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Simplify i18n text and remove trailing spaces

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starylan 2025-02-12 01:47:32 +08:00
parent 4748f1a6b0
commit 9ce83c8eea
2 changed files with 127 additions and 130 deletions
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GPT_SoVITS/TTS_infer_pack/TTS.py
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@ -79,7 +79,7 @@ def set_seed(seed:int):
except: except:
pass pass
return seed return seed
class TTS_Config: class TTS_Config:
default_configs={ default_configs={
"default":{ "default":{
@ -118,18 +118,18 @@ class TTS_Config:
# "auto_yue",#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): def __init__(self, configs: Union[dict, str]=None):
# 设置默认配置文件路径 # 设置默认配置文件路径
configs_base_path:str = "GPT_SoVITS/configs/" configs_base_path:str = "GPT_SoVITS/configs/"
os.makedirs(configs_base_path, exist_ok=True) os.makedirs(configs_base_path, exist_ok=True)
self.configs_path:str = os.path.join(configs_base_path, "tts_infer.yaml") self.configs_path:str = os.path.join(configs_base_path, "tts_infer.yaml")
if configs in ["", None]: if configs in ["", None]:
if not os.path.exists(self.configs_path): if not os.path.exists(self.configs_path):
self.save_configs() self.save_configs()
print(f"Create default config file at {self.configs_path}") print(f"Create default config file at {self.configs_path}")
configs:dict = deepcopy(self.default_configs) configs:dict = deepcopy(self.default_configs)
if isinstance(configs, str): if isinstance(configs, str):
self.configs_path = configs self.configs_path = configs
configs:dict = self._load_configs(self.configs_path) configs:dict = self._load_configs(self.configs_path)
@ -142,8 +142,8 @@ class TTS_Config:
default_config_key = "default"if version=="v1" else "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.configs:dict = configs.get("custom", deepcopy(self.default_configs[default_config_key]))
self.device = self.configs.get("device", torch.device("cpu")) self.device = self.configs.get("device", torch.device("cpu"))
self.is_half = self.configs.get("is_half", False) self.is_half = self.configs.get("is_half", False)
self.version = version self.version = version
@ -153,7 +153,7 @@ class TTS_Config:
self.cnhuhbert_base_path = self.configs.get("cnhuhbert_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 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)): 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'] 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}") print(f"fall back to default t2s_weights_path: {self.t2s_weights_path}")
@ -167,8 +167,8 @@ class TTS_Config:
self.cnhuhbert_base_path = self.default_configs[default_config_key]['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}") print(f"fall back to default cnhuhbert_base_path: {self.cnhuhbert_base_path}")
self.update_configs() self.update_configs()
self.max_sec = None self.max_sec = None
self.hz:int = 50 self.hz:int = 50
self.semantic_frame_rate:str = "25hz" self.semantic_frame_rate:str = "25hz"
@ -180,7 +180,7 @@ class TTS_Config:
self.n_speakers:int = 300 self.n_speakers:int = 300
def _load_configs(self, configs_path: str)->dict: def _load_configs(self, configs_path: str)->dict:
if os.path.exists(configs_path): if os.path.exists(configs_path):
... ...
