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| import matplotlib | |
| matplotlib.use('Agg') | |
| import glob | |
| import importlib | |
| from utils.cwt import get_lf0_cwt | |
| import os | |
| import torch.optim | |
| import torch.utils.data | |
| from utils.indexed_datasets import IndexedDataset | |
| from utils.pitch_utils import norm_interp_f0 | |
| import numpy as np | |
| from training.dataset.base_dataset import BaseDataset | |
| import torch | |
| import torch.optim | |
| import torch.utils.data | |
| import utils | |
| import torch.distributions | |
| from utils.hparams import hparams | |
| class FastSpeechDataset(BaseDataset): | |
| def __init__(self, prefix, shuffle=False): | |
| super().__init__(shuffle) | |
| self.data_dir = hparams['binary_data_dir'] | |
| self.prefix = prefix | |
| self.hparams = hparams | |
| self.sizes = np.load(f'{self.data_dir}/{self.prefix}_lengths.npy') | |
| self.indexed_ds = None | |
| # self.name2spk_id={} | |
| # pitch stats | |
| f0_stats_fn = f'{self.data_dir}/train_f0s_mean_std.npy' | |
| if os.path.exists(f0_stats_fn): | |
| hparams['f0_mean'], hparams['f0_std'] = self.f0_mean, self.f0_std = np.load(f0_stats_fn) | |
| hparams['f0_mean'] = float(hparams['f0_mean']) | |
| hparams['f0_std'] = float(hparams['f0_std']) | |
| else: | |
| hparams['f0_mean'], hparams['f0_std'] = self.f0_mean, self.f0_std = None, None | |
| if prefix == 'test': | |
| if hparams['test_input_dir'] != '': | |
| self.indexed_ds, self.sizes = self.load_test_inputs(hparams['test_input_dir']) | |
| else: | |
| if hparams['num_test_samples'] > 0: | |
| self.avail_idxs = list(range(hparams['num_test_samples'])) + hparams['test_ids'] | |
| self.sizes = [self.sizes[i] for i in self.avail_idxs] | |
| if hparams['pitch_type'] == 'cwt': | |
| _, hparams['cwt_scales'] = get_lf0_cwt(np.ones(10)) | |
| def _get_item(self, index): | |
| if hasattr(self, 'avail_idxs') and self.avail_idxs is not None: | |
| index = self.avail_idxs[index] | |
| if self.indexed_ds is None: | |
| self.indexed_ds = IndexedDataset(f'{self.data_dir}/{self.prefix}') | |
| return self.indexed_ds[index] | |
| def __getitem__(self, index): | |
| hparams = self.hparams | |
| item = self._get_item(index) | |
| max_frames = hparams['max_frames'] | |
| spec = torch.Tensor(item['mel'])[:max_frames] | |
| energy = (spec.exp() ** 2).sum(-1).sqrt() | |
| mel2ph = torch.LongTensor(item['mel2ph'])[:max_frames] if 'mel2ph' in item else None | |
| f0, uv = norm_interp_f0(item["f0"][:max_frames], hparams) | |
| #phone = torch.LongTensor(item['phone'][:hparams['max_input_tokens']]) | |
| hubert=torch.Tensor(item['hubert'][:hparams['max_input_tokens']]) | |
| pitch = torch.LongTensor(item.get("pitch"))[:max_frames] | |
| # print(item.keys(), item['mel'].shape, spec.shape) | |
| sample = { | |
| "id": index, | |
| "item_name": item['item_name'], | |
| # "text": item['txt'], | |
| # "txt_token": phone, | |
| "hubert":hubert, | |
| "mel": spec, | |
| "pitch": pitch, | |
| "energy": energy, | |
| "f0": f0, | |
| "uv": uv, | |
| "mel2ph": mel2ph, | |
| "mel_nonpadding": spec.abs().sum(-1) > 0, | |
| } | |
| if self.hparams['use_spk_embed']: | |
| sample["spk_embed"] = torch.Tensor(item['spk_embed']) | |
| if self.hparams['use_spk_id']: | |
| sample["spk_id"] = item['spk_id'] | |
| # sample['spk_id'] = 0 | |
| # for key in self.name2spk_id.keys(): | |
| # if key in item['item_name']: | |
| # sample['spk_id'] = self.name2spk_id[key] | |
| # break | |
| #======not used========== | |
| # if self.hparams['pitch_type'] == 'cwt': | |
