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""" |
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VITS2 Remy - Luxembourgish TTS Inference Script |
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Usage: |
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python inference.py "Moien, wéi geet et dir?" |
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python inference.py "Moien, wéi geet et dir?" -o output.wav |
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python inference.py "Moien, wéi geet et dir?" --noise_scale 0.5 |
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""" |
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import argparse |
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import torch |
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import scipy.io.wavfile as wavfile |
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import utils |
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import commons |
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from models import SynthesizerTrn |
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from text.symbols import symbols |
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from text import text_to_sequence |
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def get_text(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 main(): |
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parser = argparse.ArgumentParser(description="VITS2 Remy TTS") |
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parser.add_argument("text", type=str, help="Text to synthesize") |
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parser.add_argument("-o", "--output", type=str, default="output.wav", help="Output WAV file") |
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parser.add_argument("--noise_scale", type=float, default=0.667, help="Noise scale (default: 0.667)") |
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parser.add_argument("--noise_scale_w", type=float, default=0.8, help="Noise scale W (default: 0.8)") |
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parser.add_argument("--length_scale", type=float, default=1.0, help="Length scale (default: 1.0)") |
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parser.add_argument("--cpu", action="store_true", help="Use CPU instead of GPU") |
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args = parser.parse_args() |
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device = "cpu" if args.cpu else "cuda" |
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hps = utils.get_hparams_from_file("config.json") |
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if getattr(hps.model, 'use_mel_posterior_encoder', False): |
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posterior_channels = hps.data.n_mel_channels |
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else: |
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posterior_channels = hps.data.filter_length // 2 + 1 |
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net_g = SynthesizerTrn( |
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len(symbols), |
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posterior_channels, |
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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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).to(device) |
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_ = utils.load_checkpoint("model.pth", net_g, None) |
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net_g.eval() |
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text = args.text.lower() |
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print(f"Synthesizing: {text}") |
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with torch.no_grad(): |
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stn_tst = get_text(text, hps) |
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x_tst = stn_tst.to(device).unsqueeze(0) |
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x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).to(device) |
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audio = net_g.infer( |
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x_tst, x_tst_lengths, |
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noise_scale=args.noise_scale, |
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noise_scale_w=args.noise_scale_w, |
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length_scale=args.length_scale |
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)[0][0, 0].data.cpu().float().numpy() |
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wavfile.write(args.output, hps.data.sampling_rate, audio) |
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print(f"Saved to: {args.output}") |
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if __name__ == "__main__": |
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main() |
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