mesolitica/VITS-multispeaker-clean-v2

Malay VITS Multispeaker clean V2

This model intended to use by malaya-speech only, it is possible to not use the library but make sure the character vocabulary is correct.

how to

from huggingface_hub import snapshot_download
from malaya_speech.torch_model.vits.model_infer import SynthesizerTrn
from malaya_speech.torch_model.vits.commons import intersperse
from malaya_speech.utils.text import TTS_SYMBOLS
from malaya_speech.tts import load_text_ids
import torch
import os
import json

try:
    from malaya_boilerplate.hparams import HParams
except BaseException:
    from malaya_boilerplate.train.config import HParams

folder = snapshot_download(repo_id="mesolitica/VITS-multispeaker-clean-v2")

with open(os.path.join(folder, 'config.json')) as fopen:
    hps = HParams(**json.load(fopen))

model = SynthesizerTrn(
    len(TTS_SYMBOLS),
    hps.data.filter_length // 2 + 1,
    hps.train.segment_size // hps.data.hop_length,
    n_speakers=hps.data.n_speakers,
    **hps.model,
).eval()
model.load_state_dict(torch.load(os.path.join(folder, 'model.pth'), map_location='cpu'))

speaker_id = {
    'Ariff': 0,
    'Ayu': 1,
    'Bunga': 2,
    'Danial': 3,
    'Elina': 4,
    'Kamarul': 5,
    'Osman': 6,
    'Yasmin': 7
}
normalizer = load_text_ids(pad_to = None, understand_punct = True, is_lower = False)

t, ids = normalizer.normalize('saya nak makan nasi ayam yang sedap, lagi lazat, dan hidup sangatlah susah kan.', add_fullstop = False)
if hps.data.add_blank:
    ids = intersperse(ids, 0)
ids = torch.LongTensor(ids)
ids_lengths = torch.LongTensor([ids.size(0)])
ids = ids.unsqueeze(0)
sid = 0
sid = torch.tensor([sid])

with torch.no_grad():
    audio = model.infer(
        ids,
        ids_lengths,
        noise_scale=0.0,
        noise_scale_w=0.0,
        length_scale=1.0,
        sid=sid,
    )
    y_ = audio[0].numpy()