| import matplotlib
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|
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| matplotlib.use('Agg')
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|
|
| import glob
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| import importlib
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| from utils.cwt import get_lf0_cwt
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| import os
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| import torch.optim
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| import torch.utils.data
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| from utils.indexed_datasets import IndexedDataset
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| from utils.pitch_utils import norm_interp_f0
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| import numpy as np
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| from tasks.base_task import BaseDataset
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| import torch
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| import torch.optim
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| import torch.utils.data
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| import utils
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| import torch.distributions
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| from utils.hparams import hparams
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|
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|
|
| class FastSpeechDataset(BaseDataset):
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| def __init__(self, prefix, shuffle=False):
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| super().__init__(shuffle)
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| self.data_dir = hparams['binary_data_dir']
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| self.prefix = prefix
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| self.hparams = hparams
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| self.sizes = np.load(f'{self.data_dir}/{self.prefix}_lengths.npy')
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| self.indexed_ds = None
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|
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| f0_stats_fn = f'{self.data_dir}/train_f0s_mean_std.npy'
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| if os.path.exists(f0_stats_fn):
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| hparams['f0_mean'], hparams['f0_std'] = self.f0_mean, self.f0_std = np.load(f0_stats_fn)
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| hparams['f0_mean'] = float(hparams['f0_mean'])
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| hparams['f0_std'] = float(hparams['f0_std'])
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| else:
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| hparams['f0_mean'], hparams['f0_std'] = self.f0_mean, self.f0_std = None, None
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|
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| if prefix == 'test':
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| if hparams['test_input_dir'] != '':
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| self.indexed_ds, self.sizes = self.load_test_inputs(hparams['test_input_dir'])
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| else:
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| if hparams['num_test_samples'] > 0:
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| self.avail_idxs = list(range(hparams['num_test_samples'])) + hparams['test_ids']
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| self.sizes = [self.sizes[i] for i in self.avail_idxs]
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|
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| if hparams['pitch_type'] == 'cwt':
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| _, hparams['cwt_scales'] = get_lf0_cwt(np.ones(10))
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|
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| def _get_item(self, index):
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| if hasattr(self, 'avail_idxs') and self.avail_idxs is not None:
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| index = self.avail_idxs[index]
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| if self.indexed_ds is None:
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| self.indexed_ds = IndexedDataset(f'{self.data_dir}/{self.prefix}')
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| return self.indexed_ds[index]
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|
|
| def __getitem__(self, index):
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| hparams = self.hparams
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| item = self._get_item(index)
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| max_frames = hparams['max_frames']
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| spec = torch.Tensor(item['mel'])[:max_frames]
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| energy = (spec.exp() ** 2).sum(-1).sqrt()
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| mel2ph = torch.LongTensor(item['mel2ph'])[:max_frames] if 'mel2ph' in item else None
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| f0, uv = norm_interp_f0(item["f0"][:max_frames], hparams)
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| phone = torch.LongTensor(item['phone'][:hparams['max_input_tokens']])
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| pitch = torch.LongTensor(item.get("pitch"))[:max_frames]
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|
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| sample = {
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| "id": index,
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| "item_name": item['item_name'],
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| "text": item['txt'],
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| "txt_token": phone,
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| "mel": spec,
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| "pitch": pitch,
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| "energy": energy,
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| "f0": f0,
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| "uv": uv,
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| "mel2ph": mel2ph,
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| "mel_nonpadding": spec.abs().sum(-1) > 0,
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| }
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| if self.hparams['use_spk_embed']:
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| sample["spk_embed"] = torch.Tensor(item['spk_embed'])
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| if self.hparams['use_spk_id']:
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| sample["spk_id"] = item['spk_id']
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|
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|
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| if self.hparams['pitch_type'] == 'cwt':
