From b65dae788df75e29e1b16cec1681918efd7ae4f7 Mon Sep 17 00:00:00 2001 From: GoHomeToMacDonal Date: Wed, 6 Mar 2024 01:37:55 +0800 Subject: [PATCH 01/36] torchscript for T2STransformer --- GPT_SoVITS/AR/models/t2s_model.py | 245 +++++++++++++++++++++++------- 1 file changed, 191 insertions(+), 54 deletions(-) diff --git a/GPT_SoVITS/AR/models/t2s_model.py b/GPT_SoVITS/AR/models/t2s_model.py index c8ad3d8..5649627 100644 --- a/GPT_SoVITS/AR/models/t2s_model.py +++ b/GPT_SoVITS/AR/models/t2s_model.py @@ -1,5 +1,7 @@ # modified from https://github.com/yangdongchao/SoundStorm/blob/master/soundstorm/s1/AR/models/t2s_model.py # reference: https://github.com/lifeiteng/vall-e +from typing import List + import torch from tqdm import tqdm @@ -35,6 +37,140 @@ 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, -1, self.hidden_dim) + 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) + kv_len = k_cache.shape[1] + + batch_size = q.shape[0] + q_len = q.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, -1, self.hidden_dim) + 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): super(Text2SemanticDecoder, self).__init__() @@ -89,6 +225,37 @@ class Text2SemanticDecoder(nn.Module): ignore_index=self.EOS, ) + 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 + ) + # (layer.self_attn.in_proj_weight, layer.self_attn.in_proj_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) x = x + self.bert_proj(bert_feature.transpose(1, 2)) @@ -343,17 +510,9 @@ class Text2SemanticDecoder(nn.Module): 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, - } + + k_cache = None + v_cache = None ################### first step ########################## if y is not None: y_emb = self.ar_audio_embedding(y) @@ -361,7 +520,6 @@ class Text2SemanticDecoder(nn.Module): 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 @@ -373,10 +531,10 @@ class Text2SemanticDecoder(nn.Module): 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, - ) + 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), @@ -385,64 +543,43 @@ class Text2SemanticDecoder(nn.Module): xy_attn_mask = torch.concat([x_attn_mask_pad, y_attn_mask], dim=0).to( x.device ) - for idx in tqdm(range(1500)): - - xy_dec, _ = self.h((xy_pos, None), mask=xy_attn_mask, cache=cache) + if xy_attn_mask is not None: + xy_dec, k_cache, v_cache = self.t2s_transformer.process_prompt(xy_pos, xy_attn_mask) + else: + xy_dec, k_cache, v_cache = self.t2s_transformer.decode_next_token(xy_pos, k_cache, v_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 + ) + + if idx == 0: + xy_attn_mask = None + logits = logits[:, :-1] samples = sample( logits[0], y, top_k=top_k, top_p=top_p, repetition_penalty=1.35, temperature=temperature )[0].unsqueeze(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) + + y = torch.concat([y, samples], dim=1) 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") 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 - ) + ####################### 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[:, prompts.shape[1] + idx] + if ref_free: return y[:, :-1], 0 - return y[:, :-1], idx-1 + return y[:, :-1], idx - 1 From 13bb68c71531f365b81e49b55f1086ab641326bd Mon Sep 17 00:00:00 2001 From: GoHomeToMacDonal Date: Fri, 8 Mar 2024 19:09:15 +0800 Subject: [PATCH 02/36] Bug fix: inference w/o prompt --- GPT_SoVITS/AR/models/t2s_model.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/GPT_SoVITS/AR/models/t2s_model.py b/GPT_SoVITS/AR/models/t2s_model.py index 5649627..00f4aa3 100644 --- a/GPT_SoVITS/AR/models/t2s_model.py +++ b/GPT_SoVITS/AR/models/t2s_model.py @@ -235,7 +235,7 @@ class Text2SemanticDecoder(nn.Module): layer.linear2.weight, layer.linear2.bias ) - # (layer.self_attn.in_proj_weight, layer.self_attn.in_proj_bias) + block = T2SBlock( self.num_head, self.model_dim, @@ -578,7 +578,7 @@ class Text2SemanticDecoder(nn.Module): ####################### 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[:, prompts.shape[1] + idx] + xy_pos = y_emb * self.ar_audio_position.x_scale + self.ar_audio_position.alpha * self.ar_audio_position.pe[:, y_len + idx] if ref_free: return y[:, :-1], 0 From 17832e5c4a38a84f8d091a6e77c9a8813ae4d430 Mon Sep 17 00:00:00 2001 From: chasonjiang <1440499136@qq.com> Date: Fri, 8 Mar 2024 23:41:59 +0800 Subject: [PATCH 03/36] =?UTF-8?q?=09=E5=BF=BD=E7=95=A5ffmpeg=20=20=20.giti?= =?UTF-8?q?gnore=20=09=E4=BD=BFt2s=E6=A8=A1=E5=9E=8B=E6=94=AF=E6=8C=81?= =?UTF-8?q?=E6=89=B9=E9=87=8F=E6=8E=A8=E7=90=86:=20=20=20GPT=5FSoVITS/AR/m?= =?UTF-8?q?odels/t2s=5Fmodel.py=20=09=E4=BF=AE=E5=A4=8Dbatch=20bug=20=20?= =?UTF-8?q?=20GPT=5FSoVITS/AR/models/utils.py=20=20=20=20=20=E9=87=8D?= =?UTF-8?q?=E6=9E=84=E7=9A=84tts=20infer=20=20=20GPT=5FSoVITS/TTS=5Finfer?= =?UTF-8?q?=5Fpack/TTS.py=20=09=E6=96=87=E6=9C=AC=E9=A2=84=E5=A4=84?= =?UTF-8?q?=E7=90=86=E6=A8=A1=E5=9D=97=20=20=20GPT=5FSoVITS/TTS=5Finfer=5F?= =?UTF-8?q?pack/TextPreprocessor.py=20=09new=20file=20=20=20GPT=5FSoVITS/T?= =?UTF-8?q?TS=5Finfer=5Fpack/=5F=5Finit=5F=5F.py=20=09=E6=96=87=E6=9C=AC?= =?UTF-8?q?=E6=8B=86=E5=88=86=E6=96=B9=E6=B3=95=E6=A8=A1=E5=9D=97=20=20=20?= =?UTF-8?q?GPT=5FSoVITS/TTS=5Finfer=5Fpack/text=5Fsegmentation=5Fmethod.py?= =?UTF-8?q?=20=09tts=20infer=E9=85=8D=E7=BD=AE=E6=96=87=E4=BB=B6=20=20=20G?= =?UTF-8?q?PT=5FSoVITS/configs/tts=5Finfer.yaml=20=09modified=20=20=20GPT?= =?UTF-8?q?=5FSoVITS/feature=5Fextractor/cnhubert.py=20=09modified=20=20?= =?UTF-8?q?=20GPT=5FSoVITS/inference=5Fgui.py=20=09=E9=87=8D=E6=9E=84?= =?UTF-8?q?=E7=9A=84webui=20=20=20GPT=5FSoVITS/inference=5Fwebui.py=20=09n?= =?UTF-8?q?ew=20file=20=20=20GPT=5FSoVITS/inference=5Fwebui=5Fold.py?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .gitignore | 3 +- GPT_SoVITS/AR/models/t2s_model.py | 53 +- GPT_SoVITS/AR/models/utils.py | 12 +- GPT_SoVITS/TTS_infer_pack/TTS.py | 546 +++++++++++++++ GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py | 176 +++++ GPT_SoVITS/TTS_infer_pack/__init__.py | 1 + .../text_segmentation_method.py | 126 ++++ GPT_SoVITS/configs/tts_infer.yaml | 14 + GPT_SoVITS/feature_extractor/cnhubert.py | 9 +- GPT_SoVITS/inference_gui.py | 2 +- GPT_SoVITS/inference_webui.py | 514 ++------------- GPT_SoVITS/inference_webui_old.py | 622 ++++++++++++++++++ 12 files changed, 1587 insertions(+), 491 deletions(-) create mode 100644 GPT_SoVITS/TTS_infer_pack/TTS.py create mode 100644 GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py create mode 100644 GPT_SoVITS/TTS_infer_pack/__init__.py create mode 100644 GPT_SoVITS/TTS_infer_pack/text_segmentation_method.py create mode 100644 GPT_SoVITS/configs/tts_infer.yaml create mode 100644 GPT_SoVITS/inference_webui_old.py 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_model.py b/GPT_SoVITS/AR/models/t2s_model.py index c8ad3d8..8c31f12 100644 --- a/GPT_SoVITS/AR/models/t2s_model.py +++ b/GPT_SoVITS/AR/models/t2s_model.py @@ -1,5 +1,4 @@ -# modified from https://github.com/yangdongchao/SoundStorm/blob/master/soundstorm/s1/AR/models/t2s_model.py -# reference: https://github.com/lifeiteng/vall-e +# modified from https://github.com/feng-yufei/shared_debugging_code/blob/main/model/t2s_model.py import torch from tqdm import tqdm @@ -386,7 +385,9 @@ class Text2SemanticDecoder(nn.Module): 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) @@ -397,17 +398,45 @@ class Text2SemanticDecoder(nn.Module): 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[0], y, top_k=top_k, top_p=top_p, repetition_penalty=1.35, temperature=temperature - )[0].unsqueeze(0) + 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 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 +472,12 @@ 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 diff --git a/GPT_SoVITS/AR/models/utils.py b/GPT_SoVITS/AR/models/utils.py index 9678c7e..34178fe 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")) diff --git a/GPT_SoVITS/TTS_infer_pack/TTS.py b/GPT_SoVITS/TTS_infer_pack/TTS.py new file mode 100644 index 0000000..9f98a24 --- /dev/null +++ b/GPT_SoVITS/TTS_infer_pack/TTS.py @@ -0,0 +1,546 @@ +import os, sys + +now_dir = os.getcwd() +sys.path.append(now_dir) +import os +from typing import Generator, List, Union +import numpy as np +import torch +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 .text_segmentation_method import splits +from .TextPreprocessor import TextPreprocessor +i18n = I18nAuto() + +# 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 + +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 + + + +""" + + + +class TTS_Config: + def __init__(self, configs: Union[dict, str]): + 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 isinstance(configs, str): + self.configs_path = configs + configs:dict = self._load_configs(configs) + + # assert isinstance(configs, dict) + self.default_configs:dict = configs.get("default", None) + if self.default_configs is None: + self.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" + } + self.configs:dict = configs.get("custom", self.default_configs) + + self.device = self.configs.get("device") + self.is_half = self.configs.get("is_half") + self.t2s_weights_path = self.configs.get("t2s_weights_path") + self.vits_weights_path = self.configs.get("vits_weights_path") + self.bert_base_path = self.configs.get("bert_base_path") + self.cnhuhbert_base_path = self.configs.get("cnhuhbert_base_path") + + + 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"] + + 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": { + "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" + }, + "custom": { + "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 + } + } + if configs_path is None: + configs_path = self.configs_path + with open(configs_path, 'w') as f: + yaml.dump(configs, f) + + +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, + } + + 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) + + + + def init_cnhuhbert_weights(self, base_path: str): + self.cnhuhbert_model = CNHubert(base_path) + self.cnhuhbert_model.eval() + if self.configs.is_half == True: + self.cnhuhbert_model = self.cnhuhbert_model.half() + self.cnhuhbert_model = self.cnhuhbert_model.to(self.configs.device) + + + + def init_bert_weights(self, base_path: str): + self.bert_tokenizer = AutoTokenizer.from_pretrained(base_path) + self.bert_model = AutoModelForMaskedLM.from_pretrained(base_path) + if self.configs.is_half: + self.bert_model = self.bert_model.half() + self.bert_model = self.bert_model.to(self.configs.device) + + + + def init_vits_weights(self, weights_path: str): + 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 + + if self.configs.is_half: + vits_model = vits_model.half() + vits_model = vits_model.to(self.configs.device) + vits_model.eval() + vits_model.load_state_dict(dict_s2["weight"], strict=False) + self.vits_model = vits_model + + + def init_t2s_weights(self, weights_path: str): + 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) + t2s_model.load_state_dict(dict_s1["weight"]) + if self.configs.is_half: + t2s_model = t2s_model.half() + t2s_model = t2s_model.to(self.configs.device) + t2s_model.eval() + self.t2s_model = t2s_model + + def set_ref_audio(self, ref_audio_path:str): + 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): + 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] + max_length = max(seq_lengths) + + 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): + + _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]) + + index_and_len_list.sort(key=lambda x: x[1]) + # index_and_len_batch_list = [index_and_len_list[idx:min(idx+batch_size,len(index_and_len_list))] for idx in range(0,len(index_and_len_list),batch_size)] + index_and_len_list = np.array(index_and_len_list, dtype=np.int64) + + # for batch_idx, index_and_len_batch in enumerate(index_and_len_batch_list): + + batch_index_list = [] + 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) + + for batch_idx, index_list in enumerate(batch_index_list): + item_list = [data[idx] for idx in index_list] + phones_list = [] + # bert_features_list = [] + all_phones_list = [] + all_bert_features_list = [] + norm_text_batch = [] + for item in item_list: + if prompt_data is not None: + all_bert_features = torch.cat([prompt_data["bert_features"].clone(), item["bert_features"]], 1) + all_phones = torch.LongTensor(prompt_data["phones"]+item["phones"]) + phones = torch.LongTensor(item["phones"]) + # norm_text = prompt_data["norm_text"]+item["norm_text"] + else: + all_bert_features = item["bert_features"] + phones = torch.LongTensor(item["phones"]) + all_phones = phones.clone() + # norm_text = item["norm_text"] + + phones_list.append(phones) + all_phones_list.append(all_phones) + all_bert_features_list.append(all_bert_features) + norm_text_batch.append(item["norm_text"]) + # phones_batch = phones_list + phones_batch = self.batch_sequences(phones_list, axis=0, pad_value=0) + all_phones_batch = self.batch_sequences(all_phones_list, axis=0, pad_value=0) + all_bert_features_batch = torch.FloatTensor(len(item_list), 1024, all_phones_batch.shape[-1]) + all_bert_features_batch.zero_() + + for idx, item in enumerate(all_bert_features_list): + if item != None: + all_bert_features_batch[idx, :, : item.shape[-1]] = item + + batch = { + "phones": phones_batch, + "all_phones": all_phones_batch, + "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: + 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 run(self, inputs:dict): + """ + Text to speech inference. + + Args: + inputs (dict): + { + "text": "", + "text_lang: "", + "ref_audio_path": "", + "prompt_text": "", + "prompt_lang": "", + "top_k": 5, + "top_p": 0.9, + "temperature": 0.6, + "text_split_method": "", + "batch_size": 1, + "batch_threshold": 0.75 + } + returns: + tulpe[int, np.ndarray]: sampling rate and audio data. + """ + 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", 20) + top_p:float = inputs.get("top_p", 0.9) + temperature:float = inputs.get("temperature", 0.6) + text_split_method:str = inputs.get("text_split_method", "") + batch_size = inputs.get("batch_size", 1) + batch_threshold = inputs.get("batch_threshold", 0.75) + + 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()") + + 