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| import os | |
| import logging | |
| from omegaconf import OmegaConf | |
| import torch | |
| from vocos import Vocos | |
| from .model.dvae import DVAE | |
| from .model.gpt import GPT_warpper | |
| from .utils.gpu_utils import select_device | |
| from .utils.io_utils import get_latest_modified_file | |
| from .infer.api import refine_text, infer_code | |
| from huggingface_hub import snapshot_download | |
| logging.basicConfig(level = logging.INFO) | |
| class Chat: | |
| def __init__(self, ): | |
| self.pretrain_models = {} | |
| self.logger = logging.getLogger(__name__) | |
| def check_model(self, level = logging.INFO, use_decoder = False): | |
| not_finish = False | |
| check_list = ['vocos', 'gpt', 'tokenizer'] | |
| if use_decoder: | |
| check_list.append('decoder') | |
| else: | |
| check_list.append('dvae') | |
| for module in check_list: | |
| if module not in self.pretrain_models: | |
| self.logger.log(logging.WARNING, f'{module} not initialized.') | |
| not_finish = True | |
| if not not_finish: | |
| self.logger.log(level, f'All initialized.') | |
| return not not_finish | |
| def load_models(self, source='huggingface', force_redownload=False, local_path='<LOCAL_PATH>'): | |
| if source == 'huggingface': | |
| hf_home = os.getenv('HF_HOME', os.path.expanduser("~/.cache/huggingface")) | |
| try: | |
| download_path = get_latest_modified_file(os.path.join(hf_home, 'hub/models--2Noise--ChatTTS/snapshots')) | |
| except: | |
| download_path = None | |
| if download_path is None or force_redownload: | |
| self.logger.log(logging.INFO, f'Download from HF: https://huggingface.co/2Noise/ChatTTS') | |
| download_path = snapshot_download(repo_id="2Noise/ChatTTS", allow_patterns=["*.pt", "*.yaml"]) | |
| else: | |
| self.logger.log(logging.INFO, f'Load from cache: {download_path}') | |
| self._load(**{k: os.path.join(download_path, v) for k, v in OmegaConf.load(os.path.join(download_path, 'config', 'path.yaml')).items()}) | |
| elif source == 'local': | |
| self.logger.log(logging.INFO, f'Load from local: {local_path}') | |
| self._load(**{k: os.path.join(local_path, v) for k, v in OmegaConf.load(os.path.join(local_path, 'config', 'path.yaml')).items()}) | |
| def _load( | |
| self, | |
| vocos_config_path: str = None, | |
| vocos_ckpt_path: str = None, | |
| dvae_config_path: str = None, | |
| dvae_ckpt_path: str = None, | |
| gpt_config_path: str = None, | |
| gpt_ckpt_path: str = None, | |
| decoder_config_path: str = None, | |
| decoder_ckpt_path: str = None, | |
| tokenizer_path: str = None, | |
| device: str = None | |
| ): | |
| if not device: | |
| device = select_device(4096) | |
| self.logger.log(logging.INFO, f'use {device}') | |
| if vocos_config_path: | |
| vocos = Vocos.from_hparams(vocos_config_path).to(device).eval() | |
| assert vocos_ckpt_path, 'vocos_ckpt_path should not be None' | |
| vocos.load_state_dict(torch.load(vocos_ckpt_path)) | |
| self.pretrain_models['vocos'] = vocos | |
| self.logger.log(logging.INFO, 'vocos loaded.') | |
| if dvae_config_path: | |
| cfg = OmegaConf.load(dvae_config_path) | |
| dvae = DVAE(**cfg).to(device).eval() | |
| assert dvae_ckpt_path, 'dvae_ckpt_path should not be None' | |
| dvae.load_state_dict(torch.load(dvae_ckpt_path, map_location='cpu')) | |
| self.pretrain_models['dvae'] = dvae | |
| self.logger.log(logging.INFO, 'dvae loaded.') | |
| if gpt_config_path: | |
| cfg = OmegaConf.load(gpt_config_path) | |
| gpt = GPT_warpper(**cfg).to(device).eval() | |
| assert gpt_ckpt_path, 'gpt_ckpt_path should not be None' | |
| gpt.load_state_dict(torch.load(gpt_ckpt_path, map_location='cpu')) | |
| self.pretrain_models['gpt'] = gpt | |
| self.logger.log(logging.INFO, 'gpt loaded.') | |
| if decoder_config_path: | |
| cfg = OmegaConf.load(decoder_config_path) | |
| decoder = DVAE(**cfg).to(device).eval() | |
| assert decoder_ckpt_path, 'decoder_ckpt_path should not be None' | |
| decoder.load_state_dict(torch.load(decoder_ckpt_path, map_location='cpu')) | |
| self.pretrain_models['decoder'] = decoder | |
| self.logger.log(logging.INFO, 'decoder loaded.') | |
| if tokenizer_path: | |
| tokenizer = torch.load(tokenizer_path, map_location='cpu') | |
| tokenizer.padding_side = 'left' | |
| self.pretrain_models['tokenizer'] = tokenizer | |
| self.logger.log(logging.INFO, 'tokenizer loaded.') | |
| self.check_model() | |
| def infer( | |
| self, | |
| text, | |
| skip_refine_text=False, | |
| refine_text_only=False, | |
| params_refine_text={}, | |
| params_infer_code={}, | |
| use_decoder=False | |
| ): | |
| assert self.check_model(use_decoder=use_decoder) | |
| if not skip_refine_text: | |
| text_tokens = refine_text(self.pretrain_models, text, **params_refine_text)['ids'] | |
| text_tokens = [i[i < self.pretrain_models['tokenizer'].convert_tokens_to_ids('[break_0]')] for i in text_tokens] | |
| text = self.pretrain_models['tokenizer'].batch_decode(text_tokens) | |
| if refine_text_only: | |
| return text | |
| text = [params_infer_code.get('prompt', '') + i for i in text] | |
| params_infer_code.pop('prompt', '') | |
| result = infer_code(self.pretrain_models, text, **params_infer_code, return_hidden=use_decoder) | |
| if use_decoder: | |
| mel_spec = [self.pretrain_models['decoder'](i[None].permute(0,2,1)) for i in result['hiddens']] | |
| else: | |
| mel_spec = [self.pretrain_models['dvae'](i[None].permute(0,2,1)) for i in result['ids']] | |
| wav = [self.pretrain_models['vocos'].decode(i).cpu().numpy() for i in mel_spec] | |
| return wav | |