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from models import SynthesizerTrn |
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from scipy.io.wavfile import write |
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from khmer_phonemizer import phonemize_single |
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import utils |
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import commons |
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import torch |
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_pad = '_' |
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_punctuation = '. ' |
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_letters_ipa = 'acefhijklmnoprstuwzĕŋŏŭɑɓɔɗəɛɡɨɲʋʔʰː' |
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symbols = [_pad] + list(_punctuation) + list(_letters_ipa) |
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SPACE_ID = symbols.index(" ") |
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_symbol_to_id = {s: i for i, s in enumerate(symbols)} |
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def text_to_sequence(text): |
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sequence = [] |
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for symbol in text: |
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symbol_id = _symbol_to_id[symbol] |
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sequence += [symbol_id] |
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return sequence |
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def get_text(text, hps): |
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text_norm = text_to_sequence(text) |
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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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hps = utils.get_hparams_from_file("config.json") |
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net_g = SynthesizerTrn( |
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len(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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**hps.model |
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) |
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_ = net_g.eval() |
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_ = utils.load_checkpoint("G_22000.pth", net_g, None) |
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text = " ".join(phonemize_single("នឹកណាស់") + ["."]) |
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stn_tst = get_text(text, hps) |
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with torch.no_grad(): |
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x_tst = stn_tst.unsqueeze(0) |
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x_tst_lengths = torch.LongTensor([stn_tst.size(0)]) |
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audio = ( |
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net_g.infer( |
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x_tst, x_tst_lengths, noise_scale=0.667, noise_scale_w=0.8, length_scale=1 |
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)[0][0, 0] |
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.data.cpu() |
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.float() |
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.numpy() |
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) |
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write("audio.wav", rate=hps.data.sampling_rate, data=audio) |
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