mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-05-26 01:19:24 +08:00
忽略ffmpeg .gitignore
使t2s模型支持批量推理: GPT_SoVITS/AR/models/t2s_model.py
修复batch bug GPT_SoVITS/AR/models/utils.py
重构的tts infer GPT_SoVITS/TTS_infer_pack/TTS.py
文本预处理模块 GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py
new file GPT_SoVITS/TTS_infer_pack/__init__.py
文本拆分方法模块 GPT_SoVITS/TTS_infer_pack/text_segmentation_method.py
tts infer配置文件 GPT_SoVITS/configs/tts_infer.yaml
modified GPT_SoVITS/feature_extractor/cnhubert.py
modified GPT_SoVITS/inference_gui.py
重构的webui GPT_SoVITS/inference_webui.py
new file GPT_SoVITS/inference_webui_old.py
This commit is contained in:
chasonjiang
parent
e04b3f6d6b
commit
17832e5c4a
3
.gitignore
vendored
3
.gitignore
vendored
@ -10,5 +10,6 @@ reference
|
||||
GPT_weights
|
||||
SoVITS_weights
|
||||
TEMP
|
||||
|
||||
ffmpeg.exe
|
||||
ffprobe.exe
|
||||
|
||||
|
||||
@ -1,5 +1,4 @@
|
||||
# modified from https://github.com/yangdongchao/SoundStorm/blob/master/soundstorm/s1/AR/models/t2s_model.py
|
||||
# reference: https://github.com/lifeiteng/vall-e
|
||||
# modified from https://github.com/feng-yufei/shared_debugging_code/blob/main/model/t2s_model.py
|
||||
import torch
|
||||
from tqdm import tqdm
|
||||
|
||||
@ -386,7 +385,9 @@ class Text2SemanticDecoder(nn.Module):
|
||||
x.device
|
||||
)
|
||||
|
||||
|
||||
y_list = [None]*y.shape[0]
|
||||
batch_idx_map = list(range(y.shape[0]))
|
||||
idx_list = [None]*y.shape[0]
|
||||
for idx in tqdm(range(1500)):
|
||||
|
||||
xy_dec, _ = self.h((xy_pos, None), mask=xy_attn_mask, cache=cache)
|
||||
@ -397,17 +398,45 @@ class Text2SemanticDecoder(nn.Module):
|
||||
if(idx==0):###第一次跑不能EOS否则没有了
|
||||
logits = logits[:, :-1] ###刨除1024终止符号的概率
|
||||
samples = sample(
|
||||
logits[0], y, top_k=top_k, top_p=top_p, repetition_penalty=1.35, temperature=temperature
|
||||
)[0].unsqueeze(0)
|
||||
logits, y, top_k=top_k, top_p=top_p, repetition_penalty=1.35, temperature=temperature
|
||||
)[0]
|
||||
# 本次生成的 semantic_ids 和之前的 y 构成新的 y
|
||||
# print(samples.shape)#[1,1]#第一个1是bs
|
||||
y = torch.concat([y, samples], dim=1)
|
||||
|
||||
# 移除已经生成完毕的序列
|
||||
reserved_idx_of_batch_for_y = None
|
||||
if (self.EOS in torch.argmax(logits, dim=-1)) or \
|
||||
(self.EOS in samples[:, 0]): ###如果生成到EOS，则停止
|
||||
l = samples[:, 0]==self.EOS
|
||||
removed_idx_of_batch_for_y = torch.where(l==True)[0].tolist()
|
||||
reserved_idx_of_batch_for_y = torch.where(l==False)[0]
|
||||
# batch_indexs = torch.tensor(batch_idx_map, device=y.device)[removed_idx_of_batch_for_y]
|
||||
for i in removed_idx_of_batch_for_y:
|
||||
batch_index = batch_idx_map[i]
|
||||
idx_list[batch_index] = idx - 1
|
||||
y_list[batch_index] = y[i, :-1]
|
||||
|
||||
batch_idx_map = [batch_idx_map[i] for i in reserved_idx_of_batch_for_y.tolist()]
|
||||
|
||||
# 只保留未生成完毕的序列
|
||||
if reserved_idx_of_batch_for_y is not None:
|
||||
# index = torch.LongTensor(batch_idx_map).to(y.device)
|
||||
y = torch.index_select(y, dim=0, index=reserved_idx_of_batch_for_y)
|
||||
if cache["y_emb"] is not None:
|
||||
cache["y_emb"] = torch.index_select(cache["y_emb"], dim=0, index=reserved_idx_of_batch_for_y)
|
||||
if cache["k"] is not None:
|
||||
for i in range(self.num_layers):
|
||||
# 因为kv转置了，所以batch dim是1
|
||||
cache["k"][i] = torch.index_select(cache["k"][i], dim=1, index=reserved_idx_of_batch_for_y)
|
||||
cache["v"][i] = torch.index_select(cache["v"][i], dim=1, index=reserved_idx_of_batch_for_y)
|
||||
|
||||
|
||||
if early_stop_num != -1 and (y.shape[1] - prefix_len) > early_stop_num:
|
||||
print("use early stop num:", early_stop_num)
|
||||
stop = True
|
||||
|
||||
if torch.argmax(logits, dim=-1)[0] == self.EOS or samples[0, 0] == self.EOS:
|
||||
|
||||
if not (None in idx_list):
|
||||
# print(torch.argmax(logits, dim=-1)[0] == self.EOS, samples[0, 0] == self.EOS)
|
||||
stop = True
|
||||
if stop:
|
||||
@ -443,6 +472,12 @@ class Text2SemanticDecoder(nn.Module):
|
||||
xy_attn_mask = torch.zeros(
|
||||
(1, x_len + y_len), dtype=torch.bool, device=xy_pos.device
|
||||
)
|
||||
|
||||
if (None in idx_list):
|
||||
for i in range(x.shape[0]):
|
||||
if idx_list[i] is None:
|
||||
idx_list[i] = 1500-1 ###如果没有生成到EOS，就用最大长度代替
|
||||
|
||||
if ref_free:
|
||||
return y[:, :-1], 0
|
||||
return y[:, :-1], idx-1
|
||||
return y_list, [0]*x.shape[0]
|
||||
return y_list, idx_list
|
||||
|
||||
@ -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"))
|
||||
|
||||
|
||||
546
GPT_SoVITS/TTS_infer_pack/TTS.py
Normal file
546
GPT_SoVITS/TTS_infer_pack/TTS.py
Normal file
@ -0,0 +1,546 @@
|
||||
import os, sys
|
||||
|
||||
now_dir = os.getcwd()
|
||||
sys.path.append(now_dir)
|
||||
import os
|
||||
from typing import Generator, List, Union
|
||||
import numpy as np
|
||||
import torch
|
||||
import yaml
|
||||
from transformers import AutoModelForMaskedLM, AutoTokenizer
|
||||
|
||||
from AR.models.t2s_lightning_module import Text2SemanticLightningModule
|
||||
from feature_extractor.cnhubert import CNHubert
|
||||
from module.models import SynthesizerTrn
|
||||
import librosa
|
||||
from time import time as ttime
|
||||
from tools.i18n.i18n import I18nAuto
|
||||
from my_utils import load_audio
|
||||
from module.mel_processing import spectrogram_torch
|
||||
from .text_segmentation_method import splits
|
||||
from .TextPreprocessor import TextPreprocessor
|
||||
i18n = I18nAuto()
|
||||
|
||||
# tts_infer.yaml
|
||||
"""
|
||||
default:
|
||||
device: cpu
|
||||
is_half: false
|
||||
bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large
|
||||
cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base
|
||||
t2s_weights_path: GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt
|
||||
vits_weights_path: GPT_SoVITS/pretrained_models/s2G488k.pth
|
||||
|
||||
custom:
|
||||
device: cuda
|
||||
is_half: true
|
||||
bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large
|
||||
cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base
|
||||
t2s_weights_path: GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt
|
||||
vits_weights_path: GPT_SoVITS/pretrained_models/s2G488k.pth
|
||||
|
||||
|
||||
|
||||
"""
|
||||
|
||||
|
||||
|
||||
class TTS_Config:
|
||||
def __init__(self, configs: Union[dict, str]):
|
||||
configs_base_path:str = "GPT_SoVITS/configs/"
|
||||
os.makedirs(configs_base_path, exist_ok=True)
|
||||
self.configs_path:str = os.path.join(configs_base_path, "tts_infer.yaml")
|
||||
if isinstance(configs, str):
|
||||
self.configs_path = configs
|
||||
configs:dict = self._load_configs(configs)
|
||||
|
||||
# assert isinstance(configs, dict)
|
||||
self.default_configs:dict = configs.get("default", None)
|
||||
if self.default_configs is None:
|
||||
self.default_configs={
|
||||
"device": "cpu",
|
||||
"is_half": False,
|
||||
"t2s_weights_path": "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt",
|
||||
"vits_weights_path": "GPT_SoVITS/pretrained_models/s2G488k.pth",
|
||||
"cnhuhbert_base_path": "GPT_SoVITS/pretrained_models/chinese-hubert-base",
|
||||
"bert_base_path": "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large"
|
||||
}
|
||||
self.configs:dict = configs.get("custom", self.default_configs)
|
||||
|
||||
self.device = self.configs.get("device")
|
||||
self.is_half = self.configs.get("is_half")
|
||||
self.t2s_weights_path = self.configs.get("t2s_weights_path")
|
||||
self.vits_weights_path = self.configs.get("vits_weights_path")
|
||||
self.bert_base_path = self.configs.get("bert_base_path")
|
||||
self.cnhuhbert_base_path = self.configs.get("cnhuhbert_base_path")
|
||||
|
||||
|
||||
self.max_sec = None
|
||||
self.hz:int = 50
|
||||
self.semantic_frame_rate:str = "25hz"
|
||||
self.segment_size:int = 20480
|
||||
self.filter_length:int = 2048
|
||||
self.sampling_rate:int = 32000
|
||||
self.hop_length:int = 640
|
||||
self.win_length:int = 2048
|
||||
self.n_speakers:int = 300
|
||||
|
||||
self.langauges:list = ["auto", "en", "zh", "ja", "all_zh", "all_ja"]
|
||||
|
||||
def _load_configs(self, configs_path: str)->dict:
|
||||
with open(configs_path, 'r') as f:
|
||||
configs = yaml.load(f, Loader=yaml.FullLoader)
|
||||
|
||||
return configs
|
||||
|
||||
|
||||
def save_configs(self, configs_path:str=None)->None:
|
||||
configs={
|
||||
"default": {
|
||||
"device": "cpu",
|
||||
"is_half": False,
|
||||
"t2s_weights_path": "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt",
|
||||
"vits_weights_path": "GPT_SoVITS/pretrained_models/s2G488k.pth",
|
||||
"cnhuhbert_base_path": "GPT_SoVITS/pretrained_models/chinese-hubert-base",
|
||||
"bert_base_path": "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large"
|
||||
},
|
||||
"custom": {
|
||||
"device": str(self.device),
|
||||
"is_half": self.is_half,
|
||||
"t2s_weights_path": self.t2s_weights_path,
|
||||
"vits_weights_path": self.vits_weights_path,
|
||||
"bert_base_path": self.bert_base_path,
|
||||
"cnhuhbert_base_path": self.cnhuhbert_base_path
|
||||
}
|
||||
}
|
||||
if configs_path is None:
|
||||
configs_path = self.configs_path
|
||||
with open(configs_path, 'w') as f:
|
||||
yaml.dump(configs, f)
|
||||
|
||||
|
||||
class TTS:
|
||||
def __init__(self, configs: Union[dict, str, TTS_Config]):
|
||||
if isinstance(configs, TTS_Config):
|
||||
self.configs = configs
|
||||
else:
|
||||
self.configs:TTS_Config = TTS_Config(configs)
|
||||
|
||||
self.t2s_model:Text2SemanticLightningModule = None
|
||||
self.vits_model:SynthesizerTrn = None
|
||||
self.bert_tokenizer:AutoTokenizer = None
|
||||
self.bert_model:AutoModelForMaskedLM = None
|
||||
self.cnhuhbert_model:CNHubert = None
|
||||
|
||||
self._init_models()
|
||||
|
||||
self.text_preprocessor:TextPreprocessor = \
|
||||
TextPreprocessor(self.bert_model,
|
||||
self.bert_tokenizer,
|
||||
self.configs.device)
|
||||
|
||||
|
||||
self.prompt_cache:dict = {
|
||||
"ref_audio_path":None,
|
||||
"prompt_semantic":None,
|
||||
"refer_spepc":None,
|
||||
"prompt_text":None,
|
||||
"prompt_lang":None,
|
||||
"phones":None,
|
||||
"bert_features":None,
|
||||
"norm_text":None,
|
||||
}
|
||||
|
||||
def _init_models(self,):
|
||||
self.init_t2s_weights(self.configs.t2s_weights_path)
|
||||
self.init_vits_weights(self.configs.vits_weights_path)
|
||||
self.init_bert_weights(self.configs.bert_base_path)
|
||||
self.init_cnhuhbert_weights(self.configs.cnhuhbert_base_path)
|
||||
|
||||
|
||||
|
||||
def init_cnhuhbert_weights(self, base_path: str):
|
||||
self.cnhuhbert_model = CNHubert(base_path)
|
||||
self.cnhuhbert_model.eval()
|
||||
if self.configs.is_half == True:
|
||||
