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https://github.com/RVC-Boss/GPT-SoVITS.git
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缓解了batch_size>1时的复读问题，缓解方法是：在T2S模型中，先对phones进行embedding、对bert_features进行project，再pad到相同长度。
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chasonjiang
parent
3c78539c44
commit
864a148d75
@ -504,18 +504,29 @@ class Text2SemanticDecoder(nn.Module):
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def infer_panel_batch_infer_with_flash_attn(
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self,
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x, #####全部文本token
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x_lens,
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prompts, ####参考音频token
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bert_feature,
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x:List[torch.LongTensor], #####全部文本token
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x_lens:torch.LongTensor,
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prompts:torch.LongTensor, ####参考音频token
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bert_feature:List[torch.LongTensor],
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top_k: int = -100,
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top_p: int = 100,
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early_stop_num: int = -1,
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temperature: float = 1.0,
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):
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bert_feature = self.bert_proj(bert_feature.transpose(1, 2))
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x = self.ar_text_embedding(x)
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# 先对phones进行embedding、对bert_features进行project，再pad到相同长度，以缓解复读问题。（可能还有其他因素导致复读）
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max_len = 0
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for x_item, bert_item in zip(x, bert_feature):
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max_len = max(max_len, x_item.shape[0], bert_item.shape[1])
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x_list = [self.ar_text_embedding(item) for item in x]
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x_list = [F.pad(item,(0,0,0,max_len-item.shape[0]),value=0) if item.shape[0]<max_len else item for item in x_list]
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x = torch.stack(x_list, dim=0)
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bert_features_list = [self.bert_proj(item.transpose(0, 1)) for item in bert_feature]
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bert_features_list = [F.pad(item,(0,0,0,max_len-item.shape[0]), value=0) if item.shape[0]<max_len else item for item in bert_features_list]
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bert_feature = torch.stack(bert_features_list, dim=0)
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# bert_feature = self.bert_proj(bert_feature.transpose(1, 2))
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# x = self.ar_text_embedding(x)
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x = x + bert_feature
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x = self.ar_text_position(x)
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@ -658,17 +669,30 @@ class Text2SemanticDecoder(nn.Module):
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def infer_panel_batch_only(
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self,
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x, #####全部文本token
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x_lens,
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prompts, ####参考音频token
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bert_feature,
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x:List[torch.LongTensor], #####全部文本token
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x_lens:torch.LongTensor,
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prompts:torch.LongTensor, ####参考音频token
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bert_feature:List[torch.LongTensor],
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top_k: int = -100,
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top_p: int = 100,
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early_stop_num: int = -1,
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temperature: float = 1.0,
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):
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x = self.ar_text_embedding(x)
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x = x + self.bert_proj(bert_feature.transpose(1, 2))
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# 先对phones进行embedding、对bert_features进行project，再pad到相同长度，以缓解复读问题。（可能还有其他因素导致复读）
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max_len = 0
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for x_item, bert_item in zip(x, bert_feature):
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max_len = max(max_len, x_item.shape[0], bert_item.shape[1])
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x_list = [self.ar_text_embedding(item) for item in x]
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x_list = [F.pad(item,(0,0,0,max_len-item.shape[0]),value=0) if item.shape[0]<max_len else item for item in x_list]
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x = torch.stack(x_list, dim=0)
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bert_features_list = [self.bert_proj(item.transpose(0, 1)) for item in bert_feature]
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bert_features_list = [F.pad(item,(0,0,0,max_len-item.shape[0]), value=0) if item.shape[0]<max_len else item for item in bert_features_list]
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bert_feature = torch.stack(bert_features_list, dim=0)
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# bert_feature = self.bert_proj(bert_feature.transpose(1, 2))
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# x = self.ar_text_embedding(x)
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x = x + bert_feature
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x = self.ar_text_position(x)
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# AR Decoder
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@ -55,6 +55,7 @@ def set_seed(seed:int):
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seed = int(seed)
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seed = seed if seed != -1 else random.randrange(1 << 32)
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print(f"Set seed to {seed}")
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os.environ['PYTHONHASHSEED'] = str(seed)
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random.seed(seed)
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np.random.seed(seed)
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torch.manual_seed(seed)
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@ -428,7 +429,14 @@ class TTS:
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batch = torch.stack(padded_sequences)
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return batch
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def to_batch(self, data:list, prompt_data:dict=None, batch_size:int=5, threshold:float=0.75, split_bucket:bool=True):
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def to_batch(self, data:list,
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prompt_data:dict=None,
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batch_size:int=5,
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threshold:float=0.75,
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split_bucket:bool=True,
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device:torch.device=torch.device("cpu"),
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precison:torch.dtype=torch.float32,
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):
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_data:list = []
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index_and_len_list = []
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@ -480,14 +488,14 @@ class TTS:
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for item in item_list:
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if prompt_data is not None:
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all_bert_features = torch.cat([prompt_data["bert_features"], item["bert_features"]], 1)\
