diff --git a/.gitignore b/.gitignore index a3b244a..e86640a 100644 --- a/.gitignore +++ b/.gitignore @@ -14,3 +14,5 @@ GPT_weights_v2 SoVITS_weights_v2 TEMP weight.json +ffmpeg* +ffprobe* \ No newline at end of file diff --git a/GPT_SoVITS/AR/models/t2s_model.py b/GPT_SoVITS/AR/models/t2s_model.py index cecfcbc..31acadc 100644 --- a/GPT_SoVITS/AR/models/t2s_model.py +++ b/GPT_SoVITS/AR/models/t2s_model.py @@ -1,11 +1,10 @@ # modified from https://github.com/yangdongchao/SoundStorm/blob/master/soundstorm/s1/AR/models/t2s_model.py # reference: https://github.com/lifeiteng/vall-e +import math +from typing import List, Optional import torch -import random -import numpy as np - from tqdm import tqdm -from typing import List + from AR.models.utils import make_pad_mask from AR.models.utils import ( topk_sampling, @@ -37,6 +36,34 @@ default_config = { "EOS": 1024, } +# @torch.jit.script ## 使用的话首次推理会非常慢，而且推理速度不稳定 +# Efficient implementation equivalent to the following: +def scaled_dot_product_attention(query:torch.Tensor, key:torch.Tensor, value:torch.Tensor, attn_mask:Optional[torch.Tensor]=None, scale:Optional[torch.Tensor]=None) -> torch.Tensor: + B, H, L, S =query.size(0), query.size(1), query.size(-2), key.size(-2) + if scale is None: + scale_factor = torch.tensor(1 / math.sqrt(query.size(-1))) + else: + scale_factor = scale + attn_bias = torch.zeros(B, H, L, S, dtype=query.dtype, device=query.device) + + if attn_mask is not None: + if attn_mask.dtype == torch.bool: + attn_bias.masked_fill_(attn_mask, float("-inf")) + else: + attn_bias += attn_mask + attn_weight = query @ key.transpose(-2, -1) * scale_factor + attn_weight += attn_bias + attn_weight = torch.softmax(attn_weight, dim=-1) + + if attn_mask is not None: + if attn_mask.dtype == torch.bool: + attn_weight.masked_fill_(attn_mask, 0) + else: + attn_mask[attn_mask!=float("-inf")] =0 + attn_mask[attn_mask==float("-inf")] =1 + attn_weight.masked_fill_(attn_mask, 0) + + return attn_weight @ value @torch.jit.script class T2SMLP: @@ -84,61 +111,112 @@ class T2SBlock: 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) + self.false = torch.tensor(False, dtype=torch.bool) + + @torch.jit.ignore + def to_mask(self, x:torch.Tensor, padding_mask:Optional[torch.Tensor]): + if padding_mask is None: + return x + + if padding_mask.dtype == torch.bool: + return x.masked_fill(padding_mask, 0) + else: + return x * padding_mask + + def process_prompt(self, x:torch.Tensor, attn_mask : torch.Tensor, padding_mask:Optional[torch.Tensor]=None, torch_sdpa:bool=True): + + + q, k, v = F.linear(self.to_mask(x, padding_mask), 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 = self.to_mask(q, padding_mask) + k_cache = self.to_mask(k, padding_mask) + v_cache = self.to_mask(v, padding_mask) 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) + if torch_sdpa: + attn = F.scaled_dot_product_attention(q, k, v, ~attn_mask) + else: + attn = 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) + 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(self.to_mask(attn, padding_mask), 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, - ) + if padding_mask is not None: + for i in range(batch_size): + # mask = padding_mask[i,:,0] + if self.false.device!= padding_mask.device: + self.false = self.false.to(padding_mask.device) + idx = torch.where(padding_mask[i,:,0]==self.false)[0] + x_item = x[i,idx,:].unsqueeze(0) + attn_item = attn[i,idx,:].unsqueeze(0) + x_item = x_item + attn_item + x_item = F.layer_norm( + x_item, [self.hidden_dim], self.norm_w1, self.norm_b1, self.norm_eps1 + ) + x_item = x_item + self.mlp.forward(x_item) + x_item = F.layer_norm( + x_item, + [self.hidden_dim], + self.norm_w2, + self.norm_b2, + self.norm_eps2, + ) + x[i,idx,:] = x_item.squeeze(0) + x = self.to_mask(x, padding_mask) + else: + x = x + attn + x = F.layer_norm( + x, [self.hidden_dim], self.norm_w1, self.norm_b1, self.norm_eps1 + ) + x = x + self.mlp.forward(x) + x = F.layer_norm( + 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): + + def decode_next_token(self, x:torch.Tensor, k_cache:torch.Tensor, v_cache:torch.Tensor, attn_mask:Optional[torch.Tensor]=None, torch_sdpa:bool=True): 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) 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) + if torch_sdpa: + attn = F.scaled_dot_product_attention(q, k, v) + else: + attn = scaled_dot_product_attention(q, k, v, attn_mask) + + attn = attn.permute(2, 0, 1, 3).reshape(batch_size*q_len, self.hidden_dim) + attn = attn.view(q_len, batch_size, self.hidden_dim).transpose(1, 0) attn = F.linear(attn, self.out_w, self.out_b) + x = x + attn x = F.layer_norm( - x + attn, [self.hidden_dim], self.norm_w1, self.norm_b1, self.norm_eps1 + x, [self.hidden_dim], self.norm_w1, self.norm_b1, self.norm_eps1 ) + x = x + self.mlp.forward(x) x = F.layer_norm( - x + self.mlp.forward(x), + x, [self.hidden_dim], self.norm_w2, self.norm_b2, @@ -149,25 +227,32 @@ class T2SBlock: @torch.jit.script class T2STransformer: - def __init__(self, num_blocks: int, blocks: List[T2SBlock]): - self.num_blocks: int = num_blocks + 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] = [] + self, x:torch.Tensor, attn_mask : torch.Tensor, + padding_mask : Optional[torch.Tensor]=None, + torch_sdpa:bool=True + ): + 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) + x, k_cache_, v_cache_ = self.blocks[i].process_prompt(x, attn_mask, padding_mask, torch_sdpa) 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] + self, x:torch.Tensor, + k_cache: List[torch.Tensor], + v_cache: List[torch.Tensor], + attn_mask : Optional[torch.Tensor]=None, + torch_sdpa:bool=True ): 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, torch_sdpa) return x, k_cache, v_cache @@ -235,7 +320,7 @@ 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, @@ -253,7 +338,7 @@ class Text2SemanticDecoder(nn.Module): ) blocks.append(block) - + self.t2s_transformer = T2STransformer(self.num_layers, blocks) def make_input_data(self, x, x_lens, y, y_lens, bert_feature): @@ -283,7 +368,7 @@ class Text2SemanticDecoder(nn.Module): (0, y_len), value=True, ) - + # x_attn_mask[:, x_len]=False y_attn_mask = F.pad( torch.triu( torch.ones(y_len, y_len, dtype=torch.bool, device=x.device), @@ -344,7 +429,7 @@ class Text2SemanticDecoder(nn.Module): 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 @@ -488,16 +573,225 @@ class Text2SemanticDecoder(nn.Module): # 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, + def infer_panel_batch_infer( + self, + x:List[torch.LongTensor], #####全部文本token + x_lens:torch.LongTensor, + prompts:torch.LongTensor, ####参考音频token + bert_feature:List[torch.LongTensor], + top_k: int = -100, + top_p: int = 100, + early_stop_num: int = -1, + temperature: float = 1.0, + repetition_penalty: float = 1.35, + **kwargs, + ): + if prompts is None: + print("Warning: Prompt free is not supported batch_infer! switch to naive_infer") + return self.infer_panel_naive_batched(x, x_lens, prompts, bert_feature, top_k=top_k, top_p=top_p, early_stop_num=early_stop_num, temperature=temperature, **kwargs) + + + max_len = kwargs.get("max_len",x_lens.max()) + x_list = [] + for x_item, bert_item in zip(x, bert_feature): + # max_len = max(max_len, x_item.shape[0], bert_item.shape[1]) + x_item = self.ar_text_embedding(x_item.unsqueeze(0)) + x_item = x_item + self.bert_proj(bert_item.transpose(0, 1).unsqueeze(0)) + x_item = self.ar_text_position(x_item).squeeze(0) + x_item = F.pad(x_item,(0,0,0,max_len-x_item.shape[0]),value=0) if x_item.shape[0] early_stop_num) or idx==1499: + print("use early stop num:", early_stop_num) + stop = True + for i, batch_index in enumerate(batch_idx_map): + batch_index = batch_idx_map[i] + idx_list[batch_index] = idx + y_list[batch_index] = y[i, :-1] + + if not (None in idx_list): + stop = True + + if stop: + if y.shape[1]==0: + y = torch.concat([y, torch.zeros_like(samples)], dim=1) + print("bad zero prediction") + print(f"T2S Decoding EOS [{prefix_len} -> {y.shape[1]}]") + break + + ####################### update next step ################################### + y_emb = self.ar_audio_embedding(y[:, -1:]) + xy_pos = y_emb * self.ar_audio_position.x_scale + self.ar_audio_position.alpha * self.ar_audio_position.pe[:, y_len + idx].to( dtype= y_emb.dtype,device=y_emb.device) + + if (None in idx_list): + for i in range(x.shape[0]): + if idx_list[i] is None: + idx_list[i] = 1500-1 ###如果没有生成到EOS，就用最大长度代替 + + if ref_free: + return y_list, [0]*x.shape[0] + # print(idx_list) + return y_list, idx_list + + def infer_panel_naive_batched(self, + x:List[torch.LongTensor], #####全部文本token + x_lens:torch.LongTensor, + prompts:torch.LongTensor, ####参考音频token + bert_feature:List[torch.LongTensor], + top_k: int = -100, + top_p: int = 100, + early_stop_num: int = -1, + temperature: float = 1.0, + repetition_penalty: float = 1.35, + **kwargs + ): + y_list = [] + idx_list = [] + for i in range(len(x)): + y, idx = self.infer_panel_naive(x[i].unsqueeze(0), + x_lens[i], + prompts[i].unsqueeze(0) if prompts is not None else None, + bert_feature[i].unsqueeze(0), + top_k, + top_p, + early_stop_num, + temperature, + repetition_penalty, + **kwargs) + y_list.append(y[0]) + idx_list.append(idx) + + return y_list, idx_list + + def infer_panel_naive( + self, + x:torch.LongTensor, #####全部文本token + x_lens:torch.LongTensor, + prompts:torch.LongTensor, ####参考音频token + bert_feature:torch.LongTensor, + top_k: int = -100, + top_p: int = 100, + early_stop_num: int = -1, + temperature: float = 1.0, + repetition_penalty: float = 1.35, + **kwargs ): x = self.ar_text_embedding(x) x = x + self.bert_proj(bert_feature.transpose(1, 2)) @@ -528,9 +822,10 @@ class Text2SemanticDecoder(nn.Module): y_pos = None xy_pos = x y = torch.zeros(x.shape[0], 0, dtype=torch.int, device=x.device) - prompts = y ref_free = True + bsz = x.shape[0] + src_len = x_len + y_len 