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改用修饰器
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XTer
parent
6591e86df3
commit
7f8892d004
@ -569,7 +569,8 @@ class TTS:
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'''
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self.stop_flag = True
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# 使用装饰器
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@torch.no_grad()
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def run(self, inputs:dict):
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"""
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Text to speech inference.
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@ -600,260 +601,259 @@ class TTS:
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# 直接给全体套一个torch.no_grad()
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with torch.no_grad():
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########## variables initialization ###########
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self.stop_flag:bool = False
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text:str = inputs.get("text", "")
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text_lang:str = inputs.get("text_lang", "")
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ref_audio_path:str = inputs.get("ref_audio_path", "")
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prompt_text:str = inputs.get("prompt_text", "")
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prompt_lang:str = inputs.get("prompt_lang", "")
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top_k:int = inputs.get("top_k", 5)
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top_p:float = inputs.get("top_p", 1)
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temperature:float = inputs.get("temperature", 1)
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text_split_method:str = inputs.get("text_split_method", "cut0")
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batch_size = inputs.get("batch_size", 1)
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batch_threshold = inputs.get("batch_threshold", 0.75)
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speed_factor = inputs.get("speed_factor", 1.0)
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split_bucket = inputs.get("split_bucket", True)
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return_fragment = inputs.get("return_fragment", False)
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fragment_interval = inputs.get("fragment_interval", 0.3)
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seed = inputs.get("seed", -1)
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seed = -1 if seed in ["", None] else seed
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actual_seed = set_seed(seed)
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if return_fragment:
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# split_bucket = False
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print(i18n("分段返回模式已开启"))
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if split_bucket:
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split_bucket = False
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print(i18n("分段返回模式不支持分桶处理，已自动关闭分桶处理"))
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########## variables initialization ###########
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self.stop_flag:bool = False
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text:str = inputs.get("text", "")
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text_lang:str = inputs.get("text_lang", "")
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ref_audio_path:str = inputs.get("ref_audio_path", "")
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prompt_text:str = inputs.get("prompt_text", "")
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prompt_lang:str = inputs.get("prompt_lang", "")
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top_k:int = inputs.get("top_k", 5)
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top_p:float = inputs.get("top_p", 1)
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temperature:float = inputs.get("temperature", 1)
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text_split_method:str = inputs.get("text_split_method", "cut0")
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batch_size = inputs.get("batch_size", 1)
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batch_threshold = inputs.get("batch_threshold", 0.75)
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speed_factor = inputs.get("speed_factor", 1.0)
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split_bucket = inputs.get("split_bucket", True)
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return_fragment = inputs.get("return_fragment", False)
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fragment_interval = inputs.get("fragment_interval", 0.3)
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seed = inputs.get("seed", -1)
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seed = -1 if seed in ["", None] else seed
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actual_seed = set_seed(seed)
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if return_fragment:
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# split_bucket = False
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print(i18n("分段返回模式已开启"))
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if split_bucket:
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print(i18n("分桶处理模式已开启"))
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split_bucket = False
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print(i18n("分段返回模式不支持分桶处理，已自动关闭分桶处理"))
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if fragment_interval<0.01:
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fragment_interval = 0.01
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print(i18n("分段间隔过小，已自动设置为0.01"))
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if split_bucket:
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print(i18n("分桶处理模式已开启"))
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no_prompt_text = False
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if prompt_text in [None, ""]:
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no_prompt_text = True
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if fragment_interval<0.01:
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fragment_interval = 0.01
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print(i18n("分段间隔过小，已自动设置为0.01"))
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assert text_lang in self.configs.languages
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if not no_prompt_text:
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assert prompt_lang in self.configs.languages
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no_prompt_text = False
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if prompt_text in [None, ""]:
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no_prompt_text = True
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if ref_audio_path in [None, ""] and \
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((self.prompt_cache["prompt_semantic"] is None) or (self.prompt_cache["refer_spec"] is None)):
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raise ValueError("ref_audio_path cannot be empty, when the reference audio is not set using set_ref_audio()")
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assert text_lang in self.configs.languages
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if not no_prompt_text:
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assert prompt_lang in self.configs.languages
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###### setting reference audio and prompt text preprocessing ########
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t0 = ttime()
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if (ref_audio_path is not None) and (ref_audio_path != self.prompt_cache["ref_audio_path"]):
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self.set_ref_audio(ref_audio_path)
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if ref_audio_path in [None, ""] and \
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((self.prompt_cache["prompt_semantic"] is None) or (self.prompt_cache["refer_spec"] is None)):
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raise ValueError("ref_audio_path cannot be empty, when the reference audio is not set using set_ref_audio()")
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if not no_prompt_text:
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prompt_text = prompt_text.strip("\n")
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if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_lang != "en" else "."