| # cwt_spec = torch.Tensor(item['cwt_spec'])[:max_frames] | |
| # f0_mean = item.get('f0_mean', item.get('cwt_mean')) | |
| # f0_std = item.get('f0_std', item.get('cwt_std')) | |
| # sample.update({"cwt_spec": cwt_spec, "f0_mean": f0_mean, "f0_std": f0_std}) | |
| # elif self.hparams['pitch_type'] == 'ph': | |
| # f0_phlevel_sum = torch.zeros_like(phone).float().scatter_add(0, mel2ph - 1, f0) | |
| # f0_phlevel_num = torch.zeros_like(phone).float().scatter_add( | |
| # 0, mel2ph - 1, torch.ones_like(f0)).clamp_min(1) | |
| # sample["f0_ph"] = f0_phlevel_sum / f0_phlevel_num | |
| return sample | |
| def collater(self, samples): | |
| if len(samples) == 0: | |
| return {} | |
| id = torch.LongTensor([s['id'] for s in samples]) | |
| item_names = [s['item_name'] for s in samples] | |
| text = [s['text'] for s in samples] | |
| txt_tokens = utils.collate_1d([s['txt_token'] for s in samples], 0) | |
| f0 = utils.collate_1d([s['f0'] for s in samples], 0.0) | |
| pitch = utils.collate_1d([s['pitch'] for s in samples],1) | |
| uv = utils.collate_1d([s['uv'] for s in samples]) | |
| energy = utils.collate_1d([s['energy'] for s in samples], 0.0) | |
| mel2ph = utils.collate_1d([s['mel2ph'] for s in samples], 0.0) \ | |
| if samples[0]['mel2ph'] is not None else None | |
| mels = utils.collate_2d([s['mel'] for s in samples], 0.0) | |
| txt_lengths = torch.LongTensor([s['txt_token'].numel() for s in samples]) | |
| mel_lengths = torch.LongTensor([s['mel'].shape[0] for s in samples]) | |
| batch = { | |
| 'id': id, | |
| 'item_name': item_names, | |
| 'nsamples': len(samples), | |
| 'text': text, | |
| 'txt_tokens': txt_tokens, | |
| 'txt_lengths': txt_lengths, | |
| 'mels': mels, | |
| 'mel_lengths': mel_lengths, | |
| 'mel2ph': mel2ph, | |
| 'energy': energy, | |
| 'pitch': pitch, | |
| 'f0': f0, | |
| 'uv': uv, | |
| } | |
| if self.hparams['use_spk_embed']: | |
| spk_embed = torch.stack([s['spk_embed'] for s in samples]) | |
| batch['spk_embed'] = spk_embed | |
| if self.hparams['use_spk_id']: | |
| spk_ids = torch.LongTensor([s['spk_id'] for s in samples]) | |
| batch['spk_ids'] = spk_ids | |
| if self.hparams['pitch_type'] == 'cwt': | |
| cwt_spec = utils.collate_2d([s['cwt_spec'] for s in samples]) | |
| f0_mean = torch.Tensor([s['f0_mean'] for s in samples]) | |
| f0_std = torch.Tensor([s['f0_std'] for s in samples]) | |
| batch.update({'cwt_spec': cwt_spec, 'f0_mean': f0_mean, 'f0_std': f0_std}) | |
| elif self.hparams['pitch_type'] == 'ph': | |
| batch['f0'] = utils.collate_1d([s['f0_ph'] for s in samples]) | |
| return batch | |
| def load_test_inputs(self, test_input_dir, spk_id=0): | |
| inp_wav_paths = glob.glob(f'{test_input_dir}/*.wav') + glob.glob(f'{test_input_dir}/*.mp3') | |
| sizes = [] | |
| items = [] | |
| binarizer_cls = hparams.get("binarizer_cls", 'basics.base_binarizer.BaseBinarizer') | |
| pkg = ".".join(binarizer_cls.split(".")[:-1]) | |
| cls_name = binarizer_cls.split(".")[-1] | |
| binarizer_cls = getattr(importlib.import_module(pkg), cls_name) | |
| binarization_args = hparams['binarization_args'] | |
| from preprocessing.hubertinfer import Hubertencoder | |
| for wav_fn in inp_wav_paths: | |
| item_name = os.path.basename(wav_fn) | |
| ph = txt = tg_fn = '' | |
| wav_fn = wav_fn | |
| encoder = Hubertencoder(hparams['hubert_path']) | |
| item = binarizer_cls.process_item(item_name, {'wav_fn':wav_fn}, encoder, binarization_args) | |
| print(item) | |
| items.append(item) | |
| sizes.append(item['len']) | |
| return items, sizes | |