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| import math | |
| import torch | |
| from torch import nn | |
| from torch.nn import functional as F | |
| from utils.hparams import hparams | |
| from modules.commons.common_layers import Embedding | |
| from utils.tts_utils import group_hidden_by_segs, expand_word2ph | |
| import transformers | |
| def convert_pad_shape(pad_shape): | |
| l = pad_shape[::-1] | |
| pad_shape = [item for sublist in l for item in sublist] | |
| return pad_shape | |
| def shift_1d(x): | |
| x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1] | |
| return x | |
| def sequence_mask(length, max_length=None): | |
| if max_length is None: | |
| max_length = length.max() | |
| x = torch.arange(max_length, dtype=length.dtype, device=length.device) | |
| return x.unsqueeze(0) < length.unsqueeze(1) | |
| class Encoder(nn.Module): | |
| def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., | |
| window_size=None, block_length=None, pre_ln=False, **kwargs): | |
| super().__init__() | |
| self.hidden_channels = hidden_channels | |
| self.filter_channels = filter_channels | |
| self.n_heads = n_heads | |
| self.n_layers = n_layers | |
| self.kernel_size = kernel_size | |
| self.p_dropout = p_dropout | |
| self.window_size = window_size | |
| self.block_length = block_length | |
| self.pre_ln = pre_ln | |
| self.drop = nn.Dropout(p_dropout) | |
| self.attn_layers = nn.ModuleList() | |
| self.norm_layers_1 = nn.ModuleList() | |
| self.ffn_layers = nn.ModuleList() | |
| self.norm_layers_2 = nn.ModuleList() | |
| for i in range(self.n_layers): | |
| self.attn_layers.append( | |
| MultiHeadAttention(hidden_channels, hidden_channels, n_heads, window_size=window_size, | |
| p_dropout=p_dropout, block_length=block_length)) | |
| self.norm_layers_1.append(LayerNorm(hidden_channels)) | |
| self.ffn_layers.append( | |
| FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout)) | |
| self.norm_layers_2.append(LayerNorm(hidden_channels)) | |
| if pre_ln: | |
| self.last_ln = LayerNorm(hidden_channels) | |
| def forward(self, x, x_mask): | |
| attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1) | |
| for i in range(self.n_layers): | |
| x = x * x_mask | |
| x_ = x | |
| if self.pre_ln: | |
| x = self.norm_layers_1[i](x) | |
| y = self.attn_layers[i](x, x, attn_mask) | |
| y = self.drop(y) | |
| x = x_ + y | |
| if not self.pre_ln: | |
| x = self.norm_layers_1[i](x) | |
| x_ = x | |
| if self.pre_ln: | |
| x = self.norm_layers_2[i](x) | |
| y = self.ffn_layers[i](x, x_mask) | |
| y = self.drop(y) | |
| x = x_ + y | |
| if not self.pre_ln: | |
| x = self.norm_layers_2[i](x) | |
| if self.pre_ln: | |
| x = self.last_ln(x) | |
| x = x * x_mask | |
| return x | |
| class MultiHeadAttention(nn.Module): | |
| def __init__(self, channels, out_channels, n_heads, window_size=None, heads_share=True, p_dropout=0., | |
| block_length=None, proximal_bias=False, proximal_init=False): | |
| super().__init__() | |
| assert channels % n_heads == 0 | |
| self.channels = channels | |
| self.out_channels = out_channels | |
| self.n_heads = n_heads | |
| self.window_size = window_size | |
| self.heads_share = heads_share | |
| self.block_length = block_length | |
| self.proximal_bias = proximal_bias | |
| self.p_dropout = p_dropout | |
| self.attn = None | |
| self.k_channels = channels // n_heads | |
| self.conv_q = nn.Conv1d(channels, channels, 1) | |
| self.conv_k = nn.Conv1d(channels, channels, 1) | |
| self.conv_v = nn.Conv1d(channels, channels, 1) | |
| if window_size is not None: | |
| n_heads_rel = 1 if heads_share else n_heads | |