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print(i18n("实际输入的参考文本:"), prompt_text)
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if self.prompt_cache["prompt_text"] != prompt_text:
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self.prompt_cache["prompt_text"] = prompt_text
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self.prompt_cache["prompt_lang"] = prompt_lang
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phones, bert_features, norm_text = \
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self.text_preprocessor.segment_and_extract_feature_for_text(
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prompt_text,
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prompt_lang)
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self.prompt_cache["phones"] = phones
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self.prompt_cache["bert_features"] = bert_features
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self.prompt_cache["norm_text"] = norm_text
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###### setting reference audio and prompt text preprocessing ########
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t0 = ttime()
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if (ref_audio_path is not None) and (ref_audio_path != self.prompt_cache["ref_audio_path"]):
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self.set_ref_audio(ref_audio_path)
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###### text preprocessing ########
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t1 = ttime()
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data:list = None
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if not return_fragment:
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data = self.text_preprocessor.preprocess(text, text_lang, text_split_method)
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if len(data) == 0:
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yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate),
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dtype=np.int16)
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return
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if not no_prompt_text:
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prompt_text = prompt_text.strip("\n")
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if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_lang != "en" else "."
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print(i18n("实际输入的参考文本:"), prompt_text)
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if self.prompt_cache["prompt_text"] != prompt_text:
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self.prompt_cache["prompt_text"] = prompt_text
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self.prompt_cache["prompt_lang"] = prompt_lang
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phones, bert_features, norm_text = \
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self.text_preprocessor.segment_and_extract_feature_for_text(
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prompt_text,
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prompt_lang)
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self.prompt_cache["phones"] = phones
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self.prompt_cache["bert_features"] = bert_features
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self.prompt_cache["norm_text"] = norm_text
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batch_index_list:list = None
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data, batch_index_list = self.to_batch(data,
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prompt_data=self.prompt_cache if not no_prompt_text else None,
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batch_size=batch_size,
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threshold=batch_threshold,
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split_bucket=split_bucket,
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device=self.configs.device,
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precision=self.precision
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)
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else:
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print(i18n("############ 切分文本 ############"))
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texts = self.text_preprocessor.pre_seg_text(text, text_lang, text_split_method)
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data = []
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for i in range(len(texts)):
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if i%batch_size == 0:
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data.append([])
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data[-1].append(texts[i])
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###### text preprocessing ########
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t1 = ttime()
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data:list = None
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if not return_fragment:
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data = self.text_preprocessor.preprocess(text, text_lang, text_split_method)
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if len(data) == 0:
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yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate),
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dtype=np.int16)
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return
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def make_batch(batch_texts):
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batch_data = []
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print(i18n("############ 提取文本Bert特征 ############"))
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for text in tqdm(batch_texts):
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phones, bert_features, norm_text = self.text_preprocessor.segment_and_extract_feature_for_text(text, text_lang)
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if phones is None:
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continue
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res={
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"phones": phones,
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"bert_features": bert_features,
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"norm_text": norm_text,
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}
