| """Configuration for Accentik's parallel Russian vowel-ranking model.""" | |
| from copy import deepcopy | |
| from transformers import PreTrainedConfig | |
| class AccentikConfig(PreTrainedConfig): | |
| model_type = "accentik" | |
| def __init__(self, encoder_config=None, context_window=512, | |
| batch_token_budget=8192, release="audio-v3", yo_threshold=0.8, **kwargs): | |
| super().__init__(**kwargs) | |
| self.encoder_config = deepcopy(encoder_config or {}) | |
| self.context_window = int(context_window) | |
| self.batch_token_budget = int(batch_token_budget) | |
| self.release = release | |
| self.yo_threshold = float(yo_threshold) | |
| self.restore_yo_default = False | |
| if not 0.0 <= self.yo_threshold <= 1.0: | |
| raise ValueError("yo_threshold must be between zero and one") | |
| if self.context_window < 8 or self.context_window % 8: | |
| raise ValueError("context_window must be a positive multiple of eight") | |
| if self.batch_token_budget < self.context_window: | |
| raise ValueError("batch_token_budget must fit one complete context window") | |