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) @@ -541,13 +836,15 @@ class Text2SemanticDecoder(nn.Module): (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 = torch.concat([x_attn_mask_pad, y_attn_mask], dim=0)\ + .unsqueeze(0)\ + .expand(bsz*self.num_head, -1, -1)\ + .view(bsz, self.num_head, src_len, src_len)\ + .to(device=x.device, dtype=torch.bool) for idx in tqdm(range(1500)): if xy_attn_mask is not None: - xy_dec, k_cache, v_cache = self.t2s_transformer.process_prompt(xy_pos, xy_attn_mask) + xy_dec, k_cache, v_cache = self.t2s_transformer.process_prompt(xy_pos, xy_attn_mask, None) else: xy_dec, k_cache, v_cache = self.t2s_transformer.decode_next_token(xy_pos, k_cache, v_cache) @@ -558,9 +855,10 @@ 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=repetition_penalty, temperature=temperature + )[0] y = torch.concat([y, samples], dim=1) @@ -583,4 +881,20 @@ class Text2SemanticDecoder(nn.Module): if ref_free: return y[:, :-1], 0 - return y[:, :-1], idx - 1 \ No newline at end of file + return y[:, :-1], idx - 1 + + + def infer_panel( + self, + x:torch.LongTensor, #####全部文本token + x_lens:torch.LongTensor, + prompts:torch.LongTensor, ####参考音频token + bert_feature:torch.LongTensor, + top_k: int = -100, + top_p: int = 100, + early_stop_num: int = -1, + temperature: float = 1.0, + repetition_penalty: float = 1.35, + **kwargs + ): + return self.infer_panel_naive(x, x_lens, prompts, bert_feature, top_k, top_p, early_stop_num, temperature, repetition_penalty, **kwargs) diff --git a/GPT_SoVITS/AR/models/utils.py b/GPT_SoVITS/AR/models/utils.py index 9678c7e..ce0a98b 100644 --- a/GPT_SoVITS/AR/models/utils.py +++ b/GPT_SoVITS/AR/models/utils.py @@ -115,17 +115,17 @@ def logits_to_probs( top_p: Optional[int] = None, repetition_penalty: float = 1.0, ): - if previous_tokens is not None: - previous_tokens = previous_tokens.squeeze() + # if previous_tokens is not None: + # previous_tokens = previous_tokens.squeeze() # print(logits.shape,previous_tokens.shape) # pdb.set_trace() if previous_tokens is not None and repetition_penalty != 1.0: previous_tokens = previous_tokens.long() - score = torch.gather(logits, dim=0, index=previous_tokens) + score = torch.gather(logits, dim=1, index=previous_tokens) score = torch.where( score < 0, score * repetition_penalty, score / repetition_penalty ) - logits.scatter_(dim=0, index=previous_tokens, src=score) + logits.scatter_(dim=1, index=previous_tokens, src=score) if top_p is not None and top_p < 1.0: sorted_logits, sorted_indices = torch.sort(logits, descending=True) @@ -133,9 +133,9 @@ def logits_to_probs( torch.nn.functional.softmax(sorted_logits, dim=-1), dim=-1 ) sorted_indices_to_remove = cum_probs > top_p - sorted_indices_to_remove[0] = False # keep at least one option + sorted_indices_to_remove[:, 0] = False # keep at least one option indices_to_remove = sorted_indices_to_remove.scatter( - dim=0, index=sorted_indices, src=sorted_indices_to_remove + dim=1, index=sorted_indices, src=sorted_indices_to_remove ) logits = logits.masked_fill(indices_to_remove, -float("Inf")) @@ -143,7 +143,7 @@ def logits_to_probs( if top_k is not None: v, _ = torch.topk(logits, min(top_k, logits.size(-1))) - pivot = v.select(-1, -1).unsqueeze(-1) + pivot = v[: , -1].unsqueeze(-1) logits = torch.where(logits < pivot, -float("Inf"), logits) probs = torch.nn.functional.softmax(logits, dim=-1) diff --git a/GPT_SoVITS/TTS_infer_pack/TTS.py b/GPT_SoVITS/TTS_infer_pack/TTS.py new file mode 100644 index 0000000..2fd39ad --- /dev/null +++ b/GPT_SoVITS/TTS_infer_pack/TTS.py @@ -0,0 +1,1023 @@ +from copy import deepcopy +import math +import os, sys, gc +import random +import traceback + +from tqdm import tqdm +now_dir = os.getcwd() +sys.path.append(now_dir) +import ffmpeg +import os +from typing import Generator, List, Tuple, Union +import numpy as np +import torch +import torch.nn.functional as F +import yaml +from transformers import AutoModelForMaskedLM, AutoTokenizer + +from AR.models.t2s_lightning_module import Text2SemanticLightningModule +from feature_extractor.cnhubert import CNHubert +from module.models import SynthesizerTrn +import librosa +from time import time as ttime +from tools.i18n.i18n import I18nAuto, scan_language_list +from tools.my_utils import load_audio +from module.mel_processing import spectrogram_torch +from TTS_infer_pack.text_segmentation_method import splits +from TTS_infer_pack.TextPreprocessor import TextPreprocessor +language=os.environ.get("language","Auto") +language=sys.argv[-1] if sys.argv[-1] in scan_language_list() else language +i18n = I18nAuto(language=language) + +# configs/tts_infer.yaml +""" +custom: + 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/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt + vits_weights_path: GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth + version: v2 +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 + version: v1 +default_v2: + 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/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt + vits_weights_path: GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth + version: v2 +""" + +def set_seed(seed:int): + seed = int(seed) + seed = seed if seed != -1 else random.randrange(1 << 32) + print(f"Set seed to {seed}") + os.environ['PYTHONHASHSEED'] = str(seed) + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + try: + if torch.cuda.is_available(): + torch.cuda.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + # torch.backends.cudnn.deterministic = True + # torch.backends.cudnn.benchmark = False + # torch.backends.cudnn.enabled = True + # 开启后会影响精度 + torch.backends.cuda.matmul.allow_tf32 = False + torch.backends.cudnn.allow_tf32 = False + except: + pass + return seed + +class TTS_Config: + default_configs={ + "default":{ + "device": "cpu", + "is_half": False, + "version": "v1", + "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", + }, + "default_v2":{ + "device": "cpu", + "is_half": False, + "version": "v2", + "t2s_weights_path": "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt", + "vits_weights_path": "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth", + "cnhuhbert_base_path": "GPT_SoVITS/pretrained_models/chinese-hubert-base", + "bert_base_path": "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large", + }, + } + configs:dict = None + v1_languages:list = ["auto", "en", "zh", "ja", "all_zh", "all_ja"] + v2_languages:list = ["auto", "auto_yue", "en", "zh", "ja", "yue", "ko", "all_zh", "all_ja", "all_yue", "all_ko"] + languages:list = v2_languages + # "all_zh",#全部按中文识别 + # "en",#全部按英文识别#######不变 + # "all_ja",#全部按日文识别 + # "all_yue",#全部按中文识别 + # "all_ko",#全部按韩文识别 + # "zh",#按中英混合识别####不变 + # "ja",#按日英混合识别####不变 + # "yue",#按粤英混合识别####不变 + # "ko",#按韩英混合识别####不变 + # "auto",#多语种启动切分识别语种 + # "auto_yue",#多语种启动切分识别语种 + + def __init__(self, configs: Union[dict, str]=None): + + # 设置默认配置文件路径 + configs_base_path:str = "GPT_SoVITS/configs/" + os.makedirs(configs_base_path, exist_ok=True) + self.configs_path:str = os.path.join(configs_base_path, "tts_infer.yaml") + + if configs in ["", None]: + if not os.path.exists(self.configs_path): + self.save_configs() + print(f"Create default config file at {self.configs_path}") + configs:dict = deepcopy(self.default_configs) + + if isinstance(configs, str): + self.configs_path = configs + configs:dict = self._load_configs(self.configs_path) + + assert isinstance(configs, dict) + version = configs.get("version", "v2").lower() + assert version in ["v1", "v2"] + self.default_configs["default"] = configs.get("default", self.default_configs["default"]) + self.default_configs["default_v2"] = configs.get("default_v2", self.default_configs["default_v2"]) + + default_config_key = "default"if version=="v1" else "default_v2" + self.configs:dict = configs.get("custom", deepcopy(self.default_configs[default_config_key])) + + + self.device = self.configs.get("device", torch.device("cpu")) + self.is_half = self.configs.get("is_half", False) + self.version = version + 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) + self.languages = self.v2_languages if self.version=="v2" else self.v1_languages + + + if (self.t2s_weights_path in [None, ""]) or (not os.path.exists(self.t2s_weights_path)): + self.t2s_weights_path = self.default_configs[default_config_key]['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[default_config_key]['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[default_config_key]['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[default_config_key]['cnhuhbert_base_path'] + print(f"fall back to default cnhuhbert_base_path: {self.cnhuhbert_base_path}") + self.update_configs() + + + self.max_sec = None + self.hz:int = 50 + self.semantic_frame_rate:str = "25hz" + self.segment_size:int = 20480 + self.filter_length:int = 2048 + self.sampling_rate:int = 32000 + self.hop_length:int = 640 + self.win_length:int = 2048 + self.n_speakers:int = 300 + + + + 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=deepcopy(self.default_configs) + if self.configs is not None: + configs["custom"] = self.update_configs() + + if configs_path is None: + configs_path = self.configs_path + with open(configs_path, 'w') as f: + yaml.dump(configs, f) + + def update_configs(self): + self.config = { + "device" : str(self.device), + "is_half" : self.is_half, + "version" : self.version, + "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, + } + return self.config + + def __str__(self): + self.configs = self.update_configs() + string = "TTS Config".center(100, '-') + '\n' + for k, v in self.configs.items(): + string += f"{str(k).ljust(20)}: {str(v)}\n" + string += "-" * 100 + '\n' + return string + + def __repr__(self): + return self.