import torch
import json
from malaya_boilerplate.train.config import HParams
from malaya_speech.torch_model.vits.commons import intersperse
from malaya_speech.torch_model.vits.model_infer import SynthesizerTrn
from malaya_speech.utils.text import TTS_SYMBOLS
from malaya_speech.utils.torch_utils import to_tensor_cuda, to_numpy
from malaya_speech.model.abstract import Abstract, TTS
[docs]class VITS(SynthesizerTrn, TTS):
def __init__(self, normalizer, pth, config, model, name, **kwargs):
with open(config) as fopen:
hps = HParams(**json.load(fopen))
self.hps = hps
TTS.__init__(self, e2e=True)
super(VITS, self).__init__(len(TTS_SYMBOLS),
hps.data.filter_length // 2 + 1,
hps.train.segment_size // hps.data.hop_length,
**hps.model)
self.eval()
self.load_state_dict(torch.load(pth))
self._normalizer = normalizer
self.__model__ = model
self.__name__ = name
[docs] def predict(
self,
string,
temperature: float = 0.6666,
temperature_durator: float = 0.6666,
length_ratio: float = 1.0,
**kwargs,
):
"""
Change string to waveform.
Parameters
----------
string: str
temperature: float, optional (default=0.6666)
Decoder model trying to decode with encoder(text) + random.normal() * temperature.
temperature_durator: float, optional (default=0.6666)
Durator trying to predict alignment with random.normal() * temperature_durator.
length_ratio: float, optional (default=1.0)
Increase this variable will increase time voice generated.
Returns
-------
result: Dict[string, ids, alignment, y]
"""
cuda = next(self.parameters()).is_cuda
t, ids = self._normalizer.normalize(string, **kwargs)
if self.hps.data.add_blank:
ids = intersperse(ids, 0)
ids = torch.LongTensor(ids)
ids_lengths = torch.LongTensor([ids.size(0)])
ids = ids.unsqueeze(0)
ids = to_tensor_cuda(ids, cuda)
ids_lengths = to_tensor_cuda(ids_lengths, cuda)
audio = self.infer(
ids,
ids_lengths,
noise_scale=temperature,
noise_scale_w=temperature_durator,
length_scale=length_ratio,
)
alignment = to_numpy(audio[1])[0, 0]
audio = to_numpy(audio[0])[0, 0]
return {
'string': t,
'ids': to_numpy(ids)[0],
'alignment': alignment,
'y': audio,
}
def __call__(self, input, **kwargs):
return self.predict(input, **kwargs)