self.cnhuhbert_model = self.cnhuhbert_model.half()
|
||||
self.cnhuhbert_model = self.cnhuhbert_model.to(self.configs.device)
|
||||
|
||||
|
||||
|
||||
def init_bert_weights(self, base_path: str):
|
||||
self.bert_tokenizer = AutoTokenizer.from_pretrained(base_path)
|
||||
self.bert_model = AutoModelForMaskedLM.from_pretrained(base_path)
|
||||
if self.configs.is_half:
|
||||
self.bert_model = self.bert_model.half()
|
||||
self.bert_model = self.bert_model.to(self.configs.device)
|
||||
|
||||
|
||||
|
||||
def init_vits_weights(self, weights_path: str):
|
||||
self.configs.vits_weights_path = weights_path
|
||||
self.configs.save_configs()
|
||||
dict_s2 = torch.load(weights_path, map_location=self.configs.device)
|
||||
hps = dict_s2["config"]
|
||||
self.configs.filter_length = hps["data"]["filter_length"]
|
||||
self.configs.segment_size = hps["train"]["segment_size"]
|
||||
self.configs.sampling_rate = hps["data"]["sampling_rate"]
|
||||
self.configs.hop_length = hps["data"]["hop_length"]
|
||||
self.configs.win_length = hps["data"]["win_length"]
|
||||
self.configs.n_speakers = hps["data"]["n_speakers"]
|
||||
self.configs.semantic_frame_rate = "25hz"
|
||||
kwargs = hps["model"]
|
||||
vits_model = SynthesizerTrn(
|
||||
self.configs.filter_length // 2 + 1,
|
||||
self.configs.segment_size // self.configs.hop_length,
|
||||
n_speakers=self.configs.n_speakers,
|
||||
**kwargs
|
||||
)
|
||||
# if ("pretrained" not in weights_path):
|
||||
if hasattr(vits_model, "enc_q"):
|
||||
del vits_model.enc_q
|
||||
|
||||
if self.configs.is_half:
|
||||
vits_model = vits_model.half()
|
||||
vits_model = vits_model.to(self.configs.device)
|
||||
vits_model.eval()
|
||||
vits_model.load_state_dict(dict_s2["weight"], strict=False)
|
||||
self.vits_model = vits_model
|
||||
|
||||
|
||||
def init_t2s_weights(self, weights_path: str):
|
||||
self.configs.t2s_weights_path = weights_path
|
||||
self.configs.save_configs()
|
||||
self.configs.hz = 50
|
||||
dict_s1 = torch.load(weights_path, map_location=self.configs.device)
|
||||
config = dict_s1["config"]
|
||||
self.configs.max_sec = config["data"]["max_sec"]
|
||||
t2s_model = Text2SemanticLightningModule(config, "****", is_train=False)
|
||||
t2s_model.load_state_dict(dict_s1["weight"])
|
||||
if self.configs.is_half:
|
||||
t2s_model = t2s_model.half()
|
||||
t2s_model = t2s_model.to(self.configs.device)
|
||||
t2s_model.eval()
|
||||
self.t2s_model = t2s_model
|
||||
|
||||
def set_ref_audio(self, ref_audio_path:str):
|
||||
self._set_prompt_semantic(ref_audio_path)
|
||||
self._set_ref_spepc(ref_audio_path)
|
||||
|
||||
def _set_ref_spepc(self, ref_audio_path):
|
||||
audio = load_audio(ref_audio_path, int(self.configs.sampling_rate))
|
||||
audio = torch.FloatTensor(audio)
|
||||
audio_norm = audio
|
||||
audio_norm = audio_norm.unsqueeze(0)
|
||||
spec = spectrogram_torch(
|
||||
audio_norm,
|
||||
self.configs.filter_length,
|
||||
self.configs.sampling_rate,
|
||||
self.configs.hop_length,
|
||||
self.configs.win_length,
|
||||
center=False,
|
||||
)
|
||||
spec = spec.to(self.configs.device)
|
||||
if self.configs.is_half:
|
||||
spec = spec.half()
|
||||
# self.refer_spepc = spec
|
||||
self.prompt_cache["refer_spepc"] = spec
|
||||
|
||||
|
||||
def _set_prompt_semantic(self, ref_wav_path:str):
|
||||
zero_wav = np.zeros(
|
||||
int(self.configs.sampling_rate * 0.3),
|
||||
dtype=np.float16 if self.configs.is_half else np.float32,
|
||||
)
|
||||
with torch.no_grad():
|
||||
wav16k, sr = librosa.load(ref_wav_path, sr=16000)
|
||||
if (wav16k.shape[0] > 160000 or wav16k.shape[0] < 48000):
|
||||
raise OSError(i18n("参考音频在3~10秒范围外，请更换！"))
|
||||
wav16k = torch.from_numpy(wav16k)
|
||||
zero_wav_torch = torch.from_numpy(zero_wav)
|
||||
wav16k = wav16k.to(self.configs.device)
|
||||
zero_wav_torch = zero_wav_torch.to(self.configs.device)
|
||||
if self.configs.is_half:
|
||||
wav16k = wav16k.half()
|
||||
zero_wav_torch = zero_wav_torch.half()
|
||||
|
||||
wav16k = torch.cat([wav16k, zero_wav_torch])
|
||||
hubert_feature = self.cnhuhbert_model.model(wav16k.unsqueeze(0))[
|
||||
"last_hidden_state"
|
||||
].transpose(
|
||||
1, 2
|
||||
) # .float()
|
||||
codes = self.vits_model.extract_latent(hubert_feature)
|
||||
|
||||
prompt_semantic = codes[0, 0].to(self.configs.device)
|
||||
self.prompt_cache["prompt_semantic"] = prompt_semantic
|
||||
|
||||
def batch_sequences(self, sequences: List[torch.Tensor], axis: int = 0, pad_value: int = 0):
|
||||
seq = sequences[0]
|
||||
ndim = seq.dim()
|
||||
if axis < 0:
|
||||
axis += ndim
|
||||
dtype:torch.dtype = seq.dtype
|
||||
pad_value = torch.tensor(pad_value, dtype=dtype)
|
||||
seq_lengths = [seq.shape[axis] for seq in sequences]
|
||||
max_length = max(seq_lengths)
|
||||
|
||||
padded_sequences = []
|
||||
for seq, length in zip(sequences, seq_lengths):
|
||||
padding = [0] * axis + [0, max_length - length] + [0] * (ndim - axis - 1)
|
||||
padded_seq = torch.nn.functional.pad(seq, padding, value=pad_value)
|
||||
padded_sequences.append(padded_seq)
|
||||
batch = torch.stack(padded_sequences)
|
||||
return batch
|
||||
|
||||
def to_batch(self, data:list, prompt_data:dict=None, batch_size:int=5, threshold:float=0.75):
|
||||
|
||||
_data:list = []
|
||||
index_and_len_list = []
|
||||
for idx, item in enumerate(data):
|
||||
norm_text_len = len(item["norm_text"])
|
||||
index_and_len_list.append([idx, norm_text_len])
|
||||
|
||||
index_and_len_list.sort(key=lambda x: x[1])
|
||||
# index_and_len_batch_list = [index_and_len_list[idx:min(idx+batch_size,len(index_and_len_list))] for idx in range(0,len(index_and_len_list),batch_size)]
|
||||
index_and_len_list = np.array(index_and_len_list, dtype=np.int64)
|
||||
|
||||
# for batch_idx, index_and_len_batch in enumerate(index_and_len_batch_list):
|
||||
|
||||
batch_index_list = []
|
||||
batch_index_list_len = 0
|
||||
pos = 0
|
||||
while pos <index_and_len_list.shape[0]:
|
||||
# batch_index_list.append(index_and_len_list[pos:min(pos+batch_size,len(index_and_len_list))])
|
||||
pos_end = min(pos+batch_size,index_and_len_list.shape[0])
|
||||
while pos < pos_end:
|
||||
batch=index_and_len_list[pos:pos_end, 1].astype(np.float32)
|
||||
score=batch[(pos_end-pos)//2]/batch.mean()
|
||||
if (score>=threshold) or (pos_end-pos==1):
|
||||
batch_index=index_and_len_list[pos:pos_end, 0].tolist()
|
||||
batch_index_list_len += len(batch_index)
|
||||
batch_index_list.append(batch_index)
|
||||
pos = pos_end
|
||||
break
|
||||
pos_end=pos_end-1
|
||||
|
||||
assert batch_index_list_len == len(data)
|
||||
|
||||
for batch_idx, index_list in enumerate(batch_index_list):
|
||||
item_list = [data[idx] for idx in index_list]
|
||||
phones_list = []
|
||||
# bert_features_list = []
|
||||
all_phones_list = []
|
||||
all_bert_features_list = []
|
||||
norm_text_batch = []
|
||||
for item in item_list:
|
||||
if prompt_data is not None:
|
||||
all_bert_features = torch.cat([prompt_data["bert_features"].clone(), item["bert_features"]], 1)
|
||||
all_phones = torch.LongTensor(prompt_data["phones"]+item["phones"])
|
||||
phones = torch.LongTensor(item["phones"])
|
||||
# norm_text = prompt_data["norm_text"]+item["norm_text"]
|
||||
else:
|
||||
all_bert_features = item["bert_features"]
|
||||
phones = torch.LongTensor(item["phones"])
|
||||
all_phones = phones.clone()
|
||||
# norm_text = item["norm_text"]
|
||||
|
||||
phones_list.append(phones)
|
||||
all_phones_list.append(all_phones)
|
||||
all_bert_features_list.append(all_bert_features)
|
||||
norm_text_batch.append(item["norm_text"])
|
||||
# phones_batch = phones_list
|
||||
phones_batch = self.batch_sequences(phones_list, axis=0, pad_value=0)
|
||||
all_phones_batch = self.batch_sequences(all_phones_list, axis=0, pad_value=0)
|
||||
all_bert_features_batch = torch.FloatTensor(len(item_list), 1024, all_phones_batch.shape[-1])
|
||||
all_bert_features_batch.zero_()
|
||||
|
||||
for idx, item in enumerate(all_bert_features_list):
|
||||
if item != None:
|
||||
all_bert_features_batch[idx, :, : item.shape[-1]] = item
|
||||
|
||||
batch = {
|
||||
"phones": phones_batch,
|
||||
"all_phones": all_phones_batch,
|
||||
"all_bert_features": all_bert_features_batch,
|
||||
"norm_text": norm_text_batch
|
||||
}
|
||||
_data.append(batch)
|
||||
|
||||
return _data, batch_index_list
|
||||
|
||||
def recovery_order(self, data:list, batch_index_list:list)->list:
|
||||
lenght = len(sum(batch_index_list, []))
|
||||
_data = [None]*lenght
|
||||
for i, index_list in enumerate(batch_index_list):
|
||||
for j, index in enumerate(index_list):
|
||||
_data[index] = data[i][j]
|
||||
return _data
|
||||
|
||||
|
||||
|
||||
|
||||
def run(self, inputs:dict):
|
||||
"""
|
||||
Text to speech inference.
|
||||
|
||||
Args:
|
||||
inputs (dict):
|
||||
{
|
||||
"text": "",
|
||||
"text_lang: "",
|
||||
"ref_audio_path": "",
|
||||
"prompt_text": "",
|
||||
"prompt_lang": "",
|
||||
"top_k": 5,
|
||||
"top_p": 0.9,
|
||||
"temperature": 0.6,
|
||||
"text_split_method": "",
|
||||
"batch_size": 1,
|
||||
"batch_threshold": 0.75
|
||||
}
|
||||
returns:
|
||||
tulpe[int, np.ndarray]: sampling rate and audio data.
|
||||
"""
|
||||
text:str = inputs.get("text", "")
|
||||
text_lang:str = inputs.get("text_lang", "")
|
||||
ref_audio_path:str = inputs.get("ref_audio_path", "")
|
||||
prompt_text:str = inputs.get("prompt_text", "")
|
||||
prompt_lang:str = inputs.get("prompt_lang", "")
|
||||
top_k:int = inputs.get("top_k", 20)
|
||||
top_p:float = inputs.get("top_p", 0.9)
|
||||
temperature:float = inputs.get("temperature", 0.6)
|
||||
text_split_method:str = inputs.get("text_split_method", "")
|
||||
batch_size = inputs.get("batch_size", 1)
|
||||
batch_threshold = inputs.get("batch_threshold", 0.75)
|
||||
|
||||
no_prompt_text = False
|
||||
if prompt_text in [None, ""]:
|
||||
no_prompt_text = True
|
||||
|
||||
assert text_lang in self.configs.langauges
|
||||
if not no_prompt_text:
|
||||
assert prompt_lang in self.configs.langauges
|
||||
|
||||
if ref_audio_path in [None, ""] and \
|
||||
((self.prompt_cache["prompt_semantic"] is None) or (self.prompt_cache["refer_spepc"] is None)):
|
||||
raise ValueError("ref_audio_path cannot be empty, when the reference audio is not set using set_ref_audio()")
|
||||
|
||||
t0 = ttime()
|
||||
if (ref_audio_path is not None) and (ref_audio_path != self.prompt_cache["ref_audio_path"]):
|
||||
self.set_ref_audio(ref_audio_path)
|
||||
|
||||
if not no_prompt_text:
|
||||
prompt_text = prompt_text.strip("\n")
|
||||
if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_lang != "en" else "."