| rel_stddev = self.k_channels ** -0.5 | |
| self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev) | |
| self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev) | |
| self.conv_o = nn.Conv1d(channels, out_channels, 1) | |
| self.drop = nn.Dropout(p_dropout) | |
| nn.init.xavier_uniform_(self.conv_q.weight) | |
| nn.init.xavier_uniform_(self.conv_k.weight) | |
| if proximal_init: | |
| self.conv_k.weight.data.copy_(self.conv_q.weight.data) | |
| self.conv_k.bias.data.copy_(self.conv_q.bias.data) | |
| nn.init.xavier_uniform_(self.conv_v.weight) | |
| def forward(self, x, c, attn_mask=None): | |
| q = self.conv_q(x) | |
| k = self.conv_k(c) | |
| v = self.conv_v(c) | |
| x, self.attn = self.attention(q, k, v, mask=attn_mask) | |
| x = self.conv_o(x) | |
| return x | |
| def attention(self, query, key, value, mask=None): | |
| # reshape [b, d, t] -> [b, n_h, t, d_k] | |
| b, d, t_s, t_t = (*key.size(), query.size(2)) | |
| query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3) | |
| key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3) | |
| value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3) | |
| scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(self.k_channels) | |
| if self.window_size is not None: | |
| assert t_s == t_t, "Relative attention is only available for self-attention." | |
| key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s) | |
| rel_logits = self._matmul_with_relative_keys(query, key_relative_embeddings) | |
| rel_logits = self._relative_position_to_absolute_position(rel_logits) | |
| scores_local = rel_logits / math.sqrt(self.k_channels) | |
| scores = scores + scores_local | |
| if self.proximal_bias: | |
| assert t_s == t_t, "Proximal bias is only available for self-attention." | |
| scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype) | |
| if mask is not None: | |
| scores = scores.masked_fill(mask == 0, -1e4) | |
| if self.block_length is not None: | |
| block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length) | |
| scores = scores * block_mask + -1e4 * (1 - block_mask) | |
| p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s] | |
| p_attn = self.drop(p_attn) | |
| output = torch.matmul(p_attn, value) | |
| if self.window_size is not None: | |
| relative_weights = self._absolute_position_to_relative_position(p_attn) | |
| value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s) | |
| output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings) | |
| output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t] | |
| return output, p_attn | |
| def _matmul_with_relative_values(self, x, y): | |
| """ | |
| x: [b, h, l, m] | |
| y: [h or 1, m, d] | |
| ret: [b, h, l, d] | |
| """ | |
| ret = torch.matmul(x, y.unsqueeze(0)) | |
| return ret | |
| def _matmul_with_relative_keys(self, x, y): | |
| """ | |
| x: [b, h, l, d] | |
| y: [h or 1, m, d] | |
| ret: [b, h, l, m] | |
| """ | |
| ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1)) | |
| return ret | |
| def _get_relative_embeddings(self, relative_embeddings, length): | |
| max_relative_position = 2 * self.window_size + 1 | |
| # Pad first before slice to avoid using cond ops. | |
| pad_length = max(length - (self.window_size + 1), 0) | |
| slice_start_position = max((self.window_size + 1) - length, 0) | |
| slice_end_position = slice_start_position + 2 * length - 1 | |
| if pad_length > 0: | |
| padded_relative_embeddings = F.pad( | |
| relative_embeddings, | |
| convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]])) | |
| else: | |
| padded_relative_embeddings = relative_embeddings | |
