diff --git a/.gitignore b/.gitignore index 96e754a..6f846a9 100644 --- a/.gitignore +++ b/.gitignore @@ -10,5 +10,6 @@ reference GPT_weights SoVITS_weights TEMP - +ffmpeg.exe +ffprobe.exe diff --git a/GPT_SoVITS/AR/models/t2s_lightning_module.py b/GPT_SoVITS/AR/models/t2s_lightning_module.py index 2dd3f39..1b60262 100644 --- a/GPT_SoVITS/AR/models/t2s_lightning_module.py +++ b/GPT_SoVITS/AR/models/t2s_lightning_module.py @@ -13,11 +13,11 @@ from AR.modules.lr_schedulers import WarmupCosineLRSchedule from AR.modules.optim import ScaledAdam class Text2SemanticLightningModule(LightningModule): - def __init__(self, config, output_dir, is_train=True): + def __init__(self, config, output_dir, is_train=True, flash_attn_enabled:bool = False): super().__init__() self.config = config self.top_k = 3 - self.model = Text2SemanticDecoder(config=config, top_k=self.top_k) + self.model = Text2SemanticDecoder(config=config, top_k=self.top_k,flash_attn_enabled=flash_attn_enabled) pretrained_s1 = config.get("pretrained_s1") if pretrained_s1 and is_train: # print(self.load_state_dict(torch.load(pretrained_s1,map_location="cpu")["state_dict"])) diff --git a/GPT_SoVITS/AR/models/t2s_model.py b/GPT_SoVITS/AR/models/t2s_model.py index c8ad3d8..b49bcfb 100644 --- a/GPT_SoVITS/AR/models/t2s_model.py +++ b/GPT_SoVITS/AR/models/t2s_model.py @@ -1,5 +1,9 @@ # modified from https://github.com/yangdongchao/SoundStorm/blob/master/soundstorm/s1/AR/models/t2s_model.py # reference: https://github.com/lifeiteng/vall-e +import os, sys +now_dir = os.getcwd() +sys.path.append(now_dir) +from typing import List import torch from tqdm import tqdm @@ -35,8 +39,144 @@ default_config = { } +@torch.jit.script +class T2SMLP: + def __init__(self, w1, b1, w2, b2): + self.w1 = w1 + self.b1 = b1 + self.w2 = w2 + self.b2 = b2 + + def forward(self, x): + x = F.relu(F.linear(x, self.w1, self.b1)) + x = F.linear(x, self.w2, self.b2) + return x + + +@torch.jit.script +class T2SBlock: + def __init__( + self, + num_heads, + hidden_dim: int, + mlp: T2SMLP, + qkv_w, + qkv_b, + out_w, + out_b, + norm_w1, + norm_b1, + norm_eps1, + norm_w2, + norm_b2, + norm_eps2, + ): + self.num_heads = num_heads + self.mlp = mlp + self.hidden_dim: int = hidden_dim + self.qkv_w = qkv_w + self.qkv_b = qkv_b + self.out_w = out_w + self.out_b = out_b + self.norm_w1 = norm_w1 + self.norm_b1 = norm_b1 + self.norm_eps1 = norm_eps1 + self.norm_w2 = norm_w2 + self.norm_b2 = norm_b2 + self.norm_eps2 = norm_eps2 + + def process_prompt(self, x, attn_mask : torch.Tensor): + q, k, v = F.linear(x, self.qkv_w, self.qkv_b).chunk(3, dim=-1) + + batch_size = q.shape[0] + q_len = q.shape[1] + kv_len = k.shape[1] + + k_cache = k + v_cache = v + + q = q.view(batch_size, q_len, self.num_heads, -1).transpose(1, 2) + k = k_cache.view(batch_size, kv_len, self.num_heads, -1).transpose(1, 2) + v = v_cache.view(batch_size, kv_len, self.num_heads, -1).transpose(1, 2) + + attn = F.scaled_dot_product_attention(q, k, v, attn_mask) + + attn = attn.permute(2, 0, 1, 3).reshape(batch_size*q_len, self.hidden_dim) + attn = attn.view(q_len, batch_size, self.hidden_dim).transpose(1, 0) + attn = F.linear(attn, self.out_w, self.out_b) + + x = F.layer_norm( + x + attn, [self.hidden_dim], self.norm_w1, self.norm_b1, self.norm_eps1 + ) + x = F.layer_norm( + x + self.mlp.forward(x), + [self.hidden_dim], + self.norm_w2, + self.norm_b2, + self.norm_eps2, + ) + return x, k_cache, v_cache + + def decode_next_token(self, x, k_cache, v_cache): + q, k, v = F.linear(x, self.qkv_w, self.qkv_b).chunk(3, dim=-1) + + k_cache = torch.cat([k_cache, k], dim=1) + v_cache = torch.cat([v_cache, v], dim=1) + + batch_size = q.shape[0] + q_len = q.shape[1] + kv_len = k_cache.shape[1] + + q = q.view(batch_size, q_len, self.num_heads, -1).transpose(1, 2) + k = k_cache.view(batch_size, kv_len, self.num_heads, -1).transpose(1, 2) + v = v_cache.view(batch_size, kv_len, self.num_heads, -1).transpose(1, 2) + + + attn = F.scaled_dot_product_attention(q, k, v) + + attn = attn.permute(2, 0, 1, 3).reshape(batch_size*q_len, self.hidden_dim) + attn = attn.view(q_len, batch_size, self.hidden_dim).transpose(1, 0) + attn = F.linear(attn, self.out_w, self.out_b) + + x = F.layer_norm( + x + attn, [self.hidden_dim], self.norm_w1, self.norm_b1, self.norm_eps1 + ) + x = F.layer_norm( + x + self.mlp.forward(x), + [self.hidden_dim], + self.norm_w2, + self.norm_b2, + self.norm_eps2, + ) + return x, k_cache, v_cache + + +@torch.jit.script +class T2STransformer: + def __init__(self, num_blocks : int, blocks: List[T2SBlock]): + self.num_blocks : int = num_blocks + self.blocks = blocks + + def process_prompt( + self, x, attn_mask : torch.Tensor): + k_cache : List[torch.Tensor] = [] + v_cache : List[torch.Tensor] = [] + for i in range(self.num_blocks): + x, k_cache_, v_cache_ = self.blocks[i].process_prompt(x, attn_mask) + k_cache.append(k_cache_) + v_cache.append(v_cache_) + return x, k_cache, v_cache + + def decode_next_token( + self, x, k_cache: List[torch.Tensor], v_cache: List[torch.Tensor] + ): + for i in range(self.num_blocks): + x, k_cache[i], v_cache[i] = self.blocks[i].decode_next_token(x, k_cache[i], v_cache[i]) + return x, k_cache, v_cache + + class Text2SemanticDecoder(nn.Module): - def __init__(self, config, norm_first=False, top_k=3): + def __init__(self, config, norm_first=False, top_k=3, flash_attn_enabled:bool=False): super(Text2SemanticDecoder, self).__init__() self.model_dim = config["model"]["hidden_dim"] self.embedding_dim = config["model"]["embedding_dim"] @@ -88,6 +228,47 @@ class Text2SemanticDecoder(nn.Module): multidim_average="global", ignore_index=self.EOS, ) + + self.enable_flash_attn(flash_attn_enabled) + + def enable_flash_attn(self, enable:bool=True): + + if not enable: + print("Not Using Flash Attention") + self.infer_panel = self.infer_panel_batch_only + else: + self.infer_panel = self.infer_panel_batch_infer_with_flash_attn + print("Using Flash Attention") + blocks = [] + + for i in range(self.num_layers): + layer = self.h.layers[i] + t2smlp = T2SMLP( + layer.linear1.weight, + layer.linear1.bias, + layer.linear2.weight, + layer.linear2.bias + ) + + block = T2SBlock( + self.num_head, + self.model_dim, + t2smlp, + layer.self_attn.in_proj_weight, + layer.self_attn.in_proj_bias, + layer.self_attn.out_proj.weight, + layer.self_attn.out_proj.bias, + layer.norm1.weight, + layer.norm1.bias, + layer.norm1.eps, + layer.norm2.weight, + layer.norm2.bias, + layer.norm2.eps + ) + + blocks.append(block) + + self.t2s_transformer = T2STransformer(self.num_layers, blocks) def make_input_data(self, x, x_lens, y, y_lens, bert_feature): x = self.ar_text_embedding(x) @@ -321,19 +502,197 @@ class Text2SemanticDecoder(nn.Module): # 错位 return targets[:, :-1], targets[:, 1:] - def infer_panel( + def infer_panel_batch_infer_with_flash_attn( self, - x, #####全部文本token - x_lens, - prompts, ####参考音频token - bert_feature, + x:List[torch.LongTensor], #####全部文本token + x_lens:torch.LongTensor, + prompts:torch.LongTensor, ####参考音频token + bert_feature:List[torch.LongTensor], top_k: int = -100, top_p: int = 100, early_stop_num: int = -1, temperature: float = 1.0, ): - x = self.ar_text_embedding(x) - x = x + self.bert_proj(bert_feature.transpose(1, 2)) + # 先对phones进行embedding、对bert_features进行project，再pad到相同长度，以缓解复读问题。（可能还有其他因素导致复读） + max_len = 0 + for x_item, bert_item in zip(x, bert_feature): + max_len = max(max_len, x_item.shape[0], bert_item.shape[1]) + x_list = [self.ar_text_embedding(item) for item in x] + x_list = [F.pad(item,(0,0,0,max_len-item.shape[0]),value=0) if item.shape[0] early_stop_num) or idx==1499: + print("use early stop num:", early_stop_num) + stop = True + for i, batch_index in enumerate(batch_idx_map): + batch_index = batch_idx_map[i] + idx_list[batch_index] = idx + y_list[batch_index] = y[i, :-1] + + if not (None in idx_list): + stop = True + + if stop: + if y.shape[1]==0: + y = torch.concat([y, torch.zeros_like(samples)], dim=1) + print("bad zero prediction") + print(f"T2S Decoding EOS [{prefix_len} -> {y.shape[1]}]") + break + + ####################### update next step ################################### + y_emb = self.ar_audio_embedding(y[:, -1:]) + xy_pos = y_emb * self.ar_audio_position.x_scale + self.ar_audio_position.alpha * self.ar_audio_position.pe[:, y_len + idx].to( dtype= y_emb.dtype,device=y_emb.device) + + if (None in idx_list): + for i in range(x.shape[0]): + if idx_list[i] is None: + idx_list[i] = 1500-1 ###如果没有生成到EOS，就用最大长度代替 + + if ref_free: + return y_list, [0]*x.shape[0] + return y_list, idx_list + + def infer_panel_batch_only( + self, + x:List[torch.LongTensor], #####全部文本token + x_lens:torch.LongTensor, + prompts:torch.LongTensor, ####参考音频token + bert_feature:List[torch.LongTensor], + top_k: int = -100, + top_p: int = 100, + early_stop_num: int = -1, + temperature: float = 1.0, + ): + # 先对phones进行embedding、对bert_features进行project，再pad到相同长度，以缓解复读问题。（可能还有其他因素导致复读） + max_len = 0 + for x_item, bert_item in zip(x, bert_feature): + max_len = max(max_len, x_item.shape[0], bert_item.shape[1]) + x_list = [self.ar_text_embedding(item) for item in x] + x_list = [F.pad(item,(0,0,0,max_len-item.shape[0]),value=0) if item.shape[0] early_stop_num: print("use early stop num:", early_stop_num) stop = True - - if torch.argmax(logits, dim=-1)[0] == self.EOS or samples[0, 0] == self.EOS: + + if not (None in idx_list): # print(torch.argmax(logits, dim=-1)[0] == self.EOS, samples[0, 0] == self.EOS) stop = True if stop: @@ -443,6 +846,12 @@ class Text2SemanticDecoder(nn.Module): xy_attn_mask = torch.zeros( (1, x_len + y_len), dtype=torch.bool, device=xy_pos.device ) + + if (None in idx_list): + for i in range(x.shape[0]): + if idx_list[i] is None: + idx_list[i] = 1500-1 ###如果没有生成到EOS，就用最大长度代替 + if ref_free: - return y[:, :-1], 0 - return y[:, :-1], idx-1 + return y_list, [0]*x.shape[0] + return y_list, idx_list \ No newline at end of file diff --git a/GPT_SoVITS/AR/models/t2s_model_batch_only.py b/GPT_SoVITS/AR/models/t2s_model_batch_only.py new file mode 100644 index 0000000..8c31f12 --- /dev/null +++ b/GPT_SoVITS/AR/models/t2s_model_batch_only.py @@ -0,0 +1,483 @@ +# modified from https://github.com/feng-yufei/shared_debugging_code/blob/main/model/t2s_model.py +import torch +from tqdm import tqdm + +from AR.models.utils import make_pad_mask +from AR.models.utils import ( + topk_sampling, + sample, + logits_to_probs, + multinomial_sample_one_no_sync, + dpo_loss, + make_reject_y, + get_batch_logps +) +from AR.modules.embedding import SinePositionalEmbedding +from AR.modules.embedding import TokenEmbedding +from AR.modules.transformer import LayerNorm +from AR.modules.transformer import TransformerEncoder +from AR.modules.transformer import TransformerEncoderLayer +from torch import nn +from torch.nn import functional as F +from torchmetrics.classification import MulticlassAccuracy + +default_config = { + "embedding_dim": 512, + "hidden_dim": 512, + "num_head": 8, + "num_layers": 12, + "num_codebook": 8, + "p_dropout": 0.0, + "vocab_size": 1024 + 1, + "phoneme_vocab_size": 512, + "EOS": 1024, +} + + +class Text2SemanticDecoder(nn.Module): + def __init__(self, config, norm_first=False, top_k=3): + super(Text2SemanticDecoder, self).__init__() + self.model_dim = config["model"]["hidden_dim"] + self.embedding_dim = config["model"]["embedding_dim"] + self.num_head = config["model"]["head"] + self.num_layers = config["model"]["n_layer"] + self.norm_first = norm_first + self.vocab_size = config["model"]["vocab_size"] + self.phoneme_vocab_size = config["model"]["phoneme_vocab_size"] + self.p_dropout = config["model"]["dropout"] + self.EOS = config["model"]["EOS"] + self.norm_first = norm_first + assert self.EOS == self.vocab_size - 1 + # should be same as num of kmeans bin + # assert self.EOS == 1024 + self.bert_proj = nn.Linear(1024, self.embedding_dim) + self.ar_text_embedding = TokenEmbedding( + self.embedding_dim, self.phoneme_vocab_size, self.p_dropout + ) + self.ar_text_position = SinePositionalEmbedding( + self.embedding_dim, dropout=0.1, scale=False, alpha=True + ) + self.ar_audio_embedding = TokenEmbedding( + self.embedding_dim, self.vocab_size, self.p_dropout + ) + self.ar_audio_position = SinePositionalEmbedding( + self.embedding_dim, dropout=0.1, scale=False, alpha=True + ) + + self.h = TransformerEncoder( + TransformerEncoderLayer( + d_model=self.model_dim, + nhead=self.num_head, + dim_feedforward=self.model_dim * 4, + dropout=0.1, + batch_first=True, + norm_first=norm_first, + ), + num_layers=self.num_layers, + norm=LayerNorm(self.model_dim) if norm_first else None, + ) + + self.ar_predict_layer = nn.Linear(self.model_dim, self.vocab_size, bias=False) + self.loss_fct = nn.CrossEntropyLoss(reduction="sum") + + self.ar_accuracy_metric = MulticlassAccuracy( + self.vocab_size, + top_k=top_k, + average="micro", + multidim_average="global", + ignore_index=self.EOS, + ) + + def make_input_data(self, x, x_lens, y, y_lens, bert_feature): + x = self.ar_text_embedding(x) + x = x + self.bert_proj(bert_feature.transpose(1, 2)) + x = self.ar_text_position(x) + x_mask = make_pad_mask(x_lens) + + y_mask = make_pad_mask(y_lens) + y_mask_int = y_mask.type(torch.int64) + codes = y.type(torch.int64) * (1 - y_mask_int) + + # Training + # AR Decoder + y, targets = self.pad_y_eos(codes, y_mask_int, eos_id=self.EOS) + x_len = x_lens.max() + y_len = y_lens.max() + y_emb = self.ar_audio_embedding(y) + y_pos = self.ar_audio_position(y_emb) + + xy_padding_mask = torch.concat([x_mask, y_mask], dim=1) + + ar_xy_padding_mask = xy_padding_mask + + x_attn_mask = F.pad( + torch.zeros((x_len, x_len), dtype=torch.bool, device=x.device), + (0, y_len), + value=True, + ) + + y_attn_mask = F.pad( + torch.triu( + torch.ones(y_len, y_len, dtype=torch.bool, device=x.device), + diagonal=1, + ), + (x_len, 0), + value=False, + ) + + xy_attn_mask = torch.concat([x_attn_mask, y_attn_mask], dim=0) + bsz, src_len = x.shape[0], x_len + y_len + _xy_padding_mask = ( + ar_xy_padding_mask.view(bsz, 1, 1, src_len) + .expand(-1, self.num_head, -1, -1) + .reshape(bsz * self.num_head, 1, src_len) + ) + xy_attn_mask = xy_attn_mask.logical_or(_xy_padding_mask) + new_attn_mask = torch.zeros_like(xy_attn_mask, dtype=x.dtype) + new_attn_mask.masked_fill_(xy_attn_mask, float("-inf")) + xy_attn_mask = new_attn_mask + # x 和完整的 y 一次性输入模型 + xy_pos = torch.concat([x, y_pos], dim=1) + + return