__str__() + + def __hash__(self): + return hash(self.configs_path) + + def __eq__(self, other): + return isinstance(other, TTS_Config) and self.configs_path == other.configs_path + + +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_spec" : [], + "prompt_text" : None, + "prompt_lang" : None, + "phones" : None, + "bert_features" : None, + "norm_text" : None, + "aux_ref_audio_paths": [], + } + + + self.stop_flag:bool = False + self.precision:torch.dtype = torch.float16 if self.configs.is_half else torch.float32 + + def _init_models(self,): + self.init_t2s_weights(self.configs.t2s_weights_path) + self.init_vits_weights(self.configs.vits_weights_path) + self.init_bert_weights(self.configs.bert_base_path) + self.init_cnhuhbert_weights(self.configs.cnhuhbert_base_path) + # self.enable_half_precision(self.configs.is_half) + + + + def init_cnhuhbert_weights(self, base_path: str): + print(f"Loading CNHuBERT weights from {base_path}") + self.cnhuhbert_model = CNHubert(base_path) + self.cnhuhbert_model=self.cnhuhbert_model.eval() + self.cnhuhbert_model = self.cnhuhbert_model.to(self.configs.device) + if self.configs.is_half and str(self.configs.device)!="cpu": + self.cnhuhbert_model = self.cnhuhbert_model.half() + + + + def init_bert_weights(self, base_path: str): + print(f"Loading BERT weights from {base_path}") + self.bert_tokenizer = AutoTokenizer.from_pretrained(base_path) + self.bert_model = AutoModelForMaskedLM.from_pretrained(base_path) + self.bert_model=self.bert_model.eval() + self.bert_model = self.bert_model.to(self.configs.device) + if self.configs.is_half and str(self.configs.device)!="cpu": + self.bert_model = self.bert_model.half() + + def init_vits_weights(self, weights_path: str): + print(f"Loading VITS weights from {weights_path}") + self.configs.vits_weights_path = weights_path + self.configs.save_configs() + dict_s2 = torch.load(weights_path, map_location=self.configs.device) + hps = dict_s2["config"] + if dict_s2['weight']['enc_p.text_embedding.weight'].shape[0] == 322: + self.configs.version = "v1" + else: + self.configs.version = "v2" + hps["model"]["version"] = self.configs.version + 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 hasattr(vits_model, "enc_q"): + del vits_model.enc_q + + vits_model = vits_model.to(self.configs.device) + vits_model = vits_model.eval() + vits_model.load_state_dict(dict_s2["weight"], strict=False) + self.vits_model = vits_model + if self.configs.is_half and str(self.configs.device)!="cpu": + self.vits_model = self.vits_model.half() + + + def init_t2s_weights(self, weights_path: str): + print(f"Loading Text2Semantic weights from {weights_path}") + self.configs.t2s_weights_path = weights_path + self.configs.save_configs() + self.configs.hz = 50 + dict_s1 = torch.load(weights_path, map_location=self.configs.device) + config = dict_s1["config"] + self.configs.max_sec = config["data"]["max_sec"] + t2s_model = Text2SemanticLightningModule(config, "****", is_train=False) + t2s_model.load_state_dict(dict_s1["weight"]) + t2s_model = t2s_model.to(self.configs.device) + t2s_model = t2s_model.eval() + self.t2s_model = t2s_model + if self.configs.is_half and str(self.configs.device)!="cpu": + self.t2s_model = self.t2s_model.half() + + def enable_half_precision(self, enable: bool = True, save: bool = True): + ''' + To enable half precision for the TTS model. + Args: + enable: bool, whether to enable half precision. + + ''' + if str(self.configs.device) == "cpu" and enable: + print("Half precision is not supported on CPU.") + return + + self.configs.is_half = enable + self.precision = torch.float16 if enable else torch.float32 + if save: + 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, save: bool = True): + ''' + To set the device for all models. + Args: + device: torch.device, the device to use for all models. + ''' + self.configs.device = device + if save: + self.configs.save_configs() + if self.t2s_model is not None: + self.t2s_model = self.t2s_model.to(device) + if self.vits_model is not None: + self.vits_model = self.vits_model.to(device) + if self.bert_model is not None: + self.bert_model = self.bert_model.to(device) + if self.cnhuhbert_model is not None: + self.cnhuhbert_model = self.cnhuhbert_model.to(device) + + def set_ref_audio(self, ref_audio_path:str): + ''' + To set the reference audio for the TTS model, + including the prompt_semantic and refer_spepc. + Args: + ref_audio_path: str, the path of the reference audio. + ''' + self._set_prompt_semantic(ref_audio_path) + self._set_ref_spec(ref_audio_path) + self._set_ref_audio_path(ref_audio_path) + + def _set_ref_audio_path(self, ref_audio_path): + self.prompt_cache["ref_audio_path"] = ref_audio_path + + def _set_ref_spec(self, ref_audio_path): + spec = self._get_ref_spec(ref_audio_path) + if self.prompt_cache["refer_spec"] in [[],None]: + self.prompt_cache["refer_spec"]=[spec] + else: + self.prompt_cache["refer_spec"][0] = spec + + def _get_ref_spec(self, ref_audio_path): + audio = load_audio(ref_audio_path, int(self.configs.sampling_rate)) + audio = torch.FloatTensor(audio) + maxx=audio.abs().max() + if(maxx>1):audio/=min(2,maxx) + 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() + return spec + + def _set_prompt_semantic(self, ref_wav_path:str): + zero_wav = np.zeros( + int(self.configs.sampling_rate * 0.3), + dtype=np.float16 if self.configs.is_half else np.float32, + ) + with torch.no_grad(): + wav16k, sr = librosa.load(ref_wav_path, sr=16000) + if (wav16k.shape[0] > 160000 or wav16k.shape[0] < 48000): + raise OSError(i18n("参考音频在3~10秒范围外，请更换！")) + wav16k = torch.from_numpy(wav16k) + zero_wav_torch = torch.from_numpy(zero_wav) + wav16k = wav16k.to(self.configs.device) + zero_wav_torch = zero_wav_torch.to(self.configs.device) + if self.configs.is_half: + wav16k = wav16k.half() + zero_wav_torch = zero_wav_torch.half() + + wav16k = torch.cat([wav16k, zero_wav_torch]) + hubert_feature = self.cnhuhbert_model.model(wav16k.unsqueeze(0))[ + "last_hidden_state" + ].transpose( + 1, 2 + ) # .float() + codes = self.vits_model.extract_latent(hubert_feature) + + prompt_semantic = codes[0, 0].to(self.configs.device) + self.prompt_cache["prompt_semantic"] = prompt_semantic + + def batch_sequences(self, sequences: List[torch.Tensor], axis: int = 0, pad_value: int = 0, max_length:int=None): + seq = sequences[0] + ndim = seq.dim() + if axis < 0: + axis += ndim + dtype:torch.dtype = seq.dtype + pad_value = torch.tensor(pad_value, dtype=dtype) + seq_lengths = [seq.shape[axis] for seq in sequences] + if max_length is None: + max_length = max(seq_lengths) + else: + max_length = max(seq_lengths) if max_length < max(seq_lengths) else max_length + + padded_sequences = [] + for seq, length in zip(sequences, seq_lengths): + padding = [0] * axis + [0, max_length - length] + [0] * (ndim - axis - 1) + padded_seq = torch.nn.functional.pad(seq, padding, value=pad_value) + padded_sequences.append(padded_seq) + batch = torch.stack(padded_sequences) + return batch + + def to_batch(self, data:list, + prompt_data:dict=None, + batch_size:int=5, + threshold:float=0.75, + split_bucket:bool=True, + device:torch.device=torch.device("cpu"), + precision:torch.dtype=torch.float32, + ): + _data:list = [] + index_and_len_list = [] + for idx, item in enumerate(data): + norm_text_len = len(item["norm_text"]) + index_and_len_list.append([idx, norm_text_len]) + + batch_index_list = [] + if split_bucket: + index_and_len_list.sort(key=lambda x: x[1]) + index_and_len_list = np.array(index_and_len_list, dtype=np.int64) + + batch_index_list_len = 0 + pos = 0 + while pos =threshold) or (pos_end-pos==1): + batch_index=index_and_len_list[pos:pos_end, 0].tolist() + batch_index_list_len += len(batch_index) + batch_index_list.append(batch_index) + pos = pos_end + break + pos_end=pos_end-1 + + assert batch_index_list_len == len(data) + + else: + for i in range(len(data)): + if i%batch_size == 0: + batch_index_list.append([]) + batch_index_list[-1].append(i) + + + for batch_idx, index_list in enumerate(batch_index_list): + item_list = [data[idx] for idx in index_list] + phones_list = [] + phones_len_list = [] + # bert_features_list = [] + all_phones_list = [] + all_phones_len_list = [] + all_bert_features_list = [] + norm_text_batch = [] + all_bert_max_len = 0 + all_phones_max_len = 0 + for item in item_list: + if prompt_data is not None: + all_bert_features = torch.cat([prompt_data["bert_features"], item["bert_features"]], 1)\ + .to(dtype=precision, device=device) + all_phones = torch.LongTensor(prompt_data["phones"]+item["phones"]).to(device) + phones = torch.LongTensor(item["phones"]).to(device) + # norm_text = prompt_data["norm_text"]+item["norm_text"] + else: + all_bert_features = item["bert_features"]\ + .to(dtype=precision, device=device) + phones = torch.LongTensor(item["phones"]).to(device) + all_phones = phones + # norm_text = item["norm_text"] + + all_bert_max_len = max(all_bert_max_len, all_bert_features.shape[-1]) + all_phones_max_len = max(all_phones_max_len, all_phones.shape[-1]) + + phones_list.append(phones) + phones_len_list.append(phones.shape[-1]) + all_phones_list.append(all_phones) + all_phones_len_list.append(all_phones.shape[-1]) + all_bert_features_list.append(all_bert_features) + norm_text_batch.append(item["norm_text"]) + + phones_batch = phones_list + all_phones_batch = all_phones_list + all_bert_features_batch = all_bert_features_list + + + max_len = max(all_bert_max_len, all_phones_max_len) + # phones_batch = self.batch_sequences(phones_list, axis=0, pad_value=0, max_length=max_len) + #### 直接对phones和bert_features进行pad。（padding策略会影响T2S模型生成的结果，但不直接影响复读概率。影响复读概率的主要因素是mask的策略） + # 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(all_bert_features_list), 1024, max_len), dtype=precision, device=device) + # for idx, item in enumerate(all_bert_features_list): + # all_bert_features_batch[idx, :, : item.shape[-1]] = item + + # #### 先对phones进行embedding、对bert_features进行project，再pad到相同长度，（padding策略会影响T2S模型生成的结果，但不直接影响复读概率。