| import torch | |
| from .char_tokenizer import CharTokenizer | |
| from transformers import AutoModelForTokenClassification | |
| class AccentModel: | |
| def __init__(self) -> None: | |
| self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| def load(self, path): | |
| self.model = AutoModelForTokenClassification.from_pretrained(path).to(self.device) | |
| self.tokenizer = CharTokenizer.from_pretrained(path) | |
| def render_stress(self, word, token_classes): | |
| if 'STRESS' in token_classes: | |
| index = token_classes.index('STRESS') | |
| word = list(word) | |
| word[index-1] = '+' + word[index-1] | |
| return ''.join(word) | |
| else: | |
| return word | |
| def put_accent(self, word): | |
| inputs = self.tokenizer(word, return_tensors="pt").to(self.device) | |
| with torch.no_grad(): | |
| logits = self.model(**inputs).logits | |
| predictions = torch.argmax(logits, dim=2) | |
| predicted_token_class = [self.model.config.id2label[t.item()] for t in predictions[0]] | |
| return self.render_stress(word, predicted_token_class) |