xy_pos, xy_attn_mask, targets + + def forward(self, x, x_lens, y, y_lens, bert_feature): + """ + x: phoneme_ids + y: semantic_ids + """ + + reject_y, reject_y_lens = make_reject_y(y, y_lens) + + xy_pos, xy_attn_mask, targets = self.make_input_data(x, x_lens, y, y_lens, bert_feature) + + xy_dec, _ = self.h( + (xy_pos, None), + mask=xy_attn_mask, + ) + x_len = x_lens.max() + logits = self.ar_predict_layer(xy_dec[:, x_len:]) + + ###### DPO ############# + reject_xy_pos, reject_xy_attn_mask, reject_targets = self.make_input_data(x, x_lens, reject_y, reject_y_lens, bert_feature) + + reject_xy_dec, _ = self.h( + (reject_xy_pos, None), + mask=reject_xy_attn_mask, + ) + x_len = x_lens.max() + reject_logits = self.ar_predict_layer(reject_xy_dec[:, x_len:]) + + # loss + # from feiteng: 每次 duration 越多, 梯度更新也应该更多, 所以用 sum + + loss_1 = F.cross_entropy(logits.permute(0, 2, 1), targets, reduction="sum") + acc = self.ar_accuracy_metric(logits.permute(0, 2, 1).detach(), targets).item() + + A_logits, R_logits = get_batch_logps(logits, reject_logits, targets, reject_targets) + loss_2, _, _ = dpo_loss(A_logits, R_logits, 0, 0, 0.2, reference_free=True) + + loss = loss_1 + loss_2 + + return loss, acc + + def forward_old(self, x, x_lens, y, y_lens, bert_feature): + """ + x: phoneme_ids + y: semantic_ids + """ + x = self.ar_text_embedding(x) + x = x + self.bert_proj(bert_feature.transpose(1, 2)) + x = self.ar_text_position(x) + x_mask = make_pad_mask(x_lens) + + y_mask = make_pad_mask(y_lens) + y_mask_int = y_mask.type(torch.int64) + codes = y.type(torch.int64) * (1 - y_mask_int) + + # Training + # AR Decoder + y, targets = self.pad_y_eos(codes, y_mask_int, eos_id=self.EOS) + x_len = x_lens.max() + y_len = y_lens.max() + y_emb = self.ar_audio_embedding(y) + y_pos = self.ar_audio_position(y_emb) + + xy_padding_mask = torch.concat([x_mask, y_mask], dim=1) + ar_xy_padding_mask = xy_padding_mask + + x_attn_mask = F.pad( + torch.zeros((x_len, x_len), dtype=torch.bool, device=x.device), + (0, y_len), + value=True, + ) + y_attn_mask = F.pad( + torch.triu( + torch.ones(y_len, y_len, dtype=torch.bool, device=x.device), + diagonal=1, + ), + (x_len, 0), + value=False, + ) + xy_attn_mask = torch.concat([x_attn_mask, y_attn_mask], dim=0) + bsz, src_len = x.shape[0], x_len + y_len + _xy_padding_mask = ( + ar_xy_padding_mask.view(bsz, 1, 1, src_len) + .expand(-1, self.num_head, -1, -1) + .reshape(bsz * self.num_head, 1, src_len) + ) + xy_attn_mask = xy_attn_mask.logical_or(_xy_padding_mask) + new_attn_mask = torch.zeros_like(xy_attn_mask, dtype=x.dtype) + new_attn_mask.masked_fill_(xy_attn_mask, float("-inf")) + xy_attn_mask = new_attn_mask + # x 和完整的 y 一次性输入模型 + xy_pos = torch.concat([x, y_pos], dim=1) + xy_dec, _ = self.h( + (xy_pos, None), + mask=xy_attn_mask, + ) + logits = self.ar_predict_layer(xy_dec[:, x_len:]).permute(0, 2, 1) + # loss + # from feiteng: 每次 duration 越多, 梯度更新也应该更多, 所以用 sum + loss = F.cross_entropy(logits, targets, reduction="sum") + acc = self.ar_accuracy_metric(logits.detach(), targets).item() + return loss, acc + + # 需要看下这个函数和 forward 的区别以及没有 semantic 的时候 prompts 输入什么 + def infer( + self, + x, + x_lens, + prompts, + bert_feature, + top_k: int = -100, + early_stop_num: int = -1, + temperature: float = 1.0, + ): + x = self.ar_text_embedding(x) + x = x + self.bert_proj(bert_feature.transpose(1, 2)) + x = self.ar_text_position(x) + + # AR Decoder + y = prompts + prefix_len = y.shape[1] + x_len = x.shape[1] + x_attn_mask = torch.zeros((x_len, x_len), dtype=torch.bool) + stop = False + for _ in tqdm(range(1500)): + y_emb = self.ar_audio_embedding(y) + y_pos = self.ar_audio_position(y_emb) + # x 和逐渐增长的 y 一起输入给模型 + xy_pos = torch.concat([x, y_pos], dim=1) + y_len = y.shape[1] + x_attn_mask_pad = F.pad( + x_attn_mask, + (0, y_len), + value=True, + ) + y_attn_mask = F.pad( + torch.triu(torch.ones(y_len, y_len, dtype=torch.bool), diagonal=1), + (x_len, 0), + value=False, + ) + xy_attn_mask = torch.concat([x_attn_mask_pad, y_attn_mask], dim=0).to( + y.device + ) + + xy_dec, _ = self.h( + (xy_pos, None), + mask=xy_attn_mask, + ) + logits = self.ar_predict_layer(xy_dec[:, -1]) + samples = topk_sampling( + logits, top_k=top_k, top_p=1.0, temperature=temperature + ) + + if early_stop_num != -1 and (y.shape[1] - prefix_len) > early_stop_num: + print("use early stop num:", early_stop_num) + stop = True + + if torch.argmax(logits, dim=-1)[0] == self.EOS or samples[0, 0] == self.EOS: + # print(torch.argmax(logits, dim=-1)[0] == self.EOS, samples[0, 0] == self.EOS) + stop = True + if stop: + if prompts.shape[1] == y.shape[1]: + y = torch.concat([y, torch.zeros_like(samples)], dim=1) + print("bad zero prediction") + print(f"T2S Decoding EOS [{prefix_len} -> {y.shape[1]}]") + break + # 本次生成的 semantic_ids 和之前的 y 构成新的 y + # print(samples.shape)#[1,1]#第一个1是bs + # import os + # os._exit(2333) + y = torch.concat([y, samples], dim=1) + return y + + def pad_y_eos(self, y, y_mask_int, eos_id): + targets = F.pad(y, (0, 1), value=0) + eos_id * F.pad( + y_mask_int, (0, 1), value=1 + ) + # 错位 + return targets[:, :-1], targets[:, 1:] + + def infer_panel( + self, + x, #####全部文本token + x_lens, + prompts, ####参考音频token + bert_feature, + top_k: int = -100, + top_p: int = 100, + early_stop_num: int = -1, + temperature: float = 1.0, + ): + x = self.ar_text_embedding(x) + x = x + self.bert_proj(bert_feature.transpose(1, 2)) + x = self.ar_text_position(x) + + # AR Decoder + y = prompts + + x_len = x.shape[1] + x_attn_mask = torch.zeros((x_len, x_len), dtype=torch.bool) + stop = False + # print(1111111,self.num_layers) + cache = { + "all_stage": self.num_layers, + "k": [None] * self.num_layers, ###根据配置自己手写 + "v": [None] * self.num_layers, + # "xy_pos":None,##y_pos位置编码每次都不一样的没法缓存，每次都要重新拼xy_pos.主要还是写法原因，其实是可以历史统一一样的，但也没啥计算量就不管了 + "y_emb": None, ##只需要对最新的samples求emb，再拼历史的就行 + # "logits":None,###原版就已经只对结尾求再拼接了，不用管 + # "xy_dec":None,###不需要，本来只需要最后一个做logits + "first_infer": 1, + "stage": 0, + } + ################### first step ########################## + if y is not None: + y_emb = self.ar_audio_embedding(y) + y_len = y_emb.shape[1] + prefix_len = y.shape[1] + y_pos = self.ar_audio_position(y_emb) + xy_pos = torch.concat([x, y_pos], dim=1) + cache["y_emb"] = y_emb + ref_free = False + else: + y_emb = None + y_len = 0 + prefix_len = 0 + y_pos = None + xy_pos = x + y = torch.zeros(x.shape[0], 0, dtype=torch.int, device=x.device) + ref_free = True + + x_attn_mask_pad = F.pad( + x_attn_mask, + (0, y_len), ###xx的纯0扩展到xx纯0+xy纯1，(x,x+y) + value=True, + ) + y_attn_mask = F.pad( ###yy的右上1扩展到左边xy的0,(y,x+y) + torch.triu(torch.ones(y_len, y_len, dtype=torch.bool), diagonal=1), + (x_len, 0), + value=False, + ) + xy_attn_mask = torch.concat([x_attn_mask_pad, y_attn_mask], dim=0).to( + x.device + ) + + y_list = [None]*y.shape[0] + batch_idx_map = list(range(y.shape[0])) + idx_list = [None]*y.shape[0] + for idx in tqdm(range(1500)): + + xy_dec, _ = self.h((xy_pos, None), mask=xy_attn_mask, cache=cache) + logits = self.ar_predict_layer( + xy_dec[:, -1] + ) ##不用改，如果用了cache的默认就是只有一帧，取最后一帧一样的 + # samples = topk_sampling(logits, top_k=top_k, top_p=1.0, temperature=temperature) + if(idx==0):###第一次跑不能EOS否则没有了 + logits = logits[:, :-1] ###刨除1024终止符号的概率 + samples = sample( + logits, y, top_k=top_k, top_p=top_p, repetition_penalty=1.35, temperature=temperature + )[0] + # 本次生成的 semantic_ids 和之前的 y 构成新的 y + # print(samples.shape)#[1,1]#第一个1是bs + y = torch.concat([y, samples], dim=1) + + # 移除已经生成完毕的序列 + reserved_idx_of_batch_for_y = None + if (self.EOS in torch.argmax(logits, dim=-1)) or \ + (self.EOS in samples[:, 0]): ###如果生成到EOS，则停止 + l = samples[:, 0]==self.EOS + removed_idx_of_batch_for_y = torch.where(l==True)[0].tolist() + reserved_idx_of_batch_for_y = torch.where(l==False)[0] + # batch_indexs = torch.tensor(batch_idx_map, device=y.device)[removed_idx_of_batch_for_y] + for i in removed_idx_of_batch_for_y: + batch_index = batch_idx_map[i] + idx_list[batch_index] = idx - 1 + y_list[batch_index] = y[i, :-1] + + batch_idx_map = [batch_idx_map[i] for i in reserved_idx_of_batch_for_y.tolist()] + + # 只保留未生成完毕的序列 + if reserved_idx_of_batch_for_y is not None: + # index = torch.LongTensor(batch_idx_map).to(y.device) + y = torch.index_select(y, dim=0, index=reserved_idx_of_batch_for_y) + if cache["y_emb"] is not None: + cache["y_emb"] = torch.index_select(cache["y_emb"], dim=0, index=reserved_idx_of_batch_for_y) + if cache["k"] is not None: + for i in range(self.num_layers): + # 因为kv转置了，所以batch dim是1 + cache["k"][i] = torch.index_select(cache["k"][i], dim=1, index=reserved_idx_of_batch_for_y) + cache["v"][i] = torch.index_select(cache["v"][i], dim=1, index=reserved_idx_of_batch_for_y) + + + if early_stop_num != -1 and (y.shape[1] - prefix_len) > early_stop_num: + print("use early stop num:", early_stop_num) + stop = True + + if not (None in idx_list): + # print(torch.argmax(logits, dim=-1)[0] == self.EOS, samples[0, 0] == self.EOS) + stop = True + if stop: + # if prompts.shape[1] == y.shape[1]: + # y = torch.concat([y, torch.zeros_like(samples)], dim=1) + # print("bad zero prediction") + if y.shape[1]==0: + y = torch.concat([y, torch.zeros_like(samples)], dim=1) + print("bad zero prediction") + print(f"T2S Decoding EOS [{prefix_len} -> {y.shape[1]}]") + break + + ####################### update next step ################################### + cache["first_infer"] = 0 + if cache["y_emb"] is not None: + y_emb = torch.cat( + [cache["y_emb"], self.ar_audio_embedding(y[:, -1:])], dim = 1 + ) + cache["y_emb"] = y_emb + y_pos = self.ar_audio_position(y_emb) + xy_pos = y_pos[:, -1:] + else: + y_emb = self.ar_audio_embedding(y[:, -1:]) + cache["y_emb"] = y_emb + y_pos = self.ar_audio_position(y_emb) + xy_pos = y_pos + y_len = y_pos.shape[1] + + ###最右边一列（是错的） + # xy_attn_mask=torch.ones((1, x_len+y_len), dtype=torch.bool,device=xy_pos.device) + # xy_attn_mask[:,-1]=False + ###最下面一行（是对的） + xy_attn_mask = torch.zeros( + (1, x_len + y_len), dtype=torch.bool, device=xy_pos.device + ) + + if (None in idx_list): + for i in range(x.shape[0]): + if idx_list[i] is None: + idx_list[i] = 1500-1 ###如果没有生成到EOS，就用最大长度代替 + + if ref_free: + return y_list, [0]*x.shape[0] + return y_list, idx_list diff --git a/GPT_SoVITS/AR/models/utils.py b/GPT_SoVITS/AR/models/utils.py index 9678c7e..ce0a98b 100644 --- a/GPT_SoVITS/AR/models/utils.py +++ b/GPT_SoVITS/AR/models/utils.py @@ -115,17 +115,17 @@ def logits_to_probs( top_p: Optional[int] = None, repetition_penalty: float = 1.0, ): - if previous_tokens is not None: - previous_tokens = previous_tokens.squeeze() + # if previous_tokens is not None: + # previous_tokens = previous_tokens.squeeze() # print(logits.shape,previous_tokens.shape) # pdb.set_trace() if previous_tokens is not None and repetition_penalty != 1.0: previous_tokens = previous_tokens.long() - score = torch.gather(logits, dim=0, index=previous_tokens) + score = torch.gather(logits, dim=1, index=previous_tokens) score = torch.where( score < 0, score * repetition_penalty, score / repetition_penalty ) - logits.scatter_(dim=0, index=previous_tokens, src=score) + logits.scatter_(dim=1, index=previous_tokens, src=score) if top_p is not None and top_p < 1.0: sorted_logits, sorted_indices = torch.sort(logits, descending=True) @@ -133,9 +133,9 @@ def logits_to_probs( torch.nn.functional.softmax(sorted_logits, dim=-1), dim=-1 ) sorted_indices_to_remove = cum_probs > top_p - sorted_indices_to_remove[0] = False # keep at least one option + sorted_indices_to_remove[:, 0] = False # keep at least one option indices_to_remove = sorted_indices_to_remove.scatter( - dim=0, index=sorted_indices, src=sorted_indices_to_remove + dim=1, index=sorted_indices, src=sorted_indices_to_remove ) logits = logits.masked_fill(indices_to_remove, -float("Inf")) @@ -143,7 +143,7 @@ def logits_to_probs( if top_k is not None: v, _ = torch.topk(logits, min(top_k, logits.size(-1))) - pivot = v.select(-1, -1).unsqueeze(-1) + pivot = v[: , -1].unsqueeze(-1) logits = torch.where(logits < pivot, -float("Inf"), logits) probs = torch.nn.functional.softmax(logits, dim=-1) diff --git a/GPT_SoVITS/TTS_infer_pack/TTS.py b/GPT_SoVITS/TTS_infer_pack/TTS.py new file mode 100644 index 0000000..566e998 --- /dev/null +++ b/GPT_SoVITS/TTS_infer_pack/TTS.py @@ -0,0 +1,920 @@ +from copy import deepcopy +import math +import os, sys +import random +import traceback + +from tqdm import tqdm +now_dir = os.getcwd() +sys.path.append(now_dir) +import ffmpeg +import os +from typing import Generator, List, Union +import numpy as np +import torch +import torch.nn.functional as F +import yaml +from transformers import AutoModelForMaskedLM, AutoTokenizer + +from AR.models.t2s_lightning_module import Text2SemanticLightningModule +from feature_extractor.cnhubert import CNHubert +from module.models import SynthesizerTrn +import librosa +from time import time as ttime +from tools.i18n.i18n import I18nAuto +from my_utils import load_audio +from module.mel_processing import spectrogram_torch +from TTS_infer_pack.text_segmentation_method import splits +from TTS_infer_pack.TextPreprocessor import TextPreprocessor +i18n = I18nAuto() + +# configs/tts_infer.yaml +""" +default: + device: cpu + is_half: false + bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large + cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base + t2s_weights_path: GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt + vits_weights_path: GPT_SoVITS/pretrained_models/s2G488k.pth + flash_attn_enabled: true + +custom: + device: cuda + is_half: true + bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large + cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base + t2s_weights_path: GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt + vits_weights_path: GPT_SoVITS/pretrained_models/s2G488k.pth + flash_attn_enabled: true + + +""" + +def set_seed(seed:int): + seed = int(seed) + seed = seed if seed != -1 else random.randrange(1 << 32) + print(f"Set seed to {seed}") + os.environ['PYTHONHASHSEED'] = str(seed) + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + try: + if torch.cuda.is_available(): + torch.cuda.