mirror of
https://github.com/FunAudioLLM/CosyVoice.git
synced 2026-02-04 17:39:25 +08:00
89 lines
2.9 KiB
Python
89 lines
2.9 KiB
Python
# Copyright 2024 Bytedance Ltd. and/or its affiliates
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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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"""
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Preprocess the Text to Speech dataset to parquet format
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"""
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import argparse
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import os
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import re
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import datasets
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from verl.utils.hdfs_io import copy, makedirs
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--train_file", required=True, help="Path to training JSON/JSONL file")
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parser.add_argument("--test_file", required=True, help="Path to test JSON/JSONL file")
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parser.add_argument("--local_dir", default=None, required=True)
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parser.add_argument("--hdfs_dir", default=None)
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args = parser.parse_args()
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# Load datasets from local JSON files
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train_dataset = datasets.load_dataset("json", data_files=args.train_file)['train']
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test_dataset = datasets.load_dataset("json", data_files=args.test_file)['train']
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# add a row to each data item that represents a unique id
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def make_map_fn(split):
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def process_fn(example, idx):
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text = example.pop("text")
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# use cosyvoice2 official huggingface compatible checkpoint template
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question = text
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answer = ""
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data = {
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"data_source": f"{args.train_file}_{args.test_file}", # Use file names as data source
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"prompt": [
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{
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"role": "user",
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"content": question,
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},
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{
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"role": "assistant",
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"content": answer,
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},
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],
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"ability": "text-to-speech",
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"reward_model": {"style": "rule", "ground_truth": text},
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"extra_info": {
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"split": split,
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"index": idx,
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"text": text,
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},
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}
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return data
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return process_fn
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train_dataset = train_dataset.map(function=make_map_fn("train"), with_indices=True)
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test_dataset = test_dataset.map(function=make_map_fn("test"), with_indices=True)
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local_dir = args.local_dir
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hdfs_dir = args.hdfs_dir
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print(train_dataset)
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print(test_dataset)
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train_dataset.to_parquet(os.path.join(local_dir, "train.parquet"))
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test_dataset.to_parquet(os.path.join(local_dir, "test.parquet"))
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if hdfs_dir is not None:
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makedirs(hdfs_dir)
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copy(src=local_dir, dst=hdfs_dir)
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