convert_ja_tar.py

import sys
import os

# 将 scripts/speech_recognition 添加到 sys.path，以便导入 convert_to_tarred_audio_dataset
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "scripts", "speech_recognition")))

import convert_to_tarred_audio_dataset

def main():
    datasets = [
        {
            "manifest_path": "data/common_voice_11_0/ja/train/train_common_voice_11_0_manifest.json",
            "target_dir": "data/common_voice_11_0/ja/train_tarred_1bk",
            "num_shards": 1024
        },
        {
            "manifest_path": "data/common_voice_11_0/ja/validation/validation_common_voice_11_0_manifest.json",
            "target_dir": "data/common_voice_11_0/ja/validation_tarred_1bk",
            "num_shards": 32  # 验证集通常比训练集小，使用较少的 shard
        },
        {
            "manifest_path": "data/common_voice_11_0/ja/test/test_common_voice_11_0_manifest.json",
            "target_dir": "data/common_voice_11_0/ja/test_tarred_1bk",
            "num_shards": 32  # 测试集通常比训练集小，使用较少的 shard
        }
    ]

    for dataset in datasets:
        print(f"Processing dataset: {dataset['manifest_path']}")
        convert_to_tarred_audio_dataset.create_tar_datasets(
            manifest_path=dataset["manifest_path"],
            target_dir=dataset["target_dir"],
            num_shards=dataset["num_shards"],
            max_duration=15.0,
            min_duration=1.0,
            shuffle=True,
            shuffle_seed=1,
            sort_in_shards=True,
            workers=-1
        )
        print(f"Finished processing dataset: {dataset['manifest_path']}\n")

if __name__ == "__main__":
    main()