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + # torch.backends.cudnn.deterministic = True + # torch.backends.cudnn.benchmark = False + # torch.backends.cudnn.enabled = True + except: + pass + return seed + +class TTS_Config: + default_configs={ + "device": "cpu", + "is_half": False, + "t2s_weights_path": "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt", + "vits_weights_path": "GPT_SoVITS/pretrained_models/s2G488k.pth", + "cnhuhbert_base_path": "GPT_SoVITS/pretrained_models/chinese-hubert-base", + "bert_base_path": "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large", + "flash_attn_enabled": True + } + configs:dict = None + def __init__(self, configs: Union[dict, str]=None): + + # 设置默认配置文件路径 + configs_base_path:str = "GPT_SoVITS/configs/" + os.makedirs(configs_base_path, exist_ok=True) + self.configs_path:str = os.path.join(configs_base_path, "tts_infer.yaml") + + if configs in ["", None]: + if not os.path.exists(self.configs_path): + self.save_configs() + print(f"Create default config file at {self.configs_path}") + configs:dict = {"default": deepcopy(self.default_configs)} + + if isinstance(configs, str): + self.configs_path = configs + configs:dict = self._load_configs(self.configs_path) + + assert isinstance(configs, dict) + default_configs:dict = configs.get("default", None) + if default_configs is not None: + self.default_configs = default_configs + + self.configs:dict = configs.get("custom", deepcopy(self.default_configs)) + + + self.device = self.configs.get("device", torch.device("cpu")) + self.is_half = self.configs.get("is_half", False) + self.flash_attn_enabled = self.configs.get("flash_attn_enabled", True) + self.t2s_weights_path = self.configs.get("t2s_weights_path", None) + self.vits_weights_path = self.configs.get("vits_weights_path", None) + self.bert_base_path = self.configs.get("bert_base_path", None) + self.cnhuhbert_base_path = self.configs.get("cnhuhbert_base_path", None) + + + if (self.t2s_weights_path in [None, ""]) or (not os.path.exists(self.t2s_weights_path)): + self.t2s_weights_path = self.default_configs['t2s_weights_path'] + print(f"fall back to default t2s_weights_path: {self.t2s_weights_path}") + if (self.vits_weights_path in [None, ""]) or (not os.path.exists(self.vits_weights_path)): + self.vits_weights_path = self.default_configs['vits_weights_path'] + print(f"fall back to default vits_weights_path: {self.vits_weights_path}") + if (self.bert_base_path in [None, ""]) or (not os.path.exists(self.bert_base_path)): + self.bert_base_path = self.default_configs['bert_base_path'] + print(f"fall back to default bert_base_path: {self.bert_base_path}") + if (self.cnhuhbert_base_path in [None, ""]) or (not os.path.exists(self.cnhuhbert_base_path)): + self.cnhuhbert_base_path = self.default_configs['cnhuhbert_base_path'] + print(f"fall back to default cnhuhbert_base_path: {self.cnhuhbert_base_path}") + self.update_configs() + + + self.max_sec = None + self.hz:int = 50 + self.semantic_frame_rate:str = "25hz" + self.segment_size:int = 20480 + self.filter_length:int = 2048 + self.sampling_rate:int = 32000 + self.hop_length:int = 640 + self.win_length:int = 2048 + self.n_speakers:int = 300 + + self.langauges:list = ["auto", "en", "zh", "ja", "all_zh", "all_ja"] + # print(self) + + def _load_configs(self, configs_path: str)->dict: + with open(configs_path, 'r') as f: + configs = yaml.load(f, Loader=yaml.FullLoader) + + return configs + + def save_configs(self, configs_path:str=None)->None: + configs={ + "default":self.default_configs, + } + if self.configs is not None: + configs["custom"] = self.update_configs() + + if configs_path is None: + configs_path = self.configs_path + with open(configs_path, 'w') as f: + yaml.dump(configs, f) + + def update_configs(self): + self.config = { + "device" : str(self.device), + "is_half" : self.is_half, + "t2s_weights_path" : self.t2s_weights_path, + "vits_weights_path" : self.vits_weights_path, + "bert_base_path" : self.bert_base_path, + "cnhuhbert_base_path": self.cnhuhbert_base_path, + "flash_attn_enabled" : self.flash_attn_enabled + } + return self.config + + def __str__(self): + self.configs = self.update_configs() + string = "TTS Config".center(100, '-') + '\n' + for k, v in self.configs.items(): + string += f"{str(k).ljust(20)}: {str(v)}\n" + string += "-" * 100 + '\n' + return string + + def __repr__(self): + return self.__str__() + + +class TTS: + def __init__(self, configs: Union[dict, str, TTS_Config]): + if isinstance(configs, TTS_Config): + self.configs = configs + else: + self.configs:TTS_Config = TTS_Config(configs) + + self.t2s_model:Text2SemanticLightningModule = None + self.vits_model:SynthesizerTrn = None + self.bert_tokenizer:AutoTokenizer = None + self.bert_model:AutoModelForMaskedLM = None + self.cnhuhbert_model:CNHubert = None + + self._init_models() + + self.text_preprocessor:TextPreprocessor = \ + TextPreprocessor(self.bert_model, + self.bert_tokenizer, + self.configs.device) + + + self.prompt_cache:dict = { + "ref_audio_path":None, + "prompt_semantic":None, + "refer_spepc":None, + "prompt_text":None, + "prompt_lang":None, + "phones":None, + "bert_features":None, + "norm_text":None, + } + + + self.stop_flag:bool = False + self.precison:torch.dtype = torch.float16 if self.configs.is_half else torch.float32 + + def _init_models(self,): + self.init_t2s_weights(self.configs.t2s_weights_path) + self.init_vits_weights(self.configs.vits_weights_path) + self.init_bert_weights(self.configs.bert_base_path) + self.init_cnhuhbert_weights(self.configs.cnhuhbert_base_path) + # self.enable_half_precision(self.configs.is_half) + + + + def init_cnhuhbert_weights(self, base_path: str): + print(f"Loading CNHuBERT weights from {base_path}") + self.cnhuhbert_model = CNHubert(base_path) + self.cnhuhbert_model=self.cnhuhbert_model.eval() + self.cnhuhbert_model = self.cnhuhbert_model.to(self.configs.device) + if self.configs.is_half and str(self.configs.device)!="cpu": + self.cnhuhbert_model = self.cnhuhbert_model.half() + + + + def init_bert_weights(self, base_path: str): + print(f"Loading BERT weights from {base_path}") + self.bert_tokenizer = AutoTokenizer.from_pretrained(base_path) + self.bert_model = AutoModelForMaskedLM.from_pretrained(base_path) + self.bert_model=self.bert_model.eval() + self.bert_model = self.bert_model.to(self.configs.device) + if self.configs.is_half and str(self.configs.device)!="cpu": + self.bert_model = self.bert_model.half() + + + + def init_vits_weights(self, weights_path: str): + print(f"Loading VITS weights from {weights_path}") + self.configs.vits_weights_path = weights_path + self.configs.save_configs() + dict_s2 = torch.load(weights_path, map_location=self.configs.device) + hps = dict_s2["config"] + self.configs.filter_length = hps["data"]["filter_length"] + self.configs.segment_size = hps["train"]["segment_size"] + self.configs.sampling_rate = hps["data"]["sampling_rate"] + self.configs.hop_length = hps["data"]["hop_length"] + self.configs.win_length = hps["data"]["win_length"] + self.configs.n_speakers = hps["data"]["n_speakers"] + self.configs.semantic_frame_rate = "25hz" + kwargs = hps["model"] + vits_model = SynthesizerTrn( + self.configs.filter_length // 2 + 1, + self.configs.segment_size // self.configs.hop_length, + n_speakers=self.configs.n_speakers, + **kwargs + ) + # if ("pretrained" not in weights_path): + if hasattr(vits_model, "enc_q"): + del vits_model.enc_q + + vits_model = vits_model.to(self.configs.device) + vits_model = vits_model.eval() + vits_model.load_state_dict(dict_s2["weight"], strict=False) + self.vits_model = vits_model + if self.configs.is_half and str(self.configs.device)!="cpu": + self.vits_model = self.vits_model.half() + + + def init_t2s_weights(self, weights_path: str): + print(f"Loading Text2Semantic weights from {weights_path}") + self.configs.t2s_weights_path = weights_path + self.configs.save_configs() + self.configs.hz = 50 + dict_s1 = torch.load(weights_path, map_location=self.configs.device) + config = dict_s1["config"] + self.configs.max_sec = config["data"]["max_sec"] + t2s_model = Text2SemanticLightningModule(config, "****", is_train=False, + flash_attn_enabled=self.configs.flash_attn_enabled) + t2s_model.load_state_dict(dict_s1["weight"]) + t2s_model = t2s_model.to(self.configs.device) + t2s_model = t2s_model.eval() + self.t2s_model = t2s_model + if self.configs.is_half and str(self.configs.device)!="cpu": + self.t2s_model = self.t2s_model.half() + + def enable_half_precision(self, enable: bool = True): + ''' + To enable half precision for the TTS model. + Args: + enable: bool, whether to enable half precision. + + ''' + if str(self.configs.device) == "cpu" and enable: + print("Half precision is not supported on CPU.") + return + + self.configs.is_half = enable + self.precison = torch.float16 if enable else torch.float32 + self.configs.save_configs() + if enable: + if self.t2s_model is not None: + self.t2s_model =self.t2s_model.half() + if self.vits_model is not None: + self.vits_model = self.vits_model.half() + if self.bert_model is not None: + self.bert_model =self.bert_model.half() + if self.cnhuhbert_model is not None: + self.cnhuhbert_model = self.cnhuhbert_model.half() + else: + if self.t2s_model is not None: + self.t2s_model = self.t2s_model.float() + if self.vits_model is not None: + self.vits_model = self.vits_model.float() + if self.bert_model is not None: + self.bert_model = self.bert_model.float() + if self.cnhuhbert_model is not None: + self.cnhuhbert_model = self.cnhuhbert_model.float() + + def set_device(self, device: torch.device): + ''' + To set the device for all models. + Args: + device: torch.device, the device to use for all models. + ''' + self.configs.device = device + self.configs.save_configs() + if self.t2s_model is not None: + self.t2s_model = self.t2s_model.to(device) + if self.vits_model is not None: + self.vits_model = self.vits_model.to(device) + if self.bert_model is not None: + self.bert_model = self.bert_model.to(device) + if self.cnhuhbert_model is not None: + self.cnhuhbert_model = self.cnhuhbert_model.to(device) + + def set_ref_audio(self, ref_audio_path:str): + ''' + To set the reference audio for the TTS model, + including the prompt_semantic and refer_spepc. + Args: + ref_audio_path: str, the path of the reference audio. + ''' + self._set_prompt_semantic(ref_audio_path) + self._set_ref_spepc(ref_audio_path) + + def _set_ref_spepc(self, ref_audio_path): + audio = load_audio(ref_audio_path, int(self.configs.sampling_rate)) + audio = torch.FloatTensor(audio) + audio_norm = audio + audio_norm = audio_norm.unsqueeze(0) + spec = spectrogram_torch( + audio_norm, + self.configs.filter_length, + self.configs.sampling_rate, + self.configs.hop_length, + self.configs.win_length, + center=False, + ) + spec = spec.to(self.configs.device) + if self.configs.is_half: + spec = spec.half() + # self.refer_spepc = spec + self.prompt_cache["refer_spepc"] = spec + + + def _set_prompt_semantic(self, ref_wav_path:str): + zero_wav = np.zeros( + int(self.configs.sampling_rate * 0.3), + dtype=np.float16 if self.configs.is_half else np.float32, + ) + with torch.no_grad(): + wav16k, sr = librosa.load(ref_wav_path, sr=16000) + if (wav16k.shape[0] > 160000 or wav16k.shape[0] < 48000): + raise OSError(i18n("参考音频在3~10秒范围外，请更换！")) + wav16k = torch.from_numpy(wav16k) + zero_wav_torch = torch.from_numpy(zero_wav) + wav16k = wav16k.to(self.configs.device) + zero_wav_torch = zero_wav_torch.to(self.configs.device) + if self.configs.is_half: + wav16k = wav16k.half() + zero_wav_torch = zero_wav_torch.half() + + wav16k = torch.cat([wav16k, zero_wav_torch]) + hubert_feature = self.cnhuhbert_model.model(wav16k.unsqueeze(0))[ + "last_hidden_state" + ].transpose( + 1, 2 + ) # .float() + codes = self.vits_model.extract_latent(hubert_feature) + + prompt_semantic = codes[0, 0].to(self.configs.device) + self.prompt_cache["prompt_semantic"] = prompt_semantic + + def batch_sequences(self, sequences: List[torch.Tensor], axis: int = 0, pad_value: int = 0, max_length:int=None): + seq = sequences[0] + ndim = seq.dim() + if axis < 0: + axis += ndim + dtype:torch.dtype = seq.dtype + pad_value = torch.tensor(pad_value, dtype=dtype) + seq_lengths = [seq.shape[axis] for seq in sequences] + if max_length is None: + max_length = max(seq_lengths) + else: + max_length = max(seq_lengths) if max_length < max(seq_lengths) else max_length + + padded_sequences = [] + for seq, length in zip(sequences, seq_lengths): + padding = [0] * axis + [0, max_length - length] + [0] * (ndim - axis - 1) + padded_seq = torch.nn.functional.pad(seq, padding, value=pad_value) + padded_sequences.append(padded_seq) + batch = torch.stack(padded_sequences) + return batch + + def to_batch(self, data:list, + prompt_data:dict=None, + batch_size:int=5, + threshold:float=0.75, + split_bucket:bool=True, + device:torch.device=torch.device("cpu"), + precison:torch.dtype=torch.float32, + ): + + _data:list = [] + index_and_len_list = [] + for idx, item in enumerate(data): + norm_text_len = len(item["norm_text"]) + index_and_len_list.append([idx, norm_text_len]) + + batch_index_list = [] + if split_bucket: + index_and_len_list.sort(key=lambda x: x[1]) + index_and_len_list = np.array(index_and_len_list, dtype=np.int64) + + batch_index_list_len = 0 + pos = 0 + while pos =threshold) or (pos_end-pos==1): + batch_index=index_and_len_list[pos:pos_end, 0].tolist() + batch_index_list_len += len(batch_index) + batch_index_list.append(batch_index) + pos = pos_end + break + pos_end=pos_end-1 + + assert batch_index_list_len == len(data) + + else: + for i in range(len(data)): + if i%batch_size == 0: + batch_index_list.append([]) + batch_index_list[-1].append(i) + + + for batch_idx, index_list in enumerate(batch_index_list): + item_list = [data[idx] for idx in index_list] + phones_list = [] + phones_len_list = [] + # bert_features_list = [] + all_phones_list = [] + all_phones_len_list = [] + all_bert_features_list = [] + norm_text_batch = [] + bert_max_len = 0 + phones_max_len = 0 + for item in item_list: + if prompt_data is not None: + all_bert_features = torch.cat([prompt_data["bert_features"], item["bert_features"]], 1)\ + .to(dtype=precison, device=device) + all_phones = torch.LongTensor(prompt_data["phones"]+item["phones"]).to(device) + phones = torch.LongTensor(item["phones"]).to(device) + # norm_text = prompt_data["norm_text"]+item["norm_text"] + else: + all_bert_features = item["bert_features"]\ + .to(dtype=precison, device=device) + phones = torch.LongTensor(item["phones"]).to(device) + all_phones = phones + # norm_text = item["norm_text"] + + bert_max_len = max(bert_max_len, all_bert_features.shape[-1]) + phones_max_len = max(phones_max_len, phones.shape[-1]) + + phones_list.append(phones) + phones_len_list.append(phones.shape[-1]) + all_phones_list.append(all_phones) + all_phones_len_list.append(all_phones.shape[-1]) + all_bert_features_list.append(all_bert_features) + norm_text_batch.append(item["norm_text"]) + + phones_batch = phones_list + all_phones_batch = all_phones_list + all_bert_features_batch = all_bert_features_list + + + # max_len = max(bert_max_len, phones_max_len) + # phones_batch = self.batch_sequences(phones_list, axis=0, pad_value=0, max_length=max_len) + #### 直接对phones和bert_features进行pad，会增大复读概率。 + # all_phones_batch = self.batch_sequences(all_phones_list, axis=0, pad_value=0, max_length=max_len) + # all_bert_features_batch = all_bert_features_list + # all_bert_features_batch = torch.zeros(len(item_list), 1024, max_len, dtype=precison, device=device) + # for idx, item in enumerate(all_bert_features_list): + # all_bert_features_batch[idx, :, : item.shape[-1]] = item + + # #### 先对phones进行embedding、对bert_features进行project，再pad到相同长度，以缓解复读问题。（可能还有其他因素导致复读） + # all_phones_list = [self.t2s_model.model.ar_text_embedding(item.to(self.t2s_model.device)) for item in all_phones_list] + # all_phones_list = [F.pad(item,(0,0,0,max_len-item.shape[0]),value=0) for item in all_phones_list] + # all_phones_batch = torch.stack(all_phones_list, dim=0) + + # all_bert_features_list = [self.t2s_model.model.bert_proj(item.to(self.t2s_model.device).transpose(0, 1)) for item in all_bert_features_list] + # all_bert_features_list = [F.pad(item,(0,0,0,max_len-item.shape[0]), value=0) for item in all_bert_features_list] + # all_bert_features_batch = torch.stack(all_bert_features_list, dim=0) + + batch = { + "phones": phones_batch, + "phones_len": torch.LongTensor(phones_len_list).to(device), + "all_phones": all_phones_batch, + "all_phones_len": torch.LongTensor(all_phones_len_list).to(device), + "all_bert_features": all_bert_features_batch, + "norm_text": norm_text_batch + } + _data.append(batch) + + return _data, batch_index_list + + def recovery_order(self, data:list, batch_index_list:list)->list: + ''' + Recovery the order of the audio according to the batch_index_list. + + Args: + data (List[list(np.ndarray)]): the out of order audio . + batch_index_list (List[list[int]]): the batch index list. + + Returns: + list (List[np.ndarray]): the data in the original order. + ''' + lenght = len(sum(batch_index_list, [])) + _data = [None]*lenght + for i, index_list in enumerate(batch_index_list): + for j, index in enumerate(index_list): + _data[index] = data[i][j] + return _data + + def stop(self,): + ''' + Stop the inference process. + ''' + self.stop_flag = True + + + def run(self, inputs:dict): + """ + Text to speech inference. + + Args: + inputs (dict): + { + "text": "", # str. text to be synthesized + "text_lang: "", # str. language of the text to be synthesized + "ref_audio_path": "", # str. reference audio path + "prompt_text": "", # str. prompt text for the reference audio + "prompt_lang": "", # str. language of the prompt text for the reference audio + "top_k": 5, # int. top k sampling + "top_p": 1, # float. top p sampling + "temperature": 1, # float. temperature for sampling + "text_split_method": "", # str. text split method, see text_segmentaion_method.py for details. + "batch_size": 1, # int. batch size for inference + "batch_threshold": 0.75, # float. threshold for batch splitting. + "split_bucket: True, # bool. whether to split the batch into multiple buckets. + "return_fragment": False, # bool. step by step return the audio fragment. + "speed_factor":1.0, # float. control the speed of the synthesized audio. + "fragment_interval":0.3, # float. to control the interval of the audio fragment. + "seed": -1, # int. random seed for reproducibility. + } + returns: + tulpe[int, np.ndarray]: sampling rate and audio data. + """ + ########## variables initialization ########### + self.stop_flag:bool = False + text:str = inputs.get("text", "") + text_lang:str = inputs.get("text_lang", "") + ref_audio_path:str = inputs.get("ref_audio_path", "") + prompt_text:str = inputs.get("prompt_text", "") + prompt_lang:str = inputs.get("prompt_lang", "") + top_k:int = inputs.get("top_k", 5) + top_p:float = inputs.get("top_p", 1) + temperature:float = inputs.get("temperature", 1) + text_split_method:str = inputs.get("text_split_method", "") + batch_size = inputs.get("batch_size", 1) + batch_threshold = inputs.get("batch_threshold", 0.75) + speed_factor = inputs.get("speed_factor", 1.0) + split_bucket = inputs.get("split_bucket", True) + return_fragment = inputs.get("return_fragment", False) + fragment_interval = inputs.get("fragment_interval", 0.3) + seed = inputs.get("seed", -1) + seed = -1 if seed in ["", None] else seed + set_seed(seed) + + if return_fragment: + # split_bucket = False + print(i18n("分段返回模式已开启")) + if split_bucket: + split_bucket = False + print(i18n("分段返回模式不支持分桶处理，已自动关闭分桶处理")) + + if split_bucket: + print(i18n("分桶处理模式已开启")) + + if fragment_interval<0.01: + fragment_interval = 0.01 + print(i18n("分段间隔过小，已自动设置为0.01")) + + no_prompt_text = False + if prompt_text in [None, ""]: + no_prompt_text = True + + assert text_lang in self.configs.langauges + if not no_prompt_text: + assert prompt_lang in self.configs.langauges + + if ref_audio_path in [None, ""] and \ + ((self.prompt_cache["prompt_semantic"] is None) or (self.prompt_cache["refer_spepc"] is None)): + raise ValueError("ref_audio_path cannot be empty, when the reference audio is not set using set_ref_audio()") + + + ###### setting reference audio and prompt text preprocessing ######## + t0 = ttime() + if (ref_audio_path is not None) and (ref_audio_path != self.prompt_cache["ref_audio_path"]): + self.set_ref_audio(ref_audio_path) + + if not no_prompt_text: + prompt_text = prompt_text.strip("\n") + if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_lang != "en" else "." + print(i18n("实际输入的参考文本:"), prompt_text) + if self.prompt_cache["prompt_text"] != prompt_text: + self.prompt_cache["prompt_text"] = prompt_text + self.prompt_cache["prompt_lang"] = prompt_lang + phones, bert_features, norm_text = \ + self.text_preprocessor.segment_and_extract_feature_for_text( + prompt_text, + prompt_lang) + self.prompt_cache["phones"] = phones + self.prompt_cache["bert_features"] = bert_features + self.prompt_cache["norm_text"] = norm_text + + + ###### text preprocessing ######## + t1 = ttime() + data:list = None + if not return_fragment: + data = self.text_preprocessor.preprocess(text, text_lang, text_split_method) + if len(data) == 0: + yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate), + dtype=np.int16) + return + + batch_index_list:list = None + data, batch_index_list = self.to_batch(data, + prompt_data=self.prompt_cache if not no_prompt_text else None, + batch_size=batch_size, + threshold=batch_threshold, + split_bucket=split_bucket, + device=self.configs.device, + precison=self.precison + ) + else: + print(i18n("############ 切分文本 ############")) + texts = self.text_preprocessor.pre_seg_text(text, text_lang, text_split_method) + data = [] + for i in range(len(texts)): + if i%batch_size == 0: + data.append([]) + data[-1].append(texts[i]) + + def make_batch(batch_texts): + batch_data = [] + print(i18n("############ 提取文本Bert特征 ############")) + for text in tqdm(batch_texts): + phones, bert_features, norm_text = self.text_preprocessor.segment_and_extract_feature_for_text(text, text_lang) + if phones is None: + continue + res={ + "phones": phones, + "bert_features": bert_features, + "norm_text": norm_text, + } + batch_data.append(res) + if len(batch_data) == 0: + return None + batch, _ = self.to_batch(batch_data, + prompt_data=self.prompt_cache if not no_prompt_text else None, + batch_size=batch_size, + threshold=batch_threshold, + split_bucket=False, + device=self.configs.device, + precison=self.precison + ) + return batch[0] + + t2 = ttime() + try: + print("############ 推理 ############") + ###### inference ###### + t_34 = 0.0 + t_45 = 0.0 + audio = [] + for item in data: + t3 = ttime() + if return_fragment: + item = make_batch(item) + if item is None: + continue + + batch_phones:List[torch.LongTensor] = item["phones"] + batch_phones_len:torch.LongTensor = item["phones_len"] + all_phoneme_ids:List[torch.LongTensor] = item["all_phones"] + all_phoneme_lens:torch.LongTensor = item["all_phones_len"] + all_bert_features:List[torch.LongTensor] = item["all_bert_features"] + norm_text:str = item["norm_text"] + + print(i18n("前端处理后的文本(每句):"), norm_text) + if no_prompt_text : + prompt = None + else: + prompt = self.prompt_cache["prompt_semantic"].expand(len(all_phoneme_ids), -1).to(self.configs.device) + + with torch.no_grad(): + pred_semantic_list, idx_list = self.t2s_model.model.infer_panel( + all_phoneme_ids, + all_phoneme_lens, + prompt, + all_bert_features, + # prompt_phone_len=ph_offset, + top_k=top_k, + top_p=top_p, + temperature=temperature, + early_stop_num=self.configs.hz * self.configs.max_sec, + ) + t4 = ttime() + t_34 += t4 - t3 + + refer_audio_spepc:torch.Tensor = self.prompt_cache["refer_spepc"]\ + .to(dtype=self.precison, device=self.configs.device) + + batch_audio_fragment = [] + + # ## vits并行推理 method 1 + # pred_semantic_list = [item[-idx:] for item, idx in zip(pred_semantic_list, idx_list)] + # pred_semantic_len = torch.LongTensor([item.shape[0] for item in pred_semantic_list]).to(self.configs.device) + # pred_semantic = self.batch_sequences(pred_semantic_list, axis=0, pad_value=0).unsqueeze(0) + # max_len = 0 + # for i in range(0, len(batch_phones)): + # max_len = max(max_len, batch_phones[i].shape[-1]) + # batch_phones = self.batch_sequences(batch_phones, axis=0, pad_value=0, max_length=max_len) + # batch_phones = batch_phones.to(self.configs.device) + # batch_audio_fragment = (self.vits_model.batched_decode( + # pred_semantic, pred_semantic_len, batch_phones, batch_phones_len,refer_audio_spepc + # )) + + # ## vits并行推理 method 2 + pred_semantic_list = [item[-idx:] for item, idx in zip(pred_semantic_list, idx_list)] + upsample_rate = math.prod(self.vits_model.upsample_rates) + audio_frag_idx = [pred_semantic_list[i].shape[0]*2*upsample_rate for i in range(0, len(pred_semantic_list))] + audio_frag_end_idx = [ sum(audio_frag_idx[:i+1]) for i in range(0, len(audio_frag_idx))] + all_pred_semantic = torch.cat(pred_semantic_list).unsqueeze(0).unsqueeze(0).to(self.configs.device) + _batch_phones = torch.cat(batch_phones).unsqueeze(0).to(self.configs.device) + _batch_audio_fragment = (self.vits_model.decode( + all_pred_semantic, _batch_phones,refer_audio_spepc + ).detach()[0, 0, :]) + audio_frag_end_idx.insert(0, 0) + batch_audio_fragment= [_batch_audio_fragment[audio_frag_end_idx[i-1]:audio_frag_end_idx[i]] for i in range(1, len(audio_frag_end_idx))] + + + # ## vits串行推理 + # for i, idx in enumerate(idx_list): + # phones = batch_phones[i].unsqueeze(0).to(self.configs.device) + # _pred_semantic = (pred_semantic_list[i][-idx:].unsqueeze(0).unsqueeze(0)) # .unsqueeze(0)#mq要多unsqueeze一次 + # audio_fragment =(self.vits_model.decode( + # _pred_semantic, phones, refer_audio_spepc + # ).detach()[0, 0, :]) + # batch_audio_fragment.append( + # audio_fragment + # ) ###试试重建不带上prompt部分 + + t5 = ttime() + t_45 += t5 - t4 + if return_fragment: + print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t4 - t3, t5 - t4)) + yield self.audio_postprocess([batch_audio_fragment], + self.configs.sampling_rate, + None, + speed_factor, + False, + fragment_interval + ) + else: + audio.append(batch_audio_fragment) + + if self.stop_flag: + yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate), + dtype=np.int16) + return + + if not return_fragment: + print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t_34, t_45)) + yield self.audio_postprocess(audio, + self.configs.sampling_rate, + batch_index_list, + speed_factor, + split_bucket, + fragment_interval + ) + except Exception as e: + traceback.print_exc() + # 必须返回一个空音频, 否则会导致显存不释放。 + yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate), + dtype=np.int16) + # 重置模型, 否则会导致显存释放不完全。 + del self.t2s_model + del self.vits_model + self.t2s_model = None + self.vits_model = None + self.init_t2s_weights(self.configs.t2s_weights_path) + self.init_vits_weights(self.configs.vits_weights_path) + finally: + self.empty_cache() + + def empty_cache(self): + try: + if "cuda" in str(self.configs.device): + torch.cuda.empty_cache() + elif str(self.configs.device) == "mps": + torch.mps.empty_cache() + except: + pass + + def audio_postprocess(self, + audio:List[torch.Tensor], + sr:int, + batch_index_list:list=None, + speed_factor:float=1.0, + split_bucket:bool=True, + fragment_interval:float=0.3 + )->tuple[int, np.ndarray]: + zero_wav = torch.zeros( + int(self.configs.sampling_rate * fragment_interval), + dtype=self.precison, + device=self.configs.device + ) + + for i, batch in enumerate(audio): + for j, audio_fragment in enumerate(batch): + max_audio=torch.abs(audio_fragment).max()#简单防止16bit爆音 + if max_audio>1: audio_fragment/=max_audio + audio_fragment:torch.Tensor = torch.cat([audio_fragment, zero_wav], dim=0) + audio[i][j] = audio_fragment.cpu().numpy() + + + if split_bucket: + audio = self.recovery_order(audio, batch_index_list) + else: + # audio = [item for batch in audio for item in batch] + audio = sum(audio, []) + + + audio = np.concatenate(audio, 0) + audio = (audio * 32768).astype(np.int16) + + try: + if speed_factor != 1.0: + audio = speed_change(audio, speed=speed_factor, sr=int(sr)) + except Exception as e: + print(f"Failed to change speed of audio: \n{e}") + + return sr, audio + + + + +def speed_change(input_audio:np.ndarray, speed:float, sr:int): + # 将 NumPy 数组转换为原始 PCM 流 + raw_audio = input_audio.astype(np.int16).tobytes() + + # 设置 ffmpeg 输入流 + input_stream = ffmpeg.input('pipe:', format='s16le', acodec='pcm_s16le', ar=str(sr), ac=1) + + # 变速处理 + output_stream = input_stream.filter('atempo', speed) + + # 输出流到管道 + out, _ = ( + output_stream.output('pipe:', format='s16le', acodec='pcm_s16le') + .run(input=raw_audio, capture_stdout=True, capture_stderr=True) + ) + + # 将管道输出解码为 NumPy 数组 + processed_audio = np.frombuffer(out, np.int16) + + return processed_audio \ No newline at end of file diff --git a/GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py b/GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py