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 + + zero_wav = np.zeros( + int(self.configs.sampling_rate * 0.3), + dtype=np.float16 if self.configs.is_half else np.float32, + ) + + + data = self.text_preprocessor.preprocess(text, text_lang, text_split_method) + audio = [] + t1 = ttime() + 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) + t2 = ttime() + zero_wav = torch.zeros( + int(self.configs.sampling_rate * 0.3), + dtype=torch.float16 if self.configs.is_half else torch.float32, + device=self.configs.device + ) + + t_34 = 0.0 + t_45 = 0.0 + for item in data: + t3 = ttime() + batch_phones = item["phones"] + all_phoneme_ids = item["all_phones"] + all_bert_features = item["all_bert_features"] + norm_text = item["norm_text"] + + # phones = phones.to(self.configs.device) + all_phoneme_ids = all_phoneme_ids.to(self.configs.device) + all_bert_features = all_bert_features.to(self.configs.device) + if self.configs.is_half: + all_bert_features = all_bert_features.half() + # all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]*all_phoneme_ids.shape[0], device=self.configs.device) + + 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"].clone().repeat(all_phoneme_ids.shape[0], 1).to(self.configs.device) + + with torch.no_grad(): + # pred_semantic = t2s_model.model.infer( + pred_semantic_list, idx_list = self.t2s_model.model.infer_panel( + all_phoneme_ids, + None, + 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"].clone().to(self.configs.device) + if self.configs.is_half: + refer_audio_spepc = refer_audio_spepc.half() + + ## 直接对batch进行decode 生成的音频会有问题 + # pred_semantic_list = [item[-idx:] for item, idx in zip(pred_semantic_list, idx_list)] + # pred_semantic = self.batch_sequences(pred_semantic_list, axis=0, pad_value=0).unsqueeze(0) + # batch_phones = batch_phones.to(self.configs.device) + # batch_audio_fragment =(self.vits_model.decode( + # pred_semantic, batch_phones, refer_audio_spepc + # ).detach()[:, 0, :]) + # max_audio=torch.abs(batch_audio_fragment).max()#简单防止16bit爆音 + # if max_audio>1: batch_audio_fragment/=max_audio + # batch_audio_fragment = batch_audio_fragment.cpu().numpy() + + ## 改成串行处理 + batch_audio_fragment = [] + for i, idx in enumerate(idx_list): + phones = batch_phones[i].clone().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, :]) + 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.cat([audio_fragment, zero_wav], dim=0) + batch_audio_fragment.append( + audio_fragment.cpu().numpy() + ) ###试试重建不带上prompt部分 + + audio.append(batch_audio_fragment) + # audio.append(zero_wav) + t5 = ttime() + t_45 += t5 - t4 + + audio = self.recovery_order(audio, batch_index_list) + print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t_34, t_45)) + yield self.configs.sampling_rate, (np.concatenate(audio, 0) * 32768).astype( + np.int16 + ) + \ 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..1504a53 --- /dev/null +++ b/GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py @@ -0,0 +1,176 @@ + + +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 .text_segmentation_method import 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]: + texts = self.pre_seg_text(text, lang, text_split_method) + result = [] + for text in texts: + phones, bert_features, norm_text = self.segment_and_extract_feature_for_text(text, lang) + 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:"), text) + + seg_method = get_seg_method(text_split_method) + text = seg_method(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) + 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 "." + texts.append(text) + + 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) + 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["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(" ", " ") + 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_","") + 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, 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 \ No newline at end of file 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..7bc6b00 --- /dev/null +++ b/GPT_SoVITS/TTS_infer_pack/text_segmentation_method.py @@ -0,0 +1,126 @@ + + + + +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(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("cut1") + 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..5f56a4e --- /dev/null +++ b/GPT_SoVITS/configs/tts_infer.yaml @@ -0,0 +1,14 @@ +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 + 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 + 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 ee09962..68a2136 100644 --- a/GPT_SoVITS/inference_webui.py +++ b/GPT_SoVITS/inference_webui.py @@ -7,7 +7,7 @@ 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) @@ -17,32 +17,12 @@ logging.getLogger("charset_normalizer").setLevel(logging.ERROR) logging.getLogger("torchaudio._extension").setLevel(logging.ERROR) import pdb import torch +# modified from https://github.com/feng-yufei/shared_debugging_code/blob/main/model/t2s_lightning_module.py +import os, sys -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") +now_dir = os.getcwd() +sys.path.append(now_dir) -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") @@ -51,22 +31,9 @@ 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 TTS_infer_pack.TTS import TTS, TTS_Config +from TTS_infer_pack.text_segmentation_method import cut1, cut2, cut3, cut4, cut5 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。 @@ -76,128 +43,6 @@ if torch.cuda.is_available(): 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 @@ -207,313 +52,36 @@ 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 - - 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 +tts_config = TTS_Config("GPT_SoVITS/configs/tts_infer.yaml") +tts_config.device = device +tts_config.is_half = is_half +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): + inputs={ + "text": text, + "text_lang": dict_language[text_lang], + "ref_audio_path": ref_audio_path, + "prompt_text": prompt_text, + "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), + } + yield next(tts_pipline.run(inputs)) 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 @@ -559,8 +127,8 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app: 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], []) + SoVITS_dropdown.change(tts_pipline.init_vits_weights, [SoVITS_dropdown], []) + GPT_dropdown.change(tts_pipline.init_t2s_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") @@ -585,15 +153,19 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app: ) with gr.Row(): gr.Markdown(value=i18n("gpt采样参数(无参考文本时不要太低)：")) + batch_size = gr.Slider(minimum=1,maximum=20,step=1,label=i18n("batch_size"),value=1,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")) + + + 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], [output], ) 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, +) From 7556e8cc9629fc79549e48171e04ad71985764e8 Mon Sep 17 00:00:00 2001 From: chasonjiang <1440499136@qq.com> Date: Sat, 9 Mar 2024 01:02:09 +0800 Subject: [PATCH 04/36] fix some bugs GPT_SoVITS/TTS_infer_pack/TTS.py --- GPT_SoVITS/TTS_infer_pack/TTS.py | 20 ++++++++++++++------ 1 file changed, 14 insertions(+), 6 deletions(-) diff --git a/GPT_SoVITS/TTS_infer_pack/TTS.py b/GPT_SoVITS/TTS_infer_pack/TTS.py index 9f98a24..09f3175 100644 --- a/GPT_SoVITS/TTS_infer_pack/TTS.py +++ b/GPT_SoVITS/TTS_infer_pack/TTS.py @@ -275,7 +275,7 @@ class TTS: 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): + 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: @@ -283,7 +283,10 @@ class TTS: dtype:torch.dtype = seq.dtype pad_value = torch.tensor(pad_value, dtype=dtype) seq_lengths = [seq.shape[axis] for seq in sequences] - max_length = max(seq_lengths) + 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): @@ -333,6 +336,8 @@ class TTS: all_phones_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"].clone(), item["bert_features"]], 1) @@ -344,15 +349,18 @@ class TTS: phones = torch.LongTensor(item["phones"]) all_phones = phones.clone() # 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) all_phones_list.append(all_phones) all_bert_features_list.append(all_bert_features) norm_text_batch.append(item["norm_text"]) # phones_batch = phones_list - phones_batch = self.batch_sequences(phones_list, axis=0, pad_value=0) - all_phones_batch = self.batch_sequences(all_phones_list, axis=0, pad_value=0) - all_bert_features_batch = torch.FloatTensor(len(item_list), 1024, all_phones_batch.shape[-1]) + 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) + all_phones_batch = self.batch_sequences(all_phones_list, axis=0, pad_value=0, max_length=max_len) + all_bert_features_batch = torch.FloatTensor(len(item_list), 1024, max_len) all_bert_features_batch.zero_() for idx, item in enumerate(all_bert_features_list): From 61453b59b21bee4c30501ce3ea4772b781b238d1 Mon Sep 17 00:00:00 2001 From: chasonjiang <1440499136@qq.com> Date: Sat, 9 Mar 2024 02:05:03 +0800 Subject: [PATCH 05/36] =?UTF-8?q?=09=E6=B7=BB=E5=8A=A0=E9=9F=B3=E9=A2=91?= =?UTF-8?q?=E5=80=8D=E9=80=9F=E6=94=AF=E6=8C=81:=20=20=20GPT=5FSoVITS/TTS?= =?UTF-8?q?=5Finfer=5Fpack/TTS.py=20=09=E6=B7=BB=E5=8A=A0=E9=9F=B3?= =?UTF-8?q?=E9=A2=91=E5=80=8D=E9=80=9F=E6=94=AF=E6=8C=81:=20=20=20GPT=5FSo?= =?UTF-8?q?VITS/inference=5Fwebui.py?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- GPT_SoVITS/TTS_infer_pack/TTS.py | 40 ++++++++++++++++++++++++++++---- GPT_SoVITS/inference_webui.py | 6 +++-- 2 files changed, 39 insertions(+), 7 deletions(-) diff --git a/GPT_SoVITS/TTS_infer_pack/TTS.py b/GPT_SoVITS/TTS_infer_pack/TTS.py index 09f3175..70d0cc9 100644 --- a/GPT_SoVITS/TTS_infer_pack/TTS.py +++ b/GPT_SoVITS/TTS_infer_pack/TTS.py @@ -1,5 +1,6 @@ import os, sys +import ffmpeg now_dir = os.getcwd() sys.path.append(now_dir) import os @@ -405,7 +406,8 @@ class TTS: "temperature": 0.6, "text_split_method": "", "batch_size": 1, - "batch_threshold": 0.75 + "batch_threshold": 0.75, + "speed_factor":1.0, } returns: tulpe[int, np.ndarray]: sampling rate and audio data. @@ -421,6 +423,7 @@ class TTS: 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) no_prompt_text = False if prompt_text in [None, ""]: @@ -548,7 +551,34 @@ class TTS: audio = self.recovery_order(audio, batch_index_list) print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t_34, t_45)) - yield self.configs.sampling_rate, (np.concatenate(audio, 0) * 32768).astype( - np.int16 - ) - \ No newline at end of file + + audio = np.concatenate(audio, 0) + audio = (audio * 32768).astype(np.int16) + if speed_factor != 1.0: + audio = speed_change(audio, speed=speed_factor, sr=int(self.configs.sampling_rate)) + + yield self.configs.sampling_rate, 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/inference_webui.py b/GPT_SoVITS/inference_webui.py index 68a2136..f0336bb 100644 --- a/GPT_SoVITS/inference_webui.py +++ b/GPT_SoVITS/inference_webui.py @@ -68,7 +68,7 @@ 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): +def inference(text, text_lang, ref_audio_path, prompt_text, prompt_lang, top_k, top_p, temperature, text_split_method, batch_size, speed_factor): inputs={ "text": text, "text_lang": dict_language[text_lang], @@ -80,6 +80,7 @@ def inference(text, text_lang, ref_audio_path, prompt_text, prompt_lang, top_k, "temperature": temperature, "text_split_method": cut_method[text_split_method], "batch_size":int(batch_size), + "speed_factor":float(speed_factor) } yield next(tts_pipline.run(inputs)) @@ -154,6 +155,7 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app: with gr.Row(): gr.Markdown(value=i18n("gpt采样参数(无参考文本时不要太低)：")) batch_size = gr.Slider(minimum=1,maximum=20,step=1,label=i18n("batch_size"),value=1,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) @@ -165,7 +167,7 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app: inference_button.click( inference, - [text,text_language, inp_ref, prompt_text, prompt_language, top_k, top_p, temperature, how_to_cut, batch_size], + [text,text_language, inp_ref, prompt_text, prompt_language, top_k, top_p, temperature, how_to_cut, batch_size, speed_factor], [output], ) From c85b29f5a8903aba2792f7519832701b838237a3 Mon Sep 17 00:00:00 2001 From: chasonjiang <1440499136@qq.com> Date: Sat, 9 Mar 2024 02:12:20 +0800 Subject: [PATCH 06/36] =?UTF-8?q?=09=E5=A2=9E=E5=8A=A0=E5=81=A5=E5=A3=AE?= =?UTF-8?q?=E6=80=A7:=20=20=20GPT=5FSoVITS/TTS=5Finfer=5Fpack/TTS.py?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- GPT_SoVITS/TTS_infer_pack/TTS.py | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/GPT_SoVITS/TTS_infer_pack/TTS.py b/GPT_SoVITS/TTS_infer_pack/TTS.py index 70d0cc9..f54bf3b 100644 --- a/GPT_SoVITS/TTS_infer_pack/TTS.py +++ b/GPT_SoVITS/TTS_infer_pack/TTS.py @@ -554,9 +554,13 @@ class TTS: audio = np.concatenate(audio, 0) audio = (audio * 32768).astype(np.int16) - if speed_factor != 1.0: - audio = speed_change(audio, speed=speed_factor, sr=int(self.configs.sampling_rate)) - + + try: + if speed_factor != 1.0: + audio = speed_change(audio, speed=speed_factor, sr=int(self.configs.sampling_rate)) + except Exception as e: + print(f"Failed to change speed of audio: \n{e}") + yield self.configs.sampling_rate, audio From 4096a17e7efda622b857a34928f5ab5feb0cdcc2 Mon Sep 17 00:00:00 2001 From: chasonjiang <1440499136@qq.com> Date: Sat, 9 Mar 2024 19:51:49 +0800 Subject: [PATCH 07/36] =?UTF-8?q?=09=E5=85=BC=E5=AE=B9=E4=BA=86flash=5Fatt?= =?UTF-8?q?ention=E7=9A=84=E6=89=B9=E9=87=8F=E6=8E=A8=E7=90=86,=E5=B9=B6?= =?UTF-8?q?=E4=BF=AE=E5=A4=8D=E4=BA=86=E4=B8=80=E4=BA=9Bbug=20=20=20GPT=5F?= =?UTF-8?q?SoVITS/AR/models/t2s=5Fmodel.py=20=09=E6=89=B9=E9=87=8F?= =?UTF-8?q?=E6=8E=A8=E7=90=86=E5=A4=87=E4=BB=BD=E6=96=87=E4=BB=B6:=20=20?= =?UTF-8?q?=20GPT=5FSoVITS/AR/models/t2s=5Fmodel=5Fbatch=5Fonly.py?