@ -189,14 +189,14 @@ class TTS_Config:
self.save_configs(configs_path) self.save_configs(configs_path)
with open(configs_path, 'r') as f: with open(configs_path, 'r') as f:
configs = yaml.load(f, Loader=yaml.FullLoader) configs = yaml.load(f, Loader=yaml.FullLoader)
return configs return configs
def save_configs(self, configs_path:str=None)->None: def save_configs(self, configs_path:str=None)->None:
configs=deepcopy(self.default_configs) configs=deepcopy(self.default_configs)
if self.configs is not None: if self.configs is not None:
configs["custom"] = self.update_configs() configs["custom"] = self.update_configs()
if configs_path is None: if configs_path is None:
configs_path = self.configs_path configs_path = self.configs_path
with open(configs_path, 'w') as f: with open(configs_path, 'w') as f:
@ -217,7 +217,7 @@ class TTS_Config:
def update_version(self, version:str)->None: def update_version(self, version:str)->None:
self.version = version self.version = version
self.languages = self.v2_languages if self.version=="v2" else self.v1_languages self.languages = self.v2_languages if self.version=="v2" else self.v1_languages
def __str__(self): def __str__(self):
self.configs = self.update_configs() self.configs = self.update_configs()
string = "TTS Config".center(100, '-') + '\n' string = "TTS Config".center(100, '-') + '\n'
@ -225,7 +225,7 @@ class TTS_Config:
string += f"{str(k).ljust(20)}: {str(v)}\n" string += f"{str(k).ljust(20)}: {str(v)}\n"
string += "-" * 100 + '\n' string += "-" * 100 + '\n'
return string return string
def __repr__(self): def __repr__(self):
return self.__str__() return self.__str__()
@ -242,21 +242,21 @@ class TTS:
self.configs = configs self.configs = configs
else: else:
self.configs:TTS_Config = TTS_Config(configs) self.configs:TTS_Config = TTS_Config(configs)
self.t2s_model:Text2SemanticLightningModule = None self.t2s_model:Text2SemanticLightningModule = None
self.vits_model:SynthesizerTrn = None self.vits_model:SynthesizerTrn = None
self.bert_tokenizer:AutoTokenizer = None self.bert_tokenizer:AutoTokenizer = None
self.bert_model:AutoModelForMaskedLM = None self.bert_model:AutoModelForMaskedLM = None
self.cnhuhbert_model:CNHubert = None self.cnhuhbert_model:CNHubert = None
self._init_models() self._init_models()
self.text_preprocessor:TextPreprocessor = \ self.text_preprocessor:TextPreprocessor = \
TextPreprocessor(self.bert_model, TextPreprocessor(self.bert_model,
self.bert_tokenizer, self.bert_tokenizer,
self.configs.device) self.configs.device)
self.prompt_cache:dict = { self.prompt_cache:dict = {
"ref_audio_path" : None, "ref_audio_path" : None,
"prompt_semantic": None, "prompt_semantic": None,
@ -268,8 +268,8 @@ class TTS:
"norm_text" : None, "norm_text" : None,
"aux_ref_audio_paths": [], "aux_ref_audio_paths": [],
} }
self.stop_flag:bool = False self.stop_flag:bool = False
self.precision:torch.dtype = torch.float16 if self.configs.is_half else torch.float32 self.precision:torch.dtype = torch.float16 if self.configs.is_half else torch.float32
@ -279,9 +279,9 @@ class TTS:
self.init_bert_weights(self.configs.bert_base_path) self.init_bert_weights(self.configs.bert_base_path)
self.init_cnhuhbert_weights(self.configs.cnhuhbert_base_path) self.init_cnhuhbert_weights(self.configs.cnhuhbert_base_path)
# self.enable_half_precision(self.configs.is_half) # self.enable_half_precision(self.configs.is_half)
def init_cnhuhbert_weights(self, base_path: str): def init_cnhuhbert_weights(self, base_path: str):
print(f"Loading CNHuBERT weights from {base_path}") print(f"Loading CNHuBERT weights from {base_path}")
self.cnhuhbert_model = CNHubert(base_path) self.cnhuhbert_model = CNHubert(base_path)
@ -289,9 +289,9 @@ class TTS:
self.cnhuhbert_model = self.cnhuhbert_model.to(self.configs.device) self.cnhuhbert_model = self.cnhuhbert_model.to(self.configs.device)
if self.configs.is_half and str(self.configs.device)!="cpu": if self.configs.is_half and str(self.configs.device)!="cpu":
self.cnhuhbert_model = self.cnhuhbert_model.half() self.cnhuhbert_model = self.cnhuhbert_model.half()
def init_bert_weights(self, base_path: str): def init_bert_weights(self, base_path: str):
print(f"Loading BERT weights from {base_path}") print(f"Loading BERT weights from {base_path}")
