_path = model_path.resolve()
iter = int(str(model_path).split("_")[-1].split(".")[0])
model_file = str(model_path).split("/")[-1]
os.system("cp %s %s" % (model_path, output_dir))
records_file = output_dir / "records.jsonl"
with open(records_file, "w") as f:
line = "\"time\": \"2022-08-06 07:51:53.463650\", \"path\": \"%s\", \"iteration\": %d" % (
str(output_dir / model_file), iter)
f.write("{" + line + "}" + "\n")
if __name__ == '__main__':
# parse config and args
parser = argparse.ArgumentParser(
description="Preprocess audio and then extract features.")
parser.add_argument(
"--pretrained_model_dir",
type=str,
default="./pretrained_models/fastspeech2_aishell3_ckpt_1.1.0",
help="Path to pretrained model")
parser.add_argument(
"--output_dir",
type=str,
default="./exp/default/",
help="directory to save finetune model.")
args = parser.parse_args()
output_dir = Path(args.output_dir).expanduser()
output_dir.mkdir(parents=True, exist_ok=True)
pretrained_model_dir = Path(args.pretrained_model_dir).expanduser()
generate_finetune_env(output_dir, pretrained_model_dir)