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| import librosa | |
| import re | |
| import numpy as np | |
| 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.text import text_to_sequence | |
| from vits.models import SynthesizerTrn | |
| class W2V2_VITS: | |
| def __init__(self, model_path, config, device=torch.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.use_f0 = getattr(self.hps_ms.data, 'use_f0', False) | |
| self.emotion_embedding = getattr(self.hps_ms.data, 'emotion_embedding', | |
| getattr(self.hps_ms.model, 'emotion_embedding', False)) | |
| self.hps_ms.model.emotion_embedding = self.emotion_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 | |
| self.lang = lang_dict.get(self.text_cleaners, ["unknown"]) | |
| def load_model(self, emotion_reference, dimensional_emotion_model): | |
| self.emotion_reference = emotion_reference | |
| self.dimensional_emotion_model = dimensional_emotion_model | |
| 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 get_cleaned_text(self, text, hps, cleaned=False): | |
| if cleaned: | |
| text_norm = text_to_sequence(text, hps.symbols, []) | |
| 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, emotion, cleaned=False, **kwargs): | |
| stn_tst = self.get_cleaned_text(text, self.hps_ms, cleaned=cleaned) | |
| id = LongTensor([id]) | |
| if isinstance(emotion, int): | |
| emotion_emb = self.emotion_reference[emotion] | |
| elif isinstance(emotion, str) and emotion.endswith('.npy'): | |
| emotion_emb = np.load(emotion).reshape(-1, 1024)[0] | |
| else: | |
| audio16000, sampling_rate = librosa.load(emotion, sr=16000, mono=True) | |
| emotion_emb = self.dimensional_emotion_model(audio16000, sampling_rate)['hidden_states'] | |
| emotion_emb = re.sub(r'\..*$', '', emotion_emb) | |
| with no_grad(): | |
| x_tst = stn_tst.unsqueeze(0).to(self.device) | |
| x_tst_lengths = LongTensor([stn_tst.size(0)]).to(self.device) | |
| id = id.to(self.device) | |
| emotion_emb = torch.FloatTensor(emotion_emb).unsqueeze(0).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, | |
| emotion_embedding=emotion_emb)[0][0, 0].data.float().cpu().numpy() | |
| torch.cuda.empty_cache() | |
| return audio | |