| used_relative_embeddings = padded_relative_embeddings[:, slice_start_position:slice_end_position] | |
| return used_relative_embeddings | |
| def _relative_position_to_absolute_position(self, x): | |
| """ | |
| x: [b, h, l, 2*l-1] | |
| ret: [b, h, l, l] | |
| """ | |
| batch, heads, length, _ = x.size() | |
| # Concat columns of pad to shift from relative to absolute indexing. | |
| x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]])) | |
| # Concat extra elements so to add up to shape (len+1, 2*len-1). | |
| x_flat = x.view([batch, heads, length * 2 * length]) | |
| x_flat = F.pad(x_flat, convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])) | |
| # Reshape and slice out the padded elements. | |
| x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[:, :, :length, length - 1:] | |
| return x_final | |
| def _absolute_position_to_relative_position(self, x): | |
| """ | |
| x: [b, h, l, l] | |
| ret: [b, h, l, 2*l-1] | |
| """ | |
| batch, heads, length, _ = x.size() | |
| # padd along column | |
| x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])) | |
| x_flat = x.view([batch, heads, length ** 2 + length * (length - 1)]) | |
| # add 0's in the beginning that will skew the elements after reshape | |
| x_flat = F.pad(x_flat, convert_pad_shape([[0, 0], [0, 0], [length, 0]])) | |
| x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:] | |
| return x_final | |
| def _attention_bias_proximal(self, length): | |
| """Bias for self-attention to encourage attention to close positions. | |
| Args: | |
| length: an integer scalar. | |
| Returns: | |
| a Tensor with shape [1, 1, length, length] | |
| """ | |
| r = torch.arange(length, dtype=torch.float32) | |
| diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1) | |
| return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0) | |
| class FFN(nn.Module): | |
| def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None): | |
| super().__init__() | |
| self.in_channels = in_channels | |
| self.out_channels = out_channels | |
| self.filter_channels = filter_channels | |
| self.kernel_size = kernel_size | |
| self.p_dropout = p_dropout | |
| self.activation = activation | |
| self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2) | |
| self.conv_2 = nn.Conv1d(filter_channels, out_channels, 1) | |
| self.drop = nn.Dropout(p_dropout) | |
| def forward(self, x, x_mask): | |
| x = self.conv_1(x * x_mask) | |
| if self.activation == "gelu": | |
| x = x * torch.sigmoid(1.702 * x) | |
| else: | |
| x = torch.relu(x) | |
| x = self.drop(x) | |
| x = self.conv_2(x * x_mask) | |
| return x * x_mask | |
| class LayerNorm(nn.Module): | |
| def __init__(self, channels, eps=1e-4): | |
| super().__init__() | |
| self.channels = channels | |
| self.eps = eps | |
| self.gamma = nn.Parameter(torch.ones(channels)) | |
| self.beta = nn.Parameter(torch.zeros(channels)) | |
| def forward(self, x): | |
| n_dims = len(x.shape) | |
| mean = torch.mean(x, 1, keepdim=True) | |
| variance = torch.mean((x - mean) ** 2, 1, keepdim=True) | |
| x = (x - mean) * torch.rsqrt(variance + self.eps) | |
| shape = [1, -1] + [1] * (n_dims - 2) | |
| x = x * self.gamma.view(*shape) + self.beta.view(*shape) | |
| return x | |
| class ConvReluNorm(nn.Module): | |
| def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout): | |
| super().__init__() | |
| self.in_channels = in_channels | |
| self.hidden_channels = hidden_channels | |
| self.out_channels = out_channels | |
| self.kernel_size = kernel_size | |
| self.n_layers = n_layers | |
| self.p_dropout = p_dropout | |
| assert n_layers > 1, "Number of layers should be larger than 0." | |