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batch_data.append(res)
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if len(batch_data) == 0:
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return None
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batch, _ = self.to_batch(batch_data,
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batch_index_list:list = None
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data, batch_index_list = self.to_batch(data,
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prompt_data=self.prompt_cache if not no_prompt_text else None,
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batch_size=batch_size,
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threshold=batch_threshold,
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split_bucket=False,
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split_bucket=split_bucket,
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device=self.configs.device,
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precision=self.precision
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)
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return batch[0]
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else:
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print(i18n("############ 切分文本 ############"))
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texts = self.text_preprocessor.pre_seg_text(text, text_lang, text_split_method)
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data = []
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for i in range(len(texts)):
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if i%batch_size == 0:
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data.append([])
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data[-1].append(texts[i])
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def make_batch(batch_texts):
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batch_data = []
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print(i18n("############ 提取文本Bert特征 ############"))
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for text in tqdm(batch_texts):
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phones, bert_features, norm_text = self.text_preprocessor.segment_and_extract_feature_for_text(text, text_lang)
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if phones is None:
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continue
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res={
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"phones": phones,
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"bert_features": bert_features,
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"norm_text": norm_text,
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}
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batch_data.append(res)
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if len(batch_data) == 0:
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return None
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batch, _ = self.to_batch(batch_data,
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prompt_data=self.prompt_cache if not no_prompt_text else None,
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batch_size=batch_size,
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threshold=batch_threshold,
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split_bucket=False,
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device=self.configs.device,
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precision=self.precision
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)
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return batch[0]
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t2 = ttime()
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try:
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print("############ 推理 ############")
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###### inference ######
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t_34 = 0.0
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t_45 = 0.0
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audio = []
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for item in data:
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t3 = ttime()
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if return_fragment:
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item = make_batch(item)
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if item is None:
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continue
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t2 = ttime()
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try:
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print("############ 推理 ############")
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###### inference ######
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t_34 = 0.0
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t_45 = 0.0
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audio = []
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for item in data:
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t3 = ttime()
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if return_fragment:
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item = make_batch(item)
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if item is None:
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continue
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batch_phones:List[torch.LongTensor] = item["phones"]
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batch_phones_len:torch.LongTensor = item["phones_len"]
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all_phoneme_ids:List[torch.LongTensor] = item["all_phones"]
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all_phoneme_lens:torch.LongTensor = item["all_phones_len"]
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all_bert_features:List[torch.LongTensor] = item["all_bert_features"]
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norm_text:str = item["norm_text"]
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batch_phones:List[torch.LongTensor] = item["phones"]
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batch_phones_len:torch.LongTensor = item["phones_len"]
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all_phoneme_ids:List[torch.LongTensor] = item["all_phones"]
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all_phoneme_lens:torch.LongTensor = item["all_phones_len"]
