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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
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