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.to(dtype=self.precison)
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all_phones = torch.LongTensor(prompt_data["phones"]+item["phones"])
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phones = torch.LongTensor(item["phones"])
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.to(dtype=precison, device=device)
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all_phones = torch.LongTensor(prompt_data["phones"]+item["phones"]).to(device)
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phones = torch.LongTensor(item["phones"]).to(device)
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# norm_text = prompt_data["norm_text"]+item["norm_text"]
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else:
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all_bert_features = item["bert_features"]\
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.to(dtype=self.precison)
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phones = torch.LongTensor(item["phones"])
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.to(dtype=precison, device=device)
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phones = torch.LongTensor(item["phones"]).to(device)
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all_phones = phones
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# norm_text = item["norm_text"]
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@ -502,19 +510,33 @@ class TTS:
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norm_text_batch.append(item["norm_text"])
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phones_batch = phones_list
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max_len = max(bert_max_len, phones_max_len)
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all_phones_batch = all_phones_list
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all_bert_features_batch = all_bert_features_list
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# max_len = max(bert_max_len, phones_max_len)
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# phones_batch = self.batch_sequences(phones_list, axis=0, pad_value=0, max_length=max_len)
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all_phones_batch = self.batch_sequences(all_phones_list, axis=0, pad_value=0, max_length=max_len)
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#### 直接对phones和bert_features进行pad，会增大复读概率。
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# all_phones_batch = self.batch_sequences(all_phones_list, axis=0, pad_value=0, max_length=max_len)
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# all_bert_features_batch = all_bert_features_list
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all_bert_features_batch = torch.zeros(len(item_list), 1024, max_len, dtype=self.precison)
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for idx, item in enumerate(all_bert_features_list):
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all_bert_features_batch[idx, :, : item.shape[-1]] = item
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# all_bert_features_batch = torch.zeros(len(item_list), 1024, max_len, dtype=precison, device=device)
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# for idx, item in enumerate(all_bert_features_list):
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# all_bert_features_batch[idx, :, : item.shape[-1]] = item
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# #### 先对phones进行embedding、对bert_features进行project，再pad到相同长度，以缓解复读问题。（可能还有其他因素导致复读）
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# all_phones_list = [self.t2s_model.model.ar_text_embedding(item.to(self.t2s_model.device)) for item in all_phones_list]
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# all_phones_list = [F.pad(item,(0,0,0,max_len-item.shape[0]),value=0) for item in all_phones_list]
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# all_phones_batch = torch.stack(all_phones_list, dim=0)
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# 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]
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# all_bert_features_list = [F.pad(item,(0,0,0,max_len-item.shape[0]), value=0) for item in all_bert_features_list]
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# all_bert_features_batch = torch.stack(all_bert_features_list, dim=0)
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batch = {
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"phones": phones_batch,
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"phones_len": torch.LongTensor(phones_len_list),
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"phones_len": torch.LongTensor(phones_len_list).to(device),
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"all_phones": all_phones_batch,
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"all_phones_len": torch.LongTensor(all_phones_len_list),
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"all_phones_len": torch.LongTensor(all_phones_len_list).to(device),
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"all_bert_features": all_bert_features_batch,
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"norm_text": norm_text_batch
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}
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@ -658,7 +680,9 @@ class TTS:
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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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split_bucket=split_bucket,
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device=self.configs.device,
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precison=self.precison
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)
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else:
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print(i18n("############ 切分文本 ############"))
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@ -688,7 +712,9 @@ class TTS:
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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=False,
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device=self.configs.device,
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precison=self.precison
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)
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return batch[0]
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@ -706,26 +732,18 @@ class TTS:
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if item is None:
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continue
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batch_phones = item["phones"]
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batch_phones_len = item["phones_len"]
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all_phoneme_ids = item["all_phones"]
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all_phoneme_lens = item["all_phones_len"]
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all_bert_features = item["all_bert_features"]
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norm_text = item["norm_text"]
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# batch_phones = batch_phones.to(self.configs.device)
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batch_phones_len = batch_phones_len.to(self.configs.device)
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all_phoneme_ids = all_phoneme_ids.to(self.configs.device)
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all_phoneme_lens = all_phoneme_lens.to(self.configs.device)
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all_bert_features = all_bert_features.to(self.configs.device)
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if self.configs.is_half:
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all_bert_features = all_bert_features.half()
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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(all_phoneme_ids.shape[0], -1).to(self.configs.device)
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prompt = self.prompt_cache["prompt_semantic"].expand(len(all_phoneme_ids), -1).to(self.configs.device)
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with torch.no_grad():
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pred_semantic_list, idx_list = self.t2s_model.model.infer_panel(
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