| self.conv_layers = nn.ModuleList() | |
| self.norm_layers = nn.ModuleList() | |
| self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size // 2)) | |
| self.norm_layers.append(LayerNorm(hidden_channels)) | |
| self.relu_drop = nn.Sequential( | |
| nn.ReLU(), | |
| nn.Dropout(p_dropout)) | |
| for _ in range(n_layers - 1): | |
| self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size // 2)) | |
| self.norm_layers.append(LayerNorm(hidden_channels)) | |
| self.proj = nn.Conv1d(hidden_channels, out_channels, 1) | |
| self.proj.weight.data.zero_() | |
| self.proj.bias.data.zero_() | |
| def forward(self, x, x_mask): | |
| x_org = x | |
| for i in range(self.n_layers): | |
| x = self.conv_layers[i](x * x_mask) | |
| x = self.norm_layers[i](x) | |
| x = self.relu_drop(x) | |
| x = x_org + self.proj(x) | |
| return x * x_mask | |
| class RelTransformerEncoder(nn.Module): | |
| def __init__(self, | |
| n_vocab, | |
| out_channels, | |
| hidden_channels, | |
| filter_channels, | |
| n_heads, | |
| n_layers, | |
| kernel_size, | |
| p_dropout=0.0, | |
| window_size=4, | |
| block_length=None, | |
| prenet=True, | |
| pre_ln=True, | |
| ): | |
| super().__init__() | |
| self.n_vocab = n_vocab | |
| self.out_channels = out_channels | |
| self.hidden_channels = hidden_channels | |
| self.filter_channels = filter_channels | |
| self.n_heads = n_heads | |
| self.n_layers = n_layers | |
| self.kernel_size = kernel_size | |
| self.p_dropout = p_dropout | |
| self.window_size = window_size | |
| self.block_length = block_length | |
| self.prenet = prenet | |
| if n_vocab > 0: | |
| self.emb = Embedding(n_vocab, hidden_channels, padding_idx=0) | |
| if prenet: | |
| self.pre = ConvReluNorm(hidden_channels, hidden_channels, hidden_channels, | |
| kernel_size=5, n_layers=3, p_dropout=0) | |
| self.encoder = Encoder( | |
| hidden_channels, | |
| filter_channels, | |
| n_heads, | |
| n_layers, | |
| kernel_size, | |
| p_dropout, | |
| window_size=window_size, | |
| block_length=block_length, | |
| pre_ln=pre_ln, | |
| ) | |
| def forward(self, x, x_mask=None): | |
| if self.n_vocab > 0: | |
| x_lengths = (x > 0).long().sum(-1) | |
| x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h] | |
| else: | |
| x_lengths = (x.abs().sum(-1) > 0).long().sum(-1) | |
| x = torch.transpose(x, 1, -1) # [b, h, t] | |
| x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype) | |
| if self.prenet: | |
| x = self.pre(x, x_mask) | |
| x = self.encoder(x, x_mask) | |
| return x.transpose(1, 2) | |
| class Pooler(nn.Module): | |
| """ | |
| Parameter-free poolers to get the sentence embedding | |
| 'cls': [CLS] representation with BERT/RoBERTa's MLP pooler. | |
| 'cls_before_pooler': [CLS] representation without the original MLP pooler. | |
| 'avg': average of the last layers' hidden states at each token. | |
| 'avg_top2': average of the last two layers. | |
| 'avg_first_last': average of the first and the last layers. | |
| """ | |
| def __init__(self, pooler_type): | |
| super().__init__() | |
| self.pooler_type = pooler_type | |
| assert self.pooler_type in ["cls", "cls_before_pooler", "avg", "avg_top2", "avg_first_last"], "unrecognized pooling type %s" % self.pooler_type | |
| def forward(self, attention_mask, outputs): | |
| last_hidden = outputs.last_hidden_state | |
| pooler_output = outputs.pooler_output | |
| hidden_states = outputs.hidden_states | |
| if self.pooler_type in ['cls_before_pooler', 'cls']: | |
| return last_hidden[:, 0] | |
| elif self.pooler_type == "avg": | |
| return ((last_hidden * attention_mask.unsqueeze(-1)).sum(1) / attention_mask.sum(-1).unsqueeze(-1)) | |