|
||||
print(i18n("实际输入的参考文本:"), prompt_text)
|
||||
if self.prompt_cache["prompt_text"] != prompt_text:
|
||||
self.prompt_cache["prompt_text"] = prompt_text
|
||||
self.prompt_cache["prompt_lang"] = prompt_lang
|
||||
phones, bert_features, norm_text = \
|
||||
self.text_preprocessor.segment_and_extract_feature_for_text(
|
||||
prompt_text,
|
||||
prompt_lang)
|
||||
self.prompt_cache["phones"] = phones
|
||||
self.prompt_cache["bert_features"] = bert_features
|
||||
self.prompt_cache["norm_text"] = norm_text
|
||||
|
||||
zero_wav = np.zeros(
|
||||
int(self.configs.sampling_rate * 0.3),
|
||||
dtype=np.float16 if self.configs.is_half else np.float32,
|
||||
)
|
||||
|
||||
|
||||
data = self.text_preprocessor.preprocess(text, text_lang, text_split_method)
|
||||
audio = []
|
||||
t1 = ttime()
|
||||
data, batch_index_list = self.to_batch(data,
|
||||
prompt_data=self.prompt_cache if not no_prompt_text else None,
|
||||
batch_size=batch_size,
|
||||
threshold=batch_threshold)
|
||||
t2 = ttime()
|
||||
zero_wav = torch.zeros(
|
||||
int(self.configs.sampling_rate * 0.3),
|
||||
dtype=torch.float16 if self.configs.is_half else torch.float32,
|
||||
device=self.configs.device
|
||||
)
|
||||
|
||||
t_34 = 0.0
|
||||
t_45 = 0.0
|
||||
for item in data:
|
||||
t3 = ttime()
|
||||
batch_phones = item["phones"]
|
||||
all_phoneme_ids = item["all_phones"]
|
||||
all_bert_features = item["all_bert_features"]
|
||||
norm_text = item["norm_text"]
|
||||
|
||||
# phones = phones.to(self.configs.device)
|
||||
all_phoneme_ids = all_phoneme_ids.to(self.configs.device)
|
||||
all_bert_features = all_bert_features.to(self.configs.device)
|
||||
if self.configs.is_half:
|
||||
all_bert_features = all_bert_features.half()
|
||||
# all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]*all_phoneme_ids.shape[0], device=self.configs.device)
|
||||
|
||||
print(i18n("前端处理后的文本(每句):"), norm_text)
|
||||
if no_prompt_text :
|
||||
prompt = None
|
||||
else:
|
||||
prompt = self.prompt_cache["prompt_semantic"].clone().repeat(all_phoneme_ids.shape[0], 1).to(self.configs.device)
|
||||
|
||||
with torch.no_grad():
|
||||
# pred_semantic = t2s_model.model.infer(
|
||||
pred_semantic_list, idx_list = self.t2s_model.model.infer_panel(
|
||||
all_phoneme_ids,
|
||||
None,
|
||||
prompt,
|
||||
all_bert_features,
|
||||
# prompt_phone_len=ph_offset,
|
||||
top_k=top_k,
|
||||
top_p=top_p,
|
||||
temperature=temperature,
|
||||
early_stop_num=self.configs.hz * self.configs.max_sec,
|
||||
)
|
||||
t4 = ttime()
|
||||
t_34 += t4 - t3
|
||||
|
||||
refer_audio_spepc:torch.Tensor = self.prompt_cache["refer_spepc"].clone().to(self.configs.device)
|
||||
if self.configs.is_half:
|
||||
refer_audio_spepc = refer_audio_spepc.half()
|
||||
|
||||
## 直接对batch进行decode 生成的音频会有问题
|
||||
# pred_semantic_list = [item[-idx:] for item, idx in zip(pred_semantic_list, idx_list)]
|
||||
# pred_semantic = self.batch_sequences(pred_semantic_list, axis=0, pad_value=0).unsqueeze(0)
|
||||
# batch_phones = batch_phones.to(self.configs.device)
|
||||
# batch_audio_fragment =(self.vits_model.decode(
|
||||
# pred_semantic, batch_phones, refer_audio_spepc
|
||||
# ).detach()[:, 0, :])
|
||||
# max_audio=torch.abs(batch_audio_fragment).max()#简单防止16bit爆音
|
||||
# if max_audio>1: batch_audio_fragment/=max_audio
|
||||
# batch_audio_fragment = batch_audio_fragment.cpu().numpy()
|
||||
|
||||
## 改成串行处理
|
||||
batch_audio_fragment = []
|
||||
for i, idx in enumerate(idx_list):
|
||||
phones = batch_phones[i].clone().unsqueeze(0).to(self.configs.device)
|
||||
_pred_semantic = (pred_semantic_list[i][-idx:].unsqueeze(0).unsqueeze(0)) # .unsqueeze(0)#mq要多unsqueeze一次
|
||||
audio_fragment =(self.vits_model.decode(
|
||||
_pred_semantic, phones, refer_audio_spepc
|
||||
).detach()[0, 0, :])
|
||||
max_audio=torch.abs(audio_fragment).max()#简单防止16bit爆音
|
||||
if max_audio>1: audio_fragment/=max_audio
|
||||
audio_fragment = torch.cat([audio_fragment, zero_wav], dim=0)
|
||||
batch_audio_fragment.append(
|
||||
audio_fragment.cpu().numpy()
|
||||
) ###试试重建不带上prompt部分
|
||||
|
||||
audio.append(batch_audio_fragment)
|
||||
# audio.append(zero_wav)
|
||||
t5 = ttime()
|
||||
t_45 += t5 - t4
|
||||
|
||||
audio = self.recovery_order(audio, batch_index_list)
|
||||
print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t_34, t_45))
|
||||
yield self.configs.sampling_rate, (np.concatenate(audio, 0) * 32768).astype(
|
||||
np.int16
|
||||
)
|
||||
|
||||
176
GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py
Normal file
176
GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py
Normal file
@ -0,0 +1,176 @@
|
||||
|
||||
|
||||
import re
|
||||
import torch
|
||||
import LangSegment
|
||||
from typing import Dict, List, Tuple
|
||||
from text.cleaner import clean_text
|
||||
from text import cleaned_text_to_sequence
|
||||
from transformers import AutoModelForMaskedLM, AutoTokenizer
|
||||
from .text_segmentation_method import splits, get_method as get_seg_method
|
||||
|
||||
# from tools.i18n.i18n import I18nAuto
|
||||
|
||||
# i18n = I18nAuto()
|
||||
|
||||
def get_first(text:str) -> str:
|
||||
pattern = "[" + "".join(re.escape(sep) for sep in splits) + "]"
|
||||
text = re.split(pattern, text)[0].strip()
|
||||
return text
|
||||
|
||||
def merge_short_text_in_array(texts:str, threshold:int) -> list:
|
||||
if (len(texts)) < 2:
|
||||
return texts
|
||||
result = []
|
||||
text = ""
|
||||
for ele in texts:
|
||||
text += ele
|
||||
if len(text) >= threshold:
|
||||
result.append(text)
|
||||
text = ""
|
||||
if (len(text) > 0):
|
||||
if len(result) == 0:
|
||||
result.append(text)
|
||||
else:
|
||||
result[len(result) - 1] += text
|
||||
return result
|
||||
|
||||
|
||||
class TextPreprocessor:
|
||||
def __init__(self, bert_model:AutoModelForMaskedLM,
|
||||
tokenizer:AutoTokenizer, device:torch.device):
|
||||
self.bert_model = bert_model
|
||||
self.tokenizer = tokenizer
|
||||
self.device = device
|
||||
|
||||
def preprocess(self, text:str, lang:str, text_split_method:str)->List[Dict]:
|
||||
texts = self.pre_seg_text(text, lang, text_split_method)
|
||||
result = []
|
||||
for text in texts:
|
||||
phones, bert_features, norm_text = self.segment_and_extract_feature_for_text(text, lang)
|
||||
res={
|
||||
"phones": phones,
|
||||
"bert_features": bert_features,
|
||||
"norm_text": norm_text,
|
||||
}
|
||||
result.append(res)
|
||||
return result
|
||||
|
||||
def pre_seg_text(self, text:str, lang:str, text_split_method:str):
|
||||
text = text.strip("\n")
|
||||
if (text[0] not in splits and len(get_first(text)) < 4):
|
||||
text = "。" + text if lang != "en" else "." + text
|
||||
# print(i18n("实际输入的目标文本:"), text)
|
||||
|
||||
seg_method = get_seg_method(text_split_method)
|
||||
text = seg_method(text)
|
||||
|
||||
while "\n\n" in text:
|
||||
text = text.replace("\n\n", "\n")
|
||||
# print(i18n("实际输入的目标文本(切句后):"), text)
|
||||
_texts = text.split("\n")
|
||||
_texts = merge_short_text_in_array(_texts, 5)
|
||||
texts = []
|
||||
for text in _texts:
|
||||
# 解决输入目标文本的空行导致报错的问题
|
||||
if (len(text.strip()) == 0):
|
||||
continue
|
||||
if (text[-1] not in splits): text += "。" if lang != "en" else "."