self.bert_tokenizer = AutoTokenizer.from_pretrained(base_path) self.bert_tokenizer = AutoTokenizer.from_pretrained(base_path)
@ -300,7 +300,7 @@ class TTS:
self.bert_model = self.bert_model.to(self.configs.device) self.bert_model = self.bert_model.to(self.configs.device)
if self.configs.is_half and str(self.configs.device)!="cpu": if self.configs.is_half and str(self.configs.device)!="cpu":
self.bert_model = self.bert_model.half() self.bert_model = self.bert_model.half()
def init_vits_weights(self, weights_path: str): def init_vits_weights(self, weights_path: str):
print(f"Loading VITS weights from {weights_path}") print(f"Loading VITS weights from {weights_path}")
self.configs.vits_weights_path = weights_path self.configs.vits_weights_path = weights_path
@ -311,11 +311,11 @@ class TTS:
else: else:
self.configs.update_version("v2") self.configs.update_version("v2")
self.configs.save_configs() self.configs.save_configs()
hps["model"]["version"] = self.configs.version hps["model"]["version"] = self.configs.version
self.configs.filter_length = hps["data"]["filter_length"] self.configs.filter_length = hps["data"]["filter_length"]
self.configs.segment_size = hps["train"]["segment_size"] self.configs.segment_size = hps["train"]["segment_size"]
self.configs.sampling_rate = hps["data"]["sampling_rate"] self.configs.sampling_rate = hps["data"]["sampling_rate"]
self.configs.hop_length = hps["data"]["hop_length"] self.configs.hop_length = hps["data"]["hop_length"]
self.configs.win_length = hps["data"]["win_length"] self.configs.win_length = hps["data"]["win_length"]
self.configs.n_speakers = hps["data"]["n_speakers"] self.configs.n_speakers = hps["data"]["n_speakers"]
@ -330,7 +330,7 @@ class TTS:
if hasattr(vits_model, "enc_q"): if hasattr(vits_model, "enc_q"):
del vits_model.enc_q del vits_model.enc_q
vits_model = vits_model.to(self.configs.device) vits_model = vits_model.to(self.configs.device)
vits_model = vits_model.eval() vits_model = vits_model.eval()
vits_model.load_state_dict(dict_s2["weight"], strict=False) vits_model.load_state_dict(dict_s2["weight"], strict=False)
@ -338,7 +338,7 @@ class TTS:
if self.configs.is_half and str(self.configs.device)!="cpu": if self.configs.is_half and str(self.configs.device)!="cpu":
self.vits_model = self.vits_model.half() self.vits_model = self.vits_model.half()
def init_t2s_weights(self, weights_path: str): def init_t2s_weights(self, weights_path: str):
print(f"Loading Text2Semantic weights from {weights_path}") print(f"Loading Text2Semantic weights from {weights_path}")
self.configs.t2s_weights_path = weights_path self.configs.t2s_weights_path = weights_path
@ -354,18 +354,18 @@ class TTS:
self.t2s_model = t2s_model self.t2s_model = t2s_model
if self.configs.is_half and str(self.configs.device)!="cpu": if self.configs.is_half and str(self.configs.device)!="cpu":
self.t2s_model = self.t2s_model.half() self.t2s_model = self.t2s_model.half()
def enable_half_precision(self, enable: bool = True, save: bool = True): def enable_half_precision(self, enable: bool = True, save: bool = True):
''' '''
To enable half precision for the TTS model. To enable half precision for the TTS model.
Args: Args:
enable: bool, whether to enable half precision. enable: bool, whether to enable half precision.
''' '''
if str(self.configs.device) == "cpu" and enable: if str(self.configs.device) == "cpu" and enable:
print("Half precision is not supported on CPU.") print("Half precision is not supported on CPU.")
return return
self.configs.is_half = enable self.configs.is_half = enable
self.precision = torch.float16 if enable else torch.float32 self.precision = torch.float16 if enable else torch.float32
if save: if save:
@ -388,7 +388,7 @@ class TTS:
self.bert_model = self.bert_model.float() self.bert_model = self.bert_model.float()
if self.cnhuhbert_model is not None: if self.cnhuhbert_model is not None:
self.cnhuhbert_model = self.cnhuhbert_model.float() self.cnhuhbert_model = self.cnhuhbert_model.float()
def set_device(self, device: torch.device, save: bool = True): def set_device(self, device: torch.device, save: bool = True):
''' '''
To set the device for all models. To set the device for all models.