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- GPT_SoVITS/AR/models/t2s_model.py | 80 ++- GPT_SoVITS/AR/models/t2s_model_batch_only.py | 483 +++++++++++++++++++ 2 files changed, 545 insertions(+), 18 deletions(-) create mode 100644 GPT_SoVITS/AR/models/t2s_model_batch_only.py diff --git a/GPT_SoVITS/AR/models/t2s_model.py b/GPT_SoVITS/AR/models/t2s_model.py index 00f4aa3..8e3c7fc 100644 --- a/GPT_SoVITS/AR/models/t2s_model.py +++ b/GPT_SoVITS/AR/models/t2s_model.py @@ -99,7 +99,8 @@ class T2SBlock: attn = F.scaled_dot_product_attention(q, k, v, ~attn_mask) - attn = attn.permute(2, 0, 1, 3).reshape(batch_size, -1, self.hidden_dim) + 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( @@ -114,15 +115,15 @@ class T2SBlock: ) return x, k_cache, v_cache - def decode_next_token(self, x, k_cache, v_cache): + def decode_next_token(self, x, k_cache, v_cache, attn_mask : torch.Tensor): 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) - kv_len = k_cache.shape[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) @@ -131,7 +132,8 @@ class T2SBlock: attn = F.scaled_dot_product_attention(q, k, v) - attn = attn.permute(2, 0, 1, 3).reshape(batch_size, -1, self.hidden_dim) + 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( @@ -164,10 +166,10 @@ class T2STransformer: return x, k_cache, v_cache def decode_next_token( - self, x, k_cache: List[torch.Tensor], v_cache: List[torch.Tensor] + self, x, k_cache: List[torch.Tensor], v_cache: List[torch.Tensor], attn_mask : 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]) + x, k_cache[i], v_cache[i] = self.blocks[i].decode_next_token(x, k_cache[i], v_cache[i], attn_mask) return x, k_cache, v_cache @@ -543,12 +545,16 @@ class Text2SemanticDecoder(nn.Module): 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] + cache_y_emb = y_emb for idx in tqdm(range(1500)): - if xy_attn_mask is not None: + if idx == 0: xy_dec, k_cache, v_cache = self.t2s_transformer.process_prompt(xy_pos, xy_attn_mask) else: - xy_dec, k_cache, v_cache = self.t2s_transformer.decode_next_token(xy_pos, k_cache, v_cache) + xy_dec, k_cache, v_cache = self.t2s_transformer.decode_next_token(xy_pos, k_cache, v_cache, xy_attn_mask) logits = self.ar_predict_layer( xy_dec[:, -1] @@ -557,18 +563,51 @@ class Text2SemanticDecoder(nn.Module): if idx == 0: xy_attn_mask = None logits = logits[:, :-1] + samples = sample( - logits[0], y, top_k=top_k, top_p=top_p, repetition_penalty=1.35, temperature=temperature - )[0].unsqueeze(0) + logits, y, top_k=top_k, top_p=top_p, repetition_penalty=1.35, temperature=temperature + )[0] y = torch.concat([y, samples], dim=1) - - if early_stop_num != -1 and (y.shape[1] - prefix_len) > early_stop_num: + + ####### 移除batch中已经生成完毕的序列,进一步优化计算量 + reserved_idx_of_batch_for_y = None + if (self.EOS in samples[:, 0]) or \ + (self.EOS in torch.argmax(logits, dim=-1)): ###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batch中未生成完毕的序列 + 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 k_cache is not None : + for i in range(len(k_cache)): + k_cache[i] = torch.index_select(k_cache[i], dim=0, index=reserved_idx_of_batch_for_y) + v_cache[i] = torch.index_select(v_cache[i], dim=0, index=reserved_idx_of_batch_for_y) + + + if (early_stop_num != -1 and (y.shape[1] - prefix_len) > early_stop_num) or idx==1499: 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: + 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) @@ -580,6 +619,11 @@ class Text2SemanticDecoder(nn.Module): 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] + 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 From 3b9259b0a1564dc83e35ebf8e513740fc7594bb3 Mon Sep 17 00:00:00 2001 From: chasonjiang <1440499136@qq.com> Date: Sat, 9 Mar 2024 20:21:11 +0800 Subject: [PATCH 08/36] modified: GPT_SoVITS/AR/models/t2s_model.py --- GPT_SoVITS/AR/models/t2s_model.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/GPT_SoVITS/AR/models/t2s_model.py b/GPT_SoVITS/AR/models/t2s_model.py index 8e3c7fc..23da380 100644 --- a/GPT_SoVITS/AR/models/t2s_model.py +++ b/GPT_SoVITS/AR/models/t2s_model.py @@ -166,10 +166,10 @@ class T2STransformer: return x, k_cache, v_cache def decode_next_token( - self, x, k_cache: List[torch.Tensor], v_cache: List[torch.Tensor], attn_mask : torch.Tensor + 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], attn_mask) + 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 @@ -554,7 +554,7 @@ class Text2SemanticDecoder(nn.Module): if idx == 0: xy_dec, k_cache, v_cache = self.t2s_transformer.process_prompt(xy_pos, xy_attn_mask) else: - xy_dec, k_cache, v_cache = self.t2s_transformer.decode_next_token(xy_pos, k_cache, v_cache, xy_attn_mask) + xy_dec, k_cache, v_cache = self.t2s_transformer.decode_next_token(xy_pos, k_cache, v_cache) logits = self.ar_predict_layer( xy_dec[:, -1] From be49e32505f169772aac15ffdb5caaa1491965dd Mon Sep 17 00:00:00 2001 From: chasonjiang <1440499136@qq.com> Date: Sat, 9 Mar 2024 20:23:55 +0800 Subject: [PATCH 09/36] modified: GPT_SoVITS/AR/models/t2s_model.py --- GPT_SoVITS/AR/models/t2s_model.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/GPT_SoVITS/AR/models/t2s_model.py b/GPT_SoVITS/AR/models/t2s_model.py index 23da380..4908c59 100644 --- a/GPT_SoVITS/AR/models/t2s_model.py +++ b/GPT_SoVITS/AR/models/t2s_model.py @@ -115,7 +115,7 @@ class T2SBlock: ) return x, k_cache, v_cache - def decode_next_token(self, x, k_cache, v_cache, attn_mask : torch.Tensor): + 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) From 2fe3207d713f8d166e3cbaf1dc0a55aa7dd74784 Mon Sep 17 00:00:00 2001 From: chasonjiang <1440499136@qq.com> Date: Sat, 9 Mar 2024 22:11:07 +0800 Subject: [PATCH 10/36] modified: GPT_SoVITS/TTS_infer_pack/TTS.py --- GPT_SoVITS/TTS_infer_pack/TTS.py | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/GPT_SoVITS/TTS_infer_pack/TTS.py b/GPT_SoVITS/TTS_infer_pack/TTS.py index f54bf3b..7a8ecec 100644 --- a/GPT_SoVITS/TTS_infer_pack/TTS.py +++ b/GPT_SoVITS/TTS_infer_pack/TTS.py @@ -87,6 +87,7 @@ class TTS_Config: 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: @@ -118,6 +119,18 @@ class TTS_Config: configs_path = self.configs_path with open(configs_path, 'w') as f: yaml.dump(configs, f) + + + def __str__(self): + string = "----------------TTS Config--------------\n" + string += "device: {}\n".format(self.device) + string += "is_half: {}\n".format(self.is_half) + string += "bert_base_path: {}\n".format(self.bert_base_path) + string += "t2s_weights_path: {}\n".format(self.t2s_weights_path) + string += "vits_weights_path: {}\n".format(self.vits_weights_path) + string += "cnhuhbert_base_path: {}\n".format(self.cnhuhbert_base_path) + string += "----------------------------------------\n" + return string class TTS: From ed2ffe13569b4740ad525214ed0a6441628f4f1a Mon Sep 17 00:00:00 2001 From: chasonjiang <1440499136@qq.com> Date: Sun, 10 Mar 2024 01:20:42 +0800 Subject: [PATCH 11/36] =?UTF-8?q?=09=E4=BF=AE=E5=A4=8D=E4=BA=86t2s?= =?UTF-8?q?=E6=A8=A1=E5=9E=8B=E6=97=A0prompt=E8=BE=93=E5=85=A5=E6=97=B6?= =?UTF-8?q?=E7=9A=84bug=20=20=20GPT=5FSoVITS/AR/models/t2s=5Fmodel.py=20?= =?UTF-8?q?=09=E5=A2=9E=E5=8A=A0=E4=B8=80=E4=BA=9B=E6=96=B0=E7=89=B9?= =?UTF-8?q?=E6=80=A7=EF=BC=8C=E5=B9=B6=E4=BF=AE=E5=A4=8D=E4=BA=86=E4=B8=80?= =?UTF-8?q?=E4=BA=9Bbug=20=20=20GPT=5FSoVITS/TTS=5Finfer=5Fpack/TTS.py=20?= =?UTF-8?q?=09=E4=BC=98=E5=8C=96=E7=BD=91=E9=A1=B5=E5=B8=83=E5=B1=80=20=20?= =?UTF-8?q?=20GPT=5FSoVITS/inference=5Fwebui.py?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- GPT_SoVITS/AR/models/t2s_model.py | 7 +- GPT_SoVITS/TTS_infer_pack/TTS.py | 157 ++++++++++++++++++++---------- GPT_SoVITS/inference_webui.py | 131 ++++++++++++++++--------- 3 files changed, 194 insertions(+), 101 deletions(-) diff --git a/GPT_SoVITS/AR/models/t2s_model.py b/GPT_SoVITS/AR/models/t2s_model.py index 4908c59..e140b4f 100644 --- a/GPT_SoVITS/AR/models/t2s_model.py +++ b/GPT_SoVITS/AR/models/t2s_model.py @@ -549,7 +549,6 @@ class Text2SemanticDecoder(nn.Module): y_list = [None]*y.shape[0] batch_idx_map = list(range(y.shape[0])) idx_list = [None]*y.shape[0] - cache_y_emb = y_emb for idx in tqdm(range(1500)): if idx == 0: xy_dec, k_cache, v_cache = self.t2s_transformer.process_prompt(xy_pos, xy_attn_mask) @@ -589,8 +588,6 @@ class Text2SemanticDecoder(nn.Module): 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 k_cache is not None : for i in range(len(k_cache)): k_cache[i] = torch.index_select(k_cache[i], dim=0, index=reserved_idx_of_batch_for_y) @@ -617,8 +614,8 @@ class Text2SemanticDecoder(nn.Module): ####################### 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] - + 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: diff --git a/GPT_SoVITS/TTS_infer_pack/TTS.py b/GPT_SoVITS/TTS_infer_pack/TTS.py index 7a8ecec..b26bb70 100644 --- a/GPT_SoVITS/TTS_infer_pack/TTS.py +++ b/GPT_SoVITS/TTS_infer_pack/TTS.py @@ -1,8 +1,7 @@ import os, sys - -import ffmpeg now_dir = os.getcwd() sys.path.append(now_dir) +import ffmpeg import os from typing import Generator, List, Union import numpy as np @@ -164,6 +163,9 @@ class TTS: "bert_features":None, "norm_text":None, } + + + self.stop_flag:bool = False def _init_models(self,): self.init_t2s_weights(self.configs.t2s_weights_path) @@ -310,7 +312,7 @@ class TTS: 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): + def to_batch(self, data:list, prompt_data:dict=None, batch_size:int=5, threshold:float=0.75, split_bucket:bool=True): _data:list = [] index_and_len_list = [] @@ -318,30 +320,35 @@ class TTS: norm_text_len = len(item["norm_text"]) index_and_len_list.append([idx, norm_text_len]) - index_and_len_list.sort(key=lambda x: x[1]) - # index_and_len_batch_list = [index_and_len_list[idx:min(idx+batch_size,len(index_and_len_list))] for idx in range(0,len(index_and_len_list),batch_size)] - index_and_len_list = np.array(index_and_len_list, dtype=np.int64) - - # for batch_idx, index_and_len_batch in enumerate(index_and_len_batch_list): - batch_index_list = [] - 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) + 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] @@ -399,7 +406,8 @@ class TTS: _data[index] = data[i][j] return _data - + def stop(self,): + self.stop_flag = True def run(self, inputs:dict): @@ -409,22 +417,26 @@ class TTS: Args: inputs (dict): { - "text": "", - "text_lang: "", - "ref_audio_path": "", - "prompt_text": "", - "prompt_lang": "", - "top_k": 5, - "top_p": 0.9, - "temperature": 0.6, - "text_split_method": "", - "batch_size": 1, - "batch_threshold": 0.75, - "speed_factor":1.0, + "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": 0.9, # float. top p sampling + "temperature": 0.6, # 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. } returns: tulpe[int, np.ndarray]: sampling rate and audio data. """ + 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", "") @@ -437,7 +449,20 @@ class TTS: 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) + 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")) + + no_prompt_text = False if prompt_text in [None, ""]: no_prompt_text = True @@ -481,7 +506,9 @@ class TTS: 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) + threshold=batch_threshold, + split_bucket=split_bucket + ) t2 = ttime() zero_wav = torch.zeros( int(self.configs.sampling_rate * 0.3), @@ -557,27 +584,57 @@ class TTS: audio_fragment.cpu().numpy() ) ###试试重建不带上prompt部分 - audio.append(batch_audio_fragment) - # audio.append(zero_wav) 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, + batch_index_list, + speed_factor, + split_bucket) + else: + audio.append(batch_audio_fragment) + + if self.stop_flag: + yield self.configs.sampling_rate, (zero_wav.cpu().numpy()).astype(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) + + + + def audio_postprocess(self, + audio:np.ndarray, + sr:int, + batch_index_list:list=None, + speed_factor:float=1.0, + split_bucket:bool=True)->tuple[int, np.ndarray]: + if split_bucket: + audio = self.recovery_order(audio, batch_index_list) + else: + audio = [item for batch in audio for item in batch] + - audio = self.recovery_order(audio, batch_index_list) - print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t_34, t_45)) - 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(self.configs.sampling_rate)) + audio = speed_change(audio, speed=speed_factor, sr=int(sr)) except Exception as e: print(f"Failed to change speed of audio: \n{e}") - yield self.configs.sampling_rate, audio - - - + 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 diff --git a/GPT_SoVITS/inference_webui.py b/GPT_SoVITS/inference_webui.py index f0336bb..a193220 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, re, logging +import os, sys +now_dir = os.getcwd() +sys.path.append(now_dir) +import os, re, logging logging.getLogger("markdown_it").setLevel(logging.ERROR) logging.getLogger("urllib3").setLevel(logging.ERROR) logging.getLogger("httpcore").setLevel(logging.ERROR) @@ -18,10 +21,7 @@ logging.getLogger("torchaudio._extension").setLevel(logging.ERROR) import pdb import torch # modified from https://github.com/feng-yufei/shared_debugging_code/blob/main/model/t2s_lightning_module.py -import os, sys -now_dir = os.getcwd() -sys.path.append(now_dir) infer_ttswebui = os.environ.get("infer_ttswebui", 9872) infer_ttswebui = int(infer_ttswebui) @@ -34,6 +34,7 @@ import gradio as gr from TTS_infer_pack.TTS import TTS, TTS_Config from TTS_infer_pack.text_segmentation_method import cut1, cut2, cut3, cut4, cut5 from tools.i18n.i18n import I18nAuto +from TTS_infer_pack.text_segmentation_method import get_method i18n = I18nAuto() os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = '1' # 确保直接启动推理UI时也能够设置。 @@ -68,19 +69,28 @@ 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): +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 + ): inputs={ "text": text, "text_lang": dict_language[text_lang], "ref_audio_path": ref_audio_path, - "prompt_text": prompt_text, + "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) + "speed_factor":float(speed_factor), + "split_bucket":split_bucket, + "return_fragment":False, } yield next(tts_pipline.run(inputs)) @@ -121,7 +131,9 @@ 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) @@ -130,61 +142,88 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app: refresh_button.click(fn=change_choices, inputs=[], outputs=[SoVITS_dropdown, GPT_dropdown]) SoVITS_dropdown.change(tts_pipline.init_vits_weights, [SoVITS_dropdown], []) GPT_dropdown.change(tts_pipline.init_t2s_weights, [GPT_dropdown], []) - gr.Markdown(value=i18n("*请上传并填写参考信息")) - with gr.Row(): + + 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=20,step=1,label=i18n("batch_size"),value=1,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, + ) + 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) + # 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( inference, - [text,text_language, inp_ref, prompt_text, prompt_language, top_k, top_p, temperature, how_to_cut, batch_size, speed_factor], + [ + 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 + ], [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( From cae976ef5af5db171cc1795a765e179954290b57 Mon Sep 17 00:00:00 2001 From: chasonjiang <1440499136@qq.com> Date: Sun, 10 Mar 2024 01:57:04 +0800 Subject: [PATCH 12/36] =?UTF-8?q?