|
||||
texts.append(text)
|
||||
|
||||
return texts
|
||||
|
||||
def segment_and_extract_feature_for_text(self, texts:list, language:str)->Tuple[list, torch.Tensor, str]:
|
||||
textlist, langlist = self.seg_text(texts, language)
|
||||
phones, bert_features, norm_text = self.extract_bert_feature(textlist, langlist)
|
||||
|
||||
return phones, bert_features, norm_text
|
||||
|
||||
|
||||
def seg_text(self, text:str, language:str)->Tuple[list, list]:
|
||||
|
||||
textlist=[]
|
||||
langlist=[]
|
||||
if language in ["auto", "zh", "ja"]:
|
||||
# LangSegment.setfilters(["zh","ja","en","ko"])
|
||||
for tmp in LangSegment.getTexts(text):
|
||||
if tmp["lang"] == "ko":
|
||||
langlist.append("zh")
|
||||
elif tmp["lang"] == "en":
|
||||
langlist.append("en")
|
||||
else:
|
||||
# 因无法区别中日文汉字,以用户输入为准
|
||||
langlist.append(language if language!="auto" else tmp["lang"])
|
||||
textlist.append(tmp["text"])
|
||||
elif language == "en":
|
||||
# LangSegment.setfilters(["en"])
|
||||
formattext = " ".join(tmp["text"] for tmp in LangSegment.getTexts(text))
|
||||
while " " in formattext:
|
||||
formattext = formattext.replace(" ", " ")
|
||||
textlist.append(formattext)
|
||||
langlist.append("en")
|
||||
|
||||
elif language in ["all_zh","all_ja"]:
|
||||
formattext = text
|
||||
while " " in formattext:
|
||||
formattext = formattext.replace(" ", " ")
|
||||
language = language.replace("all_","")
|
||||
textlist.append(formattext)
|
||||
langlist.append(language)
|
||||
|
||||
else:
|
||||
raise ValueError(f"language {language} not supported")
|
||||
|
||||
return textlist, langlist
|
||||
|
||||
|
||||
def extract_bert_feature(self, textlist:list, langlist:list):
|
||||
phones_list = []
|
||||
bert_feature_list = []
|
||||
norm_text_list = []
|
||||
for i in range(len(textlist)):
|
||||
lang = langlist[i]
|
||||
phones, word2ph, norm_text = self.clean_text_inf(textlist[i], lang)
|
||||
_bert_feature = self.get_bert_inf(phones, word2ph, norm_text, lang)
|
||||
# phones_list.append(phones)
|
||||
phones_list.extend(phones)
|
||||
norm_text_list.append(norm_text)
|
||||
bert_feature_list.append(_bert_feature)
|
||||
bert_feature = torch.cat(bert_feature_list, dim=1)
|
||||
# phones = sum(phones_list, [])
|
||||
norm_text = ''.join(norm_text_list)
|
||||
|
||||
return phones, bert_feature, norm_text
|
||||
|
||||
|
||||
def get_bert_feature(self, text:str, word2ph:list)->torch.Tensor:
|
||||
with torch.no_grad():
|
||||
inputs = self.tokenizer(text, return_tensors="pt")
|
||||
for i in inputs:
|
||||
inputs[i] = inputs[i].to(self.device)
|
||||
res = self.bert_model(**inputs, output_hidden_states=True)
|
||||
res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1]
|
||||
assert len(word2ph) == len(text)
|
||||
phone_level_feature = []
|
||||
for i in range(len(word2ph)):
|
||||
repeat_feature = res[i].repeat(word2ph[i], 1)
|
||||
phone_level_feature.append(repeat_feature)
|
||||
phone_level_feature = torch.cat(phone_level_feature, dim=0)
|
||||
return phone_level_feature.T
|
||||
|
||||
def clean_text_inf(self, text:str, language:str):
|
||||
phones, word2ph, norm_text = clean_text(text, language)
|
||||
phones = cleaned_text_to_sequence(phones)
|
||||
return phones, word2ph, norm_text
|
||||
|
||||
def get_bert_inf(self, phones:list, word2ph:list, norm_text:str, language:str):
|
||||
language=language.replace("all_","")
|
||||
if language == "zh":
|
||||
feature = self.get_bert_feature(norm_text, word2ph).to(self.device)
|
||||
else:
|
||||
feature = torch.zeros(
|
||||
(1024, len(phones)),
|
||||
dtype=torch.float32,
|
||||
).to(self.device)
|
||||
|
||||
return feature
|
||||
1
GPT_SoVITS/TTS_infer_pack/__init__.py
Normal file
1
GPT_SoVITS/TTS_infer_pack/__init__.py
Normal file
@ -0,0 +1 @@
|
||||
from . import TTS, text_segmentation_method
|
||||
126
GPT_SoVITS/TTS_infer_pack/text_segmentation_method.py
Normal file
126
GPT_SoVITS/TTS_infer_pack/text_segmentation_method.py
Normal file
@ -0,0 +1,126 @@
|
||||
|
||||
|
||||
|
||||
|
||||
import re
|
||||
from typing import Callable
|
||||
from tools.i18n.i18n import I18nAuto
|
||||
|
||||
i18n = I18nAuto()
|
||||
|
||||
METHODS = dict()
|
||||
|
||||
def get_method(name:str)->Callable:
|
||||
method = METHODS.get(name, None)
|
||||
if method is None:
|
||||
raise ValueError(f"Method {name} not found")
|
||||
return method
|
||||
|
||||
def register_method(name):
|
||||
def decorator(func):
|
||||
METHODS[name] = func
|
||||
return func
|
||||
return decorator
|
||||
|
||||
splits = {"，", "。", "？", "！", ",", ".", "?", "!", "~", ":", "：", "—", "…", }
|
||||
|
||||
|
||||
def split(todo_text):
|
||||
todo_text = todo_text.replace("……", "。").replace("——", "，")
|
||||
if todo_text[-1] not in splits:
|
||||
todo_text += "。"
|
||||
i_split_head = i_split_tail = 0
|
||||
len_text = len(todo_text)
|
||||
todo_texts = []
|
||||
while 1:
|
||||
if i_split_head >= len_text:
|
||||
break # 结尾一定有标点，所以直接跳出即可，最后一段在上次已加入
|
||||
if todo_text[i_split_head] in splits:
|
||||
i_split_head += 1
|
||||
todo_texts.append(todo_text[i_split_tail:i_split_head])
|
||||
i_split_tail = i_split_head
|
||||
else:
|
||||
i_split_head += 1
|
||||
return todo_texts
|
||||
|
||||
|
||||
# 不切
|
||||
@register_method("cut0")
|
||||
def cut0(inp):
|
||||
return inp
|
||||
|
||||
|
||||
# 凑四句一切
|
||||
@register_method("cut1")
|
||||
def cut1(inp):
|
||||
inp = inp.strip("\n")
|
||||
inps = split(inp)
|
||||
split_idx = list(range(0, len(inps), 4))
|
||||
split_idx[-1] = None
|
||||
if len(split_idx) > 1:
|
||||
opts = []
|
||||
for idx in range(len(split_idx) - 1):
|
||||
opts.append("".join(inps[split_idx[idx]: split_idx[idx + 1]]))
|
||||
else:
|
||||
opts = [inp]
|
||||
return "\n".join(opts)
|
||||
|
||||
# 凑50字一切
|
||||
@register_method("cut2")
|
||||
def cut2(inp):
|
||||
inp = inp.strip("\n")
|
||||
inps = split(inp)
|
||||
if len(inps) < 2:
|
||||
return inp
|
||||
opts = []
|
||||
summ = 0
|
||||
tmp_str = ""
|
||||
for i in range(len(inps)):
|
||||
summ += len(inps[i])
|
||||
tmp_str += inps[i]
|
||||
if summ > 50:
|
||||
summ = 0
|
||||
opts.append(tmp_str)
|
||||
tmp_str = ""
|
||||
if tmp_str != "":
|
||||
opts.append(tmp_str)
|
||||
# print(opts)
|
||||
if len(opts) > 1 and len(opts[-1]) < 50: ##如果最后一个太短了，和前一个合一起
|
||||
opts[-2] = opts[-2] + opts[-1]
|
||||
opts = opts[:-1]
|
||||
return "\n".join(opts)
|
||||
|
||||
# 按中文句号。切
|
||||
@register_method("cut3")
|
||||
def cut3(inp):
|
||||
inp = inp.strip("\n")
|
||||
return "\n".join(["%s" % item for item in inp.strip("。").split("。")])
|
||||
|
||||
#按英文句号.切
|
||||
@register_method("cut4")
|
||||
def cut4(inp):
|
||||
inp = inp.strip("\n")
|
||||
return "\n".join(["%s" % item for item in inp.strip(".").split(".")])
|
||||
|
||||
# 按标点符号切
|
||||
# contributed by https://github.com/AI-Hobbyist/GPT-SoVITS/blob/main/GPT_SoVITS/inference_webui.py
|
||||
@register_method("cut5")
|
||||
def cut5(inp):
|
||||
# if not re.search(r'[^\w\s]', inp[-1]):
|
||||
# inp += '。'
|
||||
inp = inp.strip("\n")
|
||||
punds = r'[,.;?!、，。？！;：…]'
|
||||
items = re.split(f'({punds})', inp)
|
||||
mergeitems = ["".join(group) for group in zip(items[::2], items[1::2])]
|
||||
# 在句子不存在符号或句尾无符号的时候保证文本完整
|
||||
if len(items)%2 == 1:
|
||||
mergeitems.append(items[-1])
|
||||
opt = "\n".join(mergeitems)
|
||||
return opt
|
||||
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
method = get_method("cut1")
|
||||
print(method("你好，我是小明。你好，我是小红。你好，我是小刚。你好，我是小张。"))
|
||||
|
||||
14
GPT_SoVITS/configs/tts_infer.yaml
Normal file
14
GPT_SoVITS/configs/tts_infer.yaml
Normal file
@ -0,0 +1,14 @@
|
||||
custom:
|
||||
bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large
|
||||
cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base
|
||||
device: cuda
|
||||
is_half: true
|
||||
t2s_weights_path: GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt
|
||||
vits_weights_path: GPT_SoVITS/pretrained_models/s2G488k.pth
|
||||
default:
|
||||
bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large
|
||||
cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base
|
||||
device: cpu
|
||||
is_half: false
|
||||
t2s_weights_path: GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt
|
||||
vits_weights_path: GPT_SoVITS/pretrained_models/s2G488k.pth
|
||||
@ -20,13 +20,16 @@ cnhubert_base_path = None
|
||||
|
||||
|
||||
class CNHubert(nn.Module):
|
||||
def __init__(self):
|
||||
def __init__(self, base_path:str=None):
|
||||
super().__init__()
|
||||
self.model = HubertModel.from_pretrained(cnhubert_base_path)
|
||||
if base_path is None:
|
||||
base_path = cnhubert_base_path
|
||||
self.model = HubertModel.from_pretrained(base_path)
|
||||
self.feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(
|
||||
cnhubert_base_path
|
||||
base_path
|
||||
)
|
||||
|
||||
|
||||
def forward(self, x):
|
||||
input_values = self.feature_extractor(
|
||||
x, return_tensors="pt", sampling_rate=16000
|
||||
|
||||
@ -7,7 +7,7 @@ import soundfile as sf
|
||||
from tools.i18n.i18n import I18nAuto
|
||||
i18n = I18nAuto()
|
||||
|
||||
from GPT_SoVITS.inference_webui import change_gpt_weights, change_sovits_weights, get_tts_wav
|
||||
from GPT_SoVITS.inference_webui_old import change_gpt_weights, change_sovits_weights, get_tts_wav
|
||||
|
||||
|
||||
class GPTSoVITSGUI(QMainWindow):
|
||||
|
||||
@ -7,7 +7,7 @@
|
||||
全部按日文识别
|
||||
'''
|
||||
import os, re, logging
|
||||
import LangSegment
|
||||
|
||||
logging.getLogger("markdown_it").setLevel(logging.ERROR)
|
||||
logging.getLogger("urllib3").setLevel(logging.ERROR)
|
||||
logging.getLogger("httpcore").setLevel(logging.ERROR)
|
||||
@ -17,32 +17,12 @@ logging.getLogger("charset_normalizer").setLevel(logging.ERROR)
|
||||
logging.getLogger("torchaudio._extension").setLevel(logging.ERROR)
|
||||
import pdb
|
||||
import torch
|
||||
# modified from https://github.com/feng-yufei/shared_debugging_code/blob/main/model/t2s_lightning_module.py
|
||||
import os, sys
|
||||
|
||||
if os.path.exists("./gweight.txt"):
|
||||
with open("./gweight.txt", 'r', encoding="utf-8") as file:
|
||||
gweight_data = file.read()
|
||||
gpt_path = os.environ.get(
|
||||
"gpt_path", gweight_data)
|
||||
else:
|
||||
gpt_path = os.environ.get(
|
||||
"gpt_path", "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt")
|
||||
now_dir = os.getcwd()
|
||||
sys.path.append(now_dir)
|
||||
|
||||
if os.path.exists("./sweight.txt"):
|
||||
with open("./sweight.txt", 'r', encoding="utf-8") as file:
|
||||
sweight_data = file.read()
|
||||
sovits_path = os.environ.get("sovits_path", sweight_data)
|
||||
else:
|
||||
sovits_path = os.environ.get("sovits_path", "GPT_SoVITS/pretrained_models/s2G488k.pth")
|
||||
# gpt_path = os.environ.get(
|
||||
# "gpt_path", "pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"
|
||||
# )
|
||||
# sovits_path = os.environ.get("sovits_path", "pretrained_models/s2G488k.pth")
|
||||
cnhubert_base_path = os.environ.get(
|
||||
"cnhubert_base_path", "GPT_SoVITS/pretrained_models/chinese-hubert-base"
|
||||
)
|
||||
bert_path = os.environ.get(
|
||||
"bert_path", "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large"
|
||||
)
|
||||
infer_ttswebui = os.environ.get("infer_ttswebui", 9872)
|
||||
infer_ttswebui = int(infer_ttswebui)
|
||||
is_share = os.environ.get("is_share", "False")
|
||||
@ -51,22 +31,9 @@ if "_CUDA_VISIBLE_DEVICES" in os.environ:
|
||||
os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
|
||||
is_half = eval(os.environ.get("is_half", "True")) and not torch.backends.mps.is_available()
