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| from utils.cwt import get_lf0_cwt | |
| import torch.optim | |
| import torch.utils.data | |
| import importlib | |
| from utils.indexed_datasets import IndexedDataset | |
| from utils.pitch_utils import norm_interp_f0, denorm_f0, f0_to_coarse | |
| import numpy as np | |
| from tasks.base_task import BaseDataset | |
| import torch | |
| import torch.optim | |
| import torch.utils.data | |
| import utils | |
| import torch.distributions | |
| from utils.hparams import hparams | |
| from utils.pitch_utils import norm_interp_f0 | |
| from resemblyzer import VoiceEncoder | |
| import json | |
| from data_gen.tts.data_gen_utils import build_phone_encoder | |
| class BaseTTSDataset(BaseDataset): | |
| def __init__(self, prefix, shuffle=False, test_items=None, test_sizes=None, data_dir=None): | |
| super().__init__(shuffle) | |
| self.data_dir = hparams['binary_data_dir'] if data_dir is None else data_dir | |
| self.prefix = prefix | |
| self.hparams = hparams | |
| self.indexed_ds = None | |
| self.ext_mel2ph = None | |
| def load_size(): | |
| self.sizes = np.load(f'{self.data_dir}/{self.prefix}_lengths.npy') | |
| if prefix == 'test': | |
| if test_items is not None: | |
| self.indexed_ds, self.sizes = test_items, test_sizes | |
| else: | |
| load_size() | |
| if hparams['num_test_samples'] > 0: | |
| self.avail_idxs = [x for x in range(hparams['num_test_samples']) \ | |
| if x < len(self.sizes)] | |
| if len(hparams['test_ids']) > 0: | |
| self.avail_idxs = hparams['test_ids'] + self.avail_idxs | |
| else: | |
| self.avail_idxs = list(range(len(self.sizes))) | |
| else: | |
| load_size() | |
| self.avail_idxs = list(range(len(self.sizes))) | |
| if hparams['min_frames'] > 0: | |
| self.avail_idxs = [ | |
| x for x in self.avail_idxs if self.sizes[x] >= hparams['min_frames']] | |
| self.sizes = [self.sizes[i] for i in self.avail_idxs] | |
| 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) | |
| assert len(item['mel']) == self.sizes[index], (len(item['mel']), self.sizes[index]) | |
| max_frames = hparams['max_frames'] | |
| spec = torch.Tensor(item['mel'])[:max_frames] | |
| max_frames = spec.shape[0] // hparams['frames_multiple'] * hparams['frames_multiple'] | |
| spec = spec[:max_frames] | |
| phone = torch.LongTensor(item['phone'][:hparams['max_input_tokens']]) | |
| sample = { | |
| "id": index, | |
| "item_name": item['item_name'], | |
| "text": item['txt'], | |
| "txt_token": phone, | |
| "mel": spec, | |
| "mel_nonpadding": spec.abs().sum(-1) > 0, | |
| } | |
| if hparams['use_spk_embed']: | |
| sample["spk_embed"] = torch.Tensor(item['spk_embed']) | |
| if hparams['use_spk_id']: | |
| sample["spk_id"] = item['spk_id'] | |
| return sample | |
| def collater(self, samples): | |
| if len(samples) == 0: | |
| return {} | |
| hparams = self.hparams | |
| 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) | |
| 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, | |
| } | |
| if hparams['use_spk_embed']: | |
| spk_embed = torch.stack([s['spk_embed'] for s in samples]) | |
| batch['spk_embed'] = spk_embed | |
| if hparams['use_spk_id']: | |
| spk_ids = torch.LongTensor([s['spk_id'] for s in samples]) | |
| batch['spk_ids'] = spk_ids | |
| return batch | |
| class FastSpeechDataset(BaseTTSDataset): | |
| def __init__(self, prefix, shuffle=False, test_items=None, test_sizes=None, data_dir=None): | |
| super().__init__(prefix, shuffle, test_items, test_sizes, data_dir) | |
| self.f0_mean, self.f0_std = hparams.get('f0_mean', None), hparams.get('f0_std', None) | |
| if prefix == 'test' and hparams['test_input_dir'] != '': | |
| self.data_dir = hparams['test_input_dir'] | |
| self.indexed_ds = IndexedDataset(f'{self.data_dir}/{self.prefix}') | |
| self.indexed_ds = sorted(self.indexed_ds, key=lambda item: item['item_name']) | |
| items = {} | |
| for i in range(len(self.indexed_ds)): | |
| speaker = self.indexed_ds[i]['item_name'].split('_')[0] | |
| if speaker not in items.keys(): | |
| items[speaker] = [i] | |
| else: | |
| items[speaker].append(i) | |
| sort_item = sorted(items.values(), key=lambda item_pre_speaker: len(item_pre_speaker), reverse=True) | |
| self.avail_idxs = [n for a in sort_item for n in a][:hparams['num_test_samples']] | |
| self.indexed_ds, self.sizes = self.load_test_inputs() | |
| self.avail_idxs = [i for i in range(hparams['num_test_samples'])] | |
| if hparams['pitch_type'] == 'cwt': | |
| _, hparams['cwt_scales'] = get_lf0_cwt(np.ones(10)) | |
| def __getitem__(self, index): | |
| sample = super(FastSpeechDataset, self).__getitem__(index) | |
| item = self._get_item(index) | |