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all_bert_features:List[torch.LongTensor] = item["all_bert_features"]
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norm_text:str = item["norm_text"]
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print(i18n("前端处理后的文本(每句):"), norm_text)
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if no_prompt_text :
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prompt = None
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else:
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prompt = self.prompt_cache["prompt_semantic"].expand(len(all_phoneme_ids), -1).to(self.configs.device)
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print(i18n("前端处理后的文本(每句):"), norm_text)
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if no_prompt_text :
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prompt = None
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else:
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prompt = self.prompt_cache["prompt_semantic"].expand(len(all_phoneme_ids), -1).to(self.configs.device)
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pred_semantic_list, idx_list = self.t2s_model.model.infer_panel(
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all_phoneme_ids,
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all_phoneme_lens,
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prompt,
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all_bert_features,
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# prompt_phone_len=ph_offset,
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top_k=top_k,
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top_p=top_p,
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temperature=temperature,
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early_stop_num=self.configs.hz * self.configs.max_sec,
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)
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t4 = ttime()
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t_34 += t4 - t3
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pred_semantic_list, idx_list = self.t2s_model.model.infer_panel(
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all_phoneme_ids,
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all_phoneme_lens,
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prompt,
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all_bert_features,
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# prompt_phone_len=ph_offset,
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top_k=top_k,
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top_p=top_p,
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temperature=temperature,
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early_stop_num=self.configs.hz * self.configs.max_sec,
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)
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t4 = ttime()
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t_34 += t4 - t3
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refer_audio_spec:torch.Tensor = self.prompt_cache["refer_spec"]\
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.to(dtype=self.precision, device=self.configs.device)
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refer_audio_spec:torch.Tensor = self.prompt_cache["refer_spec"]\
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.to(dtype=self.precision, device=self.configs.device)
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batch_audio_fragment = []
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batch_audio_fragment = []
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# 这里要记得加 torch.no_grad() 不然速度慢一大截
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# with torch.no_grad():
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# 这里要记得加 torch.no_grad() 不然速度慢一大截
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# with torch.no_grad():
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# ## vits并行推理 method 1
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# pred_semantic_list = [item[-idx:] for item, idx in zip(pred_semantic_list, idx_list)]
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# pred_semantic_len = torch.LongTensor([item.shape[0] for item in pred_semantic_list]).to(self.configs.device)
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# pred_semantic = self.batch_sequences(pred_semantic_list, axis=0, pad_value=0).unsqueeze(0)
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# max_len = 0
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# for i in range(0, len(batch_phones)):
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# max_len = max(max_len, batch_phones[i].shape[-1])
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# batch_phones = self.batch_sequences(batch_phones, axis=0, pad_value=0, max_length=max_len)
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# batch_phones = batch_phones.to(self.configs.device)
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# batch_audio_fragment = (self.vits_model.batched_decode(
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# pred_semantic, pred_semantic_len, batch_phones, batch_phones_len,refer_audio_spec
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# ))
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# ## vits并行推理 method 1
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# pred_semantic_list = [item[-idx:] for item, idx in zip(pred_semantic_list, idx_list)]
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# pred_semantic_len = torch.LongTensor([item.shape[0] for item in pred_semantic_list]).to(self.configs.device)
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# pred_semantic = self.batch_sequences(pred_semantic_list, axis=0, pad_value=0).unsqueeze(0)
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# max_len = 0
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# for i in range(0, len(batch_phones)):
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# max_len = max(max_len, batch_phones[i].shape[-1])
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# batch_phones = self.batch_sequences(batch_phones, axis=0, pad_value=0, max_length=max_len)
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# batch_phones = batch_phones.to(self.configs.device)
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# batch_audio_fragment = (self.vits_model.batched_decode(
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# pred_semantic, pred_semantic_len, batch_phones, batch_phones_len,refer_audio_spec
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# ))