|
||||
import gradio as gr
|
||||
from transformers import AutoModelForMaskedLM, AutoTokenizer
|
||||
import numpy as np
|
||||
import librosa
|
||||
from feature_extractor import cnhubert
|
||||
|
||||
cnhubert.cnhubert_base_path = cnhubert_base_path
|
||||
|
||||
from module.models import SynthesizerTrn
|
||||
from AR.models.t2s_lightning_module import Text2SemanticLightningModule
|
||||
from text import cleaned_text_to_sequence
|
||||
from text.cleaner import clean_text
|
||||
from time import time as ttime
|
||||
from module.mel_processing import spectrogram_torch
|
||||
from my_utils import load_audio
|
||||
from TTS_infer_pack.TTS import TTS, TTS_Config
|
||||
from TTS_infer_pack.text_segmentation_method import cut1, cut2, cut3, cut4, cut5
|
||||
from tools.i18n.i18n import I18nAuto
|
||||
|
||||
i18n = I18nAuto()
|
||||
|
||||
os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = '1' # 确保直接启动推理UI时也能够设置。
|
||||
@ -76,128 +43,6 @@ if torch.cuda.is_available():
|
||||
else:
|
||||
device = "cpu"
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained(bert_path)
|
||||
bert_model = AutoModelForMaskedLM.from_pretrained(bert_path)
|
||||
if is_half == True:
|
||||
bert_model = bert_model.half().to(device)
|
||||
else:
|
||||
bert_model = bert_model.to(device)
|
||||
|
||||
|
||||
def get_bert_feature(text, word2ph):
|
||||
with torch.no_grad():
|
||||
inputs = tokenizer(text, return_tensors="pt")
|
||||
for i in inputs:
|
||||
inputs[i] = inputs[i].to(device)
|
||||
res = bert_model(**inputs, output_hidden_states=True)
|
||||
res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1]
|
||||
assert len(word2ph) == len(text)
|
||||
phone_level_feature = []
|
||||
for i in range(len(word2ph)):
|
||||
repeat_feature = res[i].repeat(word2ph[i], 1)
|
||||
phone_level_feature.append(repeat_feature)
|
||||
phone_level_feature = torch.cat(phone_level_feature, dim=0)
|
||||
return phone_level_feature.T
|
||||
|
||||
|
||||
class DictToAttrRecursive(dict):
|
||||
def __init__(self, input_dict):
|
||||
super().__init__(input_dict)
|
||||
for key, value in input_dict.items():
|
||||
if isinstance(value, dict):
|
||||
value = DictToAttrRecursive(value)
|
||||
self[key] = value
|
||||
setattr(self, key, value)
|
||||
|
||||
def __getattr__(self, item):
|
||||
try:
|
||||
return self[item]
|
||||
except KeyError:
|
||||
raise AttributeError(f"Attribute {item} not found")
|
||||
|
||||
def __setattr__(self, key, value):
|
||||
if isinstance(value, dict):
|
||||
value = DictToAttrRecursive(value)
|
||||
super(DictToAttrRecursive, self).__setitem__(key, value)
|
||||
super().__setattr__(key, value)
|
||||
|
||||
def __delattr__(self, item):
|
||||
try:
|
||||
del self[item]
|
||||
except KeyError:
|
||||
raise AttributeError(f"Attribute {item} not found")
|
||||
|
||||
|
||||
ssl_model = cnhubert.get_model()
|
||||
if is_half == True:
|
||||
ssl_model = ssl_model.half().to(device)
|
||||
else:
|
||||
ssl_model = ssl_model.to(device)
|
||||
|
||||
|
||||
def change_sovits_weights(sovits_path):
|
||||
global vq_model, hps
|
||||
dict_s2 = torch.load(sovits_path, map_location="cpu")
|
||||
hps = dict_s2["config"]
|
||||
hps = DictToAttrRecursive(hps)
|
||||
hps.model.semantic_frame_rate = "25hz"
|
||||
vq_model = SynthesizerTrn(
|
||||
hps.data.filter_length // 2 + 1,
|
||||
hps.train.segment_size // hps.data.hop_length,
|
||||
n_speakers=hps.data.n_speakers,
|
||||
**hps.model
|
||||
)
|
||||
if ("pretrained" not in sovits_path):
|
||||
del vq_model.enc_q
|
||||
if is_half == True:
|
||||
vq_model = vq_model.half().to(device)
|
||||
else:
|
||||
vq_model = vq_model.to(device)
|
||||
vq_model.eval()
|
||||
print(vq_model.load_state_dict(dict_s2["weight"], strict=False))
|
||||
with open("./sweight.txt", "w", encoding="utf-8") as f:
|
||||
f.write(sovits_path)
|
||||
|
||||
|
||||
change_sovits_weights(sovits_path)
|
||||
|
||||
|
||||
def change_gpt_weights(gpt_path):
|
||||
global hz, max_sec, t2s_model, config
|
||||
hz = 50
|
||||
dict_s1 = torch.load(gpt_path, map_location="cpu")
|
||||
config = dict_s1["config"]
|
||||
max_sec = config["data"]["max_sec"]
|
||||
t2s_model = Text2SemanticLightningModule(config, "****", is_train=False)
|
||||
t2s_model.load_state_dict(dict_s1["weight"])
|
||||
if is_half == True:
|
||||
t2s_model = t2s_model.half()
|
||||
t2s_model = t2s_model.to(device)
|
||||
t2s_model.eval()
|
||||
total = sum([param.nelement() for param in t2s_model.parameters()])
|
||||
print("Number of parameter: %.2fM" % (total / 1e6))
|
||||
with open("./gweight.txt", "w", encoding="utf-8") as f: f.write(gpt_path)
|
||||
|
||||
|
||||
change_gpt_weights(gpt_path)
|
||||
|
||||
|
||||
def get_spepc(hps, filename):
|
||||
audio = load_audio(filename, int(hps.data.sampling_rate))
|
||||
audio = torch.FloatTensor(audio)
|
||||
audio_norm = audio
|
||||
audio_norm = audio_norm.unsqueeze(0)
|
||||
spec = spectrogram_torch(
|
||||
audio_norm,
|
||||
hps.data.filter_length,
|
||||
hps.data.sampling_rate,
|
||||
hps.data.hop_length,
|
||||
hps.data.win_length,
|
||||
center=False,
|
||||
)
|
||||
return spec
|
||||
|
||||
|
||||
dict_language = {
|
||||
i18n("中文"): "all_zh",#全部按中文识别
|
||||
i18n("英文"): "en",#全部按英文识别#######不变
|
||||
@ -207,313 +52,36 @@ dict_language = {
|
||||
i18n("多语种混合"): "auto",#多语种启动切分识别语种
|
||||
}
|
||||
|
||||
cut_method = {
|
||||
i18n("不切"):"cut0",
|
||||
i18n("凑四句一切"): "cut1",
|
||||
i18n("凑50字一切"): "cut2",
|
||||
i18n("按中文句号。切"): "cut3",
|
||||
i18n("按英文句号.切"): "cut4",
|
||||
i18n("按标点符号切"): "cut5",
|
||||
}
|
||||
|
||||
def clean_text_inf(text, language):
|
||||
phones, word2ph, norm_text = clean_text(text, language)
|
||||
phones = cleaned_text_to_sequence(phones)
|
||||
return phones, word2ph, norm_text
|
||||
|
||||
dtype=torch.float16 if is_half == True else torch.float32
|
||||
def get_bert_inf(phones, word2ph, norm_text, language):
|
||||
language=language.replace("all_","")
|
||||
if language == "zh":
|
||||
bert = get_bert_feature(norm_text, word2ph).to(device)#.to(dtype)
|
||||
else:
|
||||
bert = torch.zeros(
|
||||
(1024, len(phones)),
|
||||
dtype=torch.float16 if is_half == True else torch.float32,
|
||||
).to(device)
|
||||
|
||||
return bert
|
||||
|
||||
|
||||
splits = {"，", "。", "？", "！", ",", ".", "?", "!", "~", ":", "：", "—", "…", }
|
||||
|
||||
|
||||
def get_first(text):
|
||||
pattern = "[" + "".join(re.escape(sep) for sep in splits) + "]"
|
||||
text = re.split(pattern, text)[0].strip()
|
||||
return text
|
||||
|
||||
|
||||
def get_phones_and_bert(text,language):
|
||||
if language in {"en","all_zh","all_ja"}:
|
||||
language = language.replace("all_","")
|
||||
if language == "en":
|
||||
LangSegment.setfilters(["en"])
|
||||
formattext = " ".join(tmp["text"] for tmp in LangSegment.getTexts(text))
|
||||
else:
|
||||
# 因无法区别中日文汉字,以用户输入为准
|
||||
formattext = text
|
||||
while " " in formattext:
|
||||
formattext = formattext.replace(" ", " ")
|
||||
phones, word2ph, norm_text = clean_text_inf(formattext, language)
|
||||
if language == "zh":
|
||||
bert = get_bert_feature(norm_text, word2ph).to(device)
|
||||
else:
|
||||
bert = torch.zeros(
|
||||
(1024, len(phones)),
|
||||
dtype=torch.float16 if is_half == True else torch.float32,
|
||||
).to(device)
|
||||
elif language in {"zh", "ja","auto"}:
|
||||
textlist=[]
|
||||
langlist=[]
|
||||
LangSegment.setfilters(["zh","ja","en","ko"])
|
||||
if language == "auto":
|
||||
for tmp in LangSegment.getTexts(text):
|
||||
if tmp["lang"] == "ko":
|
||||
langlist.append("zh")
|
||||
textlist.append(tmp["text"])
|
||||
else:
|
||||
langlist.append(tmp["lang"])
|
||||
textlist.append(tmp["text"])
|
||||
else:
|
||||
for tmp in LangSegment.getTexts(text):
|
||||
if tmp["lang"] == "en":
|
||||
langlist.append(tmp["lang"])
|
||||
else:
|
||||
# 因无法区别中日文汉字,以用户输入为准
|
||||
langlist.append(language)
|
||||
textlist.append(tmp["text"])
|
||||
print(textlist)
|
||||
print(langlist)
|
||||
phones_list = []
|
||||
bert_list = []
|
||||
norm_text_list = []
|
||||
for i in range(len(textlist)):
|
||||
lang = langlist[i]
|
||||
phones, word2ph, norm_text = clean_text_inf(textlist[i], lang)
|
||||
bert = get_bert_inf(phones, word2ph, norm_text, lang)
|
||||
phones_list.append(phones)
|
||||
norm_text_list.append(norm_text)
|
||||
bert_list.append(bert)
|
||||
bert = torch.cat(bert_list, dim=1)
|
||||
phones = sum(phones_list, [])
|
||||
norm_text = ''.join(norm_text_list)
|
||||
|
||||
return phones,bert.to(dtype),norm_text
|
||||
|
||||
|
||||
def merge_short_text_in_array(texts, threshold):
|
||||
if (len(texts)) < 2:
|
||||
return texts
|
||||
result = []
|
||||
text = ""
|
||||
for ele in texts:
|
||||
text += ele
|
||||
if len(text) >= threshold:
|
||||
result.append(text)
|
||||
text = ""
|
||||
if (len(text) > 0):
|
||||
if len(result) == 0:
|
||||
result.append(text)
|
||||
else:
|
||||
result[len(result) - 1] += text
|
||||
return result
|
||||
|
||||
def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language, how_to_cut=i18n("不切"), top_k=20, top_p=0.6, temperature=0.6, ref_free = False):
|
||||
if prompt_text is None or len(prompt_text) == 0:
|
||||
ref_free = True
|
||||
t0 = ttime()
|
||||
prompt_language = dict_language[prompt_language]
|
||||
text_language = dict_language[text_language]
|
||||
if not ref_free:
|
||||
prompt_text = prompt_text.strip("\n")
|
||||
if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_language != "en" else "."
|
||||
print(i18n("实际输入的参考文本:"), prompt_text)
|
||||
text = text.strip("\n")
|
||||
if (text[0] not in splits and len(get_first(text)) < 4): text = "。" + text if text_language != "en" else "." + text
|
||||
|
||||
print(i18n("实际输入的目标文本:"), text)
|
||||
zero_wav = np.zeros(
|
||||
int(hps.data.sampling_rate * 0.3),
|
||||
dtype=np.float16 if is_half == True else np.float32,
|
||||
)
|
||||
with torch.no_grad():
|
||||
wav16k, sr = librosa.load(ref_wav_path, sr=16000)
|
||||
if (wav16k.shape[0] > 160000 or wav16k.shape[0] < 48000):
|
||||
raise OSError(i18n("参考音频在3~10秒范围外，请更换！"))
|
||||
wav16k = torch.from_numpy(wav16k)
|
||||
zero_wav_torch = torch.from_numpy(zero_wav)
|
||||
if is_half == True:
|
||||
wav16k = wav16k.half().to(device)
|
||||
zero_wav_torch = zero_wav_torch.half().to(device)
|
||||
else:
|
||||
wav16k = wav16k.to(device)
|
||||
zero_wav_torch = zero_wav_torch.to(device)
|
||||
wav16k = torch.cat([wav16k, zero_wav_torch])
|
||||
ssl_content = ssl_model.model(wav16k.unsqueeze(0))[
|
||||
"last_hidden_state"
|
||||
].transpose(
|
||||
1, 2
|
||||
) # .float()
|
||||
codes = vq_model.extract_latent(ssl_content)
|
||||
|
||||
prompt_semantic = codes[0, 0]
|
||||
t1 = ttime()
|
||||
|
||||
if (how_to_cut == i18n("凑四句一切")):
|
||||
text = cut1(text)
|
||||
elif (how_to_cut == i18n("凑50字一切")):
|
||||
text = cut2(text)
|
||||
elif (how_to_cut == i18n("按中文句号。切")):
|
||||
text = cut3(text)
|
||||
elif (how_to_cut == i18n("按英文句号.切")):
|
||||
text = cut4(text)
|
||||
elif (how_to_cut == i18n("按标点符号切")):
|
||||
text = cut5(text)
|
||||
while "\n\n" in text:
|
||||
text = text.replace("\n\n", "\n")
|
||||
print(i18n("实际输入的目标文本(切句后):"), text)
|
||||
texts = text.split("\n")
|
||||
texts = merge_short_text_in_array(texts, 5)
|
||||
audio_opt = []
|
||||
if not ref_free:
|
||||
phones1,bert1,norm_text1=get_phones_and_bert(prompt_text, prompt_language)
|
||||
|
||||
for text in texts:
|
||||
# 解决输入目标文本的空行导致报错的问题
|
||||
if (len(text.strip()) == 0):
|
||||
continue
|
||||
if (text[-1] not in splits): text += "。" if text_language != "en" else "."