@ -406,10 +406,10 @@ class TTS:
self.bert_model = self.bert_model.to(device) self.bert_model = self.bert_model.to(device)
if self.cnhuhbert_model is not None: if self.cnhuhbert_model is not None:
self.cnhuhbert_model = self.cnhuhbert_model.to(device) self.cnhuhbert_model = self.cnhuhbert_model.to(device)
def set_ref_audio(self, ref_audio_path:str): def set_ref_audio(self, ref_audio_path:str):
''' '''
To set the reference audio for the TTS model, To set the reference audio for the TTS model,
including the prompt_semantic and refer_spepc. including the prompt_semantic and refer_spepc.
Args: Args:
ref_audio_path: str, the path of the reference audio. ref_audio_path: str, the path of the reference audio.
@ -417,9 +417,9 @@ class TTS:
self._set_prompt_semantic(ref_audio_path) self._set_prompt_semantic(ref_audio_path)
self._set_ref_spec(ref_audio_path) self._set_ref_spec(ref_audio_path)
self._set_ref_audio_path(ref_audio_path) self._set_ref_audio_path(ref_audio_path)
def _set_ref_audio_path(self, ref_audio_path): def _set_ref_audio_path(self, ref_audio_path):
self.prompt_cache["ref_audio_path"] = ref_audio_path self.prompt_cache["ref_audio_path"] = ref_audio_path
def _set_ref_spec(self, ref_audio_path): def _set_ref_spec(self, ref_audio_path):
spec = self._get_ref_spec(ref_audio_path) spec = self._get_ref_spec(ref_audio_path)
@ -472,10 +472,10 @@ class TTS:
1, 2 1, 2
) # .float() ) # .float()
codes = self.vits_model.extract_latent(hubert_feature) codes = self.vits_model.extract_latent(hubert_feature)
prompt_semantic = codes[0, 0].to(self.configs.device) prompt_semantic = codes[0, 0].to(self.configs.device)
self.prompt_cache["prompt_semantic"] = prompt_semantic 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): def batch_sequences(self, sequences: List[torch.Tensor], axis: int = 0, pad_value: int = 0, max_length:int=None):
seq = sequences[0] seq = sequences[0]
ndim = seq.dim() ndim = seq.dim()
@ -496,12 +496,12 @@ class TTS:
padded_sequences.append(padded_seq) padded_sequences.append(padded_seq)
batch = torch.stack(padded_sequences) batch = torch.stack(padded_sequences)
return batch return batch
def to_batch(self, data:list, def to_batch(self, data:list,
prompt_data:dict=None, prompt_data:dict=None,
batch_size:int=5, batch_size:int=5,
threshold:float=0.75, threshold:float=0.75,
split_bucket:bool=True, split_bucket:bool=True,
device:torch.device=torch.device("cpu"), device:torch.device=torch.device("cpu"),
precision:torch.dtype=torch.float32, precision:torch.dtype=torch.float32,
): ):
@ -514,8 +514,8 @@ class TTS:
batch_index_list = [] batch_index_list = []
if split_bucket: if split_bucket:
index_and_len_list.sort(key=lambda x: x[1]) index_and_len_list.sort(key=lambda x: x[1])
index_and_len_list = np.array(index_and_len_list, dtype=np.int64) index_and_len_list = np.array(index_and_len_list, dtype=np.int64)
batch_index_list_len = 0 batch_index_list_len = 0
pos = 0 pos = 0
while pos <index_and_len_list.shape[0]: while pos <index_and_len_list.shape[0]:
@ -531,16 +531,16 @@ class TTS:
pos = pos_end pos = pos_end
break break
pos_end=pos_end-1 pos_end=pos_end-1
assert batch_index_list_len == len(data) assert batch_index_list_len == len(data)
else: else:
for i in range(len(data)): for i in range(len(data)):
if i%batch_size == 0: if i%batch_size == 0:
batch_index_list.append([]) batch_index_list.append([])
batch_index_list[-1].append(i) batch_index_list[-1].append(i)
for batch_idx, index_list in enumerate(batch_index_list): for batch_idx, index_list in enumerate(batch_index_list):
item_list = [data[idx] for idx in index_list] item_list = [data[idx] for idx in index_list]
phones_list = [] phones_list = []
@ -568,19 +568,19 @@ class TTS:
all_bert_max_len = max(all_bert_max_len, all_bert_features.shape[-1]) 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]) all_phones_max_len = max(all_phones_max_len, all_phones.shape[-1])
phones_list.append(phones) phones_list.append(phones)
phones_len_list.append(phones.shape[-1]) phones_len_list.append(phones.shape[-1])
all_phones_list.append(all_phones) all_phones_list.append(all_phones)
all_phones_len_list.append(all_phones.shape[-1]) all_phones_len_list.append(all_phones.shape[-1])