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# ## vits并行推理 method 2
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pred_semantic_list = [item[-idx:] for item, idx in zip(pred_semantic_list, idx_list)]
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upsample_rate = math.prod(self.vits_model.upsample_rates)
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audio_frag_idx = [pred_semantic_list[i].shape[0]*2*upsample_rate for i in range(0, len(pred_semantic_list))]
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audio_frag_end_idx = [ sum(audio_frag_idx[:i+1]) for i in range(0, len(audio_frag_idx))]
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all_pred_semantic = torch.cat(pred_semantic_list).unsqueeze(0).unsqueeze(0).to(self.configs.device)
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_batch_phones = torch.cat(batch_phones).unsqueeze(0).to(self.configs.device)
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_batch_audio_fragment = (self.vits_model.decode(
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all_pred_semantic, _batch_phones, refer_audio_spec
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).detach()[0, 0, :])
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audio_frag_end_idx.insert(0, 0)
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batch_audio_fragment= [_batch_audio_fragment[audio_frag_end_idx[i-1]:audio_frag_end_idx[i]] for i in range(1, len(audio_frag_end_idx))]
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# ## vits并行推理 method 2
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pred_semantic_list = [item[-idx:] for item, idx in zip(pred_semantic_list, idx_list)]
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upsample_rate = math.prod(self.vits_model.upsample_rates)
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audio_frag_idx = [pred_semantic_list[i].shape[0]*2*upsample_rate for i in range(0, len(pred_semantic_list))]
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audio_frag_end_idx = [ sum(audio_frag_idx[:i+1]) for i in range(0, len(audio_frag_idx))]
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all_pred_semantic = torch.cat(pred_semantic_list).unsqueeze(0).unsqueeze(0).to(self.configs.device)
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_batch_phones = torch.cat(batch_phones).unsqueeze(0).to(self.configs.device)
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_batch_audio_fragment = (self.vits_model.decode(
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all_pred_semantic, _batch_phones, refer_audio_spec
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).detach()[0, 0, :])
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audio_frag_end_idx.insert(0, 0)
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batch_audio_fragment= [_batch_audio_fragment[audio_frag_end_idx[i-1]:audio_frag_end_idx[i]] for i in range(1, len(audio_frag_end_idx))]
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# ## vits串行推理
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# for i, idx in enumerate(idx_list):
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# phones = batch_phones[i].unsqueeze(0).to(self.configs.device)
|
||||
# _pred_semantic = (pred_semantic_list[i][-idx:].unsqueeze(0).unsqueeze(0)) # .unsqueeze(0)#mq要多unsqueeze一次
|
||||
# audio_fragment =(self.vits_model.decode(
|
||||
# _pred_semantic, phones, refer_audio_spec
|
||||
# ).detach()[0, 0, :])
|
||||
# batch_audio_fragment.append(
|
||||
# audio_fragment
|
||||
# ) ###试试重建不带上prompt部分
|
||||
# ## vits串行推理
|
||||
# for i, idx in enumerate(idx_list):
|
||||
# phones = batch_phones[i].unsqueeze(0).to(self.configs.device)
|
||||
# _pred_semantic = (pred_semantic_list[i][-idx:].unsqueeze(0).unsqueeze(0)) # .unsqueeze(0)#mq要多unsqueeze一次
|
||||
# audio_fragment =(self.vits_model.decode(
|
||||
# _pred_semantic, phones, refer_audio_spec
|
||||
# ).detach()[0, 0, :])
|
||||
# batch_audio_fragment.append(
|
||||
# audio_fragment
|
||||
# ) ###试试重建不带上prompt部分
|
||||
|
||||
t5 = ttime()
|
||||
t_45 += t5 - t4
|
||||
if return_fragment:
|
||||
print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t4 - t3, t5 - t4))
|
||||
yield self.audio_postprocess([batch_audio_fragment],
|
||||
self.configs.sampling_rate,
|
||||
None,
|
||||
speed_factor,
|
||||
False,
|
||||
fragment_interval
|
||||
)
|
||||
else:
|
||||
audio.append(batch_audio_fragment)
|
||||
|
||||
if self.stop_flag:
|
||||
yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate),
|
||||
dtype=np.int16)
|
||||
return
|
||||
|
||||
if not return_fragment:
|
||||
print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t_34, t_45))
|
||||
yield self.audio_postprocess(audio,
|
||||
t5 = ttime()
|
||||
t_45 += t5 - t4
|
||||
if return_fragment:
|
||||
print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t4 - t3, t5 - t4))
|
||||
yield self.audio_postprocess([batch_audio_fragment],
|
||||
self.configs.sampling_rate,
|
||||
batch_index_list,
|
||||
None,
|
||||
speed_factor,
|
||||
split_bucket,
|
||||
False,
|
||||
fragment_interval
|
||||
)
|
||||
else:
|
||||
audio.append(batch_audio_fragment)
|
||||
|
||||
except Exception as e:
|
||||
traceback.print_exc()
|
||||
# 必须返回一个空音频, 否则会导致显存不释放。
|
||||
yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate),
|
||||
dtype=np.int16)
|
||||
# 重置模型, 否则会导致显存释放不完全。
|
||||
del self.t2s_model
|
||||
del self.vits_model
|
||||
self.t2s_model = None
|
||||
self.vits_model = None
|
||||
self.init_t2s_weights(self.configs.t2s_weights_path)
|
||||
self.init_vits_weights(self.configs.vits_weights_path)
|
||||
raise e
|
||||
finally:
|
||||
self.empty_cache()
|
||||
if self.stop_flag:
|
||||
yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate),
|
||||
dtype=np.int16)
|
||||
return
|
||||
|
||||
if not return_fragment:
|
||||
print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t_34, t_45))
|
||||
yield self.audio_postprocess(audio,
|
||||
self.configs.sampling_rate,
|
||||
batch_index_list,
|
||||
speed_factor,
|
||||
split_bucket,
|
||||
fragment_interval
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
traceback.print_exc()
|
||||
# 必须返回一个空音频, 否则会导致显存不释放。
|
||||
yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate),
|
||||
dtype=np.int16)
|
||||
# 重置模型, 否则会导致显存释放不完全。
|
||||
del self.t2s_model
|
||||
del self.vits_model
|
||||
self.t2s_model = None
|
||||
self.vits_model = None
|
||||
self.init_t2s_weights(self.configs.t2s_weights_path)
|
||||
self.init_vits_weights(self.configs.vits_weights_path)
|
||||
raise e
|
||||
finally:
|
||||
self.empty_cache()
|
||||
|
||||
def empty_cache(self):
|
||||
try:
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user