|
||||
print(i18n("实际输入的目标文本(每句):"), text)
|
||||
phones2,bert2,norm_text2=get_phones_and_bert(text, text_language)
|
||||
print(i18n("前端处理后的文本(每句):"), norm_text2)
|
||||
if not ref_free:
|
||||
bert = torch.cat([bert1, bert2], 1)
|
||||
all_phoneme_ids = torch.LongTensor(phones1+phones2).to(device).unsqueeze(0)
|
||||
else:
|
||||
bert = bert2
|
||||
all_phoneme_ids = torch.LongTensor(phones2).to(device).unsqueeze(0)
|
||||
|
||||
bert = bert.to(device).unsqueeze(0)
|
||||
all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(device)
|
||||
prompt = prompt_semantic.unsqueeze(0).to(device)
|
||||
t2 = ttime()
|
||||
with torch.no_grad():
|
||||
# pred_semantic = t2s_model.model.infer(
|
||||
pred_semantic, idx = t2s_model.model.infer_panel(
|
||||
all_phoneme_ids,
|
||||
all_phoneme_len,
|
||||
None if ref_free else prompt,
|
||||
bert,
|
||||
# prompt_phone_len=ph_offset,
|
||||
top_k=top_k,
|
||||
top_p=top_p,
|
||||
temperature=temperature,
|
||||
early_stop_num=hz * max_sec,
|
||||
)
|
||||
t3 = ttime()
|
||||
# print(pred_semantic.shape,idx)
|
||||
pred_semantic = pred_semantic[:, -idx:].unsqueeze(
|
||||
0
|
||||
) # .unsqueeze(0)#mq要多unsqueeze一次
|
||||
refer = get_spepc(hps, ref_wav_path) # .to(device)
|
||||
if is_half == True:
|
||||
refer = refer.half().to(device)
|
||||
else:
|
||||
refer = refer.to(device)
|
||||
# audio = vq_model.decode(pred_semantic, all_phoneme_ids, refer).detach().cpu().numpy()[0, 0]
|
||||
audio = (
|
||||
vq_model.decode(
|
||||
pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refer
|
||||
)
|
||||
.detach()
|
||||
.cpu()
|
||||
.numpy()[0, 0]
|
||||
) ###试试重建不带上prompt部分
|
||||
max_audio=np.abs(audio).max()#简单防止16bit爆音
|
||||
if max_audio>1:audio/=max_audio
|
||||
audio_opt.append(audio)
|
||||
audio_opt.append(zero_wav)
|
||||
t4 = ttime()
|
||||
print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))
|
||||
yield hps.data.sampling_rate, (np.concatenate(audio_opt, 0) * 32768).astype(
|
||||
np.int16
|
||||
)
|
||||
|
||||
|
||||
def split(todo_text):
|
||||
todo_text = todo_text.replace("……", "。").replace("——", "，")
|
||||
if todo_text[-1] not in splits:
|
||||
todo_text += "。"
|
||||
i_split_head = i_split_tail = 0
|
||||
len_text = len(todo_text)
|
||||
todo_texts = []
|
||||
while 1:
|
||||
if i_split_head >= len_text:
|
||||
break # 结尾一定有标点，所以直接跳出即可，最后一段在上次已加入
|
||||
if todo_text[i_split_head] in splits:
|
||||
i_split_head += 1
|
||||
todo_texts.append(todo_text[i_split_tail:i_split_head])
|
||||
i_split_tail = i_split_head
|
||||
else:
|
||||
i_split_head += 1
|
||||
return todo_texts
|
||||
|
||||
|
||||
def cut1(inp):
|
||||
inp = inp.strip("\n")
|
||||
inps = split(inp)
|
||||
split_idx = list(range(0, len(inps), 4))
|
||||
split_idx[-1] = None
|
||||
if len(split_idx) > 1:
|
||||
opts = []
|
||||
for idx in range(len(split_idx) - 1):
|
||||
opts.append("".join(inps[split_idx[idx]: split_idx[idx + 1]]))
|
||||
else:
|
||||
opts = [inp]
|
||||
return "\n".join(opts)
|
||||
|
||||
|
||||
def cut2(inp):
|
||||
inp = inp.strip("\n")
|
||||
inps = split(inp)
|
||||
if len(inps) < 2:
|
||||
return inp
|
||||
opts = []
|
||||
summ = 0
|
||||
tmp_str = ""
|
||||
for i in range(len(inps)):
|
||||
summ += len(inps[i])
|
||||
tmp_str += inps[i]
|
||||
if summ > 50:
|
||||
summ = 0
|
||||
opts.append(tmp_str)
|
||||
tmp_str = ""
|
||||
if tmp_str != "":
|
||||
opts.append(tmp_str)
|
||||
# print(opts)
|
||||
if len(opts) > 1 and len(opts[-1]) < 50: ##如果最后一个太短了，和前一个合一起
|
||||
opts[-2] = opts[-2] + opts[-1]
|
||||
opts = opts[:-1]
|
||||
return "\n".join(opts)
|
||||
|
||||
|
||||
def cut3(inp):
|
||||
inp = inp.strip("\n")
|
||||
return "\n".join(["%s" % item for item in inp.strip("。").split("。")])
|
||||
|
||||
|
||||
def cut4(inp):
|
||||
inp = inp.strip("\n")
|
||||
return "\n".join(["%s" % item for item in inp.strip(".").split(".")])
|
||||
|
||||
|
||||
# contributed by https://github.com/AI-Hobbyist/GPT-SoVITS/blob/main/GPT_SoVITS/inference_webui.py
|
||||
def cut5(inp):
|
||||
# if not re.search(r'[^\w\s]', inp[-1]):
|
||||
# inp += '。'
|
||||
inp = inp.strip("\n")
|
||||
punds = r'[,.;?!、，。？！;：…]'
|
||||
items = re.split(f'({punds})', inp)
|
||||
mergeitems = ["".join(group) for group in zip(items[::2], items[1::2])]
|
||||
# 在句子不存在符号或句尾无符号的时候保证文本完整
|
||||
if len(items)%2 == 1:
|
||||
mergeitems.append(items[-1])
|
||||
opt = "\n".join(mergeitems)
|
||||
return opt
|
||||
tts_config = TTS_Config("GPT_SoVITS/configs/tts_infer.yaml")
|
||||
tts_config.device = device
|
||||
tts_config.is_half = is_half
|
||||
tts_pipline = TTS(tts_config)
|
||||
gpt_path = tts_config.t2s_weights_path
|
||||
sovits_path = tts_config.vits_weights_path
|
||||
|
||||
def inference(text, text_lang, ref_audio_path, prompt_text, prompt_lang, top_k, top_p, temperature, text_split_method, batch_size):
|
||||
inputs={
|
||||
"text": text,
|
||||
"text_lang": dict_language[text_lang],
|
||||
"ref_audio_path": ref_audio_path,
|
||||
"prompt_text": prompt_text,
|
||||
"prompt_lang": dict_language[prompt_lang],
|
||||
"top_k": top_k,
|
||||
"top_p": top_p,
|
||||
"temperature": temperature,
|
||||
"text_split_method": cut_method[text_split_method],
|
||||
"batch_size":int(batch_size),
|
||||
}
|
||||
yield next(tts_pipline.run(inputs))
|
||||
|
||||
def custom_sort_key(s):
|
||||
# 使用正则表达式提取字符串中的数字部分和非数字部分
|
||||
@ -559,8 +127,8 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
|
||||
SoVITS_dropdown = gr.Dropdown(label=i18n("SoVITS模型列表"), choices=sorted(SoVITS_names, key=custom_sort_key), value=sovits_path, interactive=True)
|
||||
refresh_button = gr.Button(i18n("刷新模型路径"), variant="primary")
|
||||
refresh_button.click(fn=change_choices, inputs=[], outputs=[SoVITS_dropdown, GPT_dropdown])
|
||||
SoVITS_dropdown.change(change_sovits_weights, [SoVITS_dropdown], [])
|
||||
GPT_dropdown.change(change_gpt_weights, [GPT_dropdown], [])
|
||||
SoVITS_dropdown.change(tts_pipline.init_vits_weights, [SoVITS_dropdown], [])
|
||||
GPT_dropdown.change(tts_pipline.init_t2s_weights, [GPT_dropdown], [])
|
||||
gr.Markdown(value=i18n("*请上传并填写参考信息"))
|
||||
with gr.Row():
|
||||
inp_ref = gr.Audio(label=i18n("请上传3~10秒内参考音频，超过会报错！"), type="filepath")
|
||||
@ -585,15 +153,19 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
|
||||
)
|
||||
with gr.Row():
|
||||
gr.Markdown(value=i18n("gpt采样参数(无参考文本时不要太低)："))
|
||||
batch_size = gr.Slider(minimum=1,maximum=20,step=1,label=i18n("batch_size"),value=1,interactive=True)
|
||||
top_k = gr.Slider(minimum=1,maximum=100,step=1,label=i18n("top_k"),value=5,interactive=True)
|
||||
top_p = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("top_p"),value=1,interactive=True)
|
||||
temperature = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("temperature"),value=1,interactive=True)
|
||||
inference_button = gr.Button(i18n("合成语音"), variant="primary")
|
||||
output = gr.Audio(label=i18n("输出的语音"))
|
||||
|
||||
|
||||
|
||||
|
||||
inference_button.click(
|
||||
get_tts_wav,
|
||||
[inp_ref, prompt_text, prompt_language, text, text_language, how_to_cut, top_k, top_p, temperature, ref_text_free],
|
||||
inference,
|
||||
[text,text_language, inp_ref, prompt_text, prompt_language, top_k, top_p, temperature, how_to_cut, batch_size],
|
||||
[output],
|
||||
)
|
||||
|
||||
|
||||
622
GPT_SoVITS/inference_webui_old.py
Normal file
622
GPT_SoVITS/inference_webui_old.py
Normal file
@ -0,0 +1,622 @@
|
||||
'''
|
||||
按中英混合识别
|
||||
按日英混合识别
|
||||
多语种启动切分识别语种
|
||||
全部按中文识别
|
||||
全部按英文识别
|
||||
全部按日文识别
|
||||
'''
|
||||
import os, re, logging
|
||||
import LangSegment
|
||||
logging.getLogger("markdown_it").setLevel(logging.ERROR)
|
||||
logging.getLogger("urllib3").setLevel(logging.ERROR)
|
||||
logging.getLogger("httpcore").setLevel(logging.ERROR)
|
||||
logging.getLogger("httpx").setLevel(logging.ERROR)
|
||||
logging.getLogger("asyncio").setLevel(logging.ERROR)
|
||||
logging.getLogger("charset_normalizer").setLevel(logging.ERROR)
|
||||
logging.getLogger("torchaudio._extension").setLevel(logging.ERROR)
|
||||
import pdb
|
||||
import torch
|
||||
|
||||
if os.path.exists("./gweight.txt"):
|
||||
with open("./gweight.txt", 'r', encoding="utf-8") as file:
|
||||
gweight_data = file.read()
|
||||
gpt_path = os.environ.get(
|
||||
"gpt_path", gweight_data)
|
||||
else:
|
||||
gpt_path = os.environ.get(
|
||||
"gpt_path", "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt")
|
||||
|
||||
if os.path.exists("./sweight.txt"):
|
||||
with open("./sweight.txt", 'r', encoding="utf-8") as file:
|
||||
sweight_data = file.read()
|
||||
sovits_path = os.environ.get("sovits_path", sweight_data)
|
||||
else:
|
||||
sovits_path = os.environ.get("sovits_path", "GPT_SoVITS/pretrained_models/s2G488k.pth")
|
||||
# gpt_path = os.environ.get(
|
||||
# "gpt_path", "pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"
|
||||
# )
|
||||
# sovits_path = os.environ.get("sovits_path", "pretrained_models/s2G488k.pth")
|
||||
cnhubert_base_path = os.environ.get(
|
||||
"cnhubert_base_path", "GPT_SoVITS/pretrained_models/chinese-hubert-base"
|
||||
)
|
||||
bert_path = os.environ.get(
|
||||
"bert_path", "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large"
|
||||
)
|
||||
infer_ttswebui = os.environ.get("infer_ttswebui", 9872)
|
||||
infer_ttswebui = int(infer_ttswebui)
|
||||
is_share = os.environ.get("is_share", "False")
|
||||
is_share = eval(is_share)
|
||||
if "_CUDA_VISIBLE_DEVICES" in os.environ:
|
||||
os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
|
||||
is_half = eval(os.environ.get("is_half", "True")) and not torch.backends.mps.is_available()
|
||||
import gradio as gr
|
||||
from transformers import AutoModelForMaskedLM, AutoTokenizer
|
||||
import numpy as np
|
||||
import librosa
|
||||
from feature_extractor import cnhubert
|
||||
|
||||
cnhubert.cnhubert_base_path = cnhubert_base_path
|
||||
|
||||
from module.models import SynthesizerTrn
|
||||
from AR.models.t2s_lightning_module import Text2SemanticLightningModule
|
||||
from text import cleaned_text_to_sequence
|
||||
from text.cleaner import clean_text
|
||||
from time import time as ttime
|
||||
from module.mel_processing import spectrogram_torch
|
||||
from my_utils import load_audio
|
||||
from tools.i18n.i18n import I18nAuto
|
||||
|
||||
i18n = I18nAuto()
|
||||
|
||||
os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = '1' # 确保直接启动推理UI时也能够设置。
|
||||
|
||||
if torch.cuda.is_available():
