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import torch
import numpy as np
import sys
import commons
import utils
from models import SynthesizerTrn
from text.symbols import symbols
from text import text_to_sequence
from scipy.io.wavfile import write
import logging
numba_logger = logging.getLogger('numba')
numba_logger.setLevel(logging.WARNING)
sys.path.append("../")
from resemblyzer import preprocess_wav, VoiceEncoder
device = "cpu"
def get_text(text, hps):
text_norm = text_to_sequence(text, hps.data.text_cleaners)
if hps.data.add_blank:
text_norm = commons.intersperse(text_norm, 0)
text_norm = torch.LongTensor(text_norm)
return text_norm
def get_speaker_embedding(path):
encoder = VoiceEncoder(device='cpu')
wav = preprocess_wav(path)
embed = encoder.embed_utterance(wav)
return embed
class VoiceClone():
def __init__(self, checkpoint_path):
hps = utils.get_hparams_from_file("./configs/vivos.json")
self.net_g = SynthesizerTrn(
len(symbols),
hps.data.filter_length // 2 + 1,
hps.train.segment_size // hps.data.hop_length,
n_speakers=hps.data.n_speakers,
**hps.model).to(device)
_ = self.net_g.eval()
_ = utils.load_checkpoint(checkpoint_path, self.net_g, None)
self.hps = hps
def infer(self, text, ref_audio):
stn_tst = get_text(text, self.hps)
with torch.no_grad():
x_tst = stn_tst.to(device).unsqueeze(0)
x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).to(device)
speaker_embedding = get_speaker_embedding(ref_audio)
speaker_embedding = torch.FloatTensor(torch.from_numpy(speaker_embedding)).unsqueeze(0).to(device)
audio = self.net_g.infer(x_tst, x_tst_lengths, speaker_embedding=speaker_embedding, noise_scale=.667, noise_scale_w=0.8, length_scale=1)[0][0,0].data.cpu().float().numpy()
write(ref_audio.replace(".wav", "_clone.wav"), 22050, audio)
if __name__ == "__main__":
object = VoiceClone("logs/vivos/G_9000.pth")
object.infer("hai ba hai ba", "audio/sontung.wav") |