影响复读概率的主要因素是mask的策略） + # all_phones_list = [self.t2s_model.model.ar_text_embedding(item.to(self.t2s_model.device)) for item in all_phones_list] + # all_phones_list = [F.pad(item,(0,0,0,max_len-item.shape[0]),value=0) for item in all_phones_list] + # all_phones_batch = torch.stack(all_phones_list, dim=0) + + # all_bert_features_list = [self.t2s_model.model.bert_proj(item.to(self.t2s_model.device).transpose(0, 1)) for item in all_bert_features_list] + # all_bert_features_list = [F.pad(item,(0,0,0,max_len-item.shape[0]), value=0) for item in all_bert_features_list] + # all_bert_features_batch = torch.stack(all_bert_features_list, dim=0) + + batch = { + "phones": phones_batch, + "phones_len": torch.LongTensor(phones_len_list).to(device), + "all_phones": all_phones_batch, + "all_phones_len": torch.LongTensor(all_phones_len_list).to(device), + "all_bert_features": all_bert_features_batch, + "norm_text": norm_text_batch, + "max_len": max_len, + } + _data.append(batch) + + return _data, batch_index_list + + def recovery_order(self, data:list, batch_index_list:list)->list: + ''' + Recovery the order of the audio according to the batch_index_list. + + Args: + data (List[list(np.ndarray)]): the out of order audio . + batch_index_list (List[list[int]]): the batch index list. + + Returns: + list (List[np.ndarray]): the data in the original order. + ''' + length = len(sum(batch_index_list, [])) + _data = [None]*length + for i, index_list in enumerate(batch_index_list): + for j, index in enumerate(index_list): + _data[index] = data[i][j] + return _data + + def stop(self,): + ''' + Stop the inference process. + ''' + self.stop_flag = True + + @torch.no_grad() + def run(self, inputs:dict): + """ + Text to speech inference. + + Args: + inputs (dict): + { + "text": "", # str.(required) text to be synthesized + "text_lang: "", # str.(required) language of the text to be synthesized + "ref_audio_path": "", # str.(required) reference audio path + "aux_ref_audio_paths": [], # list.(optional) auxiliary reference audio paths for multi-speaker synthesis + "prompt_text": "", # str.(optional) prompt text for the reference audio + "prompt_lang": "", # str.(required) language of the prompt text for the reference audio + "top_k": 5, # int. top k sampling + "top_p": 1, # float. top p sampling + "temperature": 1, # float. temperature for sampling + "text_split_method": "cut0", # str. text split method, see text_segmentation_method.py for details. + "batch_size": 1, # int. batch size for inference + "batch_threshold": 0.75, # float. threshold for batch splitting. + "split_bucket: True, # bool. whether to split the batch into multiple buckets. + "return_fragment": False, # bool. step by step return the audio fragment. + "speed_factor":1.0, # float. control the speed of the synthesized audio. + "fragment_interval":0.3, # float. to control the interval of the audio fragment. + "seed": -1, # int. random seed for reproducibility. + "parallel_infer": True, # bool. whether to use parallel inference. + "repetition_penalty": 1.35 # float. repetition penalty for T2S model. + } + returns: + Tuple[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", "") + aux_ref_audio_paths:list = inputs.get("aux_ref_audio_paths", []) + prompt_text:str = inputs.get("prompt_text", "") + prompt_lang:str = inputs.get("prompt_lang", "") + top_k:int = inputs.get("top_k", 5) + top_p:float = inputs.get("top_p", 1) + temperature:float = inputs.get("temperature", 1) + text_split_method:str = inputs.get("text_split_method", "cut0") + batch_size = inputs.get("batch_size", 1) + batch_threshold = inputs.get("batch_threshold", 0.75) + speed_factor = inputs.get("speed_factor", 1.0) + split_bucket = inputs.get("split_bucket", True) + return_fragment = inputs.get("return_fragment", False) + fragment_interval = inputs.get("fragment_interval", 0.3) + seed = inputs.get("seed", -1) + seed = -1 if seed in ["", None] else seed + actual_seed = set_seed(seed) + parallel_infer = inputs.get("parallel_infer", True) + repetition_penalty = inputs.get("repetition_penalty", 1.35) + + if parallel_infer: + print(i18n("并行推理模式已开启")) + self.t2s_model.model.infer_panel = self.t2s_model.model.infer_panel_batch_infer + else: + print(i18n("并行推理模式已关闭")) + self.t2s_model.model.infer_panel = self.t2s_model.model.infer_panel_naive_batched + + if return_fragment: + 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 and speed_factor==1.0: + print(i18n("分桶处理模式已开启")) + elif speed_factor!=1.0: + print(i18n("语速调节不支持分桶处理，已自动关闭分桶处理")) + split_bucket = False + else: + print(i18n("分桶处理模式已关闭")) + + if fragment_interval<0.01: + fragment_interval = 0.01 + print(i18n("分段间隔过小，已自动设置为0.01")) + + no_prompt_text = False + if prompt_text in [None, ""]: + no_prompt_text = True + + assert text_lang in self.configs.languages + if not no_prompt_text: + assert prompt_lang in self.configs.languages + + if ref_audio_path in [None, ""] and \ + ((self.prompt_cache["prompt_semantic"] is None) or (self.prompt_cache["refer_spec"] in [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"]): + if not os.path.exists(ref_audio_path): + raise ValueError(f"{ref_audio_path} not exists") + self.set_ref_audio(ref_audio_path) + + aux_ref_audio_paths = aux_ref_audio_paths if aux_ref_audio_paths is not None else [] + paths = set(aux_ref_audio_paths)&set(self.prompt_cache["aux_ref_audio_paths"]) + if not (len(list(paths)) == len(aux_ref_audio_paths) == len(self.prompt_cache["aux_ref_audio_paths"])): + self.prompt_cache["aux_ref_audio_paths"] = aux_ref_audio_paths + self.prompt_cache["refer_spec"] = [self.prompt_cache["refer_spec"][0]] + for path in aux_ref_audio_paths: + if path in [None, ""]: + continue + if not os.path.exists(path): + print(i18n("音频文件不存在，跳过：{}").format(path)) + continue + self.prompt_cache["refer_spec"].append(self._get_ref_spec(path)) + + if not no_prompt_text: + prompt_text = prompt_text.strip("\n") + if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_lang != "en" else "." + print(i18n("实际输入的参考文本:"), prompt_text) + if self.prompt_cache["prompt_text"] != prompt_text: + self.prompt_cache["prompt_text"] = prompt_text + self.prompt_cache["prompt_lang"] = prompt_lang + phones, bert_features, norm_text = \ + self.text_preprocessor.segment_and_extract_feature_for_text( + prompt_text, + prompt_lang) + self.prompt_cache["phones"] = phones + self.prompt_cache["bert_features"] = bert_features + self.prompt_cache["norm_text"] = norm_text + + + + + ###### text preprocessing ######## + t1 = ttime() + data:list = None + if not return_fragment: + data = self.text_preprocessor.preprocess(text, text_lang, text_split_method) + if len(data) == 0: + yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate), + dtype=np.int16) + return + + batch_index_list:list = None + data, batch_index_list = self.to_batch(data, + prompt_data=self.prompt_cache if not no_prompt_text else None, + batch_size=batch_size, + threshold=batch_threshold, + split_bucket=split_bucket, + device=self.configs.device, + precision=self.precision + ) + 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, self.configs.version) + if phones is None: + continue + res={ + "phones": phones, + "bert_features": bert_features, + "norm_text": norm_text, + } + batch_data.append(res) + if len(batch_data) == 0: + return None + batch, _ = self.to_batch(batch_data, + prompt_data=self.prompt_cache if not no_prompt_text else None, + batch_size=batch_size, + threshold=batch_threshold, + split_bucket=False, + device=self.configs.device, + precision=self.precision + ) + return batch[0] + + + t2 = ttime() + try: + print("############ 推理 ############") + ###### inference ###### + t_34 = 0.0 + t_45 = 0.0 + audio = [] + for item in data: + t3 = ttime() + if return_fragment: + item = make_batch(item) + if item is None: + continue + + batch_phones:List[torch.LongTensor] = item["phones"] + # batch_phones:torch.LongTensor = item["phones"] + batch_phones_len:torch.LongTensor = item["phones_len"] + all_phoneme_ids:torch.LongTensor = item["all_phones"] + all_phoneme_lens:torch.LongTensor = item["all_phones_len"] + all_bert_features:torch.LongTensor = item["all_bert_features"] + norm_text:str = item["norm_text"] + max_len = item["max_len"] + + print(i18n("前端处理后的文本(每句):"), norm_text) + if no_prompt_text : + prompt = None + else: + prompt = self.prompt_cache["prompt_semantic"].expand(len(all_phoneme_ids), -1).to(self.configs.device) + + + 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, + max_len=max_len, + repetition_penalty=repetition_penalty, + ) + t4 = ttime() + t_34 += t4 - t3 + + refer_audio_spec:torch.Tensor = [item.to(dtype=self.precision, device=self.configs.device) for item in self.prompt_cache["refer_spec"]] + + + 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_spec + # )) + + if speed_factor == 1.0: + # ## 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_spec, speed=speed_factor + ).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))] + else: + # ## 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_spec, speed=speed_factor + ).detach()[0, 0, :]) + batch_audio_fragment.append( + audio_fragment + ) ###试试重建不带上prompt部分 + + t5 = ttime() + t_45 += t5 - t4 + if return_fragment: + print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t4 - t3, t5 - t4)) + yield self.audio_postprocess([batch_audio_fragment], + self.configs.sampling_rate, + None, + speed_factor, + False, + fragment_interval + ) + else: + audio.append(batch_audio_fragment) + + if self.stop_flag: + yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate), + dtype=np.int16) + return + + if not return_fragment: + print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t_34, t_45)) + if len(audio) == 0: + yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate), + dtype=np.int16) + return + yield self.audio_postprocess(audio, + self.configs.sampling_rate, + batch_index_list, + speed_factor, + split_bucket, + fragment_interval + ) + + except Exception as e: + traceback.print_exc() + # 必须返回一个空音频, 否则会导致显存不释放。 + yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate), + dtype=np.int16) + # 重置模型, 否则会导致显存释放不完全。 + del self.t2s_model + del self.vits_model + self.t2s_model = None + self.vits_model = None + self.init_t2s_weights(self.configs.t2s_weights_path) + self.init_vits_weights(self.configs.vits_weights_path) + raise e + finally: + self.empty_cache() + + def empty_cache(self): + try: + gc.collect() # 触发gc的垃圾回收。避免内存一直增长。 + if "cuda" in str(self.configs.device): + torch.cuda.empty_cache() + elif str(self.configs.device) == "mps": + torch.mps.empty_cache() + except: + pass + + def audio_postprocess(self, + audio:List[torch.Tensor], + sr:int, + batch_index_list:list=None, + speed_factor:float=1.0, + split_bucket:bool=True, + fragment_interval:float=0.3 + )->Tuple[int, np.ndarray]: + zero_wav = torch.zeros( + int(self.configs.sampling_rate * fragment_interval), + dtype=self.precision, + device=self.configs.device + ) + + for i, batch in enumerate(audio): + for j, audio_fragment in enumerate(batch): + max_audio=torch.abs(audio_fragment).max()#简单防止16bit爆音 + if max_audio>1: audio_fragment/=max_audio + audio_fragment:torch.Tensor = torch.cat([audio_fragment, zero_wav], dim=0) + audio[i][j] = audio_fragment.cpu().numpy() + + + if split_bucket: + audio = self.recovery_order(audio, batch_index_list) + else: + # audio = [item for batch in audio for item in batch] + audio = sum(audio, []) + + + audio = np.concatenate(audio, 0) + audio = (audio * 32768).astype(np.int16) + + # try: + # if speed_factor != 1.0: + # audio = speed_change(audio, speed=speed_factor, sr=int(sr)) + # except Exception as e: + # print(f"Failed to change speed of audio: \n{e}") + + return sr, audio + + + + +def speed_change(input_audio:np.ndarray, speed:float, sr:int): + # 将 NumPy 数组转换为原始 PCM 流 + raw_audio = input_audio.astype(np.int16).tobytes() + + # 设置 ffmpeg 输入流 + input_stream = ffmpeg.input('pipe:', format='s16le', acodec='pcm_s16le', ar=str(sr), ac=1) + + # 变速处理 + output_stream = input_stream.filter('atempo', speed) + + # 输出流到管道 + out, _ = ( + output_stream.output('pipe:', format='s16le', acodec='pcm_s16le') + .run(input=raw_audio, capture_stdout=True, capture_stderr=True) + ) + + # 将管道输出解码为 NumPy 数组 + processed_audio = np.frombuffer(out, np.int16) + + return processed_audio \ No newline at end of file diff --git a/GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py b/GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py new file mode 100644 index 0000000..9f83f48 --- /dev/null +++ b/GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py @@ -0,0 +1,244 @@ + +import os, sys + +from tqdm import tqdm +now_dir = os.getcwd() +sys.path.append(now_dir) + +import re +import torch +import LangSegment +from text import chinese +from typing import Dict, List, Tuple +from text.cleaner import clean_text +from text import cleaned_text_to_sequence +from transformers import AutoModelForMaskedLM, AutoTokenizer +from TTS_infer_pack.text_segmentation_method import split_big_text, splits, get_method as get_seg_method + +from tools.i18n.i18n import I18nAuto, scan_language_list + +language=os.environ.get("language","Auto") +language=sys.argv[-1] if sys.argv[-1] in scan_language_list() else language +i18n = I18nAuto(language=language) +punctuation = set(['!', '?', '…', ',', '.', '-'," "]) + +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, version:str="v1")->List[Dict]: + print(i18n("############ 切分文本 ############")) + text = self.replace_consecutive_punctuation(text) # 变量命名应该是写错了 + texts = self.pre_seg_text(text, lang, text_split_method) + result = [] + print(i18n("############ 提取文本Bert特征 ############")) + for text in tqdm(texts): + phones, bert_features, norm_text = self.segment_and_extract_feature_for_text(text, lang, version) + if phones is None or norm_text=="": + continue + res={ + "phones": phones, + "bert_features": bert_features, + "norm_text": norm_text, + } + result.append(res) + return result + + def pre_seg_text(self, text:str, lang:str, text_split_method:str): + text = text.strip("\n") + if len(text) == 0: + return [] + if (text[0] not in splits and len(get_first(text)) < 4): + text = "。" + text if lang != "en" else "." + text + print(i18n("实际输入的目标文本:")) + print(text) + + seg_method = get_seg_method(text_split_method) + text = seg_method(text) + + while "\n\n" in text: + text = text.replace("\n\n", "\n") + + _texts = text.split("\n") + _texts = self.filter_text(_texts) + _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 not re.sub("\W+", "", text): + # 检测一下，如果是纯符号，就跳过。 + continue + if (text[-1] not in splits): text += "。" if lang != "en" else "." + + # 解决句子过长导致Bert报错的问题 + if (len(text) > 510): + texts.extend(split_big_text(text)) + else: + texts.append(text) + + print(i18n("实际输入的目标文本(切句后):")) + print(texts) + return texts + + def segment_and_extract_feature_for_text(self, text:str, language:str, version:str="v1")->Tuple[list, torch.Tensor, str]: + return self.get_phones_and_bert(text, language, version) + + def get_phones_and_bert(self, text:str, language:str, version:str, final:bool=False): + if language in {"en", "all_zh", "all_ja", "all_ko", "all_yue"}: + language = language.replace("all_","") + if language == "en": + LangSegment.setfilters(["en"]) + formattext = " ".join(tmp["text"] for tmp in LangSegment.getTexts(text)) + else: + # 因无法区别中日韩文汉字,以用户输入为准 + formattext = text + while " " in formattext: + formattext = formattext.replace(" ", " ") + if language == "zh": + if re.search(r'[A-Za-z]', formattext): + formattext = re.sub(r'[a-z]', lambda x: x.group(0).upper(), formattext) + formattext = chinese.mix_text_normalize(formattext) + return self.get_phones_and_bert(formattext,"zh",version) + else: + phones, word2ph, norm_text = self.clean_text_inf(formattext, language, version) + bert = self.get_bert_feature(norm_text, word2ph).to(self.device) + elif language == "yue" and re.search(r'[A-Za-z]', formattext): + formattext = re.sub(r'[a-z]', lambda x: x.group(0).upper(), formattext) + formattext = chinese.mix_text_normalize(formattext) + return self.get_phones_and_bert(formattext,"yue",version) + else: + phones, word2ph, norm_text = self.clean_text_inf(formattext, language, version) + bert = torch.zeros( + (1024, len(phones)), + dtype=torch.float32, + ).to(self.device) + elif language in {"zh", "ja", "ko", "yue", "auto", "auto_yue"}: + textlist=[] + langlist=[] + LangSegment.setfilters(["zh","ja","en","ko"]) + if language == "auto": + for tmp in LangSegment.getTexts(text): + langlist.append(tmp["lang"]) + textlist.append(tmp["text"]) + elif language == "auto_yue": + for tmp in LangSegment.getTexts(text): + if tmp["lang"] == "zh": + tmp["lang"] = "yue" + langlist.append(tmp["lang"]) + textlist.append(tmp["text"]) + else: + for tmp in LangSegment.getTexts(text): + if tmp["lang"] == "en": + langlist.append(tmp["lang"]) + else: + # 因无法区别中日韩文汉字,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 = self.clean_text_inf(textlist[i], lang, version) + bert = self.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) + + if not final and len(phones) < 6: + return self.get_phones_and_bert("." + text,language,version,final=True) + + return phones, bert, 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, version:str="v1"): + phones, word2ph, norm_text = clean_text(text, language, version) + phones = cleaned_text_to_sequence(phones, version) + 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 + + + def filter_text(self,texts): + _text=[] + if all(text in [None, " ", "\n",""] for text in texts): + raise ValueError(i18n("请输入有效文本")) + for text in texts: + if text in [None, " ", ""]: + pass + else: + _text.append(text) + return _text + + + def replace_consecutive_punctuation(self,text): + punctuations = ''.join(re.escape(p) for p in punctuation) + pattern = f'([{punctuations}])([{punctuations}])+' + result = re.sub(pattern, r'\1', text) + return result + + + 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..396c61f --- /dev/null +++ b/GPT_SoVITS/TTS_infer_pack/text_segmentation_method.py @@ -0,0 +1,173 @@ + + + + +import re +from typing import Callable + +punctuation = set(['!', '?', '…', ',', '.', '-'," "]) +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 get_method_names()->list: + return list(METHODS.keys()) + +def register_method(name): + def decorator(func): + METHODS[name] = func + return func + return decorator + +splits = {"，", "。", "？", "！", ",", ".", "?", "!", "~", ":", "：", "—", "…", } + +def split_big_text(text, max_len=510): + # 定义全角和半角标点符号 + punctuation = "".join(splits) + + # 切割文本 + segments = re.split('([' + punctuation + '])', text) + + # 初始化结果列表和当前片段 + result = [] + current_segment = '' + + for segment in segments: + # 如果当前片段加上新的片段长度超过max_len，就将当前片段加入结果列表，并重置当前片段 + if len(current_segment + segment) > max_len: + result.append(current_segment) + current_segment = segment + else: + current_segment += segment + + # 将最后一个片段加入结果列表 + if current_segment: + result.append(current_segment) + + return result + + + +def split(todo_text): + todo_text = todo_text.replace("……", "。").replace("——", "，") + if todo_text[-1] not in splits: + todo_text += "。" + i_split_head = i_split_tail = 0 + len_text = len(todo_text) + todo_texts = [] + while 1: + if i_split_head >= len_text: + break # 结尾一定有标点，所以直接跳出即可，最后一段在上次已加入 + if todo_text[i_split_head] in splits: + i_split_head += 1 + todo_texts.append(todo_text[i_split_tail:i_split_head]) + i_split_tail = i_split_head + else: + i_split_head += 1 + return todo_texts + + +# 不切 +@register_method("cut0") +def cut0(inp): + if not set(inp).issubset(punctuation): + return inp + else: + return "/n" + + +# 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] + opts = [item for item in opts if not set(item).issubset(punctuation)] + 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] + opts = [item for item in opts if not set(item).issubset(punctuation)] + return "\n".join(opts) + +# 按中文句号。