|
||||
device = "cuda"
|
||||
else:
|
||||
device = "cpu"
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained(bert_path)
|
||||
bert_model = AutoModelForMaskedLM.from_pretrained(bert_path)
|
||||
if is_half == True:
|
||||
bert_model = bert_model.half().to(device)
|
||||
else:
|
||||
bert_model = bert_model.to(device)
|
||||
|
||||
|
||||
def get_bert_feature(text, word2ph):
|
||||
with torch.no_grad():
|
||||
inputs = tokenizer(text, return_tensors="pt")
|
||||
for i in inputs:
|
||||
inputs[i] = inputs[i].to(device)
|
||||
res = bert_model(**inputs, output_hidden_states=True)
|
||||
res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1]
|
||||
assert len(word2ph) == len(text)
|
||||
phone_level_feature = []
|
||||
for i in range(len(word2ph)):
|
||||
repeat_feature = res[i].repeat(word2ph[i], 1)
|
||||
phone_level_feature.append(repeat_feature)
|
||||
phone_level_feature = torch.cat(phone_level_feature, dim=0)
|
||||
return phone_level_feature.T
|
||||
|
||||
|
||||
class DictToAttrRecursive(dict):
|
||||
def __init__(self, input_dict):
|
||||
super().__init__(input_dict)
|
||||
for key, value in input_dict.items():
|
||||
if isinstance(value, dict):
|
||||
value = DictToAttrRecursive(value)
|
||||
self[key] = value
|
||||
setattr(self, key, value)
|
||||
|
||||
def __getattr__(self, item):
|
||||
try:
|
||||
return self[item]
|
||||
except KeyError:
|
||||
raise AttributeError(f"Attribute {item} not found")
|
||||
|
||||
def __setattr__(self, key, value):
|
||||
if isinstance(value, dict):
|
||||
value = DictToAttrRecursive(value)
|
||||
super(DictToAttrRecursive, self).__setitem__(key, value)
|
||||
super().__setattr__(key, value)
|
||||
|
||||
def __delattr__(self, item):
|
||||
try:
|
||||
del self[item]
|
||||
except KeyError:
|
||||
raise AttributeError(f"Attribute {item} not found")
|
||||
|
||||
|
||||
ssl_model = cnhubert.get_model()
|
||||
if is_half == True:
|
||||
ssl_model = ssl_model.half().to(device)
|
||||
else:
|
||||
ssl_model = ssl_model.to(device)
|
||||
|
||||
|
||||
def change_sovits_weights(sovits_path):
|
||||
global vq_model, hps
|
||||
dict_s2 = torch.load(sovits_path, map_location="cpu")
|
||||
hps = dict_s2["config"]
|
||||
hps = DictToAttrRecursive(hps)
|
||||
hps.model.semantic_frame_rate = "25hz"
|
||||
vq_model = SynthesizerTrn(
|
||||
hps.data.filter_length // 2 + 1,
|
||||
hps.train.segment_size // hps.data.hop_length,
|
||||
n_speakers=hps.data.n_speakers,
|
||||
**hps.model
|
||||
)
|
||||
if ("pretrained" not in sovits_path):
|
||||
del vq_model.enc_q
|
||||
if is_half == True:
|
||||
vq_model = vq_model.half().to(device)
|
||||
else:
|
||||
vq_model = vq_model.to(device)
|
||||
vq_model.eval()
|
||||
print(vq_model.load_state_dict(dict_s2["weight"], strict=False))
|
||||
with open("./sweight.txt", "w", encoding="utf-8") as f:
|
||||
f.write(sovits_path)
|
||||
|
||||
|
||||
change_sovits_weights(sovits_path)
|
||||
|
||||
|
||||
def change_gpt_weights(gpt_path):
|
||||
global hz, max_sec, t2s_model, config
|
||||
hz = 50
|
||||
dict_s1 = torch.load(gpt_path, map_location="cpu")
|
||||
config = dict_s1["config"]
|
||||
max_sec = config["data"]["max_sec"]
|
||||
t2s_model = Text2SemanticLightningModule(config, "****", is_train=False)
|
||||
t2s_model.load_state_dict(dict_s1["weight"])
|
||||
if is_half == True:
|
||||
t2s_model = t2s_model.half()
|
||||
t2s_model = t2s_model.to(device)
|
||||
t2s_model.eval()
|
||||
total = sum([param.nelement() for param in t2s_model.parameters()])
|
||||
print("Number of parameter: %.2fM" % (total / 1e6))
|
||||
with open("./gweight.txt", "w", encoding="utf-8") as f: f.write(gpt_path)
|
||||
|
||||
|
||||
change_gpt_weights(gpt_path)
|
||||
|
||||
|
||||
def get_spepc(hps, filename):
|
||||
audio = load_audio(filename, int(hps.data.sampling_rate))
|
||||
audio = torch.FloatTensor(audio)
|
||||
audio_norm = audio
|
||||
audio_norm = audio_norm.unsqueeze(0)
|
||||
spec = spectrogram_torch(
|
||||
audio_norm,
|
||||
hps.data.filter_length,
|
||||
hps.data.sampling_rate,
|
||||
hps.data.hop_length,
|
||||
hps.data.win_length,
|
||||
center=False,
|
||||
)
|
||||
return spec
|
||||
|
||||
|
||||
dict_language = {
|
||||
i18n("中文"): "all_zh",#全部按中文识别
|
||||
i18n("英文"): "en",#全部按英文识别#######不变
|
||||
i18n("日文"): "all_ja",#全部按日文识别
|
||||
i18n("中英混合"): "zh",#按中英混合识别####不变
|
||||
i18n("日英混合"): "ja",#按日英混合识别####不变
|
||||
i18n("多语种混合"): "auto",#多语种启动切分识别语种
|
||||
}
|
||||
|
||||
|
||||
def clean_text_inf(text, language):
|
||||
phones, word2ph, norm_text = clean_text(text, language)
|
||||
phones = cleaned_text_to_sequence(phones)
|
||||
return phones, word2ph, norm_text
|
||||
|
||||
dtype=torch.float16 if is_half == True else torch.float32
|
||||
def get_bert_inf(phones, word2ph, norm_text, language):
|
||||
language=language.replace("all_","")
|
||||
if language == "zh":
|
||||
bert = get_bert_feature(norm_text, word2ph).to(device)#.to(dtype)
|
||||
else:
|
||||
bert = torch.zeros(
|
||||
(1024, len(phones)),
|
||||
dtype=torch.float16 if is_half == True else torch.float32,
|
||||
).to(device)
|
||||
|
||||
return bert
|
||||
|
||||
|
||||
splits = {"，", "。", "？", "！", ",", ".", "?", "!", "~", ":", "：", "—", "…", }
|
||||
|
||||
|
||||
def get_first(text):
|
||||
pattern = "[" + "".join(re.escape(sep) for sep in splits) + "]"
|
||||
text = re.split(pattern, text)[0].strip()
|
||||
return text
|
||||
|
||||
|
||||
def get_phones_and_bert(text,language):
|
||||
if language in {"en","all_zh","all_ja"}:
|
||||
language = language.replace("all_","")
|
||||
if language == "en":
|
||||
LangSegment.setfilters(["en"])
|
||||
formattext = " ".join(tmp["text"] for tmp in LangSegment.getTexts(text))
|
||||
else:
|
||||
# 因无法区别中日文汉字,以用户输入为准
|
||||
formattext = text
|
||||
while " " in formattext:
|
||||
formattext = formattext.replace(" ", " ")
|
||||
phones, word2ph, norm_text = clean_text_inf(formattext, language)
|
||||
if language == "zh":
|
||||
bert = get_bert_feature(norm_text, word2ph).to(device)
|
||||
else:
|
||||
bert = torch.zeros(
|
||||
(1024, len(phones)),
|
||||
dtype=torch.float16 if is_half == True else torch.float32,
|
||||
).to(device)
|
||||
elif language in {"zh", "ja","auto"}:
|
||||
textlist=[]
|
||||
langlist=[]
|
||||
LangSegment.setfilters(["zh","ja","en","ko"])
|
||||
if language == "auto":
|
||||
for tmp in LangSegment.getTexts(text):
|
||||
if tmp["lang"] == "ko":
|
||||
langlist.append("zh")
|
||||
textlist.append(tmp["text"])
|
||||
else:
|
||||
langlist.append(tmp["lang"])
|
||||
textlist.append(tmp["text"])
|
||||
else:
|
||||
for tmp in LangSegment.getTexts(text):
|
||||
if tmp["lang"] == "en":
|
||||
langlist.append(tmp["lang"])
|
||||
else:
|
||||
# 因无法区别中日文汉字,以用户输入为准
|
||||
langlist.append(language)
|
||||
textlist.append(tmp["text"])
|
||||
print(textlist)
|
||||
print(langlist)
|
||||
phones_list = []
|
||||
bert_list = []
|
||||
norm_text_list = []
|
||||
for i in range(len(textlist)):
|
||||
lang = langlist[i]
|
||||
phones, word2ph, norm_text = clean_text_inf(textlist[i], lang)
|
||||
bert = get_bert_inf(phones, word2ph, norm_text, lang)
|
||||
phones_list.append(phones)
|
||||
norm_text_list.append(norm_text)
|
||||
bert_list.append(bert)
|
||||
bert = torch.cat(bert_list, dim=1)
|
||||
phones = sum(phones_list, [])
|
||||
norm_text = ''.join(norm_text_list)
|
||||
|
||||
return phones,bert.to(dtype),norm_text
|
||||
|
||||
|
||||
def merge_short_text_in_array(texts, threshold):
|
||||
if (len(texts)) < 2:
|
||||
return texts
|
||||
result = []
|
||||
text = ""
|
||||
for ele in texts:
|
||||
text += ele
|
||||
if len(text) >= threshold:
|
||||
result.append(text)
|
||||
text = ""
|
||||
if (len(text) > 0):
|
||||
if len(result) == 0:
|
||||
result.append(text)
|
||||
else:
|
||||
result[len(result) - 1] += text
|
||||
return result
|
||||
|
||||
def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language, how_to_cut=i18n("不切"), top_k=20, top_p=0.6, temperature=0.6, ref_free = False):
|
||||
if prompt_text is None or len(prompt_text) == 0:
|
||||
ref_free = True
|
||||
t0 = ttime()
|
||||
prompt_language = dict_language[prompt_language]
|
||||
text_language = dict_language[text_language]
|
||||
if not ref_free:
|
||||
prompt_text = prompt_text.strip("\n")
|
||||
if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_language != "en" else "."
|
||||
print(i18n("实际输入的参考文本:"), prompt_text)
|
||||
text = text.strip("\n")
|
||||
if (text[0] not in splits and len(get_first(text)) < 4): text = "。" + text if text_language != "en" else "." + text
|
||||
|
||||
print(i18n("实际输入的目标文本:"), text)
|
||||
zero_wav = np.zeros(
|
||||
int(hps.data.sampling_rate * 0.3),
|
||||
dtype=np.float16 if is_half == True else np.float32,
|
||||
)
|
||||
with torch.no_grad():
|
||||
wav16k, sr = librosa.load(ref_wav_path, sr=16000)
|
||||
if (wav16k.shape[0] > 160000 or wav16k.shape[0] < 48000):
|
||||
raise OSError(i18n("参考音频在3~10秒范围外，请更换！"))
|
||||
wav16k = torch.from_numpy(wav16k)
|
||||
zero_wav_torch = torch.from_numpy(zero_wav)
|
||||
if is_half == True:
|
||||
wav16k = wav16k.half().to(device)
|
||||
zero_wav_torch = zero_wav_torch.half().to(device)
|
||||
else:
|
||||
wav16k = wav16k.to(device)
|
||||
zero_wav_torch = zero_wav_torch.to(device)
|
||||
wav16k = torch.cat([wav16k, zero_wav_torch])
|
||||
ssl_content = ssl_model.model(wav16k.unsqueeze(0))[
|
||||
"last_hidden_state"
|
||||
].transpose(
|
||||
1, 2
|
||||
) # .float()
|
||||
codes = vq_model.extract_latent(ssl_content)
|
||||
|
||||
prompt_semantic = codes[0, 0]
|
||||
t1 = ttime()
|
||||
|
||||
if (how_to_cut == i18n("凑四句一切")):
|
||||
text = cut1(text)
|
||||
elif (how_to_cut == i18n("凑50字一切")):
|
||||
text = cut2(text)
|
||||
elif (how_to_cut == i18n("按中文句号。切")):
|
||||
text = cut3(text)
|
||||
elif (how_to_cut == i18n("按英文句号.切")):
|
||||
text = cut4(text)
|
||||
elif (how_to_cut == i18n("按标点符号切")):
|
||||
text = cut5(text)
|
||||
while "\n\n" in text:
|
||||
text = text.replace("\n\n", "\n")
|
||||
print(i18n("实际输入的目标文本(切句后):"), text)
|
||||
texts = text.split("\n")
|
||||
texts = merge_short_text_in_array(texts, 5)
|
||||
audio_opt = []
|
||||
if not ref_free:
|
||||
phones1,bert1,norm_text1=get_phones_and_bert(prompt_text, prompt_language)
|
||||
|
||||
for text in texts:
|
||||
# 解决输入目标文本的空行导致报错的问题
|
||||
if (len(text.strip()) == 0):
|
||||
continue
|
||||
if (text[-1] not in splits): text += "。" if text_language != "en" else "."
