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https://github.com/RVC-Boss/GPT-SoVITS.git
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恢复先前缩进
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@ -280,6 +280,7 @@ class TTS:
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if self.configs.is_half and str(self.configs.device)!="cpu":
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self.vits_model = self.vits_model.half()
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def init_t2s_weights(self, weights_path: str):
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print(f"Loading Text2Semantic weights from {weights_path}")
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self.configs.t2s_weights_path = weights_path
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@ -376,6 +377,7 @@ class TTS:
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# self.refer_spec = spec
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self.prompt_cache["refer_spec"] = spec
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def _set_prompt_semantic(self, ref_wav_path:str):
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zero_wav = np.zeros(
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int(self.configs.sampling_rate * 0.3),
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@ -416,8 +418,7 @@ class TTS:
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max_length = max(seq_lengths)
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else:
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max_length = max(seq_lengths) if max_length < max(seq_lengths) else max_length
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# 我爱套 torch.no_grad()
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# with torch.no_grad():
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padded_sequences = []
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for seq, length in zip(sequences, seq_lengths):
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padding = [0] * axis + [0, max_length - length] + [0] * (ndim - axis - 1)
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@ -434,114 +435,115 @@ class TTS:
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device:torch.device=torch.device("cpu"),
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precision:torch.dtype=torch.float32,
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):
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# 但是这里不能套，反而会负优化
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# with torch.no_grad():
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_data:list = []
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index_and_len_list = []
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for idx, item in enumerate(data):
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norm_text_len = len(item["norm_text"])
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index_and_len_list.append([idx, norm_text_len])
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_data:list = []
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index_and_len_list = []
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for idx, item in enumerate(data):
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norm_text_len = len(item["norm_text"])
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index_and_len_list.append([idx, norm_text_len])
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batch_index_list = []
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if split_bucket:
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index_and_len_list.sort(key=lambda x: x[1])
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index_and_len_list = np.array(index_and_len_list, dtype=np.int64)
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batch_index_list = []
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if split_bucket:
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index_and_len_list.sort(key=lambda x: x[1])
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index_and_len_list = np.array(index_and_len_list, dtype=np.int64)
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batch_index_list_len = 0
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pos = 0
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while pos <index_and_len_list.shape[0]:
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# batch_index_list.append(index_and_len_list[pos:min(pos+batch_size,len(index_and_len_list))])
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pos_end = min(pos+batch_size,index_and_len_list.shape[0])
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while pos < pos_end:
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batch=index_and_len_list[pos:pos_end, 1].astype(np.float32)
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score=batch[(pos_end-pos)//2]/(batch.mean()+1e-8)
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if (score>=threshold) or (pos_end-pos==1):
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batch_index=index_and_len_list[pos:pos_end, 0].tolist()
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batch_index_list_len += len(batch_index)
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batch_index_list.append(batch_index)
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pos = pos_end
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break
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pos_end=pos_end-1
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batch_index_list_len = 0
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pos = 0
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while pos <index_and_len_list.shape[0]:
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# batch_index_list.append(index_and_len_list[pos:min(pos+batch_size,len(index_and_len_list))])
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pos_end = min(pos+batch_size,index_and_len_list.shape[0])
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while pos < pos_end:
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batch=index_and_len_list[pos:pos_end, 1].astype(np.float32)
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score=batch[(pos_end-pos)//2]/(batch.mean()+1e-8)
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if (score>=threshold) or (pos_end-pos==1):
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batch_index=index_and_len_list[pos:pos_end, 0].tolist()
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batch_index_list_len += len(batch_index)
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batch_index_list.append(batch_index)
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pos = pos_end
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break
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pos_end=pos_end-1
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assert batch_index_list_len == len(data)
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assert batch_index_list_len == len(data)
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else:
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for i in range(len(data)):
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if i%batch_size == 0:
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batch_index_list.append([])
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batch_index_list[-1].append(i)
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else:
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for i in range(len(data)):
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if i%batch_size == 0:
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batch_index_list.append([])
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batch_index_list[-1].append(i)
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for batch_idx, index_list in enumerate(batch_index_list):
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item_list = [data[idx] for idx in index_list]
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phones_list = []
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phones_len_list = []
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# bert_features_list = []
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all_phones_list = []
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all_phones_len_list = []
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all_bert_features_list = []
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norm_text_batch = []
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bert_max_len = 0
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phones_max_len = 0
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# 但是这里也不能套，反而会负优化
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# with torch.no_grad():
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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=precision, 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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for batch_idx, index_list in enumerate(batch_index_list):
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item_list = [data[idx] for idx in index_list]
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phones_list = []
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phones_len_list = []
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# bert_features_list = []
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all_phones_list = []
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all_phones_len_list = []
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all_bert_features_list = []
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norm_text_batch = []
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bert_max_len = 0
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phones_max_len = 0
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# 但是这里也不能套，反而会负优化
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# with torch.no_grad():
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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=precision, 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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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=precision, 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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bert_max_len = max(bert_max_len, all_bert_features.shape[-1])
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phones_max_len = max(phones_max_len, phones.shape[-1])
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bert_max_len = max(bert_max_len, all_bert_features.shape[-1])
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phones_max_len = max(phones_max_len, phones.shape[-1])
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phones_list.append(phones)
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phones_len_list.append(phones.shape[-1])
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all_phones_list.append(all_phones)
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all_phones_len_list.append(all_phones.shape[-1])
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all_bert_features_list.append(all_bert_features)
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norm_text_batch.append(item["norm_text"])
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phones_list.append(phones)
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phones_len_list.append(phones.shape[-1])
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all_phones_list.append(all_phones)
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all_phones_len_list.append(all_phones.shape[-1])
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all_bert_features_list.append(all_bert_features)
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norm_text_batch.append(item["norm_text"])
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phones_batch = phones_list
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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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phones_batch = phones_list
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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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#### 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=precision, 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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# 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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#### 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=precision, 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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# 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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# #### 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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batch = {
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"phones": phones_batch,
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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).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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_data.append(batch)
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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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return _data, batch_index_list
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batch = {
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"phones": phones_batch,
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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).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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_data.append(batch)
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return _data, batch_index_list
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def recovery_order(self, data:list, batch_index_list:list)->list:
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'''
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@ -567,6 +569,7 @@ class TTS:
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'''
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self.stop_flag = True
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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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@ -881,12 +884,14 @@ class TTS:
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audio_fragment:torch.Tensor = torch.cat([audio_fragment, zero_wav], dim=0)
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audio[i][j] = audio_fragment.cpu().numpy()
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if split_bucket:
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audio = self.recovery_order(audio, batch_index_list)
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else:
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# audio = [item for batch in audio for item in batch]
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audio = sum(audio, [])
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audio = np.concatenate(audio, 0)
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audio = (audio * 32768).astype(np.int16)
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@ -899,6 +904,8 @@ class TTS:
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return sr, audio
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def speed_change(input_audio:np.ndarray, speed:float, sr:int):
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# 将 NumPy 数组转换为原始 PCM 流
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raw_audio = input_audio.astype(np.int16).tobytes()
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