切 +@register_method("cut3") +def cut3(inp): + inp = inp.strip("\n") + opts = ["%s" % item for item in inp.strip("。").split("。")] + opts = [item for item in opts if not set(item).issubset(punctuation)] + return "\n".join(opts) + +#按英文句号.切 +@register_method("cut4") +def cut4(inp): + inp = inp.strip("\n") + opts = ["%s" % item for item in inp.strip(".").split(".")] + opts = [item for item in opts if not set(item).issubset(punctuation)] + return "\n".join(opts) + +# 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): + inp = inp.strip("\n") + punds = {',', '.', ';', '?', '!', '、', '，', '。', '？', '！', ';', '：', '…'} + mergeitems = [] + items = [] + + for i, char in enumerate(inp): + if char in punds: + if char == '.' and i > 0 and i < len(inp) - 1 and inp[i - 1].isdigit() and inp[i + 1].isdigit(): + items.append(char) + else: + items.append(char) + mergeitems.append("".join(items)) + items = [] + else: + items.append(char) + + if items: + mergeitems.append("".join(items)) + + opt = [item for item in mergeitems if not set(item).issubset(punds)] + return "\n".join(opt) + + + +if __name__ == '__main__': + method = get_method("cut5") + print(method("你好，我是小明。你好，我是小红。你好，我是小刚。你好，我是小张。")) + diff --git a/GPT_SoVITS/configs/tts_infer.yaml b/GPT_SoVITS/configs/tts_infer.yaml new file mode 100644 index 0000000..66f1193 --- /dev/null +++ b/GPT_SoVITS/configs/tts_infer.yaml @@ -0,0 +1,24 @@ +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/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt + version: v2 + vits_weights_path: GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.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 + version: v1 + vits_weights_path: GPT_SoVITS/pretrained_models/s2G488k.pth +default_v2: + 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/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt + version: v2 + vits_weights_path: GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth diff --git a/GPT_SoVITS/feature_extractor/cnhubert.py b/GPT_SoVITS/feature_extractor/cnhubert.py index d7e380b..013e462 100644 --- a/GPT_SoVITS/feature_extractor/cnhubert.py +++ b/GPT_SoVITS/feature_extractor/cnhubert.py @@ -23,13 +23,15 @@ cnhubert_base_path = None class CNHubert(nn.Module): - def __init__(self): + def __init__(self, base_path:str=None): super().__init__() - if os.path.exists(cnhubert_base_path):... - else:raise FileNotFoundError(cnhubert_base_path) - self.model = HubertModel.from_pretrained(cnhubert_base_path, local_files_only=True) + if base_path is None: + base_path = cnhubert_base_path + if os.path.exists(base_path):... + else:raise FileNotFoundError(base_path) + self.model = HubertModel.from_pretrained(base_path, local_files_only=True) self.feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained( - cnhubert_base_path, local_files_only=True + base_path, local_files_only=True ) def forward(self, x): diff --git a/GPT_SoVITS/inference_webui_fast.py b/GPT_SoVITS/inference_webui_fast.py new file mode 100644 index 0000000..ebf85c9 --- /dev/null +++ b/GPT_SoVITS/inference_webui_fast.py @@ -0,0 +1,331 @@ +''' +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 random +import os, re, logging +import sys +now_dir = os.getcwd() +sys.path.append(now_dir) +sys.path.append("%s/GPT_SoVITS" % (now_dir)) + +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 + + +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 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) +version=os.environ.get("version","v2") + +import gradio as gr +from TTS_infer_pack.TTS import TTS, TTS_Config +from TTS_infer_pack.text_segmentation_method import get_method +from tools.i18n.i18n import I18nAuto, scan_language_list + +language=os.environ.get("language","Auto") +language=sys.argv[-1] if sys.argv[-1] in scan_language_list() else language +i18n = I18nAuto(language=language) + + +# 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" +# elif torch.backends.mps.is_available(): +# device = "mps" +else: + device = "cpu" + +dict_language_v1 = { + 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 +} +dict_language_v2 = { + 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("粤语"): "all_yue",#全部按中文识别 + i18n("韩文"): "all_ko",#全部按韩文识别 + 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("粤英混合"): "yue",#按粤英混合识别####不变 + i18n("韩英混合"): "ko",#按韩英混合识别####不变 + i18n("多语种混合"): "auto",#多语种启动切分识别语种 + i18n("多语种混合(粤语)"): "auto_yue",#多语种启动切分识别语种 +} +dict_language = dict_language_v1 if version =='v1' else dict_language_v2 + +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", +} + +tts_config = TTS_Config("GPT_SoVITS/configs/tts_infer.yaml") +tts_config.device = device +tts_config.is_half = is_half +tts_config.version = version +if gpt_path is not None: + tts_config.t2s_weights_path = gpt_path +if sovits_path is not None: + tts_config.vits_weights_path = sovits_path +if cnhubert_base_path is not None: + tts_config.cnhuhbert_base_path = cnhubert_base_path +if bert_path is not None: + tts_config.bert_base_path = bert_path + +print(tts_config) +tts_pipeline = TTS(tts_config) +gpt_path = tts_config.t2s_weights_path +sovits_path = tts_config.vits_weights_path +version = tts_config.version + +def inference(text, text_lang, + ref_audio_path, + aux_ref_audio_paths, + prompt_text, + prompt_lang, top_k, + top_p, temperature, + text_split_method, batch_size, + speed_factor, ref_text_free, + split_bucket,fragment_interval, + seed, keep_random, parallel_infer, + repetition_penalty + ): + + seed = -1 if keep_random else seed + actual_seed = seed if seed not in [-1, "", None] else random.randrange(1 << 32) + inputs={ + "text": text, + "text_lang": dict_language[text_lang], + "ref_audio_path": ref_audio_path, + "aux_ref_audio_paths": [item.name for item in aux_ref_audio_paths] if aux_ref_audio_paths is not None else [], + "prompt_text": prompt_text if not ref_text_free else "", + "prompt_lang": dict_language[prompt_lang], + "top_k": top_k, + "top_p": top_p, + "temperature": temperature, + "text_split_method": cut_method[text_split_method], + "batch_size":int(batch_size), + "speed_factor":float(speed_factor), + "split_bucket":split_bucket, + "return_fragment":False, + "fragment_interval":fragment_interval, + "seed":actual_seed, + "parallel_infer": parallel_infer, + "repetition_penalty": repetition_penalty, + } + for item in tts_pipeline.run(inputs): + yield item, actual_seed + +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(GPT_weight_root, SoVITS_weight_root) + 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/gsv-v2final-pretrained/s2G2333k.pth", "GPT_SoVITS/pretrained_models/s2G488k.pth"] +pretrained_gpt_name=["GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt", "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"] +_ =[[],[]] +for i in range(2): + if os.path.exists(pretrained_gpt_name[i]): + _[0].append(pretrained_gpt_name[i]) + if os.path.exists(pretrained_sovits_name[i]): + _[-1].append(pretrained_sovits_name[i]) +pretrained_gpt_name,pretrained_sovits_name = _ + +SoVITS_weight_root=["SoVITS_weights_v2","SoVITS_weights"] +GPT_weight_root=["GPT_weights_v2","GPT_weights"] +for path in SoVITS_weight_root+GPT_weight_root: + os.makedirs(path,exist_ok=True) + +def get_weights_names(GPT_weight_root, SoVITS_weight_root): + SoVITS_names = [i for i in pretrained_sovits_name] + for path in SoVITS_weight_root: + for name in os.listdir(path): + if name.endswith(".pth"): SoVITS_names.append("%s/%s" % (path, name)) + GPT_names = [i for i in pretrained_gpt_name] + for path in GPT_weight_root: + for name in os.listdir(path): + if name.endswith(".ckpt"): GPT_names.append("%s/%s" % (path, name)) + return SoVITS_names, GPT_names + + +SoVITS_names, GPT_names = get_weights_names(GPT_weight_root, SoVITS_weight_root) + + + +def change_sovits_weights(sovits_path,prompt_language=None,text_language=None): + tts_pipeline.init_vits_weights(sovits_path) + global version, dict_language + dict_language = dict_language_v1 if tts_pipeline.configs.version =='v1' else dict_language_v2 + if prompt_language is not None and text_language is not None: + if prompt_language in list(dict_language.keys()): + prompt_text_update, prompt_language_update = {'__type__':'update'}, {'__type__':'update', 'value':prompt_language} + else: + prompt_text_update = {'__type__':'update', 'value':''} + prompt_language_update = {'__type__':'update', 'value':i18n("中文")} + if text_language in list(dict_language.keys()): + text_update, text_language_update = {'__type__':'update'}, {'__type__':'update', 'value':text_language} + else: + text_update = {'__type__':'update', 'value':''} + text_language_update = {'__type__':'update', 'value':i18n("中文")} + return {'__type__':'update', 'choices':list(dict_language.keys())}, {'__type__':'update', 'choices':list(dict_language.keys())}, prompt_text_update, prompt_language_update, text_update, text_language_update + + + +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.Column(): + # with gr.Group(): + gr.Markdown(value=i18n("模型切换")) + with gr.Row(): + GPT_dropdown = gr.Dropdown(label=i18n("GPT模型列表"), choices=sorted(GPT_names, key=custom_sort_key), value=gpt_path, interactive=True) + SoVITS_dropdown = gr.Dropdown(label=i18n("SoVITS模型列表"), choices=sorted(SoVITS_names, key=custom_sort_key), value=sovits_path, interactive=True) + refresh_button = gr.Button(i18n("刷新模型路径"), variant="primary") + refresh_button.click(fn=change_choices, inputs=[], outputs=[SoVITS_dropdown, GPT_dropdown]) + + + with gr.Row(): + with gr.Column(): + gr.Markdown(value=i18n("*请上传并填写参考信息")) + with gr.Row(): + inp_ref = gr.Audio(label=i18n("主参考音频(请上传3~10秒内参考音频，超过会报错！)"), type="filepath") + inp_refs = gr.File(label=i18n("辅参考音频(可选多个，或不选)"),file_count="multiple", type="file") + prompt_text = gr.Textbox(label=i18n("主参考音频的文本"), value="", lines=2) + with gr.Row(): + prompt_language = gr.Dropdown( + label=i18n("主参考音频的语种"), choices=list(dict_language.keys()), 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=20, max_lines=20) + text_language = gr.Dropdown( + label=i18n("需要合成的文本的语种"), choices=list(dict_language.keys()), value=i18n("中文") + ) + + + with gr.Group(): + gr.Markdown(value=i18n("推理设置")) + with gr.Row(): + + with gr.Column(): + batch_size = gr.Slider(minimum=1,maximum=200,step=1,label=i18n("batch_size"),value=20,interactive=True) + fragment_interval = gr.Slider(minimum=0.01,maximum=1,step=0.01,label=i18n("分段间隔(秒)"),value=0.3,interactive=True) + speed_factor = gr.Slider(minimum=0.6,maximum=1.65,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) + repetition_penalty = gr.Slider(minimum=0,maximum=2,step=0.05,label=i18n("重复惩罚"),value=1.35,interactive=True) + with gr.Column(): + with gr.Row(): + how_to_cut = gr.Dropdown( + 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, scale=1 + ) + parallel_infer = gr.Checkbox(label=i18n("并行推理"), value=True, interactive=True, show_label=True) + split_bucket = gr.Checkbox(label=i18n("数据分桶(并行推理时会降低一点计算量)"), value=True, interactive=True, show_label=True) + + with gr.Row(): + seed = gr.Number(label=i18n("随机种子"),value=-1) + keep_random = gr.Checkbox(label=i18n("保持随机"), value=True, interactive=True, show_label=True) + + 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, inp_refs, + prompt_text, prompt_language, + top_k, top_p, temperature, + how_to_cut, batch_size, + speed_factor, ref_text_free, + split_bucket,fragment_interval, + seed, keep_random, parallel_infer, + repetition_penalty + ], + [output, seed], + ) + stop_infer.click(tts_pipeline.stop, [], []) + SoVITS_dropdown.change(change_sovits_weights, [SoVITS_dropdown,prompt_language,text_language], [prompt_language,text_language,prompt_text,prompt_language,text,text_language]) + GPT_dropdown.change(tts_pipeline.init_t2s_weights, [GPT_dropdown], []) + + 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="", 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。")) + +if __name__ == '__main__': + app.queue(concurrency_count=511, max_size=1022).launch( + server_name="0.0.0.0", + inbrowser=True, + share=is_share, + server_port=infer_ttswebui, + quiet=True, + ) diff --git a/api_v2.py b/api_v2.py new file mode 100644 index 0000000..2414af0 --- /dev/null +++ b/api_v2.py @@ -0,0 +1,458 @@ +""" +# WebAPI文档 + +` python api_v2.py -a 127.0.0.1 -p 9880 -c GPT_SoVITS/configs/tts_infer.yaml ` + +## 执行参数: + `-a` - `绑定地址, 默认"127.0.0.1"` + `-p` - `绑定端口, 默认9880` + `-c` - `TTS配置文件路径, 默认"GPT_SoVITS/configs/tts_infer.yaml"` + +## 调用: + +### 推理 + +endpoint: `/tts` +GET: +``` +http://127.0.0.1:9880/tts?text=先帝创业未半而中道崩殂，今天下三分，益州疲弊，此诚危急存亡之秋也。&text_lang=zh&ref_audio_path=archive_jingyuan_1.wav&prompt_lang=zh&prompt_text=我是「罗浮」云骑将军景元。不必拘谨，「将军」只是一时的身份，你称呼我景元便可&text_split_method=cut5&batch_size=1&media_type=wav&streaming_mode=true +``` + +POST: +```json +{ + "text": "", # str.(required) text to be synthesized + "text_lang: "", # str.(required) language of the text to be synthesized + "ref_audio_path": "", # str.(required) reference audio path + "aux_ref_audio_paths": [], # list.(optional) auxiliary reference audio paths for multi-speaker synthesis + "prompt_text": "", # str.(optional) prompt text for the reference audio + "prompt_lang": "", # str.(required) language of the prompt text for the reference audio + "top_k": 5, # int. top k sampling + "top_p": 1, # float. top p sampling + "temperature": 1, # float. temperature for sampling + "text_split_method": "cut0", # str. text split method, see text_segmentation_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. + "streaming_mode": False, # bool. whether to return a streaming response. + "seed": -1, # int. random seed for reproducibility. + "parallel_infer": True, # bool. whether to use parallel inference. + "repetition_penalty": 1.35 # float. repetition penalty for T2S model. +} +``` + +RESP: +成功: 直接返回 wav 音频流， http code 200 +失败: 返回包含错误信息的 json, http code 400 + +### 命令控制 + +endpoint: `/control` + +command: +"restart": 重新运行 +"exit": 结束运行 + +GET: +``` +http://127.0.0.1:9880/control?command=restart +``` +POST: +```json +{ + "command": "restart" +} +``` + +RESP: 无 + + +### 切换GPT模型 + +endpoint: `/set_gpt_weights` + +GET: +``` +http://127.0.0.1:9880/set_gpt_weights?weights_path=GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt +``` +RESP: +成功: 返回"success", http code 200 +失败: 返回包含错误信息的 json, http code 400 + + +### 切换Sovits模型 + +endpoint: `/set_sovits_weights` + +GET: +``` +http://127.0.0.1:9880/set_sovits_weights?weights_path=GPT_SoVITS/pretrained_models/s2G488k.pth +``` + +RESP: +成功: 返回"success", http code 200 +失败: 返回包含错误信息的 json, http code 400 + +""" +import os +import sys +import traceback +from typing import Generator + +now_dir = os.getcwd() +sys.path.append(now_dir) +sys.path.append("%s/GPT_SoVITS" % (now_dir)) + +import argparse +import subprocess +import wave +import signal +import numpy as np +import soundfile as sf +from fastapi import FastAPI, Request, HTTPException, Response +from fastapi.responses import StreamingResponse, JSONResponse +from fastapi import FastAPI, UploadFile, File +import uvicorn +from io import BytesIO +from tools.i18n.i18n import I18nAuto +from GPT_SoVITS.TTS_infer_pack.TTS import TTS, TTS_Config +from GPT_SoVITS.TTS_infer_pack.text_segmentation_method import get_method_names as get_cut_method_names +from fastapi.responses import StreamingResponse +from pydantic import BaseModel +# print(sys.path) +i18n = I18nAuto() +cut_method_names = get_cut_method_names() + +parser = argparse.ArgumentParser(description="GPT-SoVITS api") +parser.add_argument("-c", "--tts_config", type=str, default="GPT_SoVITS/configs/tts_infer.yaml", help="tts_infer路径") +parser.add_argument("-a", "--bind_addr", type=str, default="127.0.0.1", help="default: 127.0.0.1") +parser.add_argument("-p", "--port", type=int, default="9880", help="default: 9880") +args = parser.parse_args() +config_path = args.tts_config +# device = args.device +port = args.port +host = args.bind_addr +argv = sys.argv + +if config_path in [None, ""]: + config_path = "GPT-SoVITS/configs/tts_infer.yaml" + +tts_config = TTS_Config(config_path) +print(tts_config) +tts_pipeline = TTS(tts_config) + +APP = FastAPI() +class TTS_Request(BaseModel): + text: str = None + text_lang: str = None + ref_audio_path: str = None + aux_ref_audio_paths: list = None + prompt_lang: str = None + prompt_text: str = "" + top_k:int = 5 + top_p:float = 1 + temperature:float = 1 + text_split_method:str = "cut5" + batch_size:int = 1 + batch_threshold:float = 0.75 + split_bucket:bool = True + speed_factor:float = 1.0 + fragment_interval:float = 0.3 + seed:int = -1 + media_type:str = "wav" + streaming_mode:bool = False + parallel_infer:bool = True + repetition_penalty:float = 1.35 + +### modify from https://github.com/RVC-Boss/GPT-SoVITS/pull/894/files +def pack_ogg(io_buffer:BytesIO, data:np.ndarray, rate:int): + with sf.SoundFile(io_buffer, mode='w', samplerate=rate, channels=1, format='ogg') as audio_file: + audio_file.write(data) + return io_buffer + + +def pack_raw(io_buffer:BytesIO, data:np.ndarray, rate:int): + io_buffer.write(data.tobytes()) + return io_buffer + + +def pack_wav(io_buffer:BytesIO, data:np.ndarray, rate:int): + io_buffer = BytesIO() + sf.write(io_buffer, data, rate, format='wav') + return io_buffer + +def pack_aac(io_buffer:BytesIO, data:np.ndarray, rate:int): + process = subprocess.Popen([ + 'ffmpeg', + '-f', 's16le', # 输入16位有符号小端整数PCM + '-ar', str(rate), # 设置采样率 + '-ac', '1', # 单声道 + '-i', 'pipe:0', # 从管道读取输入 + '-c:a', 'aac', # 音频编码器为AAC + '-b:a', '192k', # 比特率 + '-vn', # 不包含视频 + '-f', 'adts', # 输出AAC数据流格式 + 'pipe:1' # 将输出写入管道 + ], stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE) + out, _ = process.communicate(input=data.tobytes()) + io_buffer.write(out) + return io_buffer + +def pack_audio(io_buffer:BytesIO, data:np.ndarray, rate:int, media_type:str): + if media_type == "ogg": + io_buffer = pack_ogg(io_buffer, data, rate) + elif media_type == "aac": + io_buffer = pack_aac(io_buffer, data, rate) + elif media_type == "wav": + io_buffer = pack_wav(io_buffer, data, rate) + else: + io_buffer = pack_raw(io_buffer, data, rate) + io_buffer.seek(0) + return io_buffer + + + +# from https://huggingface.co/spaces/coqui/voice-chat-with-mistral/blob/main/app.py +def wave_header_chunk(frame_input=b"", channels=1, sample_width=2, sample_rate=32000): + # This will create a wave header then append the frame input + # It should be first on a streaming wav file + # Other frames better should not have it (else you will hear some artifacts each chunk start) + wav_buf = BytesIO() + with wave.open(wav_buf, "wb") as vfout: + vfout.setnchannels(channels) + vfout.setsampwidth(sample_width) + vfout.setframerate(sample_rate) + vfout.writeframes(frame_input) + + wav_buf.seek(0) + return wav_buf.read() + + +def handle_control(command:str): + if command == "restart": + os.execl(sys.executable, sys.executable, *argv) + elif command == "exit": + os.kill(os.getpid(), signal.SIGTERM) + exit(0) + + +def check_params(req:dict): + text:str = req.get("text", "") + text_lang:str = req.get("text_lang", "") + ref_audio_path:str = req.get("ref_audio_path", "") + streaming_mode:bool = req.get("streaming_mode", False) + media_type:str = req.get("media_type", "wav") + prompt_lang:str = req.get("prompt_lang", "") + text_split_method:str = req.get("text_split_method", "cut5") + + if ref_audio_path in [None, ""]: + return JSONResponse(status_code=400, content={"message": "ref_audio_path is required"}) + if text in [None, ""]: + return JSONResponse(status_code=400, content={"message": "text is required"}) + if (text_lang in [None, ""]) : + return JSONResponse(status_code=400, content={"message": "text_lang is required"}) + elif text_lang.lower() not in tts_config.languages: + return JSONResponse(status_code=400, content={"message": "text_lang is not supported"}) + if (prompt_lang in [None, ""]) : + return JSONResponse(status_code=400, content={"message": "prompt_lang is required"}) + elif prompt_lang.lower() not in tts_config.languages: + return JSONResponse(status_code=400, content={"message": "prompt_lang is not supported"}) + if media_type not in ["wav", "raw", "ogg", "aac"]: + return JSONResponse(status_code=400, content={"message": "media_type is not supported"}) + elif media_type == "ogg" and not streaming_mode: + return JSONResponse(status_code=400, content={"message": "ogg format is not supported in non-streaming mode"}) + + if text_split_method not in cut_method_names: + return JSONResponse(status_code=400, content={"message": f"text_split_method:{text_split_method} is not supported"}) + + return None + +async def tts_handle(req:dict): + """ + Text to speech handler. + + Args: + req (dict): + { + "text": "", # str.