mirror of
https://github.com/BoardWare-Genius/jarvis-models.git
synced 2025-12-13 16:53:24 +00:00
81 lines
2.6 KiB
Python
81 lines
2.6 KiB
Python
import io
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import sys
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sys.path.append('tts/vits')
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import soundfile
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import os
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os.environ["PYTORCH_JIT"] = "0"
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import torch
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import tts.vits.commons as commons
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import tts.vits.utils as utils
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from tts.vits.models import SynthesizerTrn
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from tts.vits.text.symbols import symbols
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from tts.vits.text import text_to_sequence
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import logging
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logging.getLogger().setLevel(logging.INFO)
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logging.basicConfig(level=logging.INFO)
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dirbaspath = __file__.split("\\")[1:-1]
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dirbaspath= "C://" + "/".join(dirbaspath)
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config = {
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'paimon': {
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'cfg': dirbaspath + '/models/paimon6k.json',
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'model': dirbaspath + '/models/paimon6k_390k.pth',
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'char': 'character_paimon',
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'speed': 1
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},
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'yunfei': {
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'cfg': dirbaspath + '/tts/models/yunfeimix2.json',
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'model': dirbaspath + '/models/yunfeimix2_53k.pth',
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'char': 'character_yunfei',
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'speed': 1.1
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},
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'catmaid': {
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'cfg': dirbaspath + '/models/catmix.json',
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'model': dirbaspath + '/models/catmix_107k.pth',
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'char': 'character_catmaid',
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'speed': 1.2
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},
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}
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class TTService():
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def __init__(self, model_name="catmaid"):
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cfg = config[model_name]
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logging.info('Initializing TTS Service for %s...' % cfg["char"])
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self.hps = utils.get_hparams_from_file(cfg["cfg"])
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self.speed = cfg["speed"]
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self.net_g = SynthesizerTrn(
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len(symbols),
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self.hps.data.filter_length // 2 + 1,
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self.hps.train.segment_size // self.hps.data.hop_length,
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**self.hps.model).cpu()
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_ = self.net_g.eval()
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_ = utils.load_checkpoint(cfg["model"], self.net_g, None)
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def get_text(self, text, hps):
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text_norm = text_to_sequence(text, hps.data.text_cleaners)
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if hps.data.add_blank:
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text_norm = commons.intersperse(text_norm, 0)
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text_norm = torch.LongTensor(text_norm)
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return text_norm
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def read(self, text, format="wav") -> io.BytesIO:
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text = text.replace('~', '！')
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stn_tst = self.get_text(text, self.hps)
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with torch.no_grad():
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x_tst = stn_tst.cpu().unsqueeze(0)
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x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).cpu()
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# tp = self.net_g.infer(x_tst, x_tst_lengths, noise_scale=.667, noise_scale_w=0.2, length_scale=self.speed)
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audio = self.net_g.infer(x_tst, x_tst_lengths, noise_scale=.667, noise_scale_w=0.2, length_scale=self.speed)[0][
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0, 0].data.cpu().float().numpy()
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f = io.BytesIO()
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soundfile.write(f, audio, self.hps.data.sampling_rate, format=format)
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f.seek(0)
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return f
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