new file mode 100644 index 0000000..58b2678 --- /dev/null +++ b/GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py @@ -0,0 +1,210 @@ + +import os, sys + +from tqdm import tqdm +now_dir = os.getcwd() +sys.path.append(now_dir) + +import re +import torch +import LangSegment +from typing import Dict, List, Tuple +from text.cleaner import clean_text +from text import cleaned_text_to_sequence +from transformers import AutoModelForMaskedLM, AutoTokenizer +from TTS_infer_pack.text_segmentation_method import split_big_text, splits, get_method as get_seg_method + +from tools.i18n.i18n import I18nAuto + +i18n = I18nAuto() + +def get_first(text:str) -> str: + pattern = "[" + "".join(re.escape(sep) for sep in splits) + "]" + text = re.split(pattern, text)[0].strip() + return text + +def merge_short_text_in_array(texts:str, threshold:int) -> list: + if (len(texts)) < 2: + return texts + result = [] + text = "" + for ele in texts: + text += ele + if len(text) >= threshold: + result.append(text) + text = "" + if (len(text) > 0): + if len(result) == 0: + result.append(text) + else: + result[len(result) - 1] += text + return result + + + + + + +class TextPreprocessor: + def __init__(self, bert_model:AutoModelForMaskedLM, + tokenizer:AutoTokenizer, device:torch.device): + self.bert_model = bert_model + self.tokenizer = tokenizer + self.device = device + + def preprocess(self, text:str, lang:str, text_split_method:str)->List[Dict]: + print(i18n("############ 切分文本 ############")) + texts = self.pre_seg_text(text, lang, text_split_method) + result = [] + print(i18n("############ 提取文本Bert特征 ############")) + for text in tqdm(texts): + phones, bert_features, norm_text = self.segment_and_extract_feature_for_text(text, lang) + if phones is None: + continue + res={ + "phones": phones, + "bert_features": bert_features, + "norm_text": norm_text, + } + result.append(res) + return result + + def pre_seg_text(self, text:str, lang:str, text_split_method:str): + text = text.strip("\n") + if (text[0] not in splits and len(get_first(text)) < 4): + text = "。" + text if lang != "en" else "." + text + print(i18n("实际输入的目标文本:")) + print(text) + + seg_method = get_seg_method(text_split_method) + text = seg_method(text) + + while "\n\n" in text: + text = text.replace("\n\n", "\n") + + _texts = text.split("\n") + _texts = merge_short_text_in_array(_texts, 5) + texts = [] + + + for text in _texts: + # 解决输入目标文本的空行导致报错的问题 + if (len(text.strip()) == 0): + continue + if (text[-1] not in splits): text += "。" if lang != "en" else "." + + # 解决句子过长导致Bert报错的问题 + if (len(text) > 510): + texts.extend(split_big_text(text)) + else: + texts.append(text) + + print(i18n("实际输入的目标文本(切句后):")) + print(texts) + return texts + + def segment_and_extract_feature_for_text(self, texts:list, language:str)->Tuple[list, torch.Tensor, str]: + textlist, langlist = self.seg_text(texts, language) + if len(textlist) == 0: + return None, None, None + + phones, bert_features, norm_text = self.extract_bert_feature(textlist, langlist) + return phones, bert_features, norm_text + + + def seg_text(self, text:str, language:str)->Tuple[list, list]: + + textlist=[] + langlist=[] + if language in ["auto", "zh", "ja"]: + LangSegment.setfilters(["zh","ja","en","ko"]) + for tmp in LangSegment.getTexts(text): + if tmp["text"] == "": + continue + if tmp["lang"] == "ko": + langlist.append("zh") + elif tmp["lang"] == "en": + langlist.append("en") + else: + # 因无法区别中日文汉字,以用户输入为准 + langlist.append(language if language!="auto" else tmp["lang"]) + textlist.append(tmp["text"]) + elif language == "en": + LangSegment.setfilters(["en"]) + formattext = " ".join(tmp["text"] for tmp in LangSegment.getTexts(text)) + while " " in formattext: + formattext = formattext.replace(" ", " ") + if formattext != "": + textlist.append(formattext) + langlist.append("en") + + elif language in ["all_zh","all_ja"]: + + formattext = text + while " " in formattext: + formattext = formattext.replace(" ", " ") + language = language.replace("all_","") + if text == "": + return [],[] + textlist.append(formattext) + langlist.append(language) + + else: + raise ValueError(f"language {language} not supported") + + return textlist, langlist + + + def extract_bert_feature(self, textlist:list, langlist:list): + phones_list = [] + bert_feature_list = [] + norm_text_list = [] + for i in range(len(textlist)): + lang = langlist[i] + phones, word2ph, norm_text = self.clean_text_inf(textlist[i], lang) + _bert_feature = self.get_bert_inf(phones, word2ph, norm_text, lang) + # phones_list.append(phones) + phones_list.extend(phones) + norm_text_list.append(norm_text) + bert_feature_list.append(_bert_feature) + bert_feature = torch.cat(bert_feature_list, dim=1) + # phones = sum(phones_list, []) + norm_text = ''.join(norm_text_list) + return phones_list, bert_feature, norm_text + + + def get_bert_feature(self, text:str, word2ph:list)->torch.Tensor: + with torch.no_grad(): + inputs = self.tokenizer(text, return_tensors="pt") + for i in inputs: + inputs[i] = inputs[i].to(self.device) + res = self.bert_model(**inputs, output_hidden_states=True) + res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1] + assert len(word2ph) == len(text) + phone_level_feature = [] + for i in range(len(word2ph)): + repeat_feature = res[i].repeat(word2ph[i], 1) + phone_level_feature.append(repeat_feature) + phone_level_feature = torch.cat(phone_level_feature, dim=0) + return phone_level_feature.T + + def clean_text_inf(self, text:str, language:str): + phones, word2ph, norm_text = clean_text(text, language) + phones = cleaned_text_to_sequence(phones) + return phones, word2ph, norm_text + + def get_bert_inf(self, phones:list, word2ph:list, norm_text:str, language:str): + language=language.replace("all_","") + if language == "zh": + feature = self.get_bert_feature(norm_text, word2ph).to(self.device) + else: + feature = torch.zeros( + (1024, len(phones)), + dtype=torch.float32, + ).to(self.device) + + return feature + + + + diff --git a/GPT_SoVITS/TTS_infer_pack/__init__.py b/GPT_SoVITS/TTS_infer_pack/__init__.py new file mode 100644 index 0000000..7438198 --- /dev/null +++ b/GPT_SoVITS/TTS_infer_pack/__init__.py @@ -0,0 +1 @@ +from . import TTS, text_segmentation_method \ No newline at end of file diff --git a/GPT_SoVITS/TTS_infer_pack/text_segmentation_method.py b/GPT_SoVITS/TTS_infer_pack/text_segmentation_method.py new file mode 100644 index 0000000..2a182b2 --- /dev/null +++ b/GPT_SoVITS/TTS_infer_pack/text_segmentation_method.py @@ -0,0 +1,152 @@ + + + + +import re +from typing import Callable +from tools.i18n.i18n import I18nAuto + +i18n = I18nAuto() + +METHODS = dict() + +def get_method(name:str)->Callable: + method = METHODS.get(name, None) + if method is None: + raise ValueError(f"Method {name} not found") + return method + +def register_method(name): + def decorator(func): + METHODS[name] = func + return func + return decorator + +splits = {"，", "。", "？", "！", ",", ".", "?", "!", "~", ":", "：", "—", "…", } + +def split_big_text(text, max_len=510): + # 定义全角和半角标点符号 + punctuation = "".join(splits) + + # 切割文本 + segments = re.split('([' + punctuation + '])', text) + + # 初始化结果列表和当前片段 + result = [] + current_segment = '' + + for segment in segments: + # 如果当前片段加上新的片段长度超过max_len，就将当前片段加入结果列表，并重置当前片段 + if len(current_segment + segment) > max_len: + result.append(current_segment) + current_segment = segment + else: + current_segment += segment + + # 将最后一个片段加入结果列表 + if current_segment: + result.append(current_segment) + + return result + + + +def split(todo_text): + todo_text = todo_text.replace("……", "。").replace("——", "，") + if todo_text[-1] not in splits: + todo_text += "。" + i_split_head = i_split_tail = 0 + len_text = len(todo_text) + todo_texts = [] + while 1: + if i_split_head >= len_text: + break # 结尾一定有标点，所以直接跳出即可，最后一段在上次已加入 + if todo_text[i_split_head] in splits: + i_split_head += 1 + todo_texts.append(todo_text[i_split_tail:i_split_head]) + i_split_tail = i_split_head + else: + i_split_head += 1 + return todo_texts + + +# 不切 +@register_method("cut0") +def cut0(inp): + return inp + + +# 凑四句一切 +@register_method("cut1") +def cut1(inp): + inp = inp.strip("\n") + inps = split(inp) + split_idx = list(range(0, len(inps), 4)) + split_idx[-1] = None + if len(split_idx) > 1: + opts = [] + for idx in range(len(split_idx) - 1): + opts.append("".join(inps[split_idx[idx]: split_idx[idx + 1]])) + else: + opts = [inp] + return "\n".join(opts) + +# 凑50字一切 +@register_method("cut2") +def cut2(inp): + inp = inp.strip("\n") + inps = split(inp) + if len(inps) < 2: + return inp + opts = [] + summ = 0 + tmp_str = "" + for i in range(len(inps)): + summ += len(inps[i]) + tmp_str += inps[i] + if summ > 50: + summ = 0 + opts.append(tmp_str) + tmp_str = "" + if tmp_str != "": + opts.append(tmp_str) + # print(opts) + if len(opts) > 1 and len(opts[-1]) < 50: ##如果最后一个太短了，和前一个合一起 + opts[-2] = opts[-2] + opts[-1] + opts = opts[:-1] + return "\n".join(opts) + +# 按中文句号。切 +@register_method("cut3") +def cut3(inp): + inp = inp.strip("\n") + return "\n".join(["%s" % item for item in inp.strip("。").split("。")]) + +#按英文句号.切 +@register_method("cut4") +def cut4(inp): + inp = inp.strip("\n") + return "\n".join(["%s" % item for item in inp.strip(".").split(".")]) + +# 按标点符号切 +# contributed by https://github.com/AI-Hobbyist/GPT-SoVITS/blob/main/GPT_SoVITS/inference_webui.py +@register_method("cut5") +def cut5(inp): + # if not re.search(r'[^\w\s]', inp[-1]): + # inp += '。' + inp = inp.strip("\n") + punds = r'[,.;?!、，。？！;：…]' + items = re.split(f'({punds})', inp) + mergeitems = ["".join(group) for group in zip(items[::2], items[1::2])] + # 在句子不存在符号或句尾无符号的时候保证文本完整 + if len(items)%2 == 1: + mergeitems.append(items[-1]) + opt = "\n".join(mergeitems) + return opt + + + +if __name__ == '__main__': + method = get_method("cut5") + print(method("你好，我是小明。你好，我是小红。你好，我是小刚。你好，我是小张。")) + \ No newline at end of file diff --git a/GPT_SoVITS/configs/tts_infer.yaml b/GPT_SoVITS/configs/tts_infer.yaml new file mode 100644 index 0000000..c772f29 --- /dev/null +++ b/GPT_SoVITS/configs/tts_infer.yaml @@ -0,0 +1,16 @@ +custom: + bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large + cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base + device: cuda + flash_attn_enabled: true + is_half: true + t2s_weights_path: GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt + vits_weights_path: GPT_SoVITS/pretrained_models/s2G488k.pth +default: + bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large + cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base + device: cpu + flash_attn_enabled: true + is_half: false + t2s_weights_path: GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt + vits_weights_path: GPT_SoVITS/pretrained_models/s2G488k.pth diff --git a/GPT_SoVITS/feature_extractor/cnhubert.py b/GPT_SoVITS/feature_extractor/cnhubert.py index dc155bd..7dffbdb 100644 --- a/GPT_SoVITS/feature_extractor/cnhubert.py +++ b/GPT_SoVITS/feature_extractor/cnhubert.py @@ -20,13 +20,16 @@ cnhubert_base_path = None class CNHubert(nn.Module): - def __init__(self): + def __init__(self, base_path:str=None): super().__init__() - self.model = HubertModel.from_pretrained(cnhubert_base_path) + if base_path is None: + base_path = cnhubert_base_path + self.model = HubertModel.from_pretrained(base_path) self.feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained( - cnhubert_base_path + base_path ) + def forward(self, x): input_values = self.feature_extractor( x, return_tensors="pt", sampling_rate=16000 diff --git a/GPT_SoVITS/inference_gui.py b/GPT_SoVITS/inference_gui.py index f6cfdc5..830c66d 100644 --- a/GPT_SoVITS/inference_gui.py +++ b/GPT_SoVITS/inference_gui.py @@ -7,7 +7,7 @@ import soundfile as sf from tools.i18n.i18n import I18nAuto i18n = I18nAuto() -from GPT_SoVITS.inference_webui import change_gpt_weights, change_sovits_weights, get_tts_wav +from GPT_SoVITS.inference_webui_old import change_gpt_weights, change_sovits_weights, get_tts_wav class GPTSoVITSGUI(QMainWindow): diff --git a/GPT_SoVITS/inference_webui.py b/GPT_SoVITS/inference_webui.py index 4fe8045..505b665 100644 --- a/GPT_SoVITS/inference_webui.py +++ b/GPT_SoVITS/inference_webui.py @@ -6,8 +6,11 @@ 全部按英文识别 全部按日文识别 ''' +import os, sys +now_dir = os.getcwd() +sys.path.append(now_dir) + import os, re, logging -import LangSegment logging.getLogger("markdown_it").setLevel(logging.ERROR) logging.getLogger("urllib3").setLevel(logging.ERROR) logging.getLogger("httpcore").setLevel(logging.ERROR) @@ -18,31 +21,7 @@ logging.getLogger("torchaudio._extension").setLevel(logging.ERROR) import pdb import torch -if os.path.exists("./gweight.txt"): - with open("./gweight.txt", 'r', encoding="utf-8") as file: - gweight_data = file.read() - gpt_path = os.environ.get( - "gpt_path", gweight_data) -else: - gpt_path = os.environ.get( - "gpt_path", "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt") -if os.path.exists("./sweight.txt"): - with open("./sweight.txt", 'r', encoding="utf-8") as file: - sweight_data = file.read() - sovits_path = os.environ.get("sovits_path", sweight_data) -else: - sovits_path = os.environ.get("sovits_path", "GPT_SoVITS/pretrained_models/s2G488k.pth") -# gpt_path = os.environ.get( -# "gpt_path", "pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt" -# ) -# sovits_path = os.environ.get("sovits_path", "pretrained_models/s2G488k.pth") -cnhubert_base_path = os.environ.get( - "cnhubert_base_path", "GPT_SoVITS/pretrained_models/chinese-hubert-base" -) -bert_path = os.environ.get( - "bert_path", "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large" -) infer_ttswebui = os.environ.get("infer_ttswebui", 9872) infer_ttswebui = int(infer_ttswebui) is_share = os.environ.get("is_share", "False") @@ -50,21 +29,14 @@ is_share = eval(is_share) if "_CUDA_VISIBLE_DEVICES" in os.environ: os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"] is_half = eval(os.environ.get("is_half", "True")) and torch.cuda.is_available() +gpt_path = os.environ.get("gpt_path", None) +sovits_path = os.environ.get("sovits_path", None) +cnhubert_base_path = os.environ.get("cnhubert_base_path", None) +bert_path = os.environ.get("bert_path", None) + import gradio as gr -from transformers import AutoModelForMaskedLM, AutoTokenizer -import numpy as np -import librosa -from feature_extractor import cnhubert - -cnhubert.cnhubert_base_path = cnhubert_base_path - -from module.models import SynthesizerTrn -from AR.models.t2s_lightning_module import Text2SemanticLightningModule -from text import cleaned_text_to_sequence -from text.cleaner import clean_text -from time import time as ttime -from module.mel_processing import spectrogram_torch -from my_utils import load_audio +from TTS_infer_pack.TTS import TTS, TTS_Config +from TTS_infer_pack.text_segmentation_method import get_method from tools.i18n.i18n import I18nAuto i18n = I18nAuto() @@ -73,131 +45,11 @@ i18n = I18nAuto() if torch.cuda.is_available(): device = "cuda" +# elif torch.backends.mps.is_available(): +# device = "mps" else: device = "cpu" - -tokenizer = AutoTokenizer.from_pretrained(bert_path) -bert_model = AutoModelForMaskedLM.from_pretrained(bert_path) -if is_half == True: - bert_model = bert_model.half().to(device) -else: - bert_model = bert_model.to(device) - - -def get_bert_feature(text, word2ph): - with torch.no_grad(): - inputs = tokenizer(text, return_tensors="pt") - for i in inputs: - inputs[i] = inputs[i].to(device) - res = bert_model(**inputs, output_hidden_states=True) - res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1] - assert len(word2ph) == len(text) - phone_level_feature = [] - for i in range(len(word2ph)): - repeat_feature = res[i].repeat(word2ph[i], 1) - phone_level_feature.append(repeat_feature) - phone_level_feature = torch.cat(phone_level_feature, dim=0) - return phone_level_feature.T - - -class DictToAttrRecursive(dict): - def __init__(self, input_dict): - super().