import torch.optim import torch.utils.data import numpy as np import torch import torch.optim import torch.utils.data import torch.distributions from text_to_speech.utils.audio.pitch.utils import norm_interp_f0, denorm_f0 from text_to_speech.utils.commons.dataset_utils import BaseDataset, collate_1d_or_2d from text_to_speech.utils.commons.indexed_datasets import IndexedDataset from text_to_speech.utils.commons.hparams import hparams import random class BaseSpeechDataset(BaseDataset): def __init__(self, prefix, shuffle=False, items=None, data_dir=None): super().__init__(shuffle) from text_to_speech.utils.commons.hparams import hparams 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 if items is not None: self.indexed_ds = items self.sizes = [1] * len(items) self.avail_idxs = list(range(len(self.sizes))) else: self.sizes = np.load(f'{self.data_dir}/{self.prefix}_lengths.npy') if prefix == 'test' and len(hparams['test_ids']) > 0: self.avail_idxs = hparams['test_ids'] else: self.avail_idxs = list(range(len(self.sizes))) if prefix == 'train' and hparams['min_frames'] > 0: self.avail_idxs = [x for x in self.avail_idxs if self.sizes[x] >= hparams['min_frames']] try: self.sizes = [self.sizes[i] for i in self.avail_idxs] except: tmp_sizes = [] for i in self.avail_idxs: try: tmp_sizes.append(self.sizes[i]) except: continue self.sizes = tmp_sizes 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] ph_token = torch.LongTensor(item['ph_token'][:hparams['max_input_tokens']]) sample = { "id": index, "item_name": item['item_name'], "text": item['txt'], "txt_token": ph_token, "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"] = int(item['spk_id']) return sample def collater(self, samples): if len(samples) == 0: return {} hparams = self.hparams ids = [s['id'] for s in samples] item_names = [s['item_name'] for s in samples] text = [s['text'] for s in samples] txt_tokens = collate_1d_or_2d([s['txt_token'] for s in samples], 0) mels = collate_1d_or_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': ids, '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(BaseSpeechDataset): def __getitem__(self, index): sample = super(FastSpeechDataset, self).__getitem__(index) item = self._get_item(index) hparams = self.hparams mel = sample['mel'] T = mel.shape[0] ph_token = sample['txt_token'] sample['mel2ph'] = mel2ph = torch.LongTensor(item['mel2ph'])[:T] if hparams['use_pitch_embed']: assert 'f0' in item pitch = torch.LongTensor(item.get(hparams.get('pitch_key', 'pitch')))[:T] f0, uv = norm_interp_f0(item["f0"][:T]) uv = torch.FloatTensor(uv) f0 = torch.FloatTensor(f0) if hparams['pitch_type'] == 'ph': if "f0_ph" in item: f0 = torch.FloatTensor(item['f0_ph']) else: f0 = denorm_f0(f0, None) f0_phlevel_sum = torch.zeros_like(ph_token).float().scatter_add(0, mel2ph - 1, f0) f0_phlevel_num = torch.zeros_like(ph_token).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) else: f0, uv, pitch = None, None, None sample["f0"], sample["uv"], sample["pitch"] = f0, uv, pitch return sample def collater(self, samples): if len(samples) == 0: return {} batch = super(FastSpeechDataset, self).collater(samples) hparams = self.hparams if hparams['use_pitch_embed']: f0 = collate_1d_or_2d([s['f0'] for s in samples], 0.0) pitch = collate_1d_or_2d([s['pitch'] for s in samples]) uv = collate_1d_or_2d([s['uv'] for s in samples]) else: f0, uv, pitch = None, None, None mel2ph = collate_1d_or_2d([s['mel2ph'] for s in samples], 0.0) batch.update({ 'mel2ph': mel2ph, 'pitch': pitch, 'f0': f0, 'uv': uv, }) return batch class FastSpeechWordDataset(FastSpeechDataset): def __init__(self, prefix, shuffle=False, items=None, data_dir=None): super().