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import torch |
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from examples.textless_nlp.gslm.unit2speech.tacotron2.model import Tacotron2 |
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from examples.textless_nlp.gslm.unit2speech.tacotron2.waveglow_denoiser import ( |
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Denoiser, |
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) |
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def load_quantized_audio_from_file(file_path): |
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base_fname_batch, quantized_units_batch = [], [] |
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with open(file_path) as f: |
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for line in f: |
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base_fname, quantized_units_str = line.rstrip().split("|") |
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quantized_units = [int(q) for q in quantized_units_str.split(" ")] |
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base_fname_batch.append(base_fname) |
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quantized_units_batch.append(quantized_units) |
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return base_fname_batch, quantized_units_batch |
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def synthesize_audio(model, waveglow, denoiser, inp, lab=None, strength=0.0): |
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assert inp.size(0) == 1 |
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inp = inp.cuda() |
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if lab is not None: |
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lab = torch.LongTensor(1).cuda().fill_(lab) |
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with torch.no_grad(): |
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_, mel, _, ali, has_eos = model.inference(inp, lab, ret_has_eos=True) |
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aud = waveglow.infer(mel, sigma=0.666) |
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aud_dn = denoiser(aud, strength=strength).squeeze(1) |
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return mel, aud, aud_dn, has_eos |
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def load_tacotron(tacotron_model_path, max_decoder_steps): |
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ckpt_dict = torch.load(tacotron_model_path) |
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hparams = ckpt_dict["hparams"] |
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hparams.max_decoder_steps = max_decoder_steps |
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sr = hparams.sampling_rate |
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model = Tacotron2(hparams) |
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model.load_state_dict(ckpt_dict["model_dict"]) |
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model = model.cuda().eval().half() |
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return model, sr, hparams |
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def load_waveglow(waveglow_path): |
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waveglow = torch.load(waveglow_path)["model"] |
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waveglow = waveglow.cuda().eval().half() |
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for k in waveglow.convinv: |
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k.float() |
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denoiser = Denoiser(waveglow) |
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return waveglow, denoiser |
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