The dataset viewer is not available for this dataset.
Cannot get the config names for the dataset.
Error code:   ConfigNamesError
Exception:    FileNotFoundError
Message:      Couldn't find a dataset script at /src/services/worker/ms903/Diff-SVC-refactor-pre-trained-model/Diff-SVC-refactor-pre-trained-model.py or any data file in the same directory. Couldn't find 'ms903/Diff-SVC-refactor-pre-trained-model' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in ms903/Diff-SVC-refactor-pre-trained-model. 
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 55, in compute_config_names_response
                  for config in sorted(get_dataset_config_names(path=dataset, token=hf_token))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 351, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1508, in dataset_module_factory
                  raise FileNotFoundError(
              FileNotFoundError: Couldn't find a dataset script at /src/services/worker/ms903/Diff-SVC-refactor-pre-trained-model/Diff-SVC-refactor-pre-trained-model.py or any data file in the same directory. Couldn't find 'ms903/Diff-SVC-refactor-pre-trained-model' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in ms903/Diff-SVC-refactor-pre-trained-model.

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YAML Metadata Warning: empty or missing yaml metadata in repo card (https://huggingface.co/docs/hub/datasets-cards)

新增由@CN_ChiTu训练,底模数据集添加了vctk数据集,修复了变调增强bug的底模,在"fix_pitch_add_vctk_xxxk"目录下,强烈推荐使用这组底模。

新增了"hubertsoft"作为units提取器的底模,在"hubertsoft_fix_pitch_add_vctk_xxxk"目录下,其他的底模都是"contentvec768l12"为units提取器的,两者不通用,在填写config和使用的时候需注意。

将名为"model_0.pt"的预训练模型, 放到config内"expdir: exp/*****"参数指定的模型导出文件夹内, 没有就新建一个, 程序会自动加载该文件夹下的预训练模型.

Move the pre-trained model named "model_0. pt" to the model export folder specified by the "expdir: exp/******" parameter in config, and the program will automatically load the pre-trained models in that folder.

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