__init__(prefix, shuffle, items, data_dir) # BERT contrastive loss & mlm loss # from transformers import AutoTokenizer # if hparams['ds_name'] in ['ljspeech', 'libritts']: # self.tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased') # elif hparams['ds_name'] == 'biaobei': # self.tokenizer = AutoTokenizer.from_pretrained('bert-base-chinese') # else: # raise NotImplementedError() # self.mlm_probability = 0.15 # if hparams.get("cl_ds_name") is None: # pass # elif hparams['cl_ds_name'] == "wiki": # from experimental_yerfor.simcse_datasets import WikiDataset # self.cl_dataset = WikiDataset(prefix=prefix) # shuffle = True if prefix == 'train' else False # endless = True # num_workers = None if prefix == 'train' else 0 # self.cl_dataloader = self.cl_dataset.build_dataloader(shuffle=shuffle, max_tokens=hparams.get("cl_max_tokens", 3200), # max_sentences=hparams.get("cl_max_sentences", 64), endless=endless, num_workers=num_workers) # self.cl_dl_iter = iter(self.cl_dataloader) # elif hparams['cl_ds_name'] == "nli": # from experimental_yerfor.simcse_datasets import NLIDataset # self.cl_dataset = NLIDataset(prefix=prefix) # shuffle = True if prefix == 'train' else False # endless = True # num_workers = None if prefix == 'train' else 0 # self.cl_dataloader = self.cl_dataset.build_dataloader(shuffle=shuffle, max_tokens=hparams.get("cl_max_tokens", 4800), # max_sentences=hparams.get("cl_max_sentences", 128), endless=endless, num_workers=num_workers) # self.cl_dl_iter = iter(self.cl_dataloader) def __getitem__(self, index): sample = super().__getitem__(index) item = self._get_item(index) max_frames = sample['mel'].shape[0] if 'word' in item: sample['words'] = item['word'] sample["ph_words"] = item["ph_gb_word"] sample["word_tokens"] = torch.LongTensor(item["word_token"]) else: sample['words'] = item['words'] sample["ph_words"] = " ".join(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'][:self.hparams['max_input_tokens']]) # SyntaSpeech related features # sample['dgl_graph'] = item['dgl_graph'] # sample['edge_types'] = item['edge_types'] # BERT related features # sample['bert_token'] = item['bert_token'] # sample['bert_input_ids'] = torch.LongTensor(item['bert_input_ids']) # sample['bert_token2word'] = torch.LongTensor(item['bert_token2word']) # sample['bert_attention_mask'] = torch.LongTensor(item['bert_attention_mask']) # sample['bert_token_type_ids'] = torch.LongTensor(item['bert_token_type_ids']) return sample def collater(self, samples): samples = [s for s in samples if s is not None] batch = super().collater(samples) ph_words = [s['ph_words'] for s in samples] batch['ph_words'] = ph_words word_tokens = collate_1d_or_2d([s['word_tokens'] for s in samples], 0) batch['word_tokens'] = word_tokens mel2word = collate_1d_or_2d([s['mel2word'] for s in samples], 0) batch['mel2word'] = mel2word ph2word = collate_1d_or_2d([s['ph2word'] for s in samples], 0) batch['ph2word'] = ph2word batch['words'] = [s['words'] for s in samples] batch['word_lengths'] = torch.LongTensor([len(s['word_tokens']) for s in samples]) if self.hparams['use_word_input']: # always False batch['txt_tokens'] = batch['word_tokens'] batch['txt_lengths'] = torch.LongTensor([s['word_tokens'].numel() for s in samples]) batch['mel2ph'] = batch['mel2word'] # SyntaSpeech # graph_lst, etypes_lst = [], [] # new features for Graph-based SDP # for s in samples: # graph_lst.append(s['dgl_graph']) # etypes_lst.append(s['edge_types']) # batch.update({ # 'graph_lst': graph_lst, # 'etypes_lst': etypes_lst, # }) # BERT # batch['bert_feats'] = {} # batch['bert_feats']['bert_tokens'] = [s['bert_token'] for s in samples] # bert_input_ids = collate_1d_or_2d([s['bert_input_ids'] for s in samples], 0) # batch['bert_feats']['bert_input_ids'] = bert_input_ids # bert_token2word = collate_1d_or_2d([s['bert_token2word'] for s in samples], 0) # batch['bert_feats']['bert_token2word'] = bert_token2word # bert_attention_mask = collate_1d_or_2d([s['bert_attention_mask'] for s in samples], 0) # batch['bert_feats']['bert_attention_mask'] = bert_attention_mask # bert_token_type_ids = collate_1d_or_2d([s['bert_token_type_ids'] for s in samples], 0) # batch['bert_feats']['bert_token_type_ids'] = bert_token_type_ids # BERT contrastive loss & mlm loss & electra loss # if hparams.get("cl_ds_name") is None: # batch['cl_feats'] = {} # batch['cl_feats']['cl_input_ids'] = batch['bert_feats']['bert_input_ids'].unsqueeze(1).repeat([1,2,1]) # batch['cl_feats']['cl_token2word'] = batch['bert_feats']['bert_token2word'].unsqueeze(1).repeat([1,2,1]) # batch['cl_feats']['cl_attention_mask'] = batch['bert_feats']['bert_attention_mask'].unsqueeze(1).repeat([1,2,1]) # batch['cl_feats']['cl_token_type_ids'] = batch['bert_feats']['bert_token_type_ids'].unsqueeze(1).repeat([1,2,1]) # bs, _, t = batch['cl_feats']['cl_input_ids'].shape # mlm_input_ids, mlm_labels = self.mask_tokens(batch['bert_feats']['bert_input_ids'].reshape([bs, t])) # batch['cl_feats']["mlm_input_ids"] = mlm_input_ids.reshape([bs, t]) # batch['cl_feats']["mlm_labels"] = mlm_labels.reshape([bs, t]) # batch['cl_feats']["mlm_attention_mask"] = batch['bert_feats']['bert_attention_mask'] # elif hparams['cl_ds_name'] in ["wiki", "nli"]: # try: # cl_feats = self.cl_dl_iter.__next__() # except: # self.cl_dl_iter = iter(self.cl_dataloader) # cl_feats = self.cl_dl_iter.__next__() # batch['cl_feats'] = cl_feats return batch # def mask_tokens(self, inputs, special_tokens_mask=None): # """ # Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original. # """ # inputs = inputs.clone() # labels = inputs.clone() # # We sample a few tokens in each sequence for MLM training (with probability `self.mlm_probability`) # probability_matrix = torch.full(labels.shape, self.mlm_probability) # if special_tokens_mask is None: # special_tokens_mask = [ # self.tokenizer.get_special_tokens_mask(val, already_has_special_tokens=True) for val in labels.tolist() # ] # special_tokens_mask = torch.tensor(special_tokens_mask, dtype=torch.bool) # else: # special_tokens_mask = special_tokens_mask.bool() # probability_matrix.masked_fill_(special_tokens_mask, value=0.0) # masked_indices = torch.bernoulli(probability_matrix).bool() # labels[~masked_indices] = -100 # We only compute loss on masked tokens # # 80% of the time, we replace masked input tokens with tokenizer.mask_token ([MASK]) # indices_replaced = torch.bernoulli(torch.full(labels.shape, 0.8)).bool() & masked_indices # inputs[indices_replaced] = self.tokenizer.convert_tokens_to_ids(self.tokenizer.mask_token) # # 10% of the time, we replace masked input tokens with random word # indices_random = torch.bernoulli(torch.full(labels.shape, 0.5)).bool() & masked_indices & ~indices_replaced # random_words = torch.randint(len(self.tokenizer), labels.shape, dtype=torch.long) # inputs[indices_random] = random_words[indices_random] # # The rest of the time (10% of the time) we keep the masked input tokens unchanged # return inputs, labels