__init__(input_dict) - for key, value in input_dict.items(): - if isinstance(value, dict): - value = DictToAttrRecursive(value) - self[key] = value - setattr(self, key, value) - - def __getattr__(self, item): - try: - return self[item] - except KeyError: - raise AttributeError(f"Attribute {item} not found") - - def __setattr__(self, key, value): - if isinstance(value, dict): - value = DictToAttrRecursive(value) - super(DictToAttrRecursive, self).__setitem__(key, value) - super().__setattr__(key, value) - - def __delattr__(self, item): - try: - del self[item] - except KeyError: - raise AttributeError(f"Attribute {item} not found") - - -ssl_model = cnhubert.get_model() -if is_half == True: - ssl_model = ssl_model.half().to(device) -else: - ssl_model = ssl_model.to(device) - - -def change_sovits_weights(sovits_path): - global vq_model, hps - dict_s2 = torch.load(sovits_path, map_location="cpu") - hps = dict_s2["config"] - hps = DictToAttrRecursive(hps) - hps.model.semantic_frame_rate = "25hz" - vq_model = SynthesizerTrn( - hps.data.filter_length // 2 + 1, - hps.train.segment_size // hps.data.hop_length, - n_speakers=hps.data.n_speakers, - **hps.model - ) - if ("pretrained" not in sovits_path): - del vq_model.enc_q - if is_half == True: - vq_model = vq_model.half().to(device) - else: - vq_model = vq_model.to(device) - vq_model.eval() - print(vq_model.load_state_dict(dict_s2["weight"], strict=False)) - with open("./sweight.txt", "w", encoding="utf-8") as f: - f.write(sovits_path) - - -change_sovits_weights(sovits_path) - - -def change_gpt_weights(gpt_path): - global hz, max_sec, t2s_model, config - hz = 50 - dict_s1 = torch.load(gpt_path, map_location="cpu") - config = dict_s1["config"] - max_sec = config["data"]["max_sec"] - t2s_model = Text2SemanticLightningModule(config, "****", is_train=False) - t2s_model.load_state_dict(dict_s1["weight"]) - if is_half == True: - t2s_model = t2s_model.half() - t2s_model = t2s_model.to(device) - t2s_model.eval() - total = sum([param.nelement() for param in t2s_model.parameters()]) - print("Number of parameter: %.2fM" % (total / 1e6)) - with open("./gweight.txt", "w", encoding="utf-8") as f: f.write(gpt_path) - - -change_gpt_weights(gpt_path) - - -def get_spepc(hps, filename): - audio = load_audio(filename, int(hps.data.sampling_rate)) - audio = torch.FloatTensor(audio) - audio_norm = audio - audio_norm = audio_norm.unsqueeze(0) - spec = spectrogram_torch( - audio_norm, - hps.data.filter_length, - hps.data.sampling_rate, - hps.data.hop_length, - hps.data.win_length, - center=False, - ) - return spec - - + dict_language = { i18n("中文"): "all_zh",#全部按中文识别 i18n("英文"): "en",#全部按英文识别#######不变 @@ -207,314 +59,62 @@ dict_language = { i18n("多语种混合"): "auto",#多语种启动切分识别语种 } +cut_method = { + i18n("不切"):"cut0", + i18n("凑四句一切"): "cut1", + i18n("凑50字一切"): "cut2", + i18n("按中文句号。切"): "cut3", + i18n("按英文句号.切"): "cut4", + i18n("按标点符号切"): "cut5", +} -def clean_text_inf(text, language): - phones, word2ph, norm_text = clean_text(text, language) - phones = cleaned_text_to_sequence(phones) - return phones, word2ph, norm_text - -dtype=torch.float16 if is_half == True else torch.float32 -def get_bert_inf(phones, word2ph, norm_text, language): - language=language.replace("all_","") - if language == "zh": - bert = get_bert_feature(norm_text, word2ph).to(device)#.to(dtype) - else: - bert = torch.zeros( - (1024, len(phones)), - dtype=torch.float16 if is_half == True else torch.float32, - ).to(device) - - return bert - - -splits = {"，", "。", "？", "！", ",", ".", "?", "!", "~", ":", "：", "—", "…", } - - -def get_first(text): - pattern = "[" + "".join(re.escape(sep) for sep in splits) + "]" - text = re.split(pattern, text)[0].strip() - return text - - -def get_phones_and_bert(text,language): - if language in {"en","all_zh","all_ja"}: - language = language.replace("all_","") - if language == "en": - LangSegment.setfilters(["en"]) - formattext = " ".join(tmp["text"] for tmp in LangSegment.getTexts(text)) - else: - # 因无法区别中日文汉字,以用户输入为准 - formattext = text - while " " in formattext: - formattext = formattext.replace(" ", " ") - phones, word2ph, norm_text = clean_text_inf(formattext, language) - if language == "zh": - bert = get_bert_feature(norm_text, word2ph).to(device) - else: - bert = torch.zeros( - (1024, len(phones)), - dtype=torch.float16 if is_half == True else torch.float32, - ).to(device) - elif language in {"zh", "ja","auto"}: - textlist=[] - langlist=[] - LangSegment.setfilters(["zh","ja","en","ko"]) - if language == "auto": - for tmp in LangSegment.getTexts(text): - if tmp["lang"] == "ko": - langlist.append("zh") - textlist.append(tmp["text"]) - else: - langlist.append(tmp["lang"]) - textlist.append(tmp["text"]) - else: - for tmp in LangSegment.getTexts(text): - if tmp["lang"] == "en": - langlist.append(tmp["lang"]) - else: - # 因无法区别中日文汉字,以用户输入为准 - langlist.append(language) - textlist.append(tmp["text"]) - print(textlist) - print(langlist) - phones_list = [] - bert_list = [] - norm_text_list = [] - for i in range(len(textlist)): - lang = langlist[i] - phones, word2ph, norm_text = clean_text_inf(textlist[i], lang) - bert = get_bert_inf(phones, word2ph, norm_text, lang) - phones_list.append(phones) - norm_text_list.append(norm_text) - bert_list.append(bert) - bert = torch.cat(bert_list, dim=1) - phones = sum(phones_list, []) - norm_text = ''.join(norm_text_list) - - return phones,bert.to(dtype),norm_text - - -def merge_short_text_in_array(texts, threshold): - if (len(texts)) < 2: - return texts - result = [] - text = "" - for ele in texts: - text += ele - if len(text) >= threshold: - result.append(text) - text = "" - if (len(text) > 0): - if len(result) == 0: - result.append(text) - else: - result[len(result) - 1] += text - return result - -def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language, how_to_cut=i18n("不切"), top_k=20, top_p=0.6, temperature=0.6, ref_free = False): - if prompt_text is None or len(prompt_text) == 0: - ref_free = True - t0 = ttime() - prompt_language = dict_language[prompt_language] - text_language = dict_language[text_language] - if not ref_free: - prompt_text = prompt_text.strip("\n") - if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_language != "en" else "." - print(i18n("实际输入的参考文本:"), prompt_text) - text = text.strip("\n") - if (text[0] not in splits and len(get_first(text)) < 4): text = "。" + text if text_language != "en" else "." + text +tts_config = TTS_Config("GPT_SoVITS/configs/tts_infer.yaml") +tts_config.device = device +tts_config.is_half = is_half +if gpt_path is not None: + tts_config.t2s_weights_path = gpt_path +if sovits_path is not None: + tts_config.vits_weights_path = sovits_path +if cnhubert_base_path is not None: + tts_config.cnhuhbert_base_path = cnhubert_base_path +if bert_path is not None: + tts_config.bert_base_path = bert_path - print(i18n("实际输入的目标文本:"), text) - zero_wav = np.zeros( - int(hps.data.sampling_rate * 0.3), - dtype=np.float16 if is_half == True else np.float32, - ) - with torch.no_grad(): - wav16k, sr = librosa.load(ref_wav_path, sr=16000) - if (wav16k.shape[0] > 160000 or wav16k.shape[0] < 48000): - raise OSError(i18n("参考音频在3~10秒范围外，请更换！")) - wav16k = torch.from_numpy(wav16k) - zero_wav_torch = torch.from_numpy(zero_wav) - if is_half == True: - wav16k = wav16k.half().to(device) - zero_wav_torch = zero_wav_torch.half().to(device) - else: - wav16k = wav16k.to(device) - zero_wav_torch = zero_wav_torch.to(device) - wav16k = torch.cat([wav16k, zero_wav_torch]) - ssl_content = ssl_model.model(wav16k.unsqueeze(0))[ - "last_hidden_state" - ].transpose( - 1, 2 - ) # .float() - codes = vq_model.extract_latent(ssl_content) - - prompt_semantic = codes[0, 0] - t1 = ttime() - - if (how_to_cut == i18n("凑四句一切")): - text = cut1(text) - elif (how_to_cut == i18n("凑50字一切")): - text = cut2(text) - elif (how_to_cut == i18n("按中文句号。切")): - text = cut3(text) - elif (how_to_cut == i18n("按英文句号.切")): - text = cut4(text) - elif (how_to_cut == i18n("按标点符号切")): - text = cut5(text) - while "\n\n" in text: - text = text.replace("\n\n", "\n") - print(i18n("实际输入的目标文本(切句后):"), text) - texts = text.split("\n") - texts = merge_short_text_in_array(texts, 5) - audio_opt = [] - if not ref_free: - phones1,bert1,norm_text1=get_phones_and_bert(prompt_text, prompt_language) - - for text in texts: - # 解决输入目标文本的空行导致报错的问题 - if (len(text.strip()) == 0): - continue - if (text[-1] not in splits): text += "。" if text_language != "en" else "." - print(i18n("实际输入的目标文本(每句):"), text) - phones2,bert2,norm_text2=get_phones_and_bert(text, text_language) - print(i18n("前端处理后的文本(每句):"), norm_text2) - if not ref_free: - bert = torch.cat([bert1, bert2], 1) - all_phoneme_ids = torch.LongTensor(phones1+phones2).to(device).unsqueeze(0) - else: - bert = bert2 - all_phoneme_ids = torch.LongTensor(phones2).to(device).unsqueeze(0) - - bert = bert.to(device).unsqueeze(0) - all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(device) - prompt = prompt_semantic.unsqueeze(0).to(device) - t2 = ttime() - with torch.no_grad(): - # pred_semantic = t2s_model.model.infer( - pred_semantic, idx = t2s_model.model.infer_panel( - all_phoneme_ids, - all_phoneme_len, - None if ref_free else prompt, - bert, - # prompt_phone_len=ph_offset, - top_k=top_k, - top_p=top_p, - temperature=temperature, - early_stop_num=hz * max_sec, - ) - t3 = ttime() - # print(pred_semantic.shape,idx) - pred_semantic = pred_semantic[:, -idx:].unsqueeze( - 0 - ) # .unsqueeze(0)#mq要多unsqueeze一次 - refer = get_spepc(hps, ref_wav_path) # .to(device) - if is_half == True: - refer = refer.half().to(device) - else: - refer = refer.to(device) - # audio = vq_model.decode(pred_semantic, all_phoneme_ids, refer).detach().cpu().numpy()[0, 0] - audio = ( - vq_model.decode( - pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refer - ) - .detach() - .cpu() - .numpy()[0, 0] - ) ###试试重建不带上prompt部分 - max_audio=np.abs(audio).max()#简单防止16bit爆音 - if max_audio>1:audio/=max_audio - audio_opt.append(audio) - audio_opt.append(zero_wav) - t4 = ttime() - print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3)) - yield hps.data.sampling_rate, (np.concatenate(audio_opt, 0) * 32768).astype( - np.int16 - ) - - -def split(todo_text): - todo_text = todo_text.replace("……", "。").replace("——", "，") - if todo_text[-1] not in splits: - todo_text += "。" - i_split_head = i_split_tail = 0 - len_text = len(todo_text) - todo_texts = [] - while 1: - if i_split_head >= len_text: - break # 结尾一定有标点，所以直接跳出即可，最后一段在上次已加入 - if todo_text[i_split_head] in splits: - i_split_head += 1 - todo_texts.append(todo_text[i_split_tail:i_split_head]) - i_split_tail = i_split_head - else: - i_split_head += 1 - return todo_texts - - -def cut1(inp): - inp = inp.strip("\n") - inps = split(inp) - split_idx = list(range(0, len(inps), 4)) - split_idx[-1] = None - if len(split_idx) > 1: - opts = [] - for idx in range(len(split_idx) - 1): - opts.append("".join(inps[split_idx[idx]: split_idx[idx + 1]])) - else: - opts = [inp] - return "\n".join(opts) - - -def cut2(inp): - inp = inp.strip("\n") - inps = split(inp) - if len(inps) < 2: - return inp - opts = [] - summ = 0 - tmp_str = "" - for i in range(len(inps)): - summ += len(inps[i]) - tmp_str += inps[i] - if summ > 50: - summ = 0 - opts.append(tmp_str) - tmp_str = "" - if tmp_str != "": - opts.append(tmp_str) - # print(opts) - if len(opts) > 1 and len(opts[-1]) < 50: ##如果最后一个太短了，和前一个合一起 - opts[-2] = opts[-2] + opts[-1] - opts = opts[:-1] - return "\n".join(opts) - - -def cut3(inp): - inp = inp.strip("\n") - return "\n".join(["%s" % item for item in inp.strip("。").split("。")]) - - -def cut4(inp): - inp = inp.strip("\n") - return "\n".join(["%s" % item for item in inp.strip(".").split(".")]) - - -# contributed by https://github.com/AI-Hobbyist/GPT-SoVITS/blob/main/GPT_SoVITS/inference_webui.py -def cut5(inp): - # if not re.search(r'[^\w\s]', inp[-1]): - # inp += '。' - inp = inp.strip("\n") - punds = r'[,.;?!、，。？！;：…]' - items = re.split(f'({punds})', inp) - mergeitems = ["".join(group) for group in zip(items[::2], items[1::2])] - # 在句子不存在符号或句尾无符号的时候保证文本完整 - if len(items)%2 == 1: - mergeitems.append(items[-1]) - opt = "\n".join(mergeitems) - return opt - +print(tts_config) +tts_pipline = TTS(tts_config) +gpt_path = tts_config.t2s_weights_path +sovits_path = tts_config.vits_weights_path +def inference(text, text_lang, + ref_audio_path, prompt_text, + prompt_lang, top_k, + top_p, temperature, + text_split_method, batch_size, + speed_factor, ref_text_free, + split_bucket,fragment_interval, + seed, + ): + inputs={ + "text": text, + "text_lang": dict_language[text_lang], + "ref_audio_path": ref_audio_path, + "prompt_text": prompt_text if not ref_text_free else "", + "prompt_lang": dict_language[prompt_lang], + "top_k": top_k, + "top_p": top_p, + "temperature": temperature, + "text_split_method": cut_method[text_split_method], + "batch_size":int(batch_size), + "speed_factor":float(speed_factor), + "split_bucket":split_bucket, + "return_fragment":False, + "fragment_interval":fragment_interval, + "seed":seed, + } + + for item in tts_pipline.run(inputs): + yield item + def custom_sort_key(s): # 使用正则表达式提取字符串中的数字部分和非数字部分 parts = re.split('(\d+)', s) @@ -552,65 +152,103 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app: gr.Markdown( value=i18n("本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责.
