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8a3de9a eecbab9 8a3de9a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 | 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()
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