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63 lines
2.1 KiB
63 lines
2.1 KiB
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import os
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from pathlib import Path
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def generate_finetune_env(output_dir: Path, pretrained_model_dir: Path):
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output_dir = output_dir / "checkpoints/"
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output_dir = output_dir.resolve()
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output_dir.mkdir(parents=True, exist_ok=True)
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model_path = sorted(list((pretrained_model_dir).rglob("*.pdz")))[0]
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model_path = model_path.resolve()
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iter = int(str(model_path).split("_")[-1].split(".")[0])
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model_file = str(model_path).split("/")[-1]
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os.system("cp %s %s" % (model_path, output_dir))
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records_file = output_dir / "records.jsonl"
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with open(records_file, "w") as f:
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line = "\"time\": \"2022-08-06 07:51:53.463650\", \"path\": \"%s\", \"iteration\": %d" % (
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str(output_dir / model_file), iter)
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f.write("{" + line + "}" + "\n")
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if __name__ == '__main__':
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# parse config and args
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parser = argparse.ArgumentParser(
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description="Preprocess audio and then extract features.")
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parser.add_argument(
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"--pretrained_model_dir",
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type=str,
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default="./pretrained_models/fastspeech2_aishell3_ckpt_1.1.0",
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help="Path to pretrained model")
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parser.add_argument(
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"--output_dir",
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type=str,
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default="./exp/default/",
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help="directory to save finetune model.")
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args = parser.parse_args()
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output_dir = Path(args.output_dir).expanduser()
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output_dir.mkdir(parents=True, exist_ok=True)
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pretrained_model_dir = Path(args.pretrained_model_dir).expanduser()
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generate_finetune_env(output_dir, pretrained_model_dir)
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