Spaces:
Runtime error
Runtime error
| import librosa | |
| import numpy as np | |
| import torch | |
| from torch import no_grad, LongTensor, inference_mode, FloatTensor | |
| 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 HuBert_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.model_path = model_path | |
| self.device = device | |
| key = getattr(self.hps_ms.data, "text_cleaners", ["none"])[0] | |
| self.lang = lang_dict.get(key, ["unknown"]) | |
| def load_model(self, hubert): | |
| self.hubert = hubert | |
| 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: | |
| 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 get_cleaner(self): | |
| return getattr(self.hps_ms.data, 'text_cleaners', [None])[0] | |
| def get_speakers(self, escape=False): | |
| return self.speakers | |
| def sampling_rate(self): | |
| return self.hps_ms.data.sampling_rate | |
| def infer(self, audio_path, id, noise, noisew, length, f0_scale=1, **kwargs): | |
| if self.use_f0: | |
| audio, sampling_rate = librosa.load(audio_path, sr=self.hps_ms.data.sampling_rate, mono=True) | |
| audio16000 = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000) | |
| else: | |
| audio16000, sampling_rate = librosa.load(audio_path, sr=16000, mono=True) | |
| with inference_mode(): | |
| units = self.hubert.units(FloatTensor(audio16000).unsqueeze(0).unsqueeze(0)).squeeze(0).numpy() | |
| if self.use_f0: | |
| f0 = librosa.pyin(audio, | |
| sr=sampling_rate, | |
| fmin=librosa.note_to_hz('C0'), | |
| fmax=librosa.note_to_hz('C7'), | |
| frame_length=1780)[0] | |
| target_length = len(units[:, 0]) | |
| f0 = np.nan_to_num(np.interp(np.arange(0, len(f0) * target_length, len(f0)) / target_length, | |
| np.arange(0, len(f0)), f0)) * f0_scale | |
| units[:, 0] = f0 / 10 | |
| stn_tst = FloatTensor(units) | |
| 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) | |
| 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)[0][0, 0].data.float().cpu().numpy() | |
| torch.cuda.empty_cache() | |
| return audio | |