| hparams = self.hparams | |
| max_frames = hparams['max_frames'] | |
| spec = sample['mel'] | |
| T = spec.shape[0] | |
| phone = sample['txt_token'] | |
| sample['energy'] = (spec.exp() ** 2).sum(-1).sqrt() | |
| sample['mel2ph'] = mel2ph = torch.LongTensor(item['mel2ph'])[:T] if 'mel2ph' in item else None | |
| if hparams['use_pitch_embed']: | |
| assert 'f0' in item | |
| if hparams.get('normalize_pitch', False): | |
| f0 = item["f0"] | |
| if len(f0 > 0) > 0 and f0[f0 > 0].std() > 0: | |
| f0[f0 > 0] = (f0[f0 > 0] - f0[f0 > 0].mean()) / f0[f0 > 0].std() * hparams['f0_std'] + \ | |
| hparams['f0_mean'] | |
| f0[f0 > 0] = f0[f0 > 0].clip(min=60, max=500) | |
| pitch = f0_to_coarse(f0) | |
| pitch = torch.LongTensor(pitch[:max_frames]) | |
| else: | |
| pitch = torch.LongTensor(item.get("pitch"))[:max_frames] if "pitch" in item else None | |
| f0, uv = norm_interp_f0(item["f0"][:max_frames], hparams) | |
| uv = torch.FloatTensor(uv) | |
| f0 = torch.FloatTensor(f0) | |
| if 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 hparams['pitch_type'] == 'ph': | |
| if "f0_ph" in item: | |
| f0 = torch.FloatTensor(item['f0_ph']) | |
| else: | |
| f0 = denorm_f0(f0, None, hparams) | |
| 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) | |
| f0_ph = f0_phlevel_sum / f0_phlevel_num | |
| f0, uv = norm_interp_f0(f0_ph, hparams) | |
| else: | |
| f0 = uv = torch.zeros_like(mel2ph) | |
| pitch = None | |
| sample["f0"], sample["uv"], sample["pitch"] = f0, uv, pitch | |
| if hparams['use_spk_embed']: | |
| sample["spk_embed"] = torch.Tensor(item['spk_embed']) | |
| if hparams['use_spk_id']: | |
| sample["spk_id"] = item['spk_id'] | |
| return sample | |
| def collater(self, samples): | |
| if len(samples) == 0: | |
| return {} | |
| hparams = self.hparams | |
| batch = super(FastSpeechDataset, self).collater(samples) | |
| f0 = utils.collate_1d([s['f0'] for s in samples], 0.0) | |
| pitch = utils.collate_1d([s['pitch'] for s in samples]) if samples[0]['pitch'] is not None else None | |
| 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 | |
| batch.update({ | |
| 'mel2ph': mel2ph, | |
| 'energy': energy, | |
| 'pitch': pitch, | |
| 'f0': f0, | |
| 'uv': uv, | |
| }) | |
| if 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}) | |
| return batch | |
| def load_test_inputs(self): | |
| binarizer_cls = hparams.get("binarizer_cls", 'data_gen.tts.base_binarizerr.BaseBinarizer') | |
| pkg = ".".join(binarizer_cls.split(".")[:-1]) | |
| cls_name = binarizer_cls.split(".")[-1] | |
| binarizer_cls = getattr(importlib.import_module(pkg), cls_name) | |
| ph_set_fn = f"{hparams['binary_data_dir']}/phone_set.json" | |
| ph_set = json.load(open(ph_set_fn, 'r')) | |
| print("| phone set: ", ph_set) | |
| phone_encoder = build_phone_encoder(hparams['binary_data_dir']) | |
| word_encoder = None | |
| voice_encoder = VoiceEncoder().cuda() | |
| encoder = [phone_encoder, word_encoder] | |
| sizes = [] | |
| items = [] | |
| for i in range(len(self.avail_idxs)): | |
| item = self._get_item(i) | |
| item2tgfn = f"{hparams['test_input_dir'].replace('binary', 'processed')}/mfa_outputs/{item['item_name']}.TextGrid" | |
| item = binarizer_cls.process_item(item['item_name'], item['ph'], item['txt'], item2tgfn, | |
| item['wav_fn'], item['spk_id'], encoder, hparams['binarization_args']) | |
| item['spk_embed'] = voice_encoder.embed_utterance(item['wav']) \ | |
| if hparams['binarization_args']['with_spk_embed'] else None # 判断是否保存embedding文件 | |
| items.append(item) | |
| sizes.append(item['len']) | |
| return items, sizes | |
| class FastSpeechWordDataset(FastSpeechDataset): | |
| def __getitem__(self, index): | |
| sample = super(FastSpeechWordDataset, self).__getitem__(index) | |
| item = self._get_item(index) | |
| max_frames = hparams['max_frames'] | |
| sample["ph_words"] = item["ph_words"] | |
| sample["word_tokens"] = torch.LongTensor(item["word_tokens"]) | |
| sample["mel2word"] = torch.LongTensor(item.get("mel2word"))[:max_frames] | |
| sample["ph2word"] = torch.LongTensor(item['ph2word'][:hparams['max_input_tokens']]) | |
| return sample | |
| def collater(self, samples): | |
| batch = super(FastSpeechWordDataset, self).collater(samples) | |
| ph_words = [s['ph_words'] for s in samples] | |
| batch['ph_words'] = ph_words | |
| word_tokens = utils.collate_1d([s['word_tokens'] for s in samples], 0) | |
| batch['word_tokens'] = word_tokens | |
| mel2word = utils.collate_1d([s['mel2word'] for s in samples], 0) | |
| batch['mel2word'] = mel2word | |
| ph2word = utils.collate_1d([s['ph2word'] for s in samples], 0) | |
| batch['ph2word'] = ph2word | |
| return batch | |