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import copy |
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from transformers.configuration_utils import PretrainedConfig |
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from transformers.utils import logging |
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from transformers import LlamaConfig, Qwen2Config |
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logger = logging.get_logger(__name__) |
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class VoiceLMConfig(PretrainedConfig): |
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def __init__( |
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self, |
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llm_input_size = 896, |
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llm_output_size = 896, |
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speech_token_size = 6561, |
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length_normalized_loss = True, |
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lsm_weight = 0, |
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llm_config=None, |
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sampling_config={ |
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'top_p': 0.8, |
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'top_k': 25, |
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'win_size': 10, |
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'tau_r': 0.1, |
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}, |
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**kwargs): |
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super().__init__(**kwargs) |
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self.llm_input_size = llm_input_size |
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self.llm_output_size = llm_output_size |
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self.speech_token_size = speech_token_size |
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self.length_normalized_loss = length_normalized_loss |
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self.lsm_weight = lsm_weight |
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self.sampling_config = sampling_config |
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if llm_config is None: |
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llm_config = {} |
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logger.info('llm_config is None. Initializing the LlamaConfig config with default values (`LlamaConfig`).') |
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self.llm_config = Qwen2Config(**llm_config) |
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pass |
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def to_dict(self): |
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""" |
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Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`]. |
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Returns: |
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`Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance, |
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""" |
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output = copy.deepcopy(self.__dict__) |
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output['llm_input_size'] = self.llm_input_size |
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output['llm_output_size'] = self.llm_output_size |
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output['speech_token_size'] = self.speech_token_size |
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output['length_normalized_loss'] = self.length_normalized_loss |
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output['lsm_weight'] = self.lsm_weight |
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output['sampling_config'] = self.sampling_config |
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output['llm_config'] = self.llm_config.to_dict() |
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return output |
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