如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录LICENSE.") ) - with gr.Group(): + + with gr.Column(): + # with gr.Group(): gr.Markdown(value=i18n("模型切换")) with gr.Row(): GPT_dropdown = gr.Dropdown(label=i18n("GPT模型列表"), choices=sorted(GPT_names, key=custom_sort_key), value=gpt_path, interactive=True) SoVITS_dropdown = gr.Dropdown(label=i18n("SoVITS模型列表"), choices=sorted(SoVITS_names, key=custom_sort_key), value=sovits_path, interactive=True) refresh_button = gr.Button(i18n("刷新模型路径"), variant="primary") refresh_button.click(fn=change_choices, inputs=[], outputs=[SoVITS_dropdown, GPT_dropdown]) - SoVITS_dropdown.change(change_sovits_weights, [SoVITS_dropdown], []) - GPT_dropdown.change(change_gpt_weights, [GPT_dropdown], []) - gr.Markdown(value=i18n("*请上传并填写参考信息")) - with gr.Row(): + SoVITS_dropdown.change(tts_pipline.init_vits_weights, [SoVITS_dropdown], []) + GPT_dropdown.change(tts_pipline.init_t2s_weights, [GPT_dropdown], []) + + with gr.Row(): + with gr.Column(): + gr.Markdown(value=i18n("*请上传并填写参考信息")) inp_ref = gr.Audio(label=i18n("请上传3~10秒内参考音频，超过会报错！"), type="filepath") - with gr.Column(): - ref_text_free = gr.Checkbox(label=i18n("开启无参考文本模式。不填参考文本亦相当于开启。"), value=False, interactive=True, show_label=True) - gr.Markdown(i18n("使用无参考文本模式时建议使用微调的GPT，听不清参考音频说的啥(不晓得写啥)可以开，开启后无视填写的参考文本。")) - prompt_text = gr.Textbox(label=i18n("参考音频的文本"), value="") - prompt_language = gr.Dropdown( - label=i18n("参考音频的语种"), choices=[i18n("中文"), i18n("英文"), i18n("日文"), i18n("中英混合"), i18n("日英混合"), i18n("多语种混合")], value=i18n("中文") - ) - gr.Markdown(value=i18n("*请填写需要合成的目标文本和语种模式")) - with gr.Row(): - text = gr.Textbox(label=i18n("需要合成的文本"), value="") + prompt_text = gr.Textbox(label=i18n("参考音频的文本"), value="", lines=2) + with gr.Row(): + prompt_language = gr.Dropdown( + label=i18n("参考音频的语种"), choices=[i18n("中文"), i18n("英文"), i18n("日文"), i18n("中英混合"), i18n("日英混合"), i18n("多语种混合")], value=i18n("中文") + ) + with gr.Column(): + ref_text_free = gr.Checkbox(label=i18n("开启无参考文本模式。不填参考文本亦相当于开启。"), value=False, interactive=True, show_label=True) + gr.Markdown(i18n("使用无参考文本模式时建议使用微调的GPT，听不清参考音频说的啥(不晓得写啥)可以开，开启后无视填写的参考文本。")) + + with gr.Column(): + gr.Markdown(value=i18n("*请填写需要合成的目标文本和语种模式")) + text = gr.Textbox(label=i18n("需要合成的文本"), value="", lines=16, max_lines=16) text_language = gr.Dropdown( label=i18n("需要合成的语种"), choices=[i18n("中文"), i18n("英文"), i18n("日文"), i18n("中英混合"), i18n("日英混合"), i18n("多语种混合")], value=i18n("中文") ) - how_to_cut = gr.Radio( - label=i18n("怎么切"), - choices=[i18n("不切"), i18n("凑四句一切"), i18n("凑50字一切"), i18n("按中文句号。切"), i18n("按英文句号.切"), i18n("按标点符号切"), ], - value=i18n("凑四句一切"), - interactive=True, - ) - with gr.Row(): - gr.Markdown(value=i18n("gpt采样参数(无参考文本时不要太低)：")) + + + with gr.Group(): + gr.Markdown(value=i18n("推理设置")) + with gr.Row(): + + with gr.Column(): + batch_size = gr.Slider(minimum=1,maximum=200,step=1,label=i18n("batch_size"),value=20,interactive=True) + fragment_interval = gr.Slider(minimum=0.01,maximum=1,step=0.01,label=i18n("分段间隔(秒)"),value=0.3,interactive=True) + speed_factor = gr.Slider(minimum=0.25,maximum=4,step=0.05,label="speed_factor",value=1.0,interactive=True) top_k = gr.Slider(minimum=1,maximum=100,step=1,label=i18n("top_k"),value=5,interactive=True) top_p = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("top_p"),value=1,interactive=True) temperature = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("temperature"),value=1,interactive=True) - inference_button = gr.Button(i18n("合成语音"), variant="primary") - output = gr.Audio(label=i18n("输出的语音")) - + with gr.Column(): + how_to_cut = gr.Radio( + label=i18n("怎么切"), + choices=[i18n("不切"), i18n("凑四句一切"), i18n("凑50字一切"), i18n("按中文句号。切"), i18n("按英文句号.切"), i18n("按标点符号切"), ], + value=i18n("凑四句一切"), + interactive=True, + ) + with gr.Row(): + split_bucket = gr.Checkbox(label=i18n("数据分桶(可能会降低一点计算量，选就对了)"), value=True, interactive=True, show_label=True) + seed = gr.Number(label=i18n("随机种子"),value=-1) + # with gr.Column(): + output = gr.Audio(label=i18n("输出的语音")) + with gr.Row(): + inference_button = gr.Button(i18n("合成语音"), variant="primary") + stop_infer = gr.Button(i18n("终止合成"), variant="primary") + + inference_button.click( - get_tts_wav, - [inp_ref, prompt_text, prompt_language, text, text_language, how_to_cut, top_k, top_p, temperature, ref_text_free], + inference, + [ + text,text_language, inp_ref, + prompt_text, prompt_language, + top_k, top_p, temperature, + how_to_cut, batch_size, + speed_factor, ref_text_free, + split_bucket,fragment_interval, + seed + ], [output], ) + stop_infer.click(tts_pipline.stop, [], []) + with gr.Group(): gr.Markdown(value=i18n("文本切分工具。太长的文本合成出来效果不一定好，所以太长建议先切。合成会根据文本的换行分开合成再拼起来。")) with gr.Row(): - text_inp = gr.Textbox(label=i18n("需要合成的切分前文本"), value="") - button1 = gr.Button(i18n("凑四句一切"), variant="primary") - button2 = gr.Button(i18n("凑50字一切"), variant="primary") - button3 = gr.Button(i18n("按中文句号。切"), variant="primary") - button4 = gr.Button(i18n("按英文句号.切"), variant="primary") - button5 = gr.Button(i18n("按标点符号切"), variant="primary") - text_opt = gr.Textbox(label=i18n("切分后文本"), value="") - button1.click(cut1, [text_inp], [text_opt]) - button2.click(cut2, [text_inp], [text_opt]) - button3.click(cut3, [text_inp], [text_opt]) - button4.click(cut4, [text_inp], [text_opt]) - button5.click(cut5, [text_inp], [text_opt]) + text_inp = gr.Textbox(label=i18n("需要合成的切分前文本"), value="", lines=4) + with gr.Column(): + _how_to_cut = gr.Radio( + label=i18n("怎么切"), + choices=[i18n("不切"), i18n("凑四句一切"), i18n("凑50字一切"), i18n("按中文句号。切"), i18n("按英文句号.切"), i18n("按标点符号切"), ], + value=i18n("凑四句一切"), + interactive=True, + ) + cut_text= gr.Button(i18n("切分"), variant="primary") + + def to_cut(text_inp, how_to_cut): + if len(text_inp.strip()) == 0 or text_inp==[]: + return "" + method = get_method(cut_method[how_to_cut]) + return method(text_inp) + + text_opt = gr.Textbox(label=i18n("切分后文本"), value="", lines=4) + cut_text.click(to_cut, [text_inp, _how_to_cut], [text_opt]) gr.Markdown(value=i18n("后续将支持转音素、手工修改音素、语音合成分步执行。")) app.queue(concurrency_count=511, max_size=1022).launch( diff --git a/GPT_SoVITS/inference_webui_old.py b/GPT_SoVITS/inference_webui_old.py new file mode 100644 index 0000000..ee09962 --- /dev/null +++ b/GPT_SoVITS/inference_webui_old.py @@ -0,0 +1,622 @@ +''' +按中英混合识别 +按日英混合识别 +多语种启动切分识别语种 +全部按中文识别 +全部按英文识别 +全部按日文识别 +''' +import os, re, logging +import LangSegment +logging.getLogger("markdown_it").setLevel(logging.ERROR) +logging.getLogger("urllib3").setLevel(logging.ERROR) +logging.getLogger("httpcore").setLevel(logging.ERROR) +logging.getLogger("httpx").setLevel(logging.ERROR) +logging.getLogger("asyncio").setLevel(logging.ERROR) +logging.getLogger("charset_normalizer").setLevel(logging.ERROR) +logging.getLogger("torchaudio._extension").setLevel(logging.ERROR) +import pdb +import torch + +if os.path.exists("./gweight.txt"): + with open("./gweight.txt", 'r', encoding="utf-8") as file: + gweight_data = file.read() + gpt_path = os.environ.get( + "gpt_path", gweight_data) +else: + gpt_path = os.environ.get( + "gpt_path", "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt") + +if os.path.exists("./sweight.txt"): + with open("./sweight.txt", 'r', encoding="utf-8") as file: + sweight_data = file.read() + sovits_path = os.environ.get("sovits_path", sweight_data) +else: + sovits_path = os.environ.get("sovits_path", "GPT_SoVITS/pretrained_models/s2G488k.pth") +# gpt_path = os.environ.get( +# "gpt_path", "pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt" +# ) +# sovits_path = os.environ.get("sovits_path", "pretrained_models/s2G488k.pth") +cnhubert_base_path = os.environ.get( + "cnhubert_base_path", "GPT_SoVITS/pretrained_models/chinese-hubert-base" +) +bert_path = os.environ.get( + "bert_path", "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large" +) +infer_ttswebui = os.environ.get("infer_ttswebui", 9872) +infer_ttswebui = int(infer_ttswebui) +is_share = os.environ.get("is_share", "False") +is_share = eval(is_share) +if "_CUDA_VISIBLE_DEVICES" in os.environ: + os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"] +is_half = eval(os.environ.get("is_half", "True")) and not torch.backends.mps.is_available() +import gradio as gr +from transformers import AutoModelForMaskedLM, AutoTokenizer +import numpy as np +import librosa +from feature_extractor import cnhubert + +cnhubert.cnhubert_base_path = cnhubert_base_path + +from module.models import SynthesizerTrn +from AR.models.t2s_lightning_module import Text2SemanticLightningModule +from text import cleaned_text_to_sequence +from text.cleaner import clean_text +from time import time as ttime +from module.mel_processing import spectrogram_torch +from my_utils import load_audio +from tools.i18n.i18n import I18nAuto + +i18n = I18nAuto() + +os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = '1' # 确保直接启动推理UI时也能够设置。 + +if torch.cuda.is_available(): + device = "cuda" +else: + device = "cpu" + +tokenizer = AutoTokenizer.from_pretrained(bert_path) +bert_model = AutoModelForMaskedLM.from_pretrained(bert_path) +if is_half == True: + bert_model = bert_model.half().to(device) +else: + bert_model = bert_model.to(device) + + +def get_bert_feature(text, word2ph): + with torch.no_grad(): + inputs = tokenizer(text, return_tensors="pt") + for i in inputs: + inputs[i] = inputs[i].to(device) + res = bert_model(**inputs, output_hidden_states=True) + res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1] + assert len(word2ph) == len(text) + phone_level_feature = [] + for i in range(len(word2ph)): + repeat_feature = res[i].repeat(word2ph[i], 1) + phone_level_feature.append(repeat_feature) + phone_level_feature = torch.cat(phone_level_feature, dim=0) + return phone_level_feature.T + + +class DictToAttrRecursive(dict): + def __init__(self, input_dict): + super().__init__(input_dict) + for key, value in input_dict.items(): + if isinstance(value, dict): + value = DictToAttrRecursive(value) + self[key] = value + setattr(self, key, value) + + def __getattr__(self, item): + try: + return self[item] + except KeyError: + raise AttributeError(f"Attribute {item} not found") + + def __setattr__(self, key, value): + if isinstance(value, dict): + value = DictToAttrRecursive(value) + super(DictToAttrRecursive, self).__setitem__(key, value) + super().__setattr__(key, value) + + def __delattr__(self, item): + try: + del self[item] + except KeyError: + raise AttributeError(f"Attribute {item} not found") + + +ssl_model = cnhubert.get_model() +if is_half == True: + ssl_model = ssl_model.half().to(device) +else: + ssl_model = ssl_model.to(device) + + +def change_sovits_weights(sovits_path): + global vq_model, hps + dict_s2 = torch.load(sovits_path, map_location="cpu") + hps = dict_s2["config"] + hps = DictToAttrRecursive(hps) + hps.model.semantic_frame_rate = "25hz" + vq_model = SynthesizerTrn( + hps.data.filter_length // 2 + 1, + hps.train.segment_size // hps.data.hop_length, + n_speakers=hps.data.n_speakers, + **hps.model + ) + if ("pretrained" not in sovits_path): + del vq_model.enc_q + if is_half == True: + vq_model = vq_model.half().to(device) + else: + vq_model = vq_model.to(device) + vq_model.eval() + print(vq_model.load_state_dict(dict_s2["weight"], strict=False)) + with open("./sweight.txt", "w", encoding="utf-8") as f: + f.write(sovits_path) + + +change_sovits_weights(sovits_path) + + +def change_gpt_weights(gpt_path): + global hz, max_sec, t2s_model, config + hz = 50 + dict_s1 = torch.load(gpt_path, map_location="cpu") + config = dict_s1["config"] + max_sec = config["data"]["max_sec"] + t2s_model = Text2SemanticLightningModule(config, "****", is_train=False) + t2s_model.load_state_dict(dict_s1["weight"]) + if is_half == True: + t2s_model = t2s_model.half() + t2s_model = t2s_model.to(device) + t2s_model.eval() + total = sum([param.nelement() for param in t2s_model.parameters()]) + print("Number of parameter: %.2fM" % (total / 1e6)) + with open("./gweight.txt", "w", encoding="utf-8") as f: f.write(gpt_path) + + +change_gpt_weights(gpt_path) + + +def get_spepc(hps, filename): + audio = load_audio(filename, int(hps.data.sampling_rate)) + audio = torch.FloatTensor(audio) + audio_norm = audio + audio_norm = audio_norm.unsqueeze(0) + spec = spectrogram_torch( + audio_norm, + hps.data.filter_length, + hps.data.sampling_rate, + hps.data.hop_length, + hps.data.win_length, + center=False, + ) + return spec + + +dict_language = { + i18n("中文"): "all_zh",#全部按中文识别 + i18n("英文"): "en",#全部按英文识别#######不变 + i18n("日文"): "all_ja",#全部按日文识别 + i18n("中英混合"): "zh",#按中英混合识别####不变 + i18n("日英混合"): "ja",#按日英混合识别####不变 + i18n("多语种混合"): "auto",#多语种启动切分识别语种 +} + + +def clean_text_inf(text, language): + phones, word2ph, norm_text = clean_text(text, language) + phones = cleaned_text_to_sequence(phones) + return phones, word2ph, norm_text + +dtype=torch.float16 if is_half == True else torch.float32 +def get_bert_inf(phones, word2ph, norm_text, language): + language=language.replace("all_","") + if language == "zh": + bert = get_bert_feature(norm_text, word2ph).to(device)#.to(dtype) + else: + bert = torch.zeros( + (1024, len(phones)), + dtype=torch.float16 if is_half == True else torch.float32, + ).to(device) + + return bert + + +splits = {"，", "。", "？", "！", ",", ".", "?", "!", "~", ":", "：", "—", "…", } + + +def get_first(text): + pattern = "[" + "".join(re.escape(sep) for sep in splits) + "]" + text = re.split(pattern, text)[0].strip() + return text + + +def get_phones_and_bert(text,language): + if language in {"en","all_zh","all_ja"}: + language = language.replace("all_","") + if language == "en": + LangSegment.setfilters(["en"]) + formattext = " ".join(tmp["text"] for tmp in LangSegment.getTexts(text)) + else: + # 因无法区别中日文汉字,以用户输入为准 + formattext = text + while " " in formattext: + formattext = formattext.replace(" ", " ") + phones, word2ph, norm_text = clean_text_inf(formattext, language) + if language == "zh": + bert = get_bert_feature(norm_text, word2ph).to(device) + else: + bert = torch.zeros( + (1024, len(phones)), + dtype=torch.float16 if is_half == True else torch.float32, + ).to(device) + elif language in {"zh", "ja","auto"}: + textlist=[] + langlist=[] + LangSegment.setfilters(["zh","ja","en","ko"]) + if language == "auto": + for tmp in LangSegment.getTexts(text): + if tmp["lang"] == "ko": + langlist.append("zh") + textlist.append(tmp["text"]) + else: + langlist.append(tmp["lang"]) + textlist.append(tmp["text"]) + else: + for tmp in LangSegment.getTexts(text): + if tmp["lang"] == "en": + langlist.append(tmp["lang"]) + else: + # 因无法区别中日文汉字,以用户输入为准 + langlist.append(language) + textlist.append(tmp["text"]) + print(textlist) + print(langlist) + phones_list = [] + bert_list = [] + norm_text_list = [] + for i in range(len(textlist)): + lang = langlist[i] + phones, word2ph, norm_text = clean_text_inf(textlist[i], lang) + bert = get_bert_inf(phones, word2ph, norm_text, lang) + phones_list.append(phones) + norm_text_list.append(norm_text) + bert_list.append(bert) + bert = torch.cat(bert_list, dim=1) + phones = sum(phones_list, []) + norm_text = ''.join(norm_text_list) + + return phones,bert.to(dtype),norm_text + + +def merge_short_text_in_array(texts, threshold): + if (len(texts)) < 2: + return texts + result = [] + text = "" + for ele in texts: + text += ele + if len(text) >= threshold: + result.append(text) + text = "" + if (len(text) > 0): + if len(result) == 0: + result.append(text) + else: + result[len(result) - 1] += text + return result + +def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language, how_to_cut=i18n("不切"), top_k=20, top_p=0.6, temperature=0.6, ref_free = False): + if prompt_text is None or len(prompt_text) == 0: + ref_free = True + t0 = ttime() + prompt_language = dict_language[prompt_language] + text_language = dict_language[text_language] + if not ref_free: + prompt_text = prompt_text.strip("\n") + if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_language != "en" else "." + print(i18n("实际输入的参考文本:"), prompt_text) + text = text.strip("\n") + if (text[0] not in splits and len(get_first(text)) < 4): text = "。" + text if text_language != "en" else "." + text + + print(i18n("实际输入的目标文本:"), text) + zero_wav = np.zeros( + int(hps.data.sampling_rate * 0.3), + dtype=np.float16 if is_half == True else np.float32, + ) + with torch.no_grad(): + wav16k, sr = librosa.load(ref_wav_path, sr=16000) + if (wav16k.shape[0] > 160000 or wav16k.shape[0] < 48000): + raise OSError(i18n("参考音频在3~10秒范围外，请更换！")) + wav16k = torch.from_numpy(wav16k) + zero_wav_torch = torch.from_numpy(zero_wav) + if is_half == True: + wav16k = wav16k.half().to(device) + zero_wav_torch = zero_wav_torch.half().to(device) + else: + wav16k = wav16k.to(device) + zero_wav_torch = zero_wav_torch.to(device) + wav16k = torch.cat([wav16k, zero_wav_torch]) + ssl_content = ssl_model.model(wav16k.unsqueeze(0))[ + "last_hidden_state" + ].transpose( + 1, 2 + ) # .float() + codes = vq_model.extract_latent(ssl_content) + + prompt_semantic = codes[0, 0] + t1 = ttime() + + if (how_to_cut == i18n("凑四句一切")): + text = cut1(text) + elif (how_to_cut == i18n("凑50字一切")): + text = cut2(text) + elif (how_to_cut == i18n("按中文句号。切")): + text = cut3(text) + elif (how_to_cut == i18n("按英文句号.切")): + text = cut4(text) + elif (how_to_cut == i18n("按标点符号切")): + text = cut5(text) + while "\n\n" in text: + text = text.replace("\n\n", "\n") + print(i18n("实际输入的目标文本(切句后):"), text) + texts = text.split("\n") + texts = merge_short_text_in_array(texts, 5) + audio_opt = [] + if not ref_free: + phones1,bert1,norm_text1=get_phones_and_bert(prompt_text, prompt_language) + + for text in texts: + # 解决输入目标文本的空行导致报错的问题 + if (len(text.strip()) == 0): + continue + if (text[-1] not in splits): text += "。" if text_language != "en" else "." + print(i18n("实际输入的目标文本(每句):"), text) + phones2,bert2,norm_text2=get_phones_and_bert(text, text_language) + print(i18n("前端处理后的文本(每句):"), norm_text2) + if not ref_free: + bert = torch.cat([bert1, bert2], 1) + all_phoneme_ids = torch.LongTensor(phones1+phones2).to(device).unsqueeze(0) + else: + bert = bert2 + all_phoneme_ids = torch.LongTensor(phones2).to(device).unsqueeze(0) + + bert = bert.to(device).unsqueeze(0) + all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(device) + prompt = prompt_semantic.unsqueeze(0).to(device) + t2 = ttime() + with torch.no_grad(): + # pred_semantic = t2s_model.model.infer( + pred_semantic, idx = t2s_model.model.infer_panel( + all_phoneme_ids, + all_phoneme_len, + None if ref_free else prompt, + bert, + # prompt_phone_len=ph_offset, + top_k=top_k, + top_p=top_p, + temperature=temperature, + early_stop_num=hz * max_sec, + ) + t3 = ttime() + # print(pred_semantic.shape,idx) + pred_semantic = pred_semantic[:, -idx:].unsqueeze( + 0 + ) # .unsqueeze(0)#mq要多unsqueeze一次 + refer = get_spepc(hps, ref_wav_path) # .to(device) + if is_half == True: + refer = refer.half().to(device) + else: + refer = refer.to(device) + # audio = vq_model.decode(pred_semantic, all_phoneme_ids, refer).detach().cpu().numpy()[0, 0] + audio = ( + vq_model.decode( + pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refer + ) + .detach() + .cpu() + .numpy()[0, 0] + ) ###试试重建不带上prompt部分 + max_audio=np.abs(audio).max()#简单防止16bit爆音 + if max_audio>1:audio/=max_audio + audio_opt.append(audio) + audio_opt.append(zero_wav) + t4 = ttime() + print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3)) + yield hps.data.sampling_rate, (np.concatenate(audio_opt, 0) * 32768).astype( + np.int16 + ) + + +def split(todo_text): + todo_text = todo_text.replace("……", "。").replace("——", "，") + if todo_text[-1] not in splits: + todo_text += "。" + i_split_head = i_split_tail = 0 + len_text = len(todo_text) + todo_texts = [] + while 1: + if i_split_head >= len_text: + break # 结尾一定有标点，所以直接跳出即可，最后一段在上次已加入 + if todo_text[i_split_head] in splits: + i_split_head += 1 + todo_texts.append(todo_text[i_split_tail:i_split_head]) + i_split_tail = i_split_head + else: + i_split_head += 1 + return todo_texts + + +def cut1(inp): + inp = inp.strip("\n") + inps = split(inp) + split_idx = list(range(0, len(inps), 4)) + split_idx[-1] = None + if len(split_idx) > 1: + opts = [] + for idx in range(len(split_idx) - 1): + opts.append("".join(inps[split_idx[idx]: split_idx[idx + 1]])) + else: + opts = [inp] + return "\n".join(opts) + + +def cut2(inp): + inp = inp.strip("\n") + inps = split(inp) + if len(inps) < 2: + return inp + opts = [] + summ = 0 + tmp_str = "" + for i in range(len(inps)): + summ += len(inps[i]) + tmp_str += inps[i] + if summ > 50: + summ = 0 + opts.append(tmp_str) + tmp_str = "" + if tmp_str != "": + opts.append(tmp_str) + # print(opts) + if len(opts) > 1 and len(opts[-1]) < 50: ##如果最后一个太短了，和前一个合一起 + opts[-2] = opts[-2] + opts[-1] + opts = opts[:-1] + return "\n".join(opts) + + +def cut3(inp): + inp = inp.strip("\n") + return "\n".join(["%s" % item for item in inp.strip("。").split("。")]) + + +def cut4(inp): + inp = inp.strip("\n") + return "\n".join(["%s" % item for item in inp.strip(".").split(".")]) + + +# contributed by https://github.com/AI-Hobbyist/GPT-SoVITS/blob/main/GPT_SoVITS/inference_webui.py +def cut5(inp): + # if not re.search(r'[^\w\s]', inp[-1]): + # inp += '。' + inp = inp.strip("\n") + punds = r'[,.;?!、，。？！;：…]' + items = re.split(f'({punds})', inp) + mergeitems = ["".join(group) for group in zip(items[::2], items[1::2])] + # 在句子不存在符号或句尾无符号的时候保证文本完整 + if len(items)%2 == 1: + mergeitems.append(items[-1]) + opt = "\n".join(mergeitems) + return opt + + +def custom_sort_key(s): + # 使用正则表达式提取字符串中的数字部分和非数字部分 + parts = re.split('(\d+)', s) + # 将数字部分转换为整数，非数字部分保持不变 + parts = [int(part) if part.isdigit() else part for part in parts] + return parts + + +def change_choices(): + SoVITS_names, GPT_names = get_weights_names() + return {"choices": sorted(SoVITS_names, key=custom_sort_key), "__type__": "update"}, {"choices": sorted(GPT_names, key=custom_sort_key), "__type__": "update"} + + +pretrained_sovits_name = "GPT_SoVITS/pretrained_models/s2G488k.pth" +pretrained_gpt_name = "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt" +SoVITS_weight_root = "SoVITS_weights" +GPT_weight_root = "GPT_weights" +os.makedirs(SoVITS_weight_root, exist_ok=True) +os.makedirs(GPT_weight_root, exist_ok=True) + + +def get_weights_names(): + SoVITS_names = [pretrained_sovits_name] + for name in os.listdir(SoVITS_weight_root): + if name.endswith(".pth"): SoVITS_names.append("%s/%s" % (SoVITS_weight_root, name)) + GPT_names = [pretrained_gpt_name] + for name in os.listdir(GPT_weight_root): + if name.endswith(".ckpt"): GPT_names.append("%s/%s" % (GPT_weight_root, name)) + return SoVITS_names, GPT_names + + +SoVITS_names, GPT_names = get_weights_names() + +with gr.Blocks(title="GPT-SoVITS WebUI") as app: + gr.Markdown( + value=i18n("本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责.
如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录LICENSE.") + ) + with gr.Group(): + gr.Markdown(value=i18n("模型切换")) + with gr.Row(): + GPT_dropdown = gr.Dropdown(label=i18n("GPT模型列表"), choices=sorted(GPT_names, key=custom_sort_key), value=gpt_path, interactive=True) + SoVITS_dropdown = gr.Dropdown(label=i18n("SoVITS模型列表"), choices=sorted(SoVITS_names, key=custom_sort_key), value=sovits_path, interactive=True) + refresh_button = gr.Button(i18n("刷新模型路径"), variant="primary") + refresh_button.click(fn=change_choices, inputs=[], outputs=[SoVITS_dropdown, GPT_dropdown]) + SoVITS_dropdown.change(change_sovits_weights, [SoVITS_dropdown], []) + GPT_dropdown.change(change_gpt_weights, [GPT_dropdown], []) + gr.Markdown(value=i18n("*请上传并填写参考信息")) + with gr.Row(): + inp_ref = gr.Audio(label=i18n("请上传3~10秒内参考音频，超过会报错！"), type="filepath") + with gr.Column(): + ref_text_free = gr.Checkbox(label=i18n("开启无参考文本模式。不填参考文本亦相当于开启。"), value=False, interactive=True, show_label=True) + gr.Markdown(i18n("使用无参考文本模式时建议使用微调的GPT，听不清参考音频说的啥(不晓得写啥)可以开，开启后无视填写的参考文本。")) + prompt_text = gr.Textbox(label=i18n("参考音频的文本"), value="") + prompt_language = gr.Dropdown( + label=i18n("参考音频的语种"), choices=[i18n("中文"), i18n("英文"), i18n("日文"), i18n("中英混合"), i18n("日英混合"), i18n("多语种混合")], value=i18n("中文") + ) + gr.Markdown(value=i18n("*请填写需要合成的目标文本和语种模式")) + with gr.Row(): + text = gr.Textbox(label=i18n("需要合成的文本"), value="") + text_language = gr.Dropdown( + label=i18n("需要合成的语种"), choices=[i18n("中文"), i18n("英文"), i18n("日文"), i18n("中英混合"), i18n("日英混合"), i18n("多语种混合")], value=i18n("中文") + ) + how_to_cut = gr.Radio( + label=i18n("怎么切"), + choices=[i18n("不切"), i18n("凑四句一切"), i18n("凑50字一切"), i18n("按中文句号。切"), i18n("按英文句号.切"), i18n("按标点符号切"), ], + value=i18n("凑四句一切"), + interactive=True, + ) + with gr.Row(): + gr.Markdown(value=i18n("gpt采样参数(无参考文本时不要太低)：")) + top_k = gr.Slider(minimum=1,maximum=100,step=1,label=i18n("top_k"),value=5,interactive=True) + top_p = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("top_p"),value=1,interactive=True) + temperature = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("temperature"),value=1,interactive=True) + inference_button = gr.Button(i18n("合成语音"), variant="primary") + output = gr.Audio(label=i18n("输出的语音")) + + inference_button.click( + get_tts_wav, + [inp_ref, prompt_text, prompt_language, text, text_language, how_to_cut, top_k, top_p, temperature, ref_text_free], + [output], + ) + + gr.Markdown(value=i18n("文本切分工具。太长的文本合成出来效果不一定好，所以太长建议先切。合成会根据文本的换行分开合成再拼起来。")) + with gr.Row(): + text_inp = gr.Textbox(label=i18n("需要合成的切分前文本"), value="") + button1 = gr.Button(i18n("凑四句一切"), variant="primary") + button2 = gr.Button(i18n("凑50字一切"), variant="primary") + button3 = gr.Button(i18n("按中文句号。切"), variant="primary") + button4 = gr.Button(i18n("按英文句号.切"), variant="primary") + button5 = gr.Button(i18n("按标点符号切"), variant="primary") + text_opt = gr.Textbox(label=i18n("切分后文本"), value="") + button1.click(cut1, [text_inp], [text_opt]) + button2.click(cut2, [text_inp], [text_opt]) + button3.click(cut3, [text_inp], [text_opt]) + button4.click(cut4, [text_inp], [text_opt]) + button5.click(cut5, [text_inp], [text_opt]) + gr.Markdown(value=i18n("后续将支持转音素、手工修改音素、语音合成分步执行。")) + +app.queue(concurrency_count=511, max_size=1022).launch( + server_name="0.0.0.0", + inbrowser=True, + share=is_share, + server_port=infer_ttswebui, + quiet=True, +) diff --git a/GPT_SoVITS/module/models.py b/GPT_SoVITS/module/models.py index 29676f4..b14e7c8 100644 --- a/GPT_SoVITS/module/models.py +++ b/GPT_SoVITS/module/models.py @@ -1,5 +1,6 @@ import copy import math +from typing import List import torch from torch import nn from torch.nn import functional as F @@ -986,6 +987,55 @@ class SynthesizerTrn(nn.Module): o = self.dec((z * y_mask)[:, :, :], g=ge) return o + + + @torch.no_grad() + def batched_decode(self, codes, y_lengths, text, text_lengths, refer, noise_scale=0.5): + ge = None + if refer is not None: + refer_lengths = torch.LongTensor([refer.size(2)]).to(refer.device) + refer_mask = torch.unsqueeze( + commons.sequence_mask(refer_lengths, refer.size(2)), 1 + ).to(refer.dtype) + ge = self.ref_enc(refer * refer_mask, refer_mask) + + # y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, codes.size(2)), 1).to( + # codes.dtype + # ) + y_lengths = (y_lengths * 2).long().to(codes.device) + text_lengths = text_lengths.long().to(text.device) + # y_lengths = torch.LongTensor([codes.size(2) * 2]).to(codes.device) + # text_lengths = torch.LongTensor([text.size(-1)]).to(text.device) + + # 假设padding之后再decode没有问题, 影响未知，但听起来好像没问题？ + quantized = self.quantizer.decode(codes) + if self.semantic_frame_rate == "25hz": + quantized = F.interpolate( + quantized, size=int(quantized.shape[-1] * 2), mode="nearest" + ) + + x, m_p, logs_p, y_mask = self.enc_p( + quantized, y_lengths, text, text_lengths, ge + ) + z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale + + z = self.flow(z_p, y_mask, g=ge, reverse=True) + z_masked = (z * y_mask)[:, :, :] + + # 串行。把padding部分去掉再decode + o_list:List[torch.Tensor] = [] + for i in range(z_masked.shape[0]): + z_slice = z_masked[i, :, :y_lengths[i]].unsqueeze(0) + o = self.dec(z_slice, g=ge)[0, 0, :].detach() + o_list.append(o) + + # 并行（会有问题）。先decode，再把padding的部分去掉 + # o = self.dec(z_masked, g=ge) + # upsample_rate = int(math.prod(self.upsample_rates)) + # o_lengths = y_lengths*upsample_rate + # o_list = [o[i, 0, :idx].detach() for i, idx in enumerate(o_lengths)] + + return o_list def extract_latent(self, x): ssl = self.ssl_proj(x)