|
||||
print(i18n("实际输入的目标文本(每句):"), text)
|
||||
phones2,bert2,norm_text2=get_phones_and_bert(text, text_language)
|
||||
print(i18n("前端处理后的文本(每句):"), norm_text2)
|
||||
if not ref_free:
|
||||
bert = torch.cat([bert1, bert2], 1)
|
||||
all_phoneme_ids = torch.LongTensor(phones1+phones2).to(device).unsqueeze(0)
|
||||
else:
|
||||
bert = bert2
|
||||
all_phoneme_ids = torch.LongTensor(phones2).to(device).unsqueeze(0)
|
||||
|
||||
bert = bert.to(device).unsqueeze(0)
|
||||
all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(device)
|
||||
prompt = prompt_semantic.unsqueeze(0).to(device)
|
||||
t2 = ttime()
|
||||
with torch.no_grad():
|
||||
# pred_semantic = t2s_model.model.infer(
|
||||
pred_semantic, idx = t2s_model.model.infer_panel(
|
||||
all_phoneme_ids,
|
||||
all_phoneme_len,
|
||||
None if ref_free else prompt,
|
||||
bert,
|
||||
# prompt_phone_len=ph_offset,
|
||||
top_k=top_k,
|
||||
top_p=top_p,
|
||||
temperature=temperature,
|
||||
early_stop_num=hz * max_sec,
|
||||
)
|
||||
t3 = ttime()
|
||||
# print(pred_semantic.shape,idx)
|
||||
pred_semantic = pred_semantic[:, -idx:].unsqueeze(
|
||||
0
|
||||
) # .unsqueeze(0)#mq要多unsqueeze一次
|
||||
refer = get_spepc(hps, ref_wav_path) # .to(device)
|
||||
if is_half == True:
|
||||
refer = refer.half().to(device)
|
||||
else:
|
||||
refer = refer.to(device)
|
||||
# audio = vq_model.decode(pred_semantic, all_phoneme_ids, refer).detach().cpu().numpy()[0, 0]
|
||||
audio = (
|
||||
vq_model.decode(
|
||||
pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refer
|
||||
)
|
||||
.detach()
|
||||
.cpu()
|
||||
.numpy()[0, 0]
|
||||
) ###试试重建不带上prompt部分
|
||||
max_audio=np.abs(audio).max()#简单防止16bit爆音
|
||||
if max_audio>1:audio/=max_audio
|
||||
audio_opt.append(audio)
|
||||
audio_opt.append(zero_wav)
|
||||
t4 = ttime()
|
||||
print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))
|
||||
yield hps.data.sampling_rate, (np.concatenate(audio_opt, 0) * 32768).astype(
|
||||
np.int16
|
||||
)
|
||||
|
||||
|
||||
def split(todo_text):
|
||||
todo_text = todo_text.replace("……", "。").replace("——", "，")
|
||||
if todo_text[-1] not in splits:
|
||||
todo_text += "。"
|
||||
i_split_head = i_split_tail = 0
|
||||
len_text = len(todo_text)
|
||||
todo_texts = []
|
||||
while 1:
|
||||
if i_split_head >= len_text:
|
||||
break # 结尾一定有标点，所以直接跳出即可，最后一段在上次已加入
|
||||
if todo_text[i_split_head] in splits:
|
||||
i_split_head += 1
|
||||
todo_texts.append(todo_text[i_split_tail:i_split_head])
|
||||
i_split_tail = i_split_head
|
||||
else:
|
||||
i_split_head += 1
|
||||
return todo_texts
|
||||
|
||||
|
||||
def cut1(inp):
|
||||
inp = inp.strip("\n")
|
||||
inps = split(inp)
|
||||
split_idx = list(range(0, len(inps), 4))
|
||||
split_idx[-1] = None
|
||||
if len(split_idx) > 1:
|
||||
opts = []
|
||||
for idx in range(len(split_idx) - 1):
|
||||
opts.append("".join(inps[split_idx[idx]: split_idx[idx + 1]]))
|
||||
else:
|
||||
opts = [inp]
|
||||
return "\n".join(opts)
|
||||
|
||||
|
||||
def cut2(inp):
|
||||
inp = inp.strip("\n")
|
||||
inps = split(inp)
|
||||
if len(inps) < 2:
|
||||
return inp
|
||||
opts = []
|
||||
summ = 0
|
||||
tmp_str = ""
|
||||
for i in range(len(inps)):
|
||||
summ += len(inps[i])
|
||||
tmp_str += inps[i]
|
||||
if summ > 50:
|
||||
summ = 0
|
||||
opts.append(tmp_str)
|
||||
tmp_str = ""
|
||||
if tmp_str != "":
|
||||
opts.append(tmp_str)
|
||||
# print(opts)
|
||||
if len(opts) > 1 and len(opts[-1]) < 50: ##如果最后一个太短了，和前一个合一起
|
||||
opts[-2] = opts[-2] + opts[-1]
|
||||
opts = opts[:-1]
|
||||
return "\n".join(opts)
|
||||
|
||||
|
||||
def cut3(inp):
|
||||
inp = inp.strip("\n")
|
||||
return "\n".join(["%s" % item for item in inp.strip("。").split("。")])
|
||||
|
||||
|
||||
def cut4(inp):
|
||||
inp = inp.strip("\n")
|
||||
return "\n".join(["%s" % item for item in inp.strip(".").split(".")])
|
||||
|
||||
|
||||
# contributed by https://github.com/AI-Hobbyist/GPT-SoVITS/blob/main/GPT_SoVITS/inference_webui.py
|
||||
def cut5(inp):
|
||||
# if not re.search(r'[^\w\s]', inp[-1]):
|
||||
# inp += '。'
|
||||
inp = inp.strip("\n")
|
||||
punds = r'[,.;?!、，。？！;：…]'
|
||||
items = re.split(f'({punds})', inp)
|
||||
mergeitems = ["".join(group) for group in zip(items[::2], items[1::2])]
|
||||
# 在句子不存在符号或句尾无符号的时候保证文本完整
|
||||
if len(items)%2 == 1:
|
||||
mergeitems.append(items[-1])
|
||||
opt = "\n".join(mergeitems)
|
||||
return opt
|
||||
|
||||
|
||||
def custom_sort_key(s):
|
||||
# 使用正则表达式提取字符串中的数字部分和非数字部分
|
||||
parts = re.split('(\d+)', s)
|
||||
# 将数字部分转换为整数，非数字部分保持不变
|
||||
parts = [int(part) if part.isdigit() else part for part in parts]
|
||||
return parts
|
||||
|
||||
|
||||
def change_choices():
|
||||
SoVITS_names, GPT_names = get_weights_names()
|
||||
return {"choices": sorted(SoVITS_names, key=custom_sort_key), "__type__": "update"}, {"choices": sorted(GPT_names, key=custom_sort_key), "__type__": "update"}
|
||||
|
||||
|
||||
pretrained_sovits_name = "GPT_SoVITS/pretrained_models/s2G488k.pth"
|
||||
pretrained_gpt_name = "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"
|
||||
SoVITS_weight_root = "SoVITS_weights"
|
||||
GPT_weight_root = "GPT_weights"
|
||||
os.makedirs(SoVITS_weight_root, exist_ok=True)
|
||||
os.makedirs(GPT_weight_root, exist_ok=True)
|
||||
|
||||
|
||||
def get_weights_names():
|
||||
SoVITS_names = [pretrained_sovits_name]
|
||||
for name in os.listdir(SoVITS_weight_root):
|
||||
if name.endswith(".pth"): SoVITS_names.append("%s/%s" % (SoVITS_weight_root, name))
|
||||
GPT_names = [pretrained_gpt_name]
|
||||
for name in os.listdir(GPT_weight_root):
|
||||
if name.endswith(".ckpt"): GPT_names.append("%s/%s" % (GPT_weight_root, name))
|
||||
return SoVITS_names, GPT_names
|
||||
|
||||
|
||||
SoVITS_names, GPT_names = get_weights_names()
|
||||
|
||||
with gr.Blocks(title="GPT-SoVITS WebUI") as app:
|
||||
gr.Markdown(
|
||||
value=i18n("本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责. <br>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<b>LICENSE</b>.")
|
||||
)
|
||||
with gr.Group():
|
||||
gr.Markdown(value=i18n("模型切换"))
|
||||
with gr.Row():
|
||||
GPT_dropdown = gr.Dropdown(label=i18n("GPT模型列表"), choices=sorted(GPT_names, key=custom_sort_key), value=gpt_path, interactive=True)
|
||||
SoVITS_dropdown = gr.Dropdown(label=i18n("SoVITS模型列表"), choices=sorted(SoVITS_names, key=custom_sort_key), value=sovits_path, interactive=True)
|
||||
refresh_button = gr.Button(i18n("刷新模型路径"), variant="primary")
|
||||
refresh_button.click(fn=change_choices, inputs=[], outputs=[SoVITS_dropdown, GPT_dropdown])
|
||||
SoVITS_dropdown.change(change_sovits_weights, [SoVITS_dropdown], [])
|
||||
GPT_dropdown.change(change_gpt_weights, [GPT_dropdown], [])
|
||||
gr.Markdown(value=i18n("*请上传并填写参考信息"))
|
||||
with gr.Row():
|
||||
inp_ref = gr.Audio(label=i18n("请上传3~10秒内参考音频，超过会报错！"), type="filepath")
|
||||
with gr.Column():
|
||||
ref_text_free = gr.Checkbox(label=i18n("开启无参考文本模式。不填参考文本亦相当于开启。"), value=False, interactive=True, show_label=True)
|
||||
gr.Markdown(i18n("使用无参考文本模式时建议使用微调的GPT，听不清参考音频说的啥(不晓得写啥)可以开，开启后无视填写的参考文本。"))
|
||||
prompt_text = gr.Textbox(label=i18n("参考音频的文本"), value="")
|
||||
prompt_language = gr.Dropdown(
|
||||
label=i18n("参考音频的语种"), choices=[i18n("中文"), i18n("英文"), i18n("日文"), i18n("中英混合"), i18n("日英混合"), i18n("多语种混合")], value=i18n("中文")
|
||||
)
|
||||
gr.Markdown(value=i18n("*请填写需要合成的目标文本和语种模式"))
|
||||
with gr.Row():
|
||||
text = gr.Textbox(label=i18n("需要合成的文本"), value="")
|
||||
text_language = gr.Dropdown(
|
||||
label=i18n("需要合成的语种"), choices=[i18n("中文"), i18n("英文"), i18n("日文"), i18n("中英混合"), i18n("日英混合"), i18n("多语种混合")], value=i18n("中文")
|
||||
)
|
||||
how_to_cut = gr.Radio(
|
||||
label=i18n("怎么切"),
|
||||
choices=[i18n("不切"), i18n("凑四句一切"), i18n("凑50字一切"), i18n("按中文句号。切"), i18n("按英文句号.切"), i18n("按标点符号切"), ],
|
||||
value=i18n("凑四句一切"),
|
||||
interactive=True,
|
||||
)
|
||||
with gr.Row():
|
||||
gr.Markdown(value=i18n("gpt采样参数(无参考文本时不要太低)："))
|
||||
top_k = gr.Slider(minimum=1,maximum=100,step=1,label=i18n("top_k"),value=5,interactive=True)
|
||||
top_p = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("top_p"),value=1,interactive=True)
|
||||
temperature = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("temperature"),value=1,interactive=True)
|
||||
inference_button = gr.Button(i18n("合成语音"), variant="primary")
|
||||
output = gr.Audio(label=i18n("输出的语音"))
|
||||
|
||||
inference_button.click(
|
||||
get_tts_wav,
|
||||
[inp_ref, prompt_text, prompt_language, text, text_language, how_to_cut, top_k, top_p, temperature, ref_text_free],
|
||||
[output],
|
||||
)
|
||||
|
||||
gr.Markdown(value=i18n("文本切分工具。太长的文本合成出来效果不一定好，所以太长建议先切。合成会根据文本的换行分开合成再拼起来。"))
|
||||
with gr.Row():
|
||||
text_inp = gr.Textbox(label=i18n("需要合成的切分前文本"), value="")
|
||||
button1 = gr.Button(i18n("凑四句一切"), variant="primary")
|
||||
button2 = gr.Button(i18n("凑50字一切"), variant="primary")
|
||||
button3 = gr.Button(i18n("按中文句号。切"), variant="primary")
|
||||
button4 = gr.Button(i18n("按英文句号.切"), variant="primary")
|
||||
button5 = gr.Button(i18n("按标点符号切"), variant="primary")
|
||||
text_opt = gr.Textbox(label=i18n("切分后文本"), value="")
|
||||
button1.click(cut1, [text_inp], [text_opt])
|
||||
button2.click(cut2, [text_inp], [text_opt])
|
||||
button3.click(cut3, [text_inp], [text_opt])
|
||||
button4.click(cut4, [text_inp], [text_opt])
|
||||
button5.click(cut5, [text_inp], [text_opt])
|
||||
gr.Markdown(value=i18n("后续将支持转音素、手工修改音素、语音合成分步执行。"))
|
||||
|
||||
app.queue(concurrency_count=511, max_size=1022).launch(
|
||||
server_name="0.0.0.0",
|
||||
inbrowser=True,
|
||||
share=is_share,
|
||||
server_port=infer_ttswebui,
|
||||
quiet=True,
|
||||
)
|
||||
Loading…
x
Reference in New Issue
Block a user