=20=20=20=20=E5=A2=9E=E5=8A=A0=E4=BA=86?= =?UTF-8?q?=E6=B3=A8=E9=87=8A=20=20=20GPT=5FSoVITS/TTS=5Finfer=5Fpack/TTS.?= =?UTF-8?q?py?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- GPT_SoVITS/TTS_infer_pack/TTS.py | 36 +++++++++++++++++++++++--------- 1 file changed, 26 insertions(+), 10 deletions(-) diff --git a/GPT_SoVITS/TTS_infer_pack/TTS.py b/GPT_SoVITS/TTS_infer_pack/TTS.py index b26bb70..cc460b8 100644 --- a/GPT_SoVITS/TTS_infer_pack/TTS.py +++ b/GPT_SoVITS/TTS_infer_pack/TTS.py @@ -21,7 +21,7 @@ from .text_segmentation_method import splits from .TextPreprocessor import TextPreprocessor i18n = I18nAuto() -# tts_infer.yaml +# configs/tts_infer.yaml """ default: device: cpu @@ -240,6 +240,12 @@ class TTS: self.t2s_model = t2s_model 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) @@ -399,6 +405,16 @@ class TTS: 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): @@ -407,6 +423,9 @@ class TTS: return _data def stop(self,): + ''' + Stop the inference process. + ''' self.stop_flag = True @@ -435,8 +454,8 @@ class TTS: 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", "") @@ -475,6 +494,8 @@ class TTS: ((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) @@ -494,12 +515,8 @@ class TTS: self.prompt_cache["bert_features"] = bert_features self.prompt_cache["norm_text"] = norm_text - zero_wav = np.zeros( - int(self.configs.sampling_rate * 0.3), - dtype=np.float16 if self.configs.is_half else np.float32, - ) - + ###### text preprocessing ######## data = self.text_preprocessor.preprocess(text, text_lang, text_split_method) audio = [] t1 = ttime() @@ -516,6 +533,8 @@ class TTS: device=self.configs.device ) + + ###### inference ###### t_34 = 0.0 t_45 = 0.0 for item in data: @@ -525,12 +544,10 @@ class TTS: all_bert_features = item["all_bert_features"] norm_text = item["norm_text"] - # phones = phones.to(self.configs.device) all_phoneme_ids = all_phoneme_ids.to(self.configs.device) all_bert_features = all_bert_features.to(self.configs.device) if self.configs.is_half: all_bert_features = all_bert_features.half() - # all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]*all_phoneme_ids.shape[0], device=self.configs.device) print(i18n("前端处理后的文本(每句):"), norm_text) if no_prompt_text : @@ -539,7 +556,6 @@ class TTS: prompt = self.prompt_cache["prompt_semantic"].clone().repeat(all_phoneme_ids.shape[0], 1).to(self.configs.device) with torch.no_grad(): - # pred_semantic = t2s_model.model.infer( pred_semantic_list, idx_list = self.t2s_model.model.infer_panel( all_phoneme_ids, None, From cd746848e6ca45ffdc04ab22756d8336cd5d8ec4 Mon Sep 17 00:00:00 2001 From: chasonjiang <1440499136@qq.com> Date: Sun, 10 Mar 2024 12:13:57 +0800 Subject: [PATCH 13/36] fixed some bugs GPT_SoVITS/AR/models/t2s_model.py fixed some bugs GPT_SoVITS/TTS_infer_pack/TTS.py --- GPT_SoVITS/AR/models/t2s_model.py | 21 ++++++++++++++++++++- GPT_SoVITS/TTS_infer_pack/TTS.py | 14 ++++++++++---- 2 files changed, 30 insertions(+), 5 deletions(-) diff --git a/GPT_SoVITS/AR/models/t2s_model.py b/GPT_SoVITS/AR/models/t2s_model.py index e140b4f..ed46b2b 100644 --- a/GPT_SoVITS/AR/models/t2s_model.py +++ b/GPT_SoVITS/AR/models/t2s_model.py @@ -97,7 +97,7 @@ class T2SBlock: 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 = 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) @@ -532,6 +532,20 @@ class Text2SemanticDecoder(nn.Module): y = torch.zeros(x.shape[0], 0, dtype=torch.int, device=x.device) ref_free = True + + ##### create mask ##### + bsz = x.shape[0] + src_len = x_len + y_len + y_lens = torch.LongTensor([y_len]*bsz).to(x.device) + y_mask = make_pad_mask(y_lens) + x_mask = make_pad_mask(x_lens) + + + xy_padding_mask = torch.concat([x_mask, y_mask], dim=1) + _xy_padding_mask = ( + xy_padding_mask.view(bsz, 1, 1, src_len).expand(-1, self.num_head, -1, -1) + ) + 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) @@ -545,7 +559,12 @@ class Text2SemanticDecoder(nn.Module): xy_attn_mask = torch.concat([x_attn_mask_pad, y_attn_mask], dim=0).to( x.device ) + 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 + ###### decode ##### y_list = [None]*y.shape[0] batch_idx_map = list(range(y.shape[0])) idx_list = [None]*y.shape[0] diff --git a/GPT_SoVITS/TTS_infer_pack/TTS.py b/GPT_SoVITS/TTS_infer_pack/TTS.py index cc460b8..ba29a03 100644 --- a/GPT_SoVITS/TTS_infer_pack/TTS.py +++ b/GPT_SoVITS/TTS_infer_pack/TTS.py @@ -361,6 +361,7 @@ class TTS: phones_list = [] # bert_features_list = [] all_phones_list = [] + all_phones_len_list = [] all_bert_features_list = [] norm_text_batch = [] bert_max_len = 0 @@ -376,16 +377,18 @@ class TTS: phones = torch.LongTensor(item["phones"]) all_phones = phones.clone() # 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) 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 + phones_batch = phones_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) + # phones_batch = self.batch_sequences(phones_list, axis=0, pad_value=0, max_length=max_len) all_phones_batch = self.batch_sequences(all_phones_list, axis=0, pad_value=0, max_length=max_len) all_bert_features_batch = torch.FloatTensor(len(item_list), 1024, max_len) all_bert_features_batch.zero_() @@ -397,6 +400,7 @@ class TTS: batch = { "phones": phones_batch, "all_phones": all_phones_batch, + "all_phones_len": torch.LongTensor(all_phones_len_list), "all_bert_features": all_bert_features_batch, "norm_text": norm_text_batch } @@ -541,10 +545,12 @@ class TTS: t3 = ttime() batch_phones = item["phones"] all_phoneme_ids = item["all_phones"] + all_phoneme_lens = item["all_phones_len"] all_bert_features = item["all_bert_features"] norm_text = item["norm_text"] all_phoneme_ids = all_phoneme_ids.to(self.configs.device) + all_phoneme_lens = all_phoneme_lens.to(self.configs.device) all_bert_features = all_bert_features.to(self.configs.device) if self.configs.is_half: all_bert_features = all_bert_features.half() @@ -558,7 +564,7 @@ class TTS: with torch.no_grad(): pred_semantic_list, idx_list = self.t2s_model.model.infer_panel( all_phoneme_ids, - None, + all_phoneme_lens, prompt, all_bert_features, # prompt_phone_len=ph_offset, @@ -588,7 +594,7 @@ class TTS: ## 改成串行处理 batch_audio_fragment = [] for i, idx in enumerate(idx_list): - phones = batch_phones[i].clone().unsqueeze(0).to(self.configs.device) + 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 From 174c4bbab3a18965b356fb0f6662edb19264ce38 Mon Sep 17 00:00:00 2001 From: chasonjiang <1440499136@qq.com> Date: Sun, 10 Mar 2024 14:07:58 +0800 Subject: [PATCH 14/36] =?UTF-8?q?=09=E5=A2=9E=E5=8A=A0flash=20attention=20?= =?UTF-8?q?=E9=80=89=E9=A1=B9:=20=20=20GPT=5FSoVITS/AR/models/t2s=5Flightn?= =?UTF-8?q?ing=5Fmodule.py=20=09=E5=A2=9E=E5=8A=A0flash=20attention=20?= =?UTF-8?q?=E9=80=89=E9=A1=B9:=20=20=20GPT=5FSoVITS/AR/models/t2s=5Fmodel.?= =?UTF-8?q?py=20=09=E5=A2=9E=E5=8A=A0flash=20attention=20=E9=80=89?= =?UTF-8?q?=E9=A1=B9:=20=20=20GPT=5FSoVITS/TTS=5Finfer=5Fpack/TTS.py=20=09?= =?UTF-8?q?=E5=A2=9E=E5=8A=A0flash=20attention=20=E9=80=89=E9=A1=B9:=20=20?= =?UTF-8?q?=20GPT=5FSoVITS/TTS=5Finfer=5Fpack/TextPreprocessor.py=20=09?= =?UTF-8?q?=E5=A2=9E=E5=8A=A0flash=20attention=20=E9=80=89=E9=A1=B9:=20=20?= =?UTF-8?q?=20GPT=5FSoVITS/configs/tts=5Finfer.yaml=20=09=E5=A2=9E?= =?UTF-8?q?=E5=8A=A0flash=20attention=20=E9=80=89=E9=A1=B9:=20=20=20GPT=5F?= =?UTF-8?q?SoVITS/inference=5Fwebui.py?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- GPT_SoVITS/AR/models/t2s_lightning_module.py | 4 +- GPT_SoVITS/AR/models/t2s_model.py | 233 +++++++++++++++--- GPT_SoVITS/TTS_infer_pack/TTS.py | 21 +- GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py | 5 +- GPT_SoVITS/configs/tts_infer.yaml | 2 + GPT_SoVITS/inference_webui.py | 4 +- 6 files changed, 225 insertions(+), 44 deletions(-) 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 ed46b2b..a3170b9 100644 --- a/GPT_SoVITS/AR/models/t2s_model.py +++ b/GPT_SoVITS/AR/models/t2s_model.py @@ -1,7 +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 @@ -174,7 +176,7 @@ class T2STransformer: 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"] @@ -226,37 +228,42 @@ class Text2SemanticDecoder(nn.Module): multidim_average="global", ignore_index=self.EOS, ) - - 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) + if not flash_attn_enabled: + print("Not Using Flash Attention") + self.infer_panel = self.infer_panel_batch_only + else: + 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) @@ -640,6 +647,168 @@ class Text2SemanticDecoder(nn.Module): 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, #####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 \ No newline at end of file diff --git a/GPT_SoVITS/TTS_infer_pack/TTS.py b/GPT_SoVITS/TTS_infer_pack/TTS.py index ba29a03..7cfaf46 100644 --- a/GPT_SoVITS/TTS_infer_pack/TTS.py +++ b/GPT_SoVITS/TTS_infer_pack/TTS.py @@ -17,8 +17,8 @@ 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 .text_segmentation_method import splits -from .TextPreprocessor import TextPreprocessor +from TTS_infer_pack.text_segmentation_method import splits +from TTS_infer_pack.TextPreprocessor import TextPreprocessor i18n = I18nAuto() # configs/tts_infer.yaml @@ -30,6 +30,7 @@ default: 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 @@ -38,7 +39,7 @@ custom: 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 """ @@ -63,7 +64,8 @@ class TTS_Config: "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" + "bert_base_path": "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large", + "flash_attn_enabled": True } self.configs:dict = configs.get("custom", self.default_configs) @@ -73,6 +75,7 @@ class TTS_Config: self.vits_weights_path = self.configs.get("vits_weights_path") self.bert_base_path = self.configs.get("bert_base_path") self.cnhuhbert_base_path = self.configs.get("cnhuhbert_base_path") + self.flash_attn_enabled = self.configs.get("flash_attn_enabled") self.max_sec = None @@ -103,7 +106,8 @@ class TTS_Config: "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" + "bert_base_path": "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large", + "flash_attn_enabled": True }, "custom": { "device": str(self.device), @@ -111,7 +115,8 @@ class TTS_Config: "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 + "cnhuhbert_base_path": self.cnhuhbert_base_path, + "flash_attn_enabled": self.flash_attn_enabled } } if configs_path is None: @@ -128,6 +133,7 @@ class TTS_Config: string += "t2s_weights_path: {}\n".format(self.t2s_weights_path) string += "vits_weights_path: {}\n".format(self.vits_weights_path) string += "cnhuhbert_base_path: {}\n".format(self.cnhuhbert_base_path) + string += "flash_attn_enabled: {}\n".format(self.flash_attn_enabled) string += "----------------------------------------\n" return string @@ -231,7 +237,8 @@ class TTS: 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) + t2s_model = Text2SemanticLightningModule(config, "****", is_train=False, + flash_attn_enabled=self.configs.flash_attn_enabled) t2s_model.load_state_dict(dict_s1["weight"]) if self.configs.is_half: t2s_model = t2s_model.half() diff --git a/GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py b/GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py index 1504a53..9fcb8e1 100644 --- a/GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py +++ b/GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py @@ -1,4 +1,7 @@ +import os, sys +now_dir = os.getcwd() +sys.path.append(now_dir) import re import torch @@ -7,7 +10,7 @@ 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 .text_segmentation_method import splits, get_method as get_seg_method +from TTS_infer_pack.text_segmentation_method import splits, get_method as get_seg_method # from tools.i18n.i18n import I18nAuto diff --git a/GPT_SoVITS/configs/tts_infer.yaml b/GPT_SoVITS/configs/tts_infer.yaml index 5f56a4e..c772f29 100644 --- a/GPT_SoVITS/configs/tts_infer.yaml +++ b/GPT_SoVITS/configs/tts_infer.yaml @@ -2,6 +2,7 @@ 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 @@ -9,6 +10,7 @@ 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/inference_webui.py b/GPT_SoVITS/inference_webui.py index a193220..3f58dbc 100644 --- a/GPT_SoVITS/inference_webui.py +++ b/GPT_SoVITS/inference_webui.py @@ -20,7 +20,6 @@ logging.getLogger("charset_normalizer").setLevel(logging.ERROR) logging.getLogger("torchaudio._extension").setLevel(logging.ERROR) import pdb import torch -# modified from https://github.com/feng-yufei/shared_debugging_code/blob/main/model/t2s_lightning_module.py infer_ttswebui = os.environ.get("infer_ttswebui", 9872) @@ -33,8 +32,9 @@ is_half = eval(os.environ.get("is_half", "True")) and not torch.backends.mps.is_ import gradio as gr from TTS_infer_pack.TTS import TTS, TTS_Config from TTS_infer_pack.text_segmentation_method import cut1, cut2, cut3, cut4, cut5 -from tools.i18n.i18n import I18nAuto from TTS_infer_pack.text_segmentation_method import get_method +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。 From 3535cfe3b0e39db91c71d6269d96f5495d4d300a Mon Sep 17 00:00:00 2001 From: chasonjiang <1440499136@qq.com> Date: Sun, 10 Mar 2024 21:37:28 +0800 Subject: [PATCH 15/36] =?UTF-8?q?=09=E6=96=B0=E5=A2=9EVITS=E6=89=B9?= =?UTF-8?q?=E9=87=8F=E6=8E=A8=E7=90=86=20=20=20GPT=5FSoVITS/TTS=5Finfer=5F?= =?UTF-8?q?pack/TTS.py=20=09fix=20some=20bugs=20=20=20GPT=5FSoVITS/TTS=5Fi?