| elif self.pooler_type == "avg_first_last": | |
| first_hidden = hidden_states[0] | |
| last_hidden = hidden_states[-1] | |
| pooled_result = ((first_hidden + last_hidden) / 2.0 * attention_mask.unsqueeze(-1)).sum(1) / attention_mask.sum(-1).unsqueeze(-1) | |
| return pooled_result | |
| elif self.pooler_type == "avg_top2": | |
| second_last_hidden = hidden_states[-2] | |
| last_hidden = hidden_states[-1] | |
| pooled_result = ((last_hidden + second_last_hidden) / 2.0 * attention_mask.unsqueeze(-1)).sum(1) / attention_mask.sum(-1).unsqueeze(-1) | |
| return pooled_result | |
| else: | |
| raise NotImplementedError | |
| class Similarity(nn.Module): | |
| """ | |
| Dot product or cosine similarity | |
| """ | |
| def __init__(self, temp): | |
| super().__init__() | |
| self.temp = temp | |
| self.cos = nn.CosineSimilarity(dim=-1) | |
| self.record = None | |
| self.pos_avg = 0.0 | |
| self.neg_avg = 0.0 | |
| def forward(self, x, y): | |
| sim = self.cos(x, y) | |
| self.record = sim.detach() # [64,64] | |
| min_size = min(self.record.shape[0], self.record.shape[1]) # 64 | |
| num_item = self.record.shape[0] * self.record.shape[1] # 4096 | |
| self.pos_avg = self.record.diag().sum() / min_size | |
| if num_item - min_size == 0: | |
| self.neg_avg = (self.record.sum() - self.record.diag().sum()) / 1 | |
| return sim / self.temp | |
| if torch.any(torch.isnan(self.record)).item() is True: | |
| print("we got self.record has nan when compute neg_avg") | |
| if torch.any(torch.isnan(self.record.diag())).item() is True: | |
| print("we got self.record.diag() has nan when compute neg_avg") | |
| self.neg_avg = (self.record.sum() - self.record.diag().sum()) / (num_item - min_size) | |
| return sim / self.temp | |
| class BertPredictionHeadTransform(nn.Module): | |
| def __init__(self, hidden_size): | |
| super().__init__() | |
| self.dense = nn.Linear(hidden_size, hidden_size) | |
| self.transform_act_fn = F.gelu | |
| self.LayerNorm = nn.LayerNorm(hidden_size, eps=1e-12) | |
| def forward(self, hidden_states): | |
| hidden_states = self.dense(hidden_states) | |
| hidden_states = self.transform_act_fn(hidden_states) | |
| hidden_states = self.LayerNorm(hidden_states) | |
| return hidden_states | |
| class BertLMPredictionHead(nn.Module): | |
| def __init__(self, hid_dim, out_dim): | |
| super().__init__() | |
| self.transform = BertPredictionHeadTransform(hid_dim) | |
| self.decoder = nn.Linear(hid_dim, out_dim, bias=False) | |
| self.bias = nn.Parameter(torch.zeros(out_dim)) | |
| self.decoder.bias = self.bias | |
| def forward(self, hidden_states): | |
| hidden_states = self.transform(hidden_states) | |
| hidden_states = self.decoder(hidden_states) | |
| return hidden_states | |
| # V2_2 | |
| # change add to concat. | |
| # now support finetune BERT | |
| # grad_bert=0.1 & trainable_block_idx=0 | |
| class BERTRelTransformerEncoder(nn.Module): | |
| def __init__(self, | |
| n_vocab, | |
| out_channels, | |
| hidden_channels, | |
| filter_channels, | |
| n_heads, | |
| n_layers, | |
| kernel_size, | |
| p_dropout=0.0, | |
| window_size=4, | |
| block_length=None, | |
| prenet=True, | |
| pre_ln=True, | |
| ): | |
| super().__init__() | |
| self.n_vocab = n_vocab | |
| self.out_channels = out_channels | |
| self.hidden_channels = hidden_channels | |
| self.filter_channels = filter_channels | |
| self.n_heads = n_heads | |
| self.n_layers = n_layers | |
| self.kernel_size = kernel_size | |
| self.p_dropout = p_dropout | |
| self.window_size = window_size | |
| self.block_length = block_length | |
| self.prenet = prenet | |
| if n_vocab > 0: | |