all_bert_features_list.append(all_bert_features) all_bert_features_list.append(all_bert_features)
norm_text_batch.append(item["norm_text"]) norm_text_batch.append(item["norm_text"])
phones_batch = phones_list phones_batch = phones_list
all_phones_batch = all_phones_list all_phones_batch = all_phones_list
all_bert_features_batch = all_bert_features_list all_bert_features_batch = all_bert_features_list
max_len = max(all_bert_max_len, all_phones_max_len) 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) # 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） #### 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）
@ -589,16 +589,16 @@ class TTS:
# all_bert_features_batch = torch.zeros((len(all_bert_features_list), 1024, max_len), dtype=precision, device=device) # 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): # for idx, item in enumerate(all_bert_features_list):
# all_bert_features_batch[idx, :, : item.shape[-1]] = item # 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） # #### 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 = [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_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_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 = [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_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) # all_bert_features_batch = torch.stack(all_bert_features_list, dim=0)
batch = { batch = {
"phones": phones_batch, "phones": phones_batch,
"phones_len": torch.LongTensor(phones_len_list).to(device), "phones_len": torch.LongTensor(phones_len_list).to(device),
@ -609,17 +609,17 @@ class TTS:
"max_len": max_len, "max_len": max_len,
} }
_data.append(batch) _data.append(batch)
return _data, batch_index_list return _data, batch_index_list
def recovery_order(self, data:list, batch_index_list:list)->list: def recovery_order(self, data:list, batch_index_list:list)->list:
''' '''
Recovery the order of the audio according to the batch_index_list. Recovery the order of the audio according to the batch_index_list.
Args: Args:
data (List[list(np.ndarray)]): the out of order audio . data (List[list(np.ndarray)]): the out of order audio .
batch_index_list (List[list[int]]): the batch index list. batch_index_list (List[list[int]]): the batch index list.
Returns: Returns:
list (List[np.ndarray]): the data in the original order. list (List[np.ndarray]): the data in the original order.
''' '''
@ -635,14 +635,14 @@ class TTS:
Stop the inference process. Stop the inference process.
''' '''
self.stop_flag = True self.stop_flag = True
@torch.no_grad() @torch.no_grad()
def run(self, inputs:dict): def run(self, inputs:dict):
""" """
Text to speech inference. Text to speech inference.
Args: Args:
inputs (dict): inputs (dict):
{ {
"text": "", # str.(required) text to be synthesized "text": "", # str.(required) text to be synthesized
"text_lang: "", # str.(required) language of the text to be synthesized "text_lang: "", # str.(required) language of the text to be synthesized
@ -734,7 +734,7 @@ class TTS:
if not os.path.exists(ref_audio_path): if not os.path.exists(ref_audio_path):
raise ValueError(f"{ref_audio_path} not exists") raise ValueError(f"{ref_audio_path} not exists")
self.set_ref_audio(ref_audio_path) self.set_ref_audio(ref_audio_path)
aux_ref_audio_paths = aux_ref_audio_paths if aux_ref_audio_paths is not None else [] 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"]) 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"])): if not (len(list(paths)) == len(aux_ref_audio_paths) == len(self.prompt_cache["aux_ref_audio_paths"])):
@ -744,10 +744,10 @@ class TTS:
if path in [None, ""]: if path in [None, ""]:
continue continue
if not os.path.exists(path): if not os.path.exists(path):
print(i18n("音频文件不存在，跳过：{}").format(path)) print(i18n("音频文件不存在，跳过："), path)
continue continue
self.prompt_cache["refer_spec"].append(self._get_ref_spec(path)) self.prompt_cache["refer_spec"].append(self._get_ref_spec(path))
if not no_prompt_text: if not no_prompt_text:
prompt_text = prompt_text.strip("\n") prompt_text = prompt_text.strip("\n")
if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_lang != "en" else "." if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_lang != "en" else "."