(required) text to be synthesized + "text_lang: "", # str.(required) language of the text to be synthesized + "ref_audio_path": "", # str.(required) reference audio path + "aux_ref_audio_paths": [], # list.(optional) auxiliary reference audio paths for multi-speaker synthesis + "prompt_text": "", # str.(optional) prompt text for the reference audio + "prompt_lang": "", # str.(required) language of the prompt text for the reference audio + "top_k": 5, # int. top k sampling + "top_p": 1, # float. top p sampling + "temperature": 1, # float. temperature for sampling + "text_split_method": "cut5", # str. text split method, see text_segmentation_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. + "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. + "media_type": "wav", # str. media type of the output audio, support "wav", "raw", "ogg", "aac". + "streaming_mode": False, # bool. whether to return a streaming response. + "parallel_infer": True, # bool.(optional) whether to use parallel inference. + "repetition_penalty": 1.35 # float.(optional) repetition penalty for T2S model. + } + returns: + StreamingResponse: audio stream response. + """ + + streaming_mode = req.get("streaming_mode", False) + media_type = req.get("media_type", "wav") + + check_res = check_params(req) + if check_res is not None: + return check_res + + if streaming_mode: + req["return_fragment"] = True + + try: + tts_generator=tts_pipeline.run(req) + + if streaming_mode: + def streaming_generator(tts_generator:Generator, media_type:str): + if media_type == "wav": + yield wave_header_chunk() + media_type = "raw" + for sr, chunk in tts_generator: + yield pack_audio(BytesIO(), chunk, sr, media_type).getvalue() + # _media_type = f"audio/{media_type}" if not (streaming_mode and media_type in ["wav", "raw"]) else f"audio/x-{media_type}" + return StreamingResponse(streaming_generator(tts_generator, media_type, ), media_type=f"audio/{media_type}") + + else: + sr, audio_data = next(tts_generator) + audio_data = pack_audio(BytesIO(), audio_data, sr, media_type).getvalue() + return Response(audio_data, media_type=f"audio/{media_type}") + except Exception as e: + return JSONResponse(status_code=400, content={"message": f"tts failed", "Exception": str(e)}) + + + + + + +@APP.get("/control") +async def control(command: str = None): + if command is None: + return JSONResponse(status_code=400, content={"message": "command is required"}) + handle_control(command) + + + +@APP.get("/tts") +async def tts_get_endpoint( + text: str = None, + text_lang: str = None, + ref_audio_path: str = None, + aux_ref_audio_paths:list = None, + prompt_lang: str = None, + prompt_text: str = "", + top_k:int = 5, + top_p:float = 1, + temperature:float = 1, + text_split_method:str = "cut0", + batch_size:int = 1, + batch_threshold:float = 0.75, + split_bucket:bool = True, + speed_factor:float = 1.0, + fragment_interval:float = 0.3, + seed:int = -1, + media_type:str = "wav", + streaming_mode:bool = False, + parallel_infer:bool = True, + repetition_penalty:float = 1.35 + ): + req = { + "text": text, + "text_lang": text_lang.lower(), + "ref_audio_path": ref_audio_path, + "aux_ref_audio_paths": aux_ref_audio_paths, + "prompt_text": prompt_text, + "prompt_lang": prompt_lang.lower(), + "top_k": top_k, + "top_p": top_p, + "temperature": temperature, + "text_split_method": text_split_method, + "batch_size":int(batch_size), + "batch_threshold":float(batch_threshold), + "speed_factor":float(speed_factor), + "split_bucket":split_bucket, + "fragment_interval":fragment_interval, + "seed":seed, + "media_type":media_type, + "streaming_mode":streaming_mode, + "parallel_infer":parallel_infer, + "repetition_penalty":float(repetition_penalty) + } + return await tts_handle(req) + + +@APP.post("/tts") +async def tts_post_endpoint(request: TTS_Request): + req = request.dict() + return await tts_handle(req) + + +@APP.get("/set_refer_audio") +async def set_refer_aduio(refer_audio_path: str = None): + try: + tts_pipeline.set_ref_audio(refer_audio_path) + except Exception as e: + return JSONResponse(status_code=400, content={"message": f"set refer audio failed", "Exception": str(e)}) + return JSONResponse(status_code=200, content={"message": "success"}) + + +# @APP.post("/set_refer_audio") +# async def set_refer_aduio_post(audio_file: UploadFile = File(...)): +# try: +# # 检查文件类型，确保是音频文件 +# if not audio_file.content_type.startswith("audio/"): +# return JSONResponse(status_code=400, content={"message": "file type is not supported"}) + +# os.makedirs("uploaded_audio", exist_ok=True) +# save_path = os.path.join("uploaded_audio", audio_file.filename) +# # 保存音频文件到服务器上的一个目录 +# with open(save_path , "wb") as buffer: +# buffer.write(await audio_file.read()) + +# tts_pipeline.set_ref_audio(save_path) +# except Exception as e: +# return JSONResponse(status_code=400, content={"message": f"set refer audio failed", "Exception": str(e)}) +# return JSONResponse(status_code=200, content={"message": "success"}) + +@APP.get("/set_gpt_weights") +async def set_gpt_weights(weights_path: str = None): + try: + if weights_path in ["", None]: + return JSONResponse(status_code=400, content={"message": "gpt weight path is required"}) + tts_pipeline.init_t2s_weights(weights_path) + except Exception as e: + return JSONResponse(status_code=400, content={"message": f"change gpt weight failed", "Exception": str(e)}) + + return JSONResponse(status_code=200, content={"message": "success"}) + + +@APP.get("/set_sovits_weights") +async def set_sovits_weights(weights_path: str = None): + try: + if weights_path in ["", None]: + return JSONResponse(status_code=400, content={"message": "sovits weight path is required"}) + tts_pipeline.init_vits_weights(weights_path) + except Exception as e: + return JSONResponse(status_code=400, content={"message": f"change sovits weight failed", "Exception": str(e)}) + return JSONResponse(status_code=200, content={"message": "success"}) + + + +if __name__ == "__main__": + try: + uvicorn.run(app=APP, host=host, port=port, workers=1) + except Exception as e: + traceback.print_exc() + os.kill(os.getpid(), signal.SIGTERM) + exit(0) diff --git a/webui.py b/webui.py index d483a67..840be7c 100644 --- a/webui.py +++ b/webui.py @@ -223,8 +223,12 @@ def change_uvr5(): p_uvr5=None yield i18n("UVR5已关闭"), {'__type__':'update','visible':True}, {'__type__':'update','visible':False} -def change_tts_inference(bert_path,cnhubert_base_path,gpu_number,gpt_path,sovits_path): +def change_tts_inference(bert_path,cnhubert_base_path,gpu_number,gpt_path,sovits_path, batched_infer_enabled): global p_tts_inference + if batched_infer_enabled: + cmd = '"%s" GPT_SoVITS/inference_webui_fast.py "%s"'%(python_exec, language) + else: + cmd = '"%s" GPT_SoVITS/inference_webui.py "%s"'%(python_exec, language) if(p_tts_inference==None): os.environ["gpt_path"]=gpt_path if "/" in gpt_path else "%s/%s"%(GPT_weight_root,gpt_path) os.environ["sovits_path"]=sovits_path if "/"in sovits_path else "%s/%s"%(SoVITS_weight_root,sovits_path) @@ -234,7 +238,6 @@ def change_tts_inference(bert_path,cnhubert_base_path,gpu_number,gpt_path,sovits os.environ["is_half"]=str(is_half) os.environ["infer_ttswebui"]=str(webui_port_infer_tts) os.environ["is_share"]=str(is_share) - cmd = '"%s" GPT_SoVITS/inference_webui.py "%s"'%(python_exec, language) yield i18n("TTS推理进程已开启"), {'__type__':'update','visible':False}, {'__type__':'update','visible':True} print(cmd) p_tts_inference = Popen(cmd, shell=True) @@ -1031,13 +1034,15 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app: 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]) with gr.Row(): + with gr.Row(): + batched_infer_enabled = gr.Checkbox(label=i18n("启用并行推理版本(推理速度更快)"), value=False, interactive=True, show_label=True) with gr.Row(): open_tts = gr.Button(value=i18n("开启TTS推理WebUI"),variant='primary',visible=True) close_tts = gr.Button(value=i18n("关闭TTS推理WebUI"),variant='primary',visible=False) with gr.Row(): tts_info = gr.Textbox(label=i18n("TTS推理WebUI进程输出信息")) - open_tts.click(change_tts_inference, [bert_pretrained_dir,cnhubert_base_dir,gpu_number_1C,GPT_dropdown,SoVITS_dropdown], [tts_info,open_tts,close_tts]) - close_tts.click(change_tts_inference, [bert_pretrained_dir,cnhubert_base_dir,gpu_number_1C,GPT_dropdown,SoVITS_dropdown], [tts_info,open_tts,close_tts]) + open_tts.click(change_tts_inference, [bert_pretrained_dir,cnhubert_base_dir,gpu_number_1C,GPT_dropdown,SoVITS_dropdown, batched_infer_enabled], [tts_info,open_tts,close_tts]) + close_tts.click(change_tts_inference, [bert_pretrained_dir,cnhubert_base_dir,gpu_number_1C,GPT_dropdown,SoVITS_dropdown, batched_infer_enabled], [tts_info,open_tts,close_tts]) version_checkbox.change(switch_version,[version_checkbox],[pretrained_s2G,pretrained_s2D,pretrained_s1,GPT_dropdown,SoVITS_dropdown]) with gr.TabItem(i18n("2-GPT-SoVITS-变声")):gr.Markdown(value=i18n("施工中，请静候佳音")) app.queue(concurrency_count=511, max_size=1022).launch(