| self.emb = Embedding(n_vocab, hidden_channels, padding_idx=0) | |
| if prenet: | |
| self.pre = ConvReluNorm(hidden_channels, hidden_channels, hidden_channels, | |
| kernel_size=5, n_layers=3, p_dropout=0) | |
| self.encoder1 = Encoder( | |
| hidden_channels, | |
| filter_channels, | |
| n_heads, | |
| n_layers//2, | |
| kernel_size, | |
| p_dropout, | |
| window_size=window_size, | |
| block_length=block_length, | |
| pre_ln=pre_ln, | |
| ) | |
| self.encoder2 = Encoder( | |
| hidden_channels, | |
| filter_channels, | |
| n_heads, | |
| n_layers - n_layers//2, | |
| kernel_size, | |
| p_dropout, | |
| window_size=window_size, | |
| block_length=block_length, | |
| pre_ln=pre_ln, | |
| ) | |
| if hparams['ds_name'] in ['ljspeech', 'libritts', 'librispeech']: | |
| model_name = 'bert-base-uncased' | |
| elif hparams['ds_name'] in ['biaobei', 'wenetspeech']: | |
| model_name = 'bert-base-chinese' | |
| else: | |
| raise NotImplementedError() | |
| self.tokenizer = transformers.AutoTokenizer.from_pretrained(model_name) | |
| config = transformers.AutoConfig.from_pretrained(model_name) | |
| if hparams.get("load_bert_from_pretrained", True): | |
| print("Load BERT from pretrained model ...") | |
| self.bert = transformers.AutoModel.from_pretrained(model_name,config=config) | |
| trainable_start_block = hparams.get("bert_trainable_start_block", 0) | |
| else: | |
| print("Initialize BERT from scratch!") | |
| self.bert = transformers.BertModel(config=config) | |
| trainable_start_block = 0 | |
| for k, v in self.bert.named_parameters(): | |
| if 'embeddings' in k: | |
| v.requires_grad = False | |
| elif 'encoder.layer' in k: | |
| block_idx = int(k.split(".")[2]) | |
| if block_idx < trainable_start_block: | |
| v.requires_grad = False | |
| else: | |
| v.requires_grad = True | |
| elif 'cls' in k: | |
| v.requires_grad = True | |
| else: | |
| print("Unhandled key: {}, set to requires_grad...".format(k)) | |
| v.requires_grad = True | |
| self.bert_combine = nn.Sequential(*[ | |
| nn.Conv1d(768 + hidden_channels, hidden_channels, 3, 1, 1), | |
| nn.ReLU(), | |
| ]) | |
| self.pooler = Pooler("avg") | |
| self.sim = Similarity(temp=0.05) | |
| def forward(self, x, x_mask=None, bert_feats=None, ph2word=None, **kwargs): | |
| if self.n_vocab > 0: | |
| x_lengths = (x > 0).long().sum(-1) | |
| x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h] | |
| else: | |
| x_lengths = (x.abs().sum(-1) > 0).long().sum(-1) | |
| x = torch.transpose(x, 1, -1) # [b, h, t] | |
| x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype) | |
| if self.prenet: | |
| x = self.pre(x, x_mask) | |
| x = self.encoder1(x, x_mask) | |
| bert_outputs = self.bert(bert_feats['bert_input_ids'], | |
| attention_mask=bert_feats['bert_attention_mask'], | |
| token_type_ids=bert_feats['bert_token_type_ids'], | |
| output_hidden_states=True) | |
| bert_num_blocks = hparams.get("bert_num_blocks", 12) # total 1+12blocks in bert | |
| bert_embedding = bert_outputs['hidden_states'][bert_num_blocks] | |
| # bert_embedding = bert_outputs['last_hidden_state'] | |
| grad_bert = hparams.get("grad_bert", 0.1) | |
| bert_embedding = bert_embedding.detach() * (1-grad_bert) + bert_embedding * grad_bert | |
| bert_word_embedding, _ = group_hidden_by_segs(bert_embedding, bert_feats['bert_token2word'], bert_feats['bert_token2word'].max().item()) | |
| bert_ph_embedding = expand_word2ph(bert_word_embedding, ph2word) | |
| bert_ph_embedding = bert_ph_embedding.transpose(1,2) | |
| x = torch.cat([x, bert_ph_embedding], dim=1) | |
| x = self.bert_combine(x) | |
| x = self.encoder2(x, x_mask) | |
| return x.transpose(1, 2) | |