@ -757,7 +757,7 @@ class TTS:
self.prompt_cache["prompt_lang"] = prompt_lang self.prompt_cache["prompt_lang"] = prompt_lang
phones, bert_features, norm_text = \ phones, bert_features, norm_text = \
self.text_preprocessor.segment_and_extract_feature_for_text( self.text_preprocessor.segment_and_extract_feature_for_text(
prompt_text, prompt_text,
prompt_lang, prompt_lang,
self.configs.version) self.configs.version)
self.prompt_cache["phones"] = phones self.prompt_cache["phones"] = phones
@ -778,26 +778,26 @@ class TTS:
return return
batch_index_list:list = None batch_index_list:list = None
data, batch_index_list = self.to_batch(data, data, batch_index_list = self.to_batch(data,
prompt_data=self.prompt_cache if not no_prompt_text else None, prompt_data=self.prompt_cache if not no_prompt_text else None,
batch_size=batch_size, batch_size=batch_size,
threshold=batch_threshold, threshold=batch_threshold,
split_bucket=split_bucket, split_bucket=split_bucket,
device=self.configs.device, device=self.configs.device,
precision=self.precision precision=self.precision
) )
else: else:
print(i18n("############ 切分文本 ############")) print(f'############ {i18n("切分文本")} ############')
texts = self.text_preprocessor.pre_seg_text(text, text_lang, text_split_method) texts = self.text_preprocessor.pre_seg_text(text, text_lang, text_split_method)
data = [] data = []
for i in range(len(texts)): for i in range(len(texts)):
if i%batch_size == 0: if i%batch_size == 0:
data.append([]) data.append([])
data[-1].append(texts[i]) data[-1].append(texts[i])
def make_batch(batch_texts): def make_batch(batch_texts):
batch_data = [] batch_data = []
print(i18n("############ 提取文本Bert特征 ############")) print(f'############ {i18n("提取文本Bert特征")} ############')
for text in tqdm(batch_texts): 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) phones, bert_features, norm_text = self.text_preprocessor.segment_and_extract_feature_for_text(text, text_lang, self.configs.version)
if phones is None: if phones is None:
@ -810,9 +810,9 @@ class TTS:
batch_data.append(res) batch_data.append(res)
if len(batch_data) == 0: if len(batch_data) == 0:
return None return None
batch, _ = self.to_batch(batch_data, batch, _ = self.to_batch(batch_data,
prompt_data=self.prompt_cache if not no_prompt_text else None, prompt_data=self.prompt_cache if not no_prompt_text else None,
batch_size=batch_size, batch_size=batch_size,
threshold=batch_threshold, threshold=batch_threshold,
split_bucket=False, split_bucket=False,
device=self.configs.device, device=self.configs.device,
@ -868,10 +868,10 @@ class TTS:
t_34 += t4 - t3 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"]] 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 = [] batch_audio_fragment = []
# ## vits并行推理 method 1 # ## vits并行推理 method 1
# pred_semantic_list = [item[-idx:] for item, idx in zip(pred_semantic_list, idx_list)] # 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_len = torch.LongTensor([item.shape[0] for item in pred_semantic_list]).to(self.configs.device)
@ -914,10 +914,10 @@ class TTS:
t_45 += t5 - t4 t_45 += t5 - t4
if return_fragment: if return_fragment:
print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t4 - t3, t5 - t4)) print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t4 - t3, t5 - t4))
yield self.audio_postprocess([batch_audio_fragment], yield self.audio_postprocess([batch_audio_fragment],
self.configs.sampling_rate, self.configs.sampling_rate,
None, None,
speed_factor, speed_factor,
False, False,
fragment_interval fragment_interval
) )
@ -935,10 +935,10 @@ class TTS:
yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate), yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate),
dtype=np.int16) dtype=np.int16)
return return
yield self.audio_postprocess(audio, yield self.audio_postprocess(audio,