= =?UTF-8?q?nfer=5Fpack/TextPreprocessor.py=20=09fix=20some=20bugs=20=20=20?= =?UTF-8?q?GPT=5FSoVITS/TTS=5Finfer=5Fpack/text=5Fsegmentation=5Fmethod.py?= =?UTF-8?q?=20=09fix=20some=20bugs=20=20=20GPT=5FSoVITS/inference=5Fwebui.?= =?UTF-8?q?py=20=09fix=20some=20bugs=20=20=20GPT=5FSoVITS/module/models.py?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- GPT_SoVITS/TTS_infer_pack/TTS.py | 108 ++++++++++++------ GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py | 20 +++- .../text_segmentation_method.py | 28 ++++- GPT_SoVITS/inference_webui.py | 20 +++- GPT_SoVITS/module/models.py | 50 ++++++++ 5 files changed, 182 insertions(+), 44 deletions(-) diff --git a/GPT_SoVITS/TTS_infer_pack/TTS.py b/GPT_SoVITS/TTS_infer_pack/TTS.py index 7cfaf46..c111034 100644 --- a/GPT_SoVITS/TTS_infer_pack/TTS.py +++ b/GPT_SoVITS/TTS_infer_pack/TTS.py @@ -1,3 +1,4 @@ +import math import os, sys now_dir = os.getcwd() sys.path.append(now_dir) @@ -366,6 +367,7 @@ class TTS: 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 = [] @@ -375,24 +377,26 @@ class TTS: phones_max_len = 0 for item in item_list: if prompt_data is not None: - all_bert_features = torch.cat([prompt_data["bert_features"].clone(), item["bert_features"]], 1) + all_bert_features = torch.cat([prompt_data["bert_features"], item["bert_features"]], 1) all_phones = torch.LongTensor(prompt_data["phones"]+item["phones"]) phones = torch.LongTensor(item["phones"]) # norm_text = prompt_data["norm_text"]+item["norm_text"] else: all_bert_features = item["bert_features"] phones = torch.LongTensor(item["phones"]) - all_phones = phones.clone() + 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 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) @@ -406,6 +410,7 @@ class TTS: batch = { "phones": phones_batch, + "phones_len": torch.LongTensor(phones_len_list), "all_phones": all_phones_batch, "all_phones_len": torch.LongTensor(all_phones_len_list), "all_bert_features": all_bert_features_batch, @@ -491,7 +496,11 @@ class TTS: if split_bucket: print(i18n("分桶处理模式已开启")) - + + # if vits_batched_inference: + # print(i18n("VITS批量推理模式已开启")) + # else: + # print(i18n("VITS单句推理模式已开启")) no_prompt_text = False if prompt_text in [None, ""]: @@ -529,7 +538,6 @@ class TTS: ###### text preprocessing ######## data = self.text_preprocessor.preprocess(text, text_lang, text_split_method) - audio = [] t1 = ttime() data, batch_index_list = self.to_batch(data, prompt_data=self.prompt_cache if not no_prompt_text else None, @@ -538,24 +546,23 @@ class TTS: split_bucket=split_bucket ) t2 = ttime() - zero_wav = torch.zeros( - int(self.configs.sampling_rate * 0.3), - dtype=torch.float16 if self.configs.is_half else torch.float32, - device=self.configs.device - ) ###### inference ###### t_34 = 0.0 t_45 = 0.0 + audio = [] for item in data: t3 = ttime() batch_phones = item["phones"] + batch_phones_len = item["phones_len"] all_phoneme_ids = item["all_phones"] all_phoneme_lens = item["all_phones_len"] all_bert_features = item["all_bert_features"] norm_text = item["norm_text"] + # batch_phones = batch_phones.to(self.configs.device) + batch_phones_len = batch_phones_len.to(self.configs.device) all_phoneme_ids = all_phoneme_ids.to(self.configs.device) all_phoneme_lens = all_phoneme_lens.to(self.configs.device) all_bert_features = all_bert_features.to(self.configs.device) @@ -566,7 +573,7 @@ class TTS: if no_prompt_text : prompt = None else: - prompt = self.prompt_cache["prompt_semantic"].clone().repeat(all_phoneme_ids.shape[0], 1).to(self.configs.device) + prompt = self.prompt_cache["prompt_semantic"].expand(all_phoneme_ids.shape[0], -1).to(self.configs.device) with torch.no_grad(): pred_semantic_list, idx_list = self.t2s_model.model.infer_panel( @@ -583,41 +590,52 @@ class TTS: t4 = ttime() t_34 += t4 - t3 - refer_audio_spepc:torch.Tensor = self.prompt_cache["refer_spepc"].clone().to(self.configs.device) + refer_audio_spepc:torch.Tensor = self.prompt_cache["refer_spepc"].to(self.configs.device) if self.configs.is_half: refer_audio_spepc = refer_audio_spepc.half() - - ## 直接对batch进行decode 生成的音频会有问题 + + + 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) # batch_phones = batch_phones.to(self.configs.device) - # batch_audio_fragment =(self.vits_model.decode( - # pred_semantic, batch_phones, refer_audio_spepc - # ).detach()[:, 0, :]) - # max_audio=torch.abs(batch_audio_fragment).max()#简单防止16bit爆音 - # if max_audio>1: batch_audio_fragment/=max_audio - # batch_audio_fragment = batch_audio_fragment.cpu().numpy() + # batch_audio_fragment = (self.vits_model.batched_decode( + # pred_semantic, pred_semantic_len, batch_phones, batch_phones_len,refer_audio_spepc + # )) - ## 改成串行处理 - batch_audio_fragment = [] - 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, :]) - 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.cat([audio_fragment, zero_wav], dim=0) - batch_audio_fragment.append( - audio_fragment.cpu().numpy() - ) ###试试重建不带上prompt部分 + # ## 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, + yield self.audio_postprocess([batch_audio_fragment], self.configs.sampling_rate, batch_index_list, speed_factor, @@ -626,7 +644,8 @@ class TTS: audio.append(batch_audio_fragment) if self.stop_flag: - yield self.configs.sampling_rate, (zero_wav.cpu().numpy()).astype(np.int16) + yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate * 0.3), + dtype=np.int16) return if not return_fragment: @@ -640,15 +659,30 @@ class TTS: def audio_postprocess(self, - audio:np.ndarray, + audio:List[torch.Tensor], sr:int, batch_index_list:list=None, speed_factor:float=1.0, split_bucket:bool=True)->tuple[int, np.ndarray]: + zero_wav = torch.zeros( + int(self.configs.sampling_rate * 0.3), + dtype=torch.float16 if self.configs.is_half else torch.float32, + 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 = [item for batch in audio for item in batch] + audio = sum(audio, []) audio = np.concatenate(audio, 0) diff --git a/GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py b/GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py index 9fcb8e1..2669bf4 100644 --- a/GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py +++ b/GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py @@ -10,7 +10,7 @@ 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 splits, get_method as get_seg_method +from TTS_infer_pack.text_segmentation_method import split_big_text, splits, get_method as get_seg_method # from tools.i18n.i18n import I18nAuto @@ -39,6 +39,10 @@ def merge_short_text_in_array(texts:str, threshold:int) -> list: return result + + + + class TextPreprocessor: def __init__(self, bert_model:AutoModelForMaskedLM, tokenizer:AutoTokenizer, device:torch.device): @@ -74,12 +78,18 @@ class TextPreprocessor: _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 "." - texts.append(text) + + # 解决句子过长导致Bert报错的问题 + texts.extend(split_big_text(text)) + return texts @@ -176,4 +186,8 @@ class TextPreprocessor: dtype=torch.float32, ).to(self.device) - return feature \ No newline at end of file + return feature + + + + diff --git a/GPT_SoVITS/TTS_infer_pack/text_segmentation_method.py b/GPT_SoVITS/TTS_infer_pack/text_segmentation_method.py index 7bc6b00..2a182b2 100644 --- a/GPT_SoVITS/TTS_infer_pack/text_segmentation_method.py +++ b/GPT_SoVITS/TTS_infer_pack/text_segmentation_method.py @@ -24,6 +24,32 @@ def register_method(name): 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("——", "，") @@ -121,6 +147,6 @@ def cut5(inp): if __name__ == '__main__': - method = get_method("cut1") + 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/inference_webui.py b/GPT_SoVITS/inference_webui.py index 3f58dbc..2d223f9 100644 --- a/GPT_SoVITS/inference_webui.py +++ b/GPT_SoVITS/inference_webui.py @@ -29,9 +29,13 @@ 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() +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 TTS_infer_pack.TTS import TTS, TTS_Config -from TTS_infer_pack.text_segmentation_method import cut1, cut2, cut3, cut4, cut5 from TTS_infer_pack.text_segmentation_method import get_method from tools.i18n.i18n import I18nAuto @@ -65,6 +69,15 @@ cut_method = { 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 + tts_pipline = TTS(tts_config) gpt_path = tts_config.t2s_weights_path sovits_path = tts_config.vits_weights_path @@ -169,7 +182,7 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app: with gr.Row(): with gr.Column(): - batch_size = gr.Slider(minimum=1,maximum=20,step=1,label=i18n("batch_size"),value=1,interactive=True) + batch_size = gr.Slider(minimum=1,maximum=100,step=1,label=i18n("batch_size"),value=20,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) @@ -181,7 +194,8 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app: value=i18n("凑四句一切"), interactive=True, ) - 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) + 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) # with gr.Column(): output = gr.Audio(label=i18n("输出的语音")) with gr.Row(): diff --git a/GPT_SoVITS/module/models.py b/GPT_SoVITS/module/models.py index a4d2235..75bc617 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 @@ -985,6 +986,55 @@ 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) From d8ee4e29111380ea337afe5fd03f378f96da45d0 Mon Sep 17 00:00:00 2001 From: SapphireLab Date: Mon, 11 Mar 2024 03:04:43 +0800 Subject: [PATCH 16/36] fix output for mix language --- GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py b/GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py index 2669bf4..946b4c5 100644 --- a/GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py +++ b/GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py @@ -152,8 +152,7 @@ class TextPreprocessor: bert_feature = torch.cat(bert_feature_list, dim=1) # phones = sum(phones_list, []) norm_text = ''.join(norm_text_list) - - return phones, bert_feature, norm_text + return phones_list, bert_feature, norm_text def get_bert_feature(self, text:str, word2ph:list)->torch.Tensor: From aa020b059d2acd3986715996a48516b97a7fbe67 Mon Sep 17 00:00:00 2001 From: SapphireLab Date: Mon, 11 Mar 2024 03:25:57 +0800 Subject: [PATCH 17/36] align tts config --- GPT_SoVITS/TTS_infer_pack/TTS.py | 14 ++++---------- 1 file changed, 4 insertions(+), 10 deletions(-) diff --git a/GPT_SoVITS/TTS_infer_pack/TTS.py b/GPT_SoVITS/TTS_infer_pack/TTS.py index c111034..62ab49a 100644 --- a/GPT_SoVITS/TTS_infer_pack/TTS.py +++ b/GPT_SoVITS/TTS_infer_pack/TTS.py @@ -125,17 +125,11 @@ class TTS_Config: with open(configs_path, 'w') as f: yaml.dump(configs, f) - def __str__(self): - string = "----------------TTS Config--------------\n" - string += "device: {}\n".format(self.device) - string += "is_half: {}\n".format(self.is_half) - string += "bert_base_path: {}\n".format(self.bert_base_path) - string += "t2s_weights_path: {}\n".format(self.t2s_weights_path) - string += "vits_weights_path: {}\n".format(self.vits_weights_path) - string += "cnhuhbert_base_path: {}\n".format(self.cnhuhbert_base_path) - string += "flash_attn_enabled: {}\n".format(self.flash_attn_enabled) - string += "----------------------------------------\n" + 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 From 778a43760a43bc9d10046c777e9ac723b5f95475 Mon Sep 17 00:00:00 2001 From: SapphireLab Date: Mon, 11 Mar 2024 13:53:56 +0800 Subject: [PATCH 18/36] change config --- GPT_SoVITS/TTS_infer_pack/TTS.py | 24 ++++++++++++++---------- 1 file changed, 14 insertions(+), 10 deletions(-) diff --git a/GPT_SoVITS/TTS_infer_pack/TTS.py b/GPT_SoVITS/TTS_infer_pack/TTS.py index 62ab49a..2e9de81 100644 --- a/GPT_SoVITS/TTS_infer_pack/TTS.py +++ b/GPT_SoVITS/TTS_infer_pack/TTS.py @@ -97,7 +97,6 @@ class TTS_Config: configs = yaml.load(f, Loader=yaml.FullLoader) return configs - def save_configs(self, configs_path:str=None)->None: configs={ @@ -110,22 +109,27 @@ class TTS_Config: "bert_base_path": "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large", "flash_attn_enabled": True }, - "custom": { - "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 - } + "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): + 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 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" From d23f3a62c44dadf5c7c366bc8ff69296caa2357d Mon Sep 17 00:00:00 2001 From: chasonjiang <1440499136@qq.com> Date: Mon, 11 Mar 2024 17:16:04 +0800 Subject: [PATCH 19/36] =?UTF-8?q?=E4=BF=AE=E5=A4=8D=E4=BA=86=E4=B8=80?= =?UTF-8?q?=E4=BA=9Bbug,=E4=BC=98=E5=8C=96=E4=BA=86=E4=B8=80=E4=BA=9B?= =?UTF-8?q?=E4=BB=A3=E7=A0=81?