huseinzol05
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Create README.md
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README.md
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---
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language:
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- ms
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---
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# Malay VITS Multispeaker clean V2
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**This model intended to use by [malaya-speech](https://github.com/mesolitica/malaya-speech) only, it is possible to not use the library but make sure the character vocabulary is correct**.
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## how to
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```python
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from huggingface_hub import snapshot_download
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from malaya_speech.torch_model.vits.model_infer import SynthesizerTrn
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from malaya_speech.torch_model.vits.commons import intersperse
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from malaya_speech.utils.text import TTS_SYMBOLS
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from malaya_speech.tts import load_text_ids
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import torch
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import os
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import json
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try:
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from malaya_boilerplate.hparams import HParams
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except BaseException:
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from malaya_boilerplate.train.config import HParams
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folder = snapshot_download(repo_id="mesolitica/VITS-multispeaker-clean-v2")
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with open(os.path.join(folder, 'config.json')) as fopen:
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hps = HParams(**json.load(fopen))
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model = SynthesizerTrn(
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len(TTS_SYMBOLS),
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hps.data.filter_length // 2 + 1,
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hps.train.segment_size // hps.data.hop_length,
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n_speakers=hps.data.n_speakers,
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**hps.model,
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).eval()
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model.load_state_dict(torch.load(os.path.join(folder, 'model.pth'), map_location='cpu'))
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speaker_id = {
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'Ariff': 0,
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'Ayu': 1,
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'Bunga': 2,
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'Danial': 3,
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'Elina': 4,
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'Kamarul': 5,
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'Osman': 6,
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'Yasmin': 7
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}
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normalizer = load_text_ids(pad_to = None, understand_punct = True, is_lower = False)
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t, ids = normalizer.normalize('saya nak makan nasi ayam yang sedap, lagi lazat, dan hidup sangatlah susah kan.', add_fullstop = False)
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if hps.data.add_blank:
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ids = intersperse(ids, 0)
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ids = torch.LongTensor(ids)
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ids_lengths = torch.LongTensor([ids.size(0)])
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ids = ids.unsqueeze(0)
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sid = 0
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sid = torch.tensor([sid])
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with torch.no_grad():
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audio = model.infer(
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ids,
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ids_lengths,
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noise_scale=0.0,
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noise_scale_w=0.0,
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length_scale=1.0,
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sid=sid,
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)
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y_ = audio[0].numpy()
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```
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