self.configs.sampling_rate, self.configs.sampling_rate,
batch_index_list, batch_index_list,
speed_factor, speed_factor,
split_bucket, split_bucket,
fragment_interval fragment_interval
) )
@ -958,7 +958,7 @@ class TTS:
raise e raise e
finally: finally:
self.empty_cache() self.empty_cache()
def empty_cache(self): def empty_cache(self):
try: try:
gc.collect() # 触发gc的垃圾回收。避免内存一直增长。 gc.collect() # 触发gc的垃圾回收。避免内存一直增长。
@ -967,13 +967,13 @@ class TTS:
elif str(self.configs.device) == "mps": elif str(self.configs.device) == "mps":
torch.mps.empty_cache() torch.mps.empty_cache()
except: except:
pass pass
def audio_postprocess(self, def audio_postprocess(self,
audio:List[torch.Tensor], audio:List[torch.Tensor],
sr:int, sr:int,
batch_index_list:list=None, batch_index_list:list=None,
speed_factor:float=1.0, speed_factor:float=1.0,
split_bucket:bool=True, split_bucket:bool=True,
fragment_interval:float=0.3 fragment_interval:float=0.3
)->Tuple[int, np.ndarray]: )->Tuple[int, np.ndarray]:
@ -982,36 +982,36 @@ class TTS:
dtype=self.precision, dtype=self.precision,
device=self.configs.device device=self.configs.device
) )
for i, batch in enumerate(audio): for i, batch in enumerate(audio):
for j, audio_fragment in enumerate(batch): for j, audio_fragment in enumerate(batch):
max_audio=torch.abs(audio_fragment).max()#简单防止16bit爆音 max_audio=torch.abs(audio_fragment).max()#简单防止16bit爆音
if max_audio>1: audio_fragment/=max_audio if max_audio>1: audio_fragment/=max_audio
audio_fragment:torch.Tensor = torch.cat([audio_fragment, zero_wav], dim=0) audio_fragment:torch.Tensor = torch.cat([audio_fragment, zero_wav], dim=0)
audio[i][j] = audio_fragment.cpu().numpy() audio[i][j] = audio_fragment.cpu().numpy()
if split_bucket: if split_bucket:
audio = self.recovery_order(audio, batch_index_list) audio = self.recovery_order(audio, batch_index_list)
else: else:
# audio = [item for batch in audio for item in batch] # audio = [item for batch in audio for item in batch]
audio = sum(audio, []) audio = sum(audio, [])
audio = np.concatenate(audio, 0) audio = np.concatenate(audio, 0)
audio = (audio * 32768).astype(np.int16) audio = (audio * 32768).astype(np.int16)
# try: # try:
# if speed_factor != 1.0: # if speed_factor != 1.0:
# audio = speed_change(audio, speed=speed_factor, sr=int(sr)) # audio = speed_change(audio, speed=speed_factor, sr=int(sr))
# except Exception as e: # except Exception as e:
# print(f"Failed to change speed of audio: \n{e}") # print(f"Failed to change speed of audio: \n{e}")
return sr, audio return sr, audio
def speed_change(input_audio:np.ndarray, speed:float, sr:int): def speed_change(input_audio:np.ndarray, speed:float, sr:int):
# 将 NumPy 数组转换为原始 PCM 流 # 将 NumPy 数组转换为原始 PCM 流
raw_audio = input_audio.astype(np.int16).tobytes() raw_audio = input_audio.astype(np.int16).tobytes()

33
GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py
View File

@ -49,18 +49,18 @@ def merge_short_text_in_array(texts:str, threshold:int) -> list:
class TextPreprocessor: class TextPreprocessor:
def __init__(self, bert_model:AutoModelForMaskedLM, def __init__(self, bert_model:AutoModelForMaskedLM,
tokenizer:AutoTokenizer, device:torch.device): tokenizer:AutoTokenizer, device:torch.device):
self.bert_model = bert_model self.bert_model = bert_model
self.tokenizer = tokenizer self.tokenizer = tokenizer
self.device = device self.device = device
def preprocess(self, text:str, lang:str, text_split_method:str, version:str="v2")->List[Dict]: def preprocess(self, text:str, lang:str, text_split_method:str, version:str="v2")->List[Dict]:
print(i18n("############ 切分文本 ############")) print(f'############ {i18n("切分文本")} ############')
text = self.replace_consecutive_punctuation(text) text = self.replace_consecutive_punctuation(text)
