| import torch | |
| from typing import Dict, Tuple, List | |
| from transformers import PretrainedConfig | |
| class PathummaAudioConfig(PretrainedConfig): | |
| model_type: str = "pathumma_audio" | |
| def __init__( | |
| self, | |
| llm_path: str = "nectec/OpenThaiLLM-DoodNiLT-V1.0.0-Beta-7B", | |
| whisper_path: str = "nectec/Pathumma-whisper-th-large-v3", | |
| beats_path: str = "", | |
| init_from_scratch: bool = True, | |
| lora: bool = True, | |
| lora_infer_mode: bool = True, | |
| lora_rank: int = 8, | |
| lora_alpha: int = 32, | |
| lora_dropout: float = 0.1, | |
| target_modules: List[str] = ["q_proj", "v_proj"], | |
| qformer_query_token: int = 1, | |
| qformer_hidden_layers: int = 2, | |
| second_per_window: float = 0.333333, | |
| second_stride: float = 0.333333, | |
| torch_dtype: torch.dtype = torch.bfloat16, | |
| **kwargs | |
| ): | |
| super().__init__(**kwargs) | |
| self.architectures = kwargs.get("architectures", ["PathummaAudioModel"]) | |
| self.auto_map = kwargs.get("auto_map", { | |
| "AutoConfig": "configuration_pathumma_audio.PathummaAudioConfig", | |
| "AutoModel": "modeling_pathumma_audio.PathummaAudioModel" | |
| }) | |
| self.llm_path = llm_path | |
| self.whisper_path = whisper_path | |
| self.beats_path = beats_path | |
| self.init_from_scratch = init_from_scratch | |
| self.lora = lora | |
| self.lora_infer_mode = lora_infer_mode | |
| self.lora_rank = lora_rank | |
| self.lora_alpha = lora_alpha | |
| self.lora_dropout = lora_dropout | |
| self.target_modules = target_modules | |
| self.qformer_query_token = qformer_query_token | |
| self.qformer_hidden_layers = qformer_hidden_layers | |
| self.second_per_window = second_per_window | |
| self.second_stride = second_stride | |
| self.torch_dtype = torch_dtype |