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import os |
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import torch |
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import sys |
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import gradio as gr |
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from huggingface_hub import hf_hub_download |
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if "vits" not in sys.path: |
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sys.path.append("vits") |
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from vits import commons, utils |
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from vits.models import SynthesizerTrn |
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class TextMapper(object): |
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def __init__(self, vocab_file): |
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self.symbols = [x.strip() for x in open(vocab_file, encoding="utf-8").readlines()] |
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self.SPACE_ID = self.symbols.index(" ") |
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self._symbol_to_id = {s: i for i, s in enumerate(self.symbols)} |
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def text_to_sequence(self, text, cleaner_names): |
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sequence = [self._symbol_to_id[symbol] for symbol in text.strip()] |
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return sequence |
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def get_text(self, text, hps): |
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text_norm = self.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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return torch.LongTensor(text_norm) |
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def filter_oov(self, text, lang=None): |
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val_chars = self._symbol_to_id |
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return "".join(filter(lambda x: x in val_chars, text)) |
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def synthesize(text, speed): |
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if speed is None: |
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speed = 1.0 |
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lang_code = "fao" |
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vocab_file = hf_hub_download(repo_id="facebook/mms-tts", filename="vocab.txt", subfolder=f"models/{lang_code}") |
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config_file = hf_hub_download(repo_id="facebook/mms-tts", filename="config.json", subfolder=f"models/{lang_code}") |
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g_pth = hf_hub_download(repo_id="facebook/mms-tts", filename="G_100000.pth", subfolder=f"models/{lang_code}") |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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hps = utils.get_hparams_from_file(config_file) |
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text_mapper = TextMapper(vocab_file) |
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net_g = SynthesizerTrn(len(text_mapper.symbols), hps.data.filter_length // 2 + 1, hps.train.segment_size // hps.data.hop_length, **hps.model) |
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net_g.to(device) |
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net_g.eval() |
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utils.load_checkpoint(g_pth, net_g, None) |
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text = text.lower() |
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text = text_mapper.filter_oov(text) |
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stn_tst = text_mapper.get_text(text, hps) |
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with torch.no_grad(): |
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x_tst = stn_tst.unsqueeze(0).to(device) |
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x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).to(device) |
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hyp = net_g.infer(x_tst, x_tst_lengths, noise_scale=0.667, noise_scale_w=0.8, length_scale=1.0 / speed)[0][0, 0].cpu().float().numpy() |
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return gr.Audio.update(value=(hps.data.sampling_rate, hyp)), text |
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