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| import sys | |
| import torch | |
| from transformers import ( | |
| AutoModelForMaskedLM, | |
| AutoTokenizer, | |
| DebertaV2Model, | |
| DebertaV2Tokenizer, | |
| ClapModel, | |
| ClapProcessor, | |
| ) | |
| from config import config | |
| from text.japanese import text2sep_kata | |
| class BertFeature: | |
| def __init__(self, model_path, language="ZH"): | |
| self.model_path = model_path | |
| self.language = language | |
| self.tokenizer = None | |
| self.model = None | |
| self.device = None | |
| self._prepare() | |
| def _get_device(self, device=config.bert_gen_config.device): | |
| if ( | |
| sys.platform == "darwin" | |
| and torch.backends.mps.is_available() | |
| and device == "cpu" | |
| ): | |
| device = "mps" | |
| if not device: | |
| device = "cuda" | |
| return device | |
| def _prepare(self): | |
| self.device = self._get_device() | |
| if self.language == "EN": | |
| self.tokenizer = DebertaV2Tokenizer.from_pretrained(self.model_path) | |
| self.model = DebertaV2Model.from_pretrained(self.model_path).to(self.device) | |
| else: | |
| self.tokenizer = AutoTokenizer.from_pretrained(self.model_path) | |
| self.model = AutoModelForMaskedLM.from_pretrained(self.model_path).to( | |
| self.device | |
| ) | |
| self.model.eval() | |
| def get_bert_feature(self, text, word2ph): | |
| if self.language == "JP": | |
| text = "".join(text2sep_kata(text)[0]) | |
| with torch.no_grad(): | |
| inputs = self.tokenizer(text, return_tensors="pt") | |
| for i in inputs: | |
| inputs[i] = inputs[i].to(self.device) | |
| res = self.model(**inputs, output_hidden_states=True) | |
| res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu() | |
| word2phone = word2ph | |
| phone_level_feature = [] | |
| for i in range(len(word2phone)): | |
| repeat_feature = res[i].repeat(word2phone[i], 1) | |
| phone_level_feature.append(repeat_feature) | |
| phone_level_feature = torch.cat(phone_level_feature, dim=0) | |
| return phone_level_feature.T | |
| class ClapFeature: | |
| def __init__(self, model_path): | |
| self.model_path = model_path | |
| self.processor = None | |
| self.model = None | |
| self.device = None | |
| self._prepare() | |
| def _get_device(self, device=config.bert_gen_config.device): | |
| if ( | |
| sys.platform == "darwin" | |
| and torch.backends.mps.is_available() | |
| and device == "cpu" | |
| ): | |
| device = "mps" | |
| if not device: | |
| device = "cuda" | |
| return device | |
| def _prepare(self): | |
| self.device = self._get_device() | |
| self.processor = ClapProcessor.from_pretrained(self.model_path) | |
| self.model = ClapModel.from_pretrained(self.model_path).to(self.device) | |
| self.model.eval() | |
| def get_clap_audio_feature(self, audio_data): | |
| with torch.no_grad(): | |
| inputs = self.processor( | |
| audios=audio_data, return_tensors="pt", sampling_rate=48000 | |
| ).to(self.device) | |
| emb = self.model.get_audio_features(**inputs) | |
| return emb.T | |
| def get_clap_text_feature(self, text): | |
| with torch.no_grad(): | |
| inputs = self.processor(text=text, return_tensors="pt").to(self.device) | |
| emb = self.model.get_text_features(**inputs) | |
| return emb.T | |