Instructions to use xcczach/YAGS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xcczach/YAGS with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="xcczach/YAGS", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("xcczach/YAGS", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Delete configuration_gpt_sovits.py
Browse files- configuration_gpt_sovits.py +0 -25
configuration_gpt_sovits.py
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from transformers import PretrainedConfig
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import torch
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class GPTSoVITSConfig(PretrainedConfig):
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model_type = "gpt_sovits"
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def __init__(
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self,
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prompt_language: str="zh",
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_hubert_config_dict: dict[str, any] = None,
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_hubert_extractor_config_dict: dict[str, any] = None,
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_bert_config_dict: dict[str, any] = None,
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_hps_dict: dict[str, any] = None,
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_gpt_config_dict: dict[str, any] = None,
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**kwargs
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):
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self.prompt_language = prompt_language
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self._hubert_config_dict = _hubert_config_dict
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self._hubert_extractor_config_dict = _hubert_extractor_config_dict
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self._bert_config_dict = _bert_config_dict
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self._hps_dict = _hps_dict
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self._gpt_config_dict = _gpt_config_dict
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super().__init__(**kwargs)
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