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- GPT_SoVITS/AR/models/t2s_model.py | 37 +++++++------ GPT_SoVITS/AR/models/utils.py | 2 +- GPT_SoVITS/TTS_infer_pack/TTS.py | 52 +++++++++++-------- GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py | 22 +++++--- GPT_SoVITS/inference_webui.py | 10 ++-- 5 files changed, 72 insertions(+), 51 deletions(-) diff --git a/GPT_SoVITS/AR/models/t2s_model.py b/GPT_SoVITS/AR/models/t2s_model.py index a3170b9..dfd6eb0 100644 --- a/GPT_SoVITS/AR/models/t2s_model.py +++ b/GPT_SoVITS/AR/models/t2s_model.py @@ -229,10 +229,15 @@ class Text2SemanticDecoder(nn.Module): ignore_index=self.EOS, ) - if not flash_attn_enabled: + 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 = [] @@ -497,7 +502,7 @@ 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, @@ -508,8 +513,10 @@ class Text2SemanticDecoder(nn.Module): early_stop_num: int = -1, temperature: float = 1.0, ): + + bert_feature = self.bert_proj(bert_feature.transpose(1, 2)) x = self.ar_text_embedding(x) - x = x + self.bert_proj(bert_feature.transpose(1, 2)) + x = x + bert_feature x = self.ar_text_position(x) # AR Decoder @@ -546,30 +553,28 @@ class Text2SemanticDecoder(nn.Module): y_lens = torch.LongTensor([y_len]*bsz).to(x.device) y_mask = make_pad_mask(y_lens) x_mask = make_pad_mask(x_lens) - + # (bsz, x_len + y_len) xy_padding_mask = torch.concat([x_mask, y_mask], dim=1) - _xy_padding_mask = ( - xy_padding_mask.view(bsz, 1, 1, src_len).expand(-1, self.num_head, -1, -1) - ) - x_attn_mask_pad = F.pad( + x_mask = 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) + y_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 - ) - xy_attn_mask = xy_attn_mask.logical_or(_xy_padding_mask) + + xy_mask = torch.concat([x_mask, y_mask], dim=0).view(1 , src_len, src_len).expand(bsz, -1, -1).to(x.device) + # xy_mask = torch.triu(torch.ones(src_len, src_len, dtype=torch.bool, device=x.device), diagonal=1) + xy_padding_mask = xy_padding_mask.view(bsz, 1, src_len).expand(-1, src_len, src_len) + xy_attn_mask = xy_mask.logical_or(xy_padding_mask) + xy_attn_mask = xy_attn_mask.unsqueeze(1).expand(-1, self.num_head, -1, -1) 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 + xy_attn_mask = new_attn_mask.masked_fill(xy_attn_mask, float("-inf")) ###### decode ##### y_list = [None]*y.shape[0] @@ -641,7 +646,7 @@ class Text2SemanticDecoder(nn.Module): ####################### 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: diff --git a/GPT_SoVITS/AR/models/utils.py b/GPT_SoVITS/AR/models/utils.py index 34178fe..ce0a98b 100644 --- a/GPT_SoVITS/AR/models/utils.py +++ b/GPT_SoVITS/AR/models/utils.py @@ -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 index c111034..791e0f4 100644 --- a/GPT_SoVITS/TTS_infer_pack/TTS.py +++ b/GPT_SoVITS/TTS_infer_pack/TTS.py @@ -1,5 +1,6 @@ import math import os, sys +import random now_dir = os.getcwd() sys.path.append(now_dir) import ffmpeg @@ -7,6 +8,7 @@ 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 @@ -130,11 +132,11 @@ class TTS_Config: string = "----------------TTS Config--------------\n" string += "device: {}\n".format(self.device) string += "is_half: {}\n".format(self.is_half) + string += "flash_attn_enabled: {}\n".format(self.flash_attn_enabled) string += "bert_base_path: {}\n".format(self.bert_base_path) string += "t2s_weights_path: {}\n".format(self.t2s_weights_path) string += "vits_weights_path: {}\n".format(self.vits_weights_path) string += "cnhuhbert_base_path: {}\n".format(self.cnhuhbert_base_path) - string += "flash_attn_enabled: {}\n".format(self.flash_attn_enabled) string += "----------------------------------------\n" return string @@ -184,7 +186,7 @@ class TTS: def init_cnhuhbert_weights(self, base_path: str): self.cnhuhbert_model = CNHubert(base_path) - self.cnhuhbert_model.eval() + self.cnhuhbert_model=self.cnhuhbert_model.eval() if self.configs.is_half == True: self.cnhuhbert_model = self.cnhuhbert_model.half() self.cnhuhbert_model = self.cnhuhbert_model.to(self.configs.device) @@ -194,6 +196,7 @@ class TTS: def init_bert_weights(self, base_path: str): self.bert_tokenizer = AutoTokenizer.from_pretrained(base_path) self.bert_model = AutoModelForMaskedLM.from_pretrained(base_path) + self.bert_model=self.bert_model.eval() if self.configs.is_half: self.bert_model = self.bert_model.half() self.bert_model = self.bert_model.to(self.configs.device) @@ -226,7 +229,7 @@ class TTS: if self.configs.is_half: vits_model = vits_model.half() vits_model = vits_model.to(self.configs.device) - vits_model.eval() + vits_model = vits_model.eval() vits_model.load_state_dict(dict_s2["weight"], strict=False) self.vits_model = vits_model @@ -244,7 +247,7 @@ class TTS: if self.configs.is_half: t2s_model = t2s_model.half() t2s_model = t2s_model.to(self.configs.device) - t2s_model.eval() + t2s_model = t2s_model.eval() self.t2s_model = t2s_model def set_ref_audio(self, ref_audio_path:str): @@ -377,12 +380,14 @@ class TTS: 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) + all_bert_features = torch.cat([prompt_data["bert_features"], item["bert_features"]], 1)\ + .to(dtype=torch.float32 if not self.configs.is_half else torch.float16) all_phones = torch.LongTensor(prompt_data["phones"]+item["phones"]) phones = torch.LongTensor(item["phones"]) # norm_text = prompt_data["norm_text"]+item["norm_text"] else: - all_bert_features = item["bert_features"] + all_bert_features = item["bert_features"]\ + .to(dtype=torch.float32 if not self.configs.is_half else torch.float16) phones = torch.LongTensor(item["phones"]) all_phones = phones # norm_text = item["norm_text"] @@ -401,12 +406,10 @@ class TTS: 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) all_phones_batch = self.batch_sequences(all_phones_list, axis=0, pad_value=0, max_length=max_len) - all_bert_features_batch = torch.FloatTensor(len(item_list), 1024, max_len) - all_bert_features_batch.zero_() - + # all_bert_features_batch = all_bert_features_list + all_bert_features_batch = torch.zeros(len(item_list), 1024, max_len, dtype=torch.float32) for idx, item in enumerate(all_bert_features_list): - if item != None: - all_bert_features_batch[idx, :, : item.shape[-1]] = item + all_bert_features_batch[idx, :, : item.shape[-1]] = item batch = { "phones": phones_batch, @@ -458,8 +461,8 @@ class TTS: "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": 0.9, # float. top p sampling - "temperature": 0.6, # float. temperature for 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. @@ -477,9 +480,9 @@ class TTS: 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", 20) - top_p:float = inputs.get("top_p", 0.9) - temperature:float = inputs.get("temperature", 0.6) + 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) @@ -497,10 +500,6 @@ class TTS: if split_bucket: print(i18n("分桶处理模式已开启")) - # if vits_batched_inference: - # print(i18n("VITS批量推理模式已开启")) - # else: - # print(i18n("VITS单句推理模式已开启")) no_prompt_text = False if prompt_text in [None, ""]: @@ -547,7 +546,7 @@ class TTS: ) t2 = ttime() - + print("############ 推理 ############") ###### inference ###### t_34 = 0.0 t_45 = 0.0 @@ -601,6 +600,10 @@ class TTS: # 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 @@ -654,7 +657,12 @@ class TTS: self.configs.sampling_rate, batch_index_list, speed_factor, - split_bucket) + split_bucket) + + try: + torch.cuda.empty_cache() + except: + pass diff --git a/GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py b/GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py index 2669bf4..986819f 100644 --- a/GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py +++ b/GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py @@ -1,5 +1,7 @@ import os, sys + +from tqdm import tqdm now_dir = os.getcwd() sys.path.append(now_dir) @@ -12,9 +14,9 @@ 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 +from tools.i18n.i18n import I18nAuto -# i18n = I18nAuto() +i18n = I18nAuto() def get_first(text:str) -> str: pattern = "[" + "".join(re.escape(sep) for sep in splits) + "]" @@ -51,9 +53,11 @@ class TextPreprocessor: 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 = [] - for text in texts: + 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) res={ "phones": phones, @@ -67,14 +71,16 @@ class TextPreprocessor: 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:"), 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") - # print(i18n("实际输入的目标文本(切句后):"), text) + print(i18n("实际输入的目标文本(切句后):")) + print(text) _texts = text.split("\n") _texts = merge_short_text_in_array(_texts, 5) texts = [] @@ -105,7 +111,7 @@ class TextPreprocessor: textlist=[] langlist=[] if language in ["auto", "zh", "ja"]: - # LangSegment.setfilters(["zh","ja","en","ko"]) + LangSegment.setfilters(["zh","ja","en","ko"]) for tmp in LangSegment.getTexts(text): if tmp["lang"] == "ko": langlist.append("zh") @@ -116,7 +122,7 @@ class TextPreprocessor: langlist.append(language if language!="auto" else tmp["lang"]) textlist.append(tmp["text"]) elif language == "en": - # LangSegment.setfilters(["en"]) + LangSegment.setfilters(["en"]) formattext = " ".join(tmp["text"] for tmp in LangSegment.getTexts(text)) while " " in formattext: formattext = formattext.replace(" ", " ") @@ -153,7 +159,7 @@ class TextPreprocessor: # phones = sum(phones_list, []) norm_text = ''.join(norm_text_list) - return phones, bert_feature, norm_text + return phones_list, bert_feature, norm_text def get_bert_feature(self, text:str, word2ph:list)->torch.Tensor: diff --git a/GPT_SoVITS/inference_webui.py b/GPT_SoVITS/inference_webui.py index 2d223f9..53333c4 100644 --- a/GPT_SoVITS/inference_webui.py +++ b/GPT_SoVITS/inference_webui.py @@ -103,10 +103,12 @@ def inference(text, text_lang, "batch_size":int(batch_size), "speed_factor":float(speed_factor), "split_bucket":split_bucket, - "return_fragment":False, + "return_fragment":False } - yield next(tts_pipline.run(inputs)) - + + 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) @@ -182,7 +184,7 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app: with gr.Row(): with gr.Column(): - batch_size = gr.Slider(minimum=1,maximum=100,step=1,label=i18n("batch_size"),value=20,interactive=True) + batch_size = gr.Slider(minimum=1,maximum=200,step=1,label=i18n("batch_size"),value=1,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) From 7a6d251e44cc4468c1ae9f8b8ddd1de8c1b2dafa Mon Sep 17 00:00:00 2001 From: chasonjiang <1440499136@qq.com> Date: Mon, 11 Mar 2024 17:31:21 +0800 Subject: [PATCH 20/36] modified: GPT_SoVITS/inference_webui.py --- GPT_SoVITS/inference_webui.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/GPT_SoVITS/inference_webui.py b/GPT_SoVITS/inference_webui.py index 53333c4..5a26615 100644 --- a/GPT_SoVITS/inference_webui.py +++ b/GPT_SoVITS/inference_webui.py @@ -45,9 +45,11 @@ os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = '1' # 确保直接启动推理UI时 if torch.cuda.is_available(): device = "cuda" +elif torch.backends.mps.is_available(): + device = "mps" else: device = "cpu" - + 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 From 134602cd99119365f4772db81c42b33037547a93 Mon Sep 17 00:00:00 2001 From: chasonjiang <1440499136@qq.com> Date: Mon, 11 Mar 2024 17:37:30 +0800 Subject: [PATCH 21/36] =?UTF-8?q?=E4=BF=AE=E6=94=B9=E4=BA=86=E9=BB=98?= =?UTF-8?q?=E8=AE=A4batch=5Fsize=E4=B8=BA20?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- GPT_SoVITS/inference_webui.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/GPT_SoVITS/inference_webui.py b/GPT_SoVITS/inference_webui.py index 5a26615..9c8c83d 100644 --- a/GPT_SoVITS/inference_webui.py +++ b/GPT_SoVITS/inference_webui.py @@ -105,7 +105,7 @@ def inference(text, text_lang, "batch_size":int(batch_size), "speed_factor":float(speed_factor), "split_bucket":split_bucket, - "return_fragment":False + "return_fragment":True } for item in tts_pipline.run(inputs): @@ -186,7 +186,7 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app: with gr.Row(): with gr.Column(): - batch_size = gr.Slider(minimum=1,maximum=200,step=1,label=i18n("batch_size"),value=1,interactive=True) + batch_size = gr.Slider(minimum=1,maximum=200,step=1,label=i18n("batch_size"),value=20,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) From 46826b28b0377d9a1f7f0625c12b40f36f911b37 Mon Sep 17 00:00:00 2001 From: chasonjiang <1440499136@qq.com> Date: Mon, 11 Mar 2024 17:43:31 +0800 Subject: [PATCH 22/36] =?UTF-8?q?=E8=AE=BE=E7=BD=AE=E9=BB=98=E8=AE=A4?= =?UTF-8?q?=E8=BF=94=E5=9B=9E=E4=B8=80=E6=95=B4=E7=AF=87=E6=96=87=E7=AB=A0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- GPT_SoVITS/inference_webui.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/GPT_SoVITS/inference_webui.py b/GPT_SoVITS/inference_webui.py index 9c8c83d..2308b38 100644 --- a/GPT_SoVITS/inference_webui.py +++ b/GPT_SoVITS/inference_webui.py @@ -105,7 +105,7 @@ def inference(text, text_lang, "batch_size":int(batch_size), "speed_factor":float(speed_factor), "split_bucket":split_bucket, - "return_fragment":True + "return_fragment":False } for item in tts_pipline.run(inputs): From bfd72860687be2a628e54f20574c6fc28371cd57 Mon Sep 17 00:00:00 2001 From: chasonjiang <1440499136@qq.com> Date: Mon, 11 Mar 2024 19:35:55 +0800 Subject: [PATCH 23/36] =?UTF-8?q?=E4=BF=AE=E5=A4=8D=E4=BA=86,=E4=B8=AD?= =?UTF-8?q?=E8=8B=B1=E6=96=87=E6=B7=B7=E5=90=88=E6=96=87=E6=9C=AC=E5=90=88?= =?UTF-8?q?=E6=88=90=E8=8B=B1=E6=96=87=E6=97=B6,=20=E5=87=BA=E7=8E=B0?= =?UTF-8?q?=E7=A9=BA=E5=AD=97=E7=AC=A6=E6=8A=A5=E9=94=99=E7=9A=84=E9=97=AE?= =?UTF-8?q?=E9=A2=98=20=E4=BC=98=E5=8C=96=E4=BA=86=E4=BB=A3=E7=A0=81,=20?= =?UTF-8?q?=E5=A2=9E=E5=8A=A0=E4=BA=86=E5=81=A5=E5=A3=AE=E6=80=A7?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- GPT_SoVITS/TTS_infer_pack/TTS.py | 85 +++++++++++++++---- GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py | 28 ++++-- 2 files changed, 88 insertions(+), 25 deletions(-) diff --git a/GPT_SoVITS/TTS_infer_pack/TTS.py b/GPT_SoVITS/TTS_infer_pack/TTS.py index 249e945..7912ddf 100644 --- a/GPT_SoVITS/TTS_infer_pack/TTS.py +++ b/GPT_SoVITS/TTS_infer_pack/TTS.py @@ -173,35 +173,36 @@ class TTS: 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() - if self.configs.is_half == True: - self.cnhuhbert_model = self.cnhuhbert_model.half() self.cnhuhbert_model = self.cnhuhbert_model.to(self.configs.device) 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() - if self.configs.is_half: - self.bert_model = self.bert_model.half() self.bert_model = self.bert_model.to(self.configs.device) 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) @@ -224,8 +225,6 @@ class TTS: if hasattr(vits_model, "enc_q"): del vits_model.enc_q - if self.configs.is_half: - vits_model = vits_model.half() vits_model = vits_model.to(self.configs.device) vits_model = vits_model.eval() vits_model.load_state_dict(dict_s2["weight"], strict=False) @@ -233,6 +232,7 @@ class TTS: 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 @@ -242,12 +242,60 @@ class TTS: t2s_model = Text2SemanticLightningModule(config, "****", is_train=False, flash_attn_enabled=self.configs.flash_attn_enabled) t2s_model.load_state_dict(dict_s1["weight"]) - if self.configs.is_half: - t2s_model = t2s_model.half() t2s_model = t2s_model.to(self.configs.device) t2s_model = t2s_model.eval() self.t2s_model = t2s_model + 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 self.configs.device == "cpu": + 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, @@ -347,7 +395,7 @@ class TTS: pos_end = min(pos+batch_size,index_and_len_list.shape[0]) while pos < pos_end: batch=index_and_len_list[pos:pos_end, 1].astype(np.float32) - score=batch[(pos_end-pos)//2]/batch.mean() + score=batch[(pos_end-pos)//2]/(batch.mean()+1e-8) if (score>=threshold) or (pos_end-pos==1): batch_index=index_and_len_list[pos:pos_end, 0].tolist() batch_index_list_len += len(batch_index) @@ -379,13 +427,13 @@ class TTS: 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=torch.float32 if not self.configs.is_half else torch.float16) + .to(dtype=self.precison) all_phones = torch.LongTensor(prompt_data["phones"]+item["phones"]) phones = torch.LongTensor(item["phones"]) # norm_text = prompt_data["norm_text"]+item["norm_text"] else: all_bert_features = item["bert_features"]\ - .to(dtype=torch.float32 if not self.configs.is_half else torch.float16) + .to(dtype=self.precison) phones = torch.LongTensor(item["phones"]) all_phones = phones # norm_text = item["norm_text"] @@ -405,7 +453,7 @@ class TTS: # phones_batch = self.batch_sequences(phones_list, axis=0, pad_value=0, max_length=max_len) 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=torch.float32) + all_bert_features_batch = torch.zeros(len(item_list), 1024, max_len, dtype=self.precison) for idx, item in enumerate(all_bert_features_list): all_bert_features_batch[idx, :, : item.shape[-1]] = item @@ -535,6 +583,11 @@ class TTS: ###### text preprocessing ######## 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 * 0.3), + dtype=np.int16) + return + t1 = ttime() data, batch_index_list = self.to_batch(data, prompt_data=self.prompt_cache if not no_prompt_text else None, @@ -587,10 +640,8 @@ class TTS: t4 = ttime() t_34 += t4 - t3 - refer_audio_spepc:torch.Tensor = self.prompt_cache["refer_spepc"].to(self.configs.device) - if self.configs.is_half: - refer_audio_spepc = refer_audio_spepc.half() - + refer_audio_spepc:torch.Tensor = self.prompt_cache["refer_spepc"]\ + .to(dtype=self.precison, device=self.configs.device) batch_audio_fragment = [] @@ -672,7 +723,7 @@ class TTS: split_bucket:bool=True)->tuple[int, np.ndarray]: zero_wav = torch.zeros( int(self.configs.sampling_rate * 0.3), - dtype=torch.float16 if self.configs.is_half else torch.float32, + dtype=self.precison, device=self.configs.device ) diff --git a/GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py b/GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py index ee61bc3..58b2678 100644 --- a/GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py +++ b/GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py @@ -59,6 +59,8 @@ class TextPreprocessor: 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, @@ -79,12 +81,10 @@ class TextPreprocessor: 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):")) - print(text) + _texts = text.split("\n") _texts = merge_short_text_in_array(_texts, 5) texts = [] - for text in _texts: @@ -94,15 +94,21 @@ class TextPreprocessor: 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 - texts.extend(split_big_text(text)) - + 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) - phones, bert_features, norm_text = self.extract_bert_feature(textlist, langlist) + 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 @@ -113,6 +119,8 @@ class TextPreprocessor: 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": @@ -126,14 +134,18 @@ class TextPreprocessor: formattext = " ".join(tmp["text"] for tmp in LangSegment.getTexts(text)) while " " in formattext: formattext = formattext.replace(" ", " ") - textlist.append(formattext) - langlist.append("en") + 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) From 511b99e4a96aafae856ab0fa5bcb22dee5c55a6c Mon Sep 17 00:00:00 2001 From: chasonjiang <1440499136@qq.com> Date: Tue, 12 Mar 2024 15:30:08 +0800 Subject: [PATCH 24/36] =?UTF-8?q?=E5=A2=9E=E5=8A=A0=E4=BA=86TTS=5FConfig?= =?UTF-8?q?=E7=B1=BB=E7=9A=84=E5=81=A5=E5=A3=AE=E6=80=A7?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- GPT_SoVITS/TTS_infer_pack/TTS.py | 94 ++++++++++++++++++++------------ GPT_SoVITS/inference_webui.py | 1 + 2 files changed, 61 insertions(+), 34 deletions(-) diff --git a/GPT_SoVITS/TTS_infer_pack/TTS.py b/GPT_SoVITS/TTS_infer_pack/TTS.py index 7912ddf..694d4a7 100644 --- a/GPT_SoVITS/TTS_infer_pack/TTS.py +++ b/GPT_SoVITS/TTS_infer_pack/TTS.py @@ -1,3 +1,4 @@ +from copy import deepcopy import math import os, sys import random @@ -50,18 +51,7 @@ custom: class TTS_Config: - def __init__(self, configs: Union[dict, str]): - 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 isinstance(configs, str): - self.configs_path = configs - configs:dict = self._load_configs(configs) - - # assert isinstance(configs, dict) - self.default_configs:dict = configs.get("default", None) - if self.default_configs is None: - self.default_configs={ + default_configs={ "device": "cpu", "is_half": False, "t2s_weights_path": "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt", @@ -70,15 +60,54 @@ class TTS_Config: "bert_base_path": "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large", "flash_attn_enabled": True } - self.configs:dict = configs.get("custom", self.default_configs) + configs:dict = None + def __init__(self, configs: Union[dict, str]=None): - self.device = self.configs.get("device") - self.is_half = self.configs.get("is_half") - self.t2s_weights_path = self.configs.get("t2s_weights_path") - self.vits_weights_path = self.configs.get("vits_weights_path") - self.bert_base_path = self.configs.get("bert_base_path") - self.cnhuhbert_base_path = self.configs.get("cnhuhbert_base_path") - self.flash_attn_enabled = self.configs.get("flash_attn_enabled") + # 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 @@ -92,7 +121,7 @@ class TTS_Config: self.n_speakers:int = 300 self.langauges:list = ["auto", "en", "zh", "ja", "all_zh", "all_ja"] - print(self) + # print(self) def _load_configs(self, configs_path: str)->dict: with open(configs_path, 'r') as f: @@ -102,24 +131,18 @@ class TTS_Config: def save_configs(self, configs_path:str=None)->None: configs={ - "default": { - "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 - }, - "custom": self.update_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): - config = { + self.config = { "device" : str(self.device), "is_half" : self.is_half, "t2s_weights_path" : self.t2s_weights_path, @@ -128,7 +151,7 @@ class TTS_Config: "cnhuhbert_base_path": self.cnhuhbert_base_path, "flash_attn_enabled" : self.flash_attn_enabled } - return config + return self.config def __str__(self): self.configs = self.update_configs() @@ -137,6 +160,9 @@ class TTS_Config: string += f"{str(k).ljust(20)}: {str(v)}\n" string += "-" * 100 + '\n' return string + + def __repr__(self): + return self.__str__() class TTS: @@ -253,7 +279,7 @@ class TTS: enable: bool, whether to enable half precision. ''' - if self.configs.device == "cpu": + if self.configs.device == "cpu" and enable: print("Half precision is not supported on CPU.") return diff --git a/GPT_SoVITS/inference_webui.py b/GPT_SoVITS/inference_webui.py index 2308b38..bc68031 100644 --- a/GPT_SoVITS/inference_webui.py +++ b/GPT_SoVITS/inference_webui.py @@ -80,6 +80,7 @@ if cnhubert_base_path is not None: if bert_path is not None: tts_config.bert_base_path = bert_path +print(tts_config) tts_pipline = TTS(tts_config) gpt_path = tts_config.t2s_weights_path sovits_path = tts_config.vits_weights_path From 345f3203f84d6017151f1075bed0e917ac784130 Mon Sep 17 00:00:00 2001 From: chasonjiang <1440499136@qq.com> Date: Tue, 12 Mar 2024 16:08:50 +0800 Subject: [PATCH 25/36] =?UTF-8?q?=E4=BF=AE=E5=A4=8D=E4=BA=86=E7=83=AD?= =?UTF-8?q?=E5=88=87=E6=8D=A2=E6=A8=A1=E5=9E=8B=E6=97=B6=EF=BC=8C=E7=B2=BE?= =?UTF-8?q?=E5=BA=A6=E4=B8=8D=E5=8C=B9=E9=85=8D=E5=AF=BC=E8=87=B4=E7=9A=84?= =?UTF-8?q?=E9=94=99=E8=AF=AF=E3=80=82?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- GPT_SoVITS/TTS_infer_pack/TTS.py | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/GPT_SoVITS/TTS_infer_pack/TTS.py b/GPT_SoVITS/TTS_infer_pack/TTS.py index 694d4a7..61ba7be 100644 --- a/GPT_SoVITS/TTS_infer_pack/TTS.py +++ b/GPT_SoVITS/TTS_infer_pack/TTS.py @@ -206,7 +206,7 @@ class TTS: 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) + # self.enable_half_precision(self.configs.is_half) @@ -215,6 +215,8 @@ class TTS: 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: + self.cnhuhbert_model = self.cnhuhbert_model.half() @@ -224,6 +226,8 @@ class TTS: 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: + self.bert_model = self.bert_model.half() @@ -255,6 +259,8 @@ class TTS: 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: + self.vits_model = self.vits_model.half() def init_t2s_weights(self, weights_path: str): @@ -271,6 +277,8 @@ class TTS: t2s_model = t2s_model.to(self.configs.device) t2s_model = t2s_model.eval() self.t2s_model = t2s_model + if self.configs.is_half: + self.t2s_model = self.t2s_model.half() def enable_half_precision(self, enable: bool = True): ''' From f2cbc826c7b8767e2dfec9399f3350c094ce3c34 Mon Sep 17 00:00:00 2001 From: chasonjiang <1440499136@qq.com> Date: Tue, 12 Mar 2024 16:31:21 +0800 Subject: [PATCH 26/36] =?UTF-8?q?=E6=B3=A8=E9=87=8A=E4=BA=86inference=5Fwe?= =?UTF-8?q?bui=E4=B8=ADmps=E7=9A=84=E9=80=89=E9=A1=B9?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- GPT_SoVITS/inference_webui.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/GPT_SoVITS/inference_webui.py b/GPT_SoVITS/inference_webui.py index bc68031..4c9d90f 100644 --- a/GPT_SoVITS/inference_webui.py +++ b/GPT_SoVITS/inference_webui.py @@ -45,8 +45,8 @@ os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = '1' # 确保直接启动推理UI时 if torch.cuda.is_available(): device = "cuda" -elif torch.backends.mps.is_available(): - device = "mps" +# elif torch.backends.mps.is_available(): +# device = "mps" else: device = "cpu" From d60d8ea3fb5c1ab741365e4ebfb7fa3b4ea0853e Mon Sep 17 00:00:00 2001 From: chasonjiang <1440499136@qq.com> Date: Wed, 13 Mar 2024 16:25:27 +0800 Subject: [PATCH 27/36] =?UTF-8?q?=E4=BF=AE=E5=A4=8D=E4=BA=86OutOfMemoryErr?= =?UTF-8?q?or=E6=97=B6=EF=BC=8C=E6=98=BE=E5=AD=98=E6=97=A0=E6=B3=95?= =?UTF-8?q?=E9=87=8A=E6=94=BE=E7=9A=84=E9=97=AE=E9=A2=98?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- GPT_SoVITS/TTS_infer_pack/TTS.py | 251 +++++++++++++++++-------------- 1 file changed, 139 insertions(+), 112 deletions(-) diff --git a/GPT_SoVITS/TTS_infer_pack/TTS.py b/GPT_SoVITS/TTS_infer_pack/TTS.py index 61ba7be..62bb2e9 100644 --- a/GPT_SoVITS/TTS_infer_pack/TTS.py +++ b/GPT_SoVITS/TTS_infer_pack/TTS.py @@ -2,6 +2,7 @@ from copy import deepcopy import math import os, sys import random +import traceback now_dir = os.getcwd() sys.path.append(now_dir) import ffmpeg @@ -48,8 +49,18 @@ custom: """ - - +# def set_seed(seed): +# random.seed(seed) +# os.environ['PYTHONHASHSEED'] = str(seed) +# np.random.seed(seed) +# torch.manual_seed(seed) +# 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 +# set_seed(1234) + class TTS_Config: default_configs={ "device": "cpu", @@ -630,125 +641,141 @@ class TTS: split_bucket=split_bucket ) t2 = ttime() + try: + print("############ 推理 ############") + ###### inference ###### + t_34 = 0.0 + t_45 = 0.0 + audio = [] + for item in data: + t3 = ttime() + batch_phones = item["phones"] + batch_phones_len = item["phones_len"] + all_phoneme_ids = item["all_phones"] + all_phoneme_lens = item["all_phones_len"] + all_bert_features = item["all_bert_features"] + norm_text = item["norm_text"] + + # batch_phones = batch_phones.to(self.configs.device) + batch_phones_len = batch_phones_len.to(self.configs.device) + all_phoneme_ids = all_phoneme_ids.to(self.configs.device) + all_phoneme_lens = all_phoneme_lens.to(self.configs.device) + all_bert_features = all_bert_features.to(self.configs.device) + if self.configs.is_half: + all_bert_features = all_bert_features.half() - print("############ 推理 ############") - ###### inference ###### - t_34 = 0.0 - t_45 = 0.0 - audio = [] - for item in data: - t3 = ttime() - batch_phones = item["phones"] - batch_phones_len = item["phones_len"] - all_phoneme_ids = item["all_phones"] - all_phoneme_lens = item["all_phones_len"] - all_bert_features = item["all_bert_features"] - norm_text = item["norm_text"] - - # batch_phones = batch_phones.to(self.configs.device) - batch_phones_len = batch_phones_len.to(self.configs.device) - all_phoneme_ids = all_phoneme_ids.to(self.configs.device) - all_phoneme_lens = all_phoneme_lens.to(self.configs.device) - all_bert_features = all_bert_features.to(self.configs.device) - if self.configs.is_half: - all_bert_features = all_bert_features.half() - - 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(all_phoneme_ids.shape[0], -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) + 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(all_phoneme_ids.shape[0], -1).to(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))] + 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], + # ## 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, + batch_index_list, + speed_factor, + split_bucket) + else: + audio.append(batch_audio_fragment) + + if self.stop_flag: + yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate * 0.3), + 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) - else: - audio.append(batch_audio_fragment) - - if self.stop_flag: - yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate * 0.3), - 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) - - try: - torch.cuda.empty_cache() + split_bucket) + 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 str(self.configs.device) == "cuda": + 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, From 252c9b7eb638958f6302b2788df08a84099a0f05 Mon Sep 17 00:00:00 2001 From: chasonjiang <1440499136@qq.com> Date: Wed, 13 Mar 2024 19:51:24 +0800 Subject: [PATCH 28/36] =?UTF-8?q?=E6=8E=A8=E7=90=86=E5=8A=9F=E8=83=BD?= =?UTF-8?q?