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| import torch | |
| from torch import no_grad, LongTensor | |
| import utils | |
| from utils import get_hparams_from_file, lang_dict | |
| from vits import commons | |
| from vits.mel_processing import spectrogram_torch | |
| from vits.text import text_to_sequence | |
| from vits.models import SynthesizerTrn | |
| class VITS: | |
| def __init__(self, model_path, config, device="cpu", **kwargs): | |
| self.hps_ms = get_hparams_from_file(config) if isinstance(config, str) else config | |
| self.n_speakers = getattr(self.hps_ms.data, 'n_speakers', 0) | |
| self.n_symbols = len(getattr(self.hps_ms, 'symbols', [])) | |
| self.speakers = getattr(self.hps_ms, 'speakers', ['0']) | |
| if not isinstance(self.speakers, list): | |
| self.speakers = [item[0] for item in sorted(list(self.speakers.items()), key=lambda x: x[1])] | |
| self.bert_embedding = getattr(self.hps_ms.data, 'bert_embedding', | |
| getattr(self.hps_ms.model, 'bert_embedding', False)) | |
| self.hps_ms.model.bert_embedding = self.bert_embedding | |
| self.text_cleaners = getattr(self.hps_ms.data, 'text_cleaners', [None])[0] | |
| self.sampling_rate = self.hps_ms.data.sampling_rate | |
| self.device = device | |
| self.model_path = model_path | |
| # load checkpoint | |
| # self.load_model() | |
| self.lang = lang_dict.get(self.text_cleaners, ["unknown"]) | |
| def load_model(self): | |
| self.net_g_ms = SynthesizerTrn( | |
| self.n_symbols, | |
| self.hps_ms.data.filter_length // 2 + 1, | |
| self.hps_ms.train.segment_size // self.hps_ms.data.hop_length, | |
| n_speakers=self.n_speakers, | |
| **self.hps_ms.model) | |
| _ = self.net_g_ms.eval() | |
| utils.load_checkpoint(self.model_path, self.net_g_ms) | |
| self.net_g_ms.to(self.device) | |
| def release_model(self): | |
| del self.net_g_ms | |
| def get_cleaned_text(self, text, hps, cleaned=False): | |
| if cleaned: | |
| text_norm = text_to_sequence(text, hps.symbols, []) | |
| else: | |
| if self.bert_embedding: | |
| text_norm, char_embed = text_to_sequence(text, hps.symbols, hps.data.text_cleaners, | |
| bert_embedding=self.bert_embedding) | |
| text_norm = LongTensor(text_norm) | |
| return text_norm, char_embed | |
| else: | |
| text_norm = text_to_sequence(text, hps.symbols, hps.data.text_cleaners) | |
| if hps.data.add_blank: | |
| text_norm = commons.intersperse(text_norm, 0) | |
| text_norm = LongTensor(text_norm) | |
| return text_norm | |
| def infer(self, text, id, noise, noisew, length, cleaned=False, **kwargs): | |
| char_embeds = None | |
| if self.bert_embedding: | |
| stn_tst, char_embeds = self.get_cleaned_text(text, self.hps_ms, cleaned=cleaned) | |
| else: | |
| stn_tst = self.get_cleaned_text(text, self.hps_ms, cleaned=cleaned) | |
| id = LongTensor([id]) | |
| with no_grad(): | |
| x_tst = stn_tst.unsqueeze(0).to(self.device) | |
| x_tst_lengths = LongTensor([stn_tst.size(0)]).to(self.device) | |
| x_tst_prosody = torch.FloatTensor(char_embeds).unsqueeze(0).to( | |
| self.device) if self.bert_embedding else None | |
| id = id.to(self.device) | |
| audio = self.net_g_ms.infer(x=x_tst, | |
| x_lengths=x_tst_lengths, | |
| sid=id, | |
| noise_scale=noise, | |
| noise_scale_w=noisew, | |
| length_scale=length, | |
| bert=x_tst_prosody)[0][0, 0].data.float().cpu().numpy() | |
| torch.cuda.empty_cache() | |
| return audio | |
| def voice_conversion(self, audio_path, original_id, target_id): | |
| audio = utils.load_audio_to_torch( | |
| audio_path, self.sampling_rate) | |
| y = audio.unsqueeze(0) | |
| spec = spectrogram_torch(y, self.hps_ms.data.filter_length, | |
| self.sampling_rate, self.hps_ms.data.hop_length, | |
| self.hps_ms.data.win_length, | |
| center=False) | |
| spec_lengths = LongTensor([spec.size(-1)]) | |
| sid_src = LongTensor([original_id]) | |
| with no_grad(): | |
| sid_tgt = LongTensor([target_id]) | |
| audio = self.net_g_ms.voice_conversion(spec.to(self.device), | |
| spec_lengths.to(self.device), | |
| sid_src=sid_src.to(self.device), | |
| sid_tgt=sid_tgt.to(self.device))[0][0, 0].data.cpu().float().numpy() | |
| torch.cuda.empty_cache() | |
| return audio | |