File size: 2,797 Bytes
d5f13b4 53a6a60 d5f13b4 | 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 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 | import os
import sys
from dataclasses import is_dataclass
from datasets import load_dataset
from datasets.utils.logging import set_verbosity_info, enable_progress_bar
from omegaconf import OmegaConf
# 开启 datasets 进度条和日志
set_verbosity_info()
enable_progress_bar()
# 添加脚本所在路径到 sys.path
sys.path.append(os.path.join(os.path.dirname(__file__), 'scripts', 'speech_recognition'))
from convert_hf_dataset_to_nemo import HFDatasetConversionConfig, prepare_output_dirs, process_dataset
def convert_ja():
# 本地数据集路径
dataset_path = os.path.join(os.path.dirname(__file__), "data", "common_voice_11_0")
# 输出路径
output_dir = os.path.join(os.path.dirname(__file__), "data", "nemo_ja")
# 构建配置
cfg = HFDatasetConversionConfig(
output_dir=output_dir,
path=dataset_path,
name="ja",
ensure_ascii=False,
use_auth_token=False,
num_proc=8, # 使用多进程加快速度
)
# 转换为 OmegaConf
if is_dataclass(cfg):
cfg = OmegaConf.structured(cfg)
# 准备输出目录
prepare_output_dirs(cfg)
print(f"开始加载数据集 {cfg.path}，语言: {cfg.name}...")
print("此过程会进行数据的准备和解压，请耐心等待（可以通过命令行查看进度条）...")
# 加载数据集
try:
dataset = load_dataset(
path=cfg.path,
name=cfg.name,
split=cfg.split,
cache_dir=None,
streaming=cfg.streaming,
token=cfg.use_auth_token,
trust_remote_code=True,
download_mode="force_redownload",
)
print("数据集加载完成！")
except Exception as e:
import traceback
print("Failed to load dataset. Traceback:")
print(traceback.format_exc())
sys.exit(1)
print("开始进行格式转换 (HF -> NeMo)...")
# 处理数据集
if isinstance(dataset, dict):
print(f"\nMultiple splits found for dataset {cfg.path}: {list(dataset.keys())}")
keys = list(dataset.keys())
for key in keys:
ds_split = dataset[key]
print(f"Processing split {key} for dataset {cfg.path}")
cfg.split_output_dir = os.path.join(cfg.resolved_output_dir, key)
process_dataset(ds_split, cfg)
del dataset[key], ds_split
cfg.split_output_dir = None
else:
print(f"Single split found for dataset {cfg.path} | Split chosen = {cfg.split}")
if cfg.split is not None:
cfg.split_output_dir = os.path.join(cfg.resolved_output_dir, cfg.split)
process_dataset(dataset, cfg)
if __name__ == '__main__':
convert_ja()
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