=E5=A2=9E=E5=BC=BA=E5=92=8C=E6=94=B9=E8=BF=9B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- GPT_SoVITS/AR/models/t2s_model.py | 32 ++++++++--- GPT_SoVITS/TTS_infer_pack/TTS.py | 93 +++++++++++++++++++++++-------- GPT_SoVITS/inference_webui.py | 8 ++- 3 files changed, 99 insertions(+), 34 deletions(-) diff --git a/GPT_SoVITS/AR/models/t2s_model.py b/GPT_SoVITS/AR/models/t2s_model.py index dfd6eb0..da95111 100644 --- a/GPT_SoVITS/AR/models/t2s_model.py +++ b/GPT_SoVITS/AR/models/t2s_model.py @@ -707,19 +707,33 @@ class Text2SemanticDecoder(nn.Module): 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) + ##### create mask ##### + bsz = x.shape[0] + src_len = x_len + y_len + y_lens = torch.LongTensor([y_len]*bsz).to(x.device) + y_mask = make_pad_mask(y_lens) + x_mask = make_pad_mask(x_lens) + + # (bsz, x_len + y_len) + xy_padding_mask = torch.concat([x_mask, y_mask], dim=1) + + x_mask = 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_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 - ) + + xy_mask = torch.concat([x_mask, y_mask], dim=0).view(1 , src_len, src_len).expand(bsz*self.num_head, -1, -1).to(x.device) + # xy_mask = torch.triu(torch.ones(src_len, src_len, dtype=torch.bool, device=x.device), diagonal=1) + xy_padding_mask = xy_padding_mask.view(bsz, 1, src_len).expand(bsz, src_len, src_len).repeat(self.num_head, 1, 1) + xy_attn_mask = xy_mask.logical_or(xy_padding_mask) + new_attn_mask = torch.zeros_like(xy_attn_mask, dtype=x.dtype) + xy_attn_mask = new_attn_mask.masked_fill(xy_attn_mask, float("-inf")) y_list = [None]*y.shape[0] batch_idx_map = list(range(y.shape[0])) diff --git a/GPT_SoVITS/TTS_infer_pack/TTS.py b/GPT_SoVITS/TTS_infer_pack/TTS.py index 62bb2e9..e569f52 100644 --- a/GPT_SoVITS/TTS_infer_pack/TTS.py +++ b/GPT_SoVITS/TTS_infer_pack/TTS.py @@ -3,6 +3,8 @@ import math import os, sys import random import traceback + +from tqdm import tqdm now_dir = os.getcwd() sys.path.append(now_dir) import ffmpeg @@ -552,14 +554,15 @@ class TTS: "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 + "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. } returns: tulpe[int, np.ndarray]: sampling rate and audio data. @@ -580,9 +583,10 @@ class TTS: 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) if return_fragment: - split_bucket = False + # split_bucket = False print(i18n("分段返回模式已开启")) if split_bucket: split_bucket = False @@ -590,7 +594,10 @@ class TTS: 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, ""]: @@ -627,19 +634,52 @@ class TTS: ###### text preprocessing ######## - 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 * 0.3), - dtype=np.int16) - return - t1 = ttime() - 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 - ) + 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 + ) + 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) + 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 + ) + return batch[0] + t2 = ttime() try: print("############ 推理 ############") @@ -649,6 +689,9 @@ class TTS: audio = [] for item in data: t3 = ttime() + if return_fragment: + item = make_batch(item) + batch_phones = item["phones"] batch_phones_len = item["phones_len"] all_phoneme_ids = item["all_phones"] @@ -734,14 +777,16 @@ class TTS: 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, - batch_index_list, + None, speed_factor, - split_bucket) + 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 * 0.3), + yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate), dtype=np.int16) return @@ -751,7 +796,9 @@ class TTS: self.configs.sampling_rate, batch_index_list, speed_factor, - split_bucket) + 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。 @@ -781,9 +828,11 @@ class TTS: sr:int, batch_index_list:list=None, speed_factor:float=1.0, - split_bucket:bool=True)->tuple[int, np.ndarray]: + split_bucket:bool=True, + fragment_interval:float=0.3 + )->tuple[int, np.ndarray]: zero_wav = torch.zeros( - int(self.configs.sampling_rate * 0.3), + int(self.configs.sampling_rate * fragment_interval), dtype=self.precison, device=self.configs.device ) diff --git a/GPT_SoVITS/inference_webui.py b/GPT_SoVITS/inference_webui.py index 4c9d90f..7ba5d1e 100644 --- a/GPT_SoVITS/inference_webui.py +++ b/GPT_SoVITS/inference_webui.py @@ -91,7 +91,7 @@ def inference(text, text_lang, top_p, temperature, text_split_method, batch_size, speed_factor, ref_text_free, - split_bucket + split_bucket,fragment_interval, ): inputs={ "text": text, @@ -106,7 +106,8 @@ def inference(text, text_lang, "batch_size":int(batch_size), "speed_factor":float(speed_factor), "split_bucket":split_bucket, - "return_fragment":False + "return_fragment":False, + "fragment_interval":fragment_interval, } for item in tts_pipline.run(inputs): @@ -188,6 +189,7 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app: 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) @@ -216,7 +218,7 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app: top_k, top_p, temperature, how_to_cut, batch_size, speed_factor, ref_text_free, - split_bucket + split_bucket,fragment_interval, ], [output], ) From 03ae7fdb03df551bc3ccaaf215e2ac598424edc0 Mon Sep 17 00:00:00 2001 From: chasonjiang <1440499136@qq.com> Date: Wed, 13 Mar 2024 20:08:32 +0800 Subject: [PATCH 29/36] =?UTF-8?q?=E5=A2=9E=E5=8A=A0=E5=81=A5=E5=A3=AE?= =?UTF-8?q?=E6=80=A7?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- GPT_SoVITS/TTS_infer_pack/TTS.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/GPT_SoVITS/TTS_infer_pack/TTS.py b/GPT_SoVITS/TTS_infer_pack/TTS.py index e569f52..bcab7e8 100644 --- a/GPT_SoVITS/TTS_infer_pack/TTS.py +++ b/GPT_SoVITS/TTS_infer_pack/TTS.py @@ -672,6 +672,8 @@ class TTS: "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, @@ -691,6 +693,8 @@ class TTS: t3 = ttime() if return_fragment: item = make_batch(item) + if item is None: + continue batch_phones = item["phones"] batch_phones_len = item["phones_len"] From 8698c28c907906da05c3c0464796766932058893 Mon Sep 17 00:00:00 2001 From: chasonjiang <1440499136@qq.com> Date: Thu, 14 Mar 2024 11:15:09 +0800 Subject: [PATCH 30/36] =?UTF-8?q?=E4=BC=98=E5=8C=96=E4=BB=A3=E7=A0=81?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- GPT_SoVITS/TTS_infer_pack/TTS.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/GPT_SoVITS/TTS_infer_pack/TTS.py b/GPT_SoVITS/TTS_infer_pack/TTS.py index bcab7e8..e1ed8bb 100644 --- a/GPT_SoVITS/TTS_infer_pack/TTS.py +++ b/GPT_SoVITS/TTS_infer_pack/TTS.py @@ -820,7 +820,7 @@ class TTS: def empty_cache(self): try: - if str(self.configs.device) == "cuda": + if "cuda" in str(self.configs.device): torch.cuda.empty_cache() elif str(self.configs.device) == "mps": torch.mps.empty_cache() From 85dea3008bce09e0cb1aabc9454dd0137611527a Mon Sep 17 00:00:00 2001 From: chasonjiang <1440499136@qq.com> Date: Thu, 14 Mar 2024 11:18:39 +0800 Subject: [PATCH 31/36] =?UTF-8?q?=E5=90=8C=E6=AD=A5=E5=90=88=E5=B9=B6main?= =?UTF-8?q?=E5=88=86=E6=94=AF=E5=88=B0fast=5Finference=5F=E5=88=86?= =?UTF-8?q?=E6=94=AF?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- GPT_SoVITS/inference_webui.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/GPT_SoVITS/inference_webui.py b/GPT_SoVITS/inference_webui.py index 418c399..be0aad6 100644 --- a/GPT_SoVITS/inference_webui.py +++ b/GPT_SoVITS/inference_webui.py @@ -28,13 +28,12 @@ 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() +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) -is_half = eval(os.environ.get("is_half", "True")) and torch.cuda.is_available() import gradio as gr from TTS_infer_pack.TTS import TTS, TTS_Config from TTS_infer_pack.text_segmentation_method import get_method From b8ce03fd1bf30d5c0072d6a1946baf028eeab846 Mon Sep 17 00:00:00 2001 From: chasonjiang <1440499136@qq.com> Date: Thu, 14 Mar 2024 11:24:10 +0800 Subject: [PATCH 32/36] =?UTF-8?q?=E5=A2=9E=E5=8A=A0=E5=81=A5=E5=A3=AE?= =?UTF-8?q?=E6=80=A7=EF=BC=8C=E9=98=B2=E6=AD=A2=E5=9C=A8cpu=E6=8E=A8?= =?UTF-8?q?=E7=90=86=E6=97=B6=E8=AE=BE=E7=BD=AE=E5=8D=8A=E7=B2=BE=E5=BA=A6?= =?UTF-8?q?=E6=8A=A5=E9=94=99?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- GPT_SoVITS/TTS_infer_pack/TTS.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/GPT_SoVITS/TTS_infer_pack/TTS.py b/GPT_SoVITS/TTS_infer_pack/TTS.py index e1ed8bb..d82ed5d 100644 --- a/GPT_SoVITS/TTS_infer_pack/TTS.py +++ b/GPT_SoVITS/TTS_infer_pack/TTS.py @@ -228,7 +228,7 @@ class TTS: 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: + if self.configs.is_half and str(self.configs.device)!="cpu": self.cnhuhbert_model = self.cnhuhbert_model.half() @@ -239,7 +239,7 @@ class TTS: 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: + if self.configs.is_half and str(self.configs.device)!="cpu": self.bert_model = self.bert_model.half() @@ -272,7 +272,7 @@ class TTS: 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: + if self.configs.is_half and str(self.configs.device)!="cpu": self.vits_model = self.vits_model.half() @@ -290,7 +290,7 @@ class TTS: t2s_model = t2s_model.to(self.configs.device) t2s_model = t2s_model.eval() self.t2s_model = t2s_model - if self.configs.is_half: + 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): @@ -300,7 +300,7 @@ class TTS: enable: bool, whether to enable half precision. ''' - if self.configs.device == "cpu" and enable: + if str(self.configs.device) == "cpu" and enable: print("Half precision is not supported on CPU.") return From fabb00af794e5beaf007e6ee7039f3b453c5df10 Mon Sep 17 00:00:00 2001 From: KamioRinn Date: Fri, 15 Mar 2024 03:13:09 +0800 Subject: [PATCH 33/36] Fix chinese ellipsis --- GPT_SoVITS/text/zh_normalization/text_normlization.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/GPT_SoVITS/text/zh_normalization/text_normlization.py b/GPT_SoVITS/text/zh_normalization/text_normlization.py index 712537d..b4c1494 100644 --- a/GPT_SoVITS/text/zh_normalization/text_normlization.py +++ b/GPT_SoVITS/text/zh_normalization/text_normlization.py @@ -113,7 +113,7 @@ class TextNormalizer(): sentence = sentence.replace('ψ', '普赛').replace('Ψ', '普赛') sentence = sentence.replace('ω', '欧米伽').replace('Ω', '欧米伽') # re filter special characters, have one more character "-" than line 68 - sentence = re.sub(r'[-——《》【】<=>{}()（）#&@“”^_|…\\]', '', sentence) + sentence = re.sub(r'[-——《》【】<=>{}()（）#&@“”^_|\\]', '', sentence) return sentence def normalize_sentence(self, sentence: str) -> str: From a2f2a5f4a7d05ba908a34290f5baab1425ea5955 Mon Sep 17 00:00:00 2001 From: chasonjiang <1440499136@qq.com> Date: Fri, 15 Mar 2024 14:34:10 +0800 Subject: [PATCH 34/36] =?UTF-8?q?=E6=B7=BB=E5=8A=A0=E4=BA=86=E8=87=AA?= =?UTF-8?q?=E5=AE=9A=E4=B9=89=E4=BF=AE=E6=94=B9=E9=9A=8F=E6=9C=BA=E6=95=B0?= =?UTF-8?q?=E7=A7=8D=E5=AD=90=EF=BC=8C=E6=96=B9=E4=BE=BF=E5=A4=8D=E7=8E=B0?= =?UTF-8?q?=E7=BB=93=E6=9E=9C=E3=80=82?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- GPT_SoVITS/TTS_infer_pack/TTS.py | 32 +++++++++++++++++++++----------- GPT_SoVITS/inference_webui.py | 4 ++++ 2 files changed, 25 insertions(+), 11 deletions(-) diff --git a/GPT_SoVITS/TTS_infer_pack/TTS.py b/GPT_SoVITS/TTS_infer_pack/TTS.py index d82ed5d..17d72b3 100644 --- a/GPT_SoVITS/TTS_infer_pack/TTS.py +++ b/GPT_SoVITS/TTS_infer_pack/TTS.py @@ -51,17 +51,23 @@ custom: """ -# def set_seed(seed): -# random.seed(seed) -# os.environ['PYTHONHASHSEED'] = str(seed) -# np.random.seed(seed) -# torch.manual_seed(seed) -# 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 -# set_seed(1234) +def set_seed(seed:int): + seed = int(seed) + seed = seed if seed != -1 else random.randrange(1 << 32) + print(f"Set seed to {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={ @@ -563,6 +569,7 @@ class TTS: "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. @@ -584,6 +591,9 @@ class TTS: 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) + + set_seed(seed) if return_fragment: # split_bucket = False diff --git a/GPT_SoVITS/inference_webui.py b/GPT_SoVITS/inference_webui.py index be0aad6..505b665 100644 --- a/GPT_SoVITS/inference_webui.py +++ b/GPT_SoVITS/inference_webui.py @@ -92,6 +92,7 @@ def inference(text, text_lang, text_split_method, batch_size, speed_factor, ref_text_free, split_bucket,fragment_interval, + seed, ): inputs={ "text": text, @@ -108,6 +109,7 @@ def inference(text, text_lang, "split_bucket":split_bucket, "return_fragment":False, "fragment_interval":fragment_interval, + "seed":seed, } for item in tts_pipline.run(inputs): @@ -203,6 +205,7 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app: ) 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(): @@ -219,6 +222,7 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app: how_to_cut, batch_size, speed_factor, ref_text_free, split_bucket,fragment_interval, + seed ], [output], ) From 3c78539c44b4092c281914dc1cd462c480be92c4 Mon Sep 17 00:00:00 2001 From: chasonjiang <1440499136@qq.com> Date: Fri, 15 Mar 2024 14:58:25 +0800 Subject: [PATCH 35/36] =?UTF-8?q?=E5=A2=9E=E5=8A=A0=E5=81=A5=E5=A3=AE?= =?UTF-8?q?=E6=80=A7?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- GPT_SoVITS/TTS_infer_pack/TTS.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/GPT_SoVITS/TTS_infer_pack/TTS.py b/GPT_SoVITS/TTS_infer_pack/TTS.py index 17d72b3..45c5c78 100644 --- a/GPT_SoVITS/TTS_infer_pack/TTS.py +++ b/GPT_SoVITS/TTS_infer_pack/TTS.py @@ -592,7 +592,7 @@ class TTS: 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: From 864a148d75c235d3d7c75316ba02254bcbdf9fb3 Mon Sep 17 00:00:00 2001 From: chasonjiang <1440499136@qq.com> Date: Sat, 16 Mar 2024 21:04:49 +0800 Subject: [PATCH 36/36] =?UTF-8?q?=E7=BC=93=E8=A7=A3=E4=BA=86batch=5Fsize>1?= =?UTF-8?q?=E6=97=B6=E7=9A=84=E5=A4=8D=E8=AF=BB=E9=97=AE=E9=A2=98=EF=BC=8C?= =?UTF-8?q?=E7=BC=93=E8=A7=A3=E6=96=B9=E6=B3=95=E6=98=AF=EF=BC=9A=E5=9C=A8?= =?UTF-8?q?T2S=E6=A8=A1=E5=9E=8B=E4=B8=AD=EF=BC=8C=E5=85=88=E5=AF=B9phones?= =?UTF-8?q?=E8=BF=9B=E8=A1=8Cembedding=E3=80=81=E5=AF=B9bert=5Ffeatures?= =?UTF-8?q?=E8=BF=9B=E8=A1=8Cproject=EF=BC=8C=E5=86=8Dpad=E5=88=B0?= =?UTF-8?q?=E7=9B=B8=E5=90=8C=E9=95=BF=E5=BA=A6=E3=80=82?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- GPT_SoVITS/AR/models/t2s_model.py | 50 ++++++++++++++------ GPT_SoVITS/TTS_infer_pack/TTS.py | 78 +++++++++++++++++++------------ 2 files changed, 85 insertions(+), 43 deletions(-) diff --git a/GPT_SoVITS/AR/models/t2s_model.py b/GPT_SoVITS/AR/models/t2s_model.py index da95111..b49bcfb 100644 --- a/GPT_SoVITS/AR/models/t2s_model.py +++ b/GPT_SoVITS/AR/models/t2s_model.py @@ -504,18 +504,29 @@ class Text2SemanticDecoder(nn.Module): 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, ): - - bert_feature = self.bert_proj(bert_feature.transpose(1, 2)) - x = self.ar_text_embedding(x) + # 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]