texts = self.pre_seg_text(text, lang, text_split_method) texts = self.pre_seg_text(text, lang, text_split_method)
result = [] result = []
print(i18n("############ 提取文本Bert特征 ############")) print(f'############ {i18n("提取文本Bert特征")} ############')
for text in tqdm(texts): for text in tqdm(texts):
phones, bert_features, norm_text = self.segment_and_extract_feature_for_text(text, lang, version) phones, bert_features, norm_text = self.segment_and_extract_feature_for_text(text, lang, version)
if phones is None or norm_text=="": if phones is None or norm_text=="":
@ -77,14 +77,14 @@ class TextPreprocessor:
text = text.strip("\n") text = text.strip("\n")
if len(text) == 0: if len(text) == 0:
return [] return []
if (text[0] not in splits and len(get_first(text)) < 4): if (text[0] not in splits and len(get_first(text)) < 4):
text = "。" + text if lang != "en" else "." + text text = "。" + text if lang != "en" else "." + text
print(i18n("实际输入的目标文本:")) print(i18n("实际输入的目标文本:"))
print(text) print(text)
seg_method = get_seg_method(text_split_method) seg_method = get_seg_method(text_split_method)
text = seg_method(text) text = seg_method(text)
while "\n\n" in text: while "\n\n" in text:
text = text.replace("\n\n", "\n") text = text.replace("\n\n", "\n")
@ -93,29 +93,29 @@ class TextPreprocessor:
_texts = merge_short_text_in_array(_texts, 5) _texts = merge_short_text_in_array(_texts, 5)
texts = [] texts = []
for text in _texts: for text in _texts:
# 解决输入目标文本的空行导致报错的问题 # 解决输入目标文本的空行导致报错的问题
if (len(text.strip()) == 0): if (len(text.strip()) == 0):
continue continue
if not re.sub("\W+", "", text): if not re.sub("\W+", "", text):
# 检测一下，如果是纯符号，就跳过。 # 检测一下，如果是纯符号，就跳过。
continue continue
if (text[-1] not in splits): text += "。" if lang != "en" else "." 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 # 解决句子过长导致Bert报错的问题
if (len(text) > 510): if (len(text) > 510):
texts.extend(split_big_text(text)) texts.extend(split_big_text(text))
else: else:
texts.append(text) texts.append(text)
print(i18n("实际输入的目标文本(切句后):")) print(i18n("实际输入的目标文本(切句后):"))
print(texts) print(texts)
return texts return texts
def segment_and_extract_feature_for_text(self, text:str, language:str, version:str="v1")->Tuple[list, torch.Tensor, str]: 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) return self.get_phones_and_bert(text, language, version)
def get_phones_and_bert(self, text:str, language:str, version:str, final:bool=False): 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"}: if language in {"en", "all_zh", "all_ja", "all_ko", "all_yue"}:
language = language.replace("all_","") language = language.replace("all_","")
@ -203,7 +203,7 @@ class TextPreprocessor:
phone_level_feature.append(repeat_feature) phone_level_feature.append(repeat_feature)
phone_level_feature = torch.cat(phone_level_feature, dim=0) phone_level_feature = torch.cat(phone_level_feature, dim=0)
return phone_level_feature.T return phone_level_feature.T
def clean_text_inf(self, text:str, language:str, version:str="v2"): def clean_text_inf(self, text:str, language:str, version:str="v2"):
phones, word2ph, norm_text = clean_text(text, language, version) phones, word2ph, norm_text = clean_text(text, language, version)
phones = cleaned_text_to_sequence(phones, version) phones = cleaned_text_to_sequence(phones, version)
@ -232,13 +232,10 @@ class TextPreprocessor:
else: else:
_text.append(text) _text.append(text)
return _text return _text
def replace_consecutive_punctuation(self,text): def replace_consecutive_punctuation(self,text):
punctuations = ''.join(re.escape(p) for p in punctuation) punctuations = ''.join(re.escape(p) for p in punctuation)
pattern = f'([{punctuations}])([{punctuations}])+' pattern = f'([{punctuations}])([{punctuations}])+'
result = re.sub(pattern, r'\1', text) result = re.sub(pattern, r'\1', text)
return result return result