63 lines
2.7 KiB
Python
63 lines
2.7 KiB
Python
from itertools import groupby
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import torch
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from transformers import Wav2Vec2CTCTokenizer
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from models.audio.tts.ctc_code_generator import CtcCodeGenerator
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def get_ctc_metadata(codes):
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if isinstance(codes, torch.Tensor):
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codes = codes.tolist()
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grouped = groupby(codes)
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rcodes, repeats, pads = [], [], [0]
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for val, group in grouped:
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if val == 0:
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pads[-1] = len(list(
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group)) # This is a very important distinction! It means the padding belongs to the character proceeding it.
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else:
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rcodes.append(val)
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repeats.append(len(list(group)))
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pads.append(0)
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rcodes = torch.tensor(rcodes)
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# These clip values are sane maximum values which I did not see in the datasets I have access to.
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repeats = torch.clip(torch.tensor(repeats), min=1, max=30)
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pads = torch.clip(torch.tensor(pads[:-1]), max=120)
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return rcodes, pads, repeats
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if __name__ == '__main__':
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model = CtcCodeGenerator(model_dim=512, layers=16, dropout=0).eval().cuda()
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model.load_state_dict(torch.load('../experiments/train_encoder_build_ctc_alignments_toy/models/76000_generator_ema.pth'))
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tokenizer = Wav2Vec2CTCTokenizer.from_pretrained('jbetker/tacotron-symbols')
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text = "and now, what do you want."
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seq = [0, 0, 0, 38, 51, 51, 41, 11, 11, 51, 51, 0, 0, 0, 0, 52, 0, 60, 0, 0, 0, 0, 0, 0, 6, 11, 11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 60, 45, 0, 38, 57, 57, 11, 0, 41, 52, 52, 11, 11, 62, 52, 52, 58, 0, 11, 11, 60, 0, 0, 0, 0, 38, 0, 0, 51, 51, 0, 0, 57, 0, 0, 7, 7, 0, 0, 0]
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codes, pads, repeats = get_ctc_metadata(seq)
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with torch.no_grad():
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codes = codes.cuda().unsqueeze(0)
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pads = pads.cuda().unsqueeze(0)
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repeats = repeats.cuda().unsqueeze(0)
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ppads = pads.clone()
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prepeats = repeats.clone()
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mask = torch.zeros_like(pads)
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conf_str = tokenizer.decode(codes[0].tolist())
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for s in range(codes.shape[-1]):
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logits, confidences = model.inference(codes, pads * mask, repeats * mask)
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confidences = confidences * mask.logical_not() # prevent prediction of tokens that have already been predicted.
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i = confidences.argmax(dim=-1)
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pred = logits[0,i].argmax()
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pred_pads = pred % model.max_pad
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pred_repeats = pred // model.max_pad
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ppads[0,i] = pred_pads
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prepeats[0,i] = pred_repeats
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mask[0,i] = 1
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conf_str = conf_str[:i] + conf_str[i].upper() + conf_str[i+1:]
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print(f"conf: {conf_str} pads={pred_pads}:{pads[0,i].item()} repeats={pred_repeats}:{repeats[0,i].item()}") |