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| cwt_spec = torch.Tensor(item['cwt_spec'])[:max_frames]
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| f0_mean = item.get('f0_mean', item.get('cwt_mean'))
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| f0_std = item.get('f0_std', item.get('cwt_std'))
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| sample.update({"cwt_spec": cwt_spec, "f0_mean": f0_mean, "f0_std": f0_std})
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| elif self.hparams['pitch_type'] == 'ph':
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| f0_phlevel_sum = torch.zeros_like(phone).float().scatter_add(0, mel2ph - 1, f0)
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| f0_phlevel_num = torch.zeros_like(phone).float().scatter_add(
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| 0, mel2ph - 1, torch.ones_like(f0)).clamp_min(1)
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| sample["f0_ph"] = f0_phlevel_sum / f0_phlevel_num
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| return sample
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|
|
| def collater(self, samples):
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| if len(samples) == 0:
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| return {}
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| id = torch.LongTensor([s['id'] for s in samples])
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| item_names = [s['item_name'] for s in samples]
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| text = [s['text'] for s in samples]
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| txt_tokens = utils.collate_1d([s['txt_token'] for s in samples], 0)
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| f0 = utils.collate_1d([s['f0'] for s in samples], 0.0)
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| pitch = utils.collate_1d([s['pitch'] for s in samples])
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| uv = utils.collate_1d([s['uv'] for s in samples])
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| energy = utils.collate_1d([s['energy'] for s in samples], 0.0)
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| mel2ph = utils.collate_1d([s['mel2ph'] for s in samples], 0.0) \
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| if samples[0]['mel2ph'] is not None else None
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| mels = utils.collate_2d([s['mel'] for s in samples], 0.0)
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| txt_lengths = torch.LongTensor([s['txt_token'].numel() for s in samples])
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| mel_lengths = torch.LongTensor([s['mel'].shape[0] for s in samples])
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|
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| batch = {
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| 'id': id,
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| 'item_name': item_names,
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| 'nsamples': len(samples),
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| 'text': text,
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| 'txt_tokens': txt_tokens,
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| 'txt_lengths': txt_lengths,
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| 'mels': mels,
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| 'mel_lengths': mel_lengths,
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| 'mel2ph': mel2ph,
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| 'energy': energy,
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| 'pitch': pitch,
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| 'f0': f0,
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| 'uv': uv,
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| }
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|
|
| if self.hparams['use_spk_embed']:
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| spk_embed = torch.stack([s['spk_embed'] for s in samples])
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| batch['spk_embed'] = spk_embed
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| if self.hparams['use_spk_id']:
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| spk_ids = torch.LongTensor([s['spk_id'] for s in samples])
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| batch['spk_ids'] = spk_ids
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| if self.hparams['pitch_type'] == 'cwt':
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| cwt_spec = utils.collate_2d([s['cwt_spec'] for s in samples])
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| f0_mean = torch.Tensor([s['f0_mean'] for s in samples])
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| f0_std = torch.Tensor([s['f0_std'] for s in samples])
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| batch.update({'cwt_spec': cwt_spec, 'f0_mean': f0_mean, 'f0_std': f0_std})
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| elif self.hparams['pitch_type'] == 'ph':
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| batch['f0'] = utils.collate_1d([s['f0_ph'] for s in samples])
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|
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| return batch
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|
|
| def load_test_inputs(self, test_input_dir, spk_id=0):
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| inp_wav_paths = glob.glob(f'{test_input_dir}/*.wav') + glob.glob(f'{test_input_dir}/*.mp3')
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| sizes = []
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| items = []
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|
|
| binarizer_cls = hparams.get("binarizer_cls", 'data_gen.tts.base_binarizerr.BaseBinarizer')
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| pkg = ".".join(binarizer_cls.split(".")[:-1])
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| cls_name = binarizer_cls.split(".")[-1]
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| binarizer_cls = getattr(importlib.import_module(pkg), cls_name)
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| binarization_args = hparams['binarization_args']
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|
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| for wav_fn in inp_wav_paths:
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| item_name = os.path.basename(wav_fn)
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| ph = txt = tg_fn = ''
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| wav_fn = wav_fn
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| encoder = None
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| item = binarizer_cls.process_item(item_name, ph, txt, tg_fn, wav_fn, spk_id, encoder, binarization_args)
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| items.append(item)
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| sizes.append(item['len'])
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| return items, sizes
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|
|
