mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-10-08 16:00:01 +08:00
添加with torch.no_grad()，速度快一大截
This commit is contained in:
XTer
parent
ec7647e08d
commit
adb7f71b64
4
.gitignore
vendored
4
.gitignore
vendored
@ -10,6 +10,8 @@ reference
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GPT_weights
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SoVITS_weights
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TEMP
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PortableGit
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ffmpeg.exe
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ffprobe.exe
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tmp_audio
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trained
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@ -249,8 +249,6 @@ class TTS:
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if self.configs.is_half and str(self.configs.device)!="cpu":
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self.bert_model = self.bert_model.half()
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def init_vits_weights(self, weights_path: str):
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print(f"Loading VITS weights from {weights_path}")
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self.configs.vits_weights_path = weights_path
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@ -282,7 +280,6 @@ 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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@ -379,7 +376,6 @@ 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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@ -420,7 +416,8 @@ 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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@ -438,111 +435,113 @@ class TTS:
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precision: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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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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# 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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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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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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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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.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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.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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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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# 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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# #### 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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# 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).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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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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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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@ -568,7 +567,6 @@ 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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@ -596,156 +594,159 @@ class TTS:
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returns:
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tuple[int, np.ndarray]: sampling rate and audio data.
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"""
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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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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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# 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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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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print(i18n("分桶处理模式已开启"))
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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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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 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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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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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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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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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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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 ref_audio_path in [None, ""] and \
|
||||
((self.prompt_cache["prompt_semantic"] is None) or (self.prompt_cache["refer_spec"] is None)):
|
||||
raise ValueError("ref_audio_path cannot be empty, when the reference audio is not set using set_ref_audio()")
|
||||
|
||||
|
||||
###### setting reference audio and prompt text preprocessing ########
|
||||
t0 = ttime()
|
||||
if (ref_audio_path is not None) and (ref_audio_path != self.prompt_cache["ref_audio_path"]):
|
||||
###### setting reference audio and prompt text preprocessing ########
|
||||
t0 = ttime()
|
||||
if (ref_audio_path is not None) and (ref_audio_path != self.prompt_cache["ref_audio_path"]):
|
||||
self.set_ref_audio(ref_audio_path)
|
||||
|
||||
if not no_prompt_text:
|
||||
prompt_text = prompt_text.strip("\n")
|
||||
if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_lang != "en" else "."
|
||||
print(i18n("实际输入的参考文本:"), prompt_text)
|
||||
if self.prompt_cache["prompt_text"] != prompt_text:
|
||||
self.prompt_cache["prompt_text"] = prompt_text
|
||||
self.prompt_cache["prompt_lang"] = prompt_lang
|
||||
phones, bert_features, norm_text = \
|
||||
self.text_preprocessor.segment_and_extract_feature_for_text(
|
||||
prompt_text,
|
||||
prompt_lang)
|
||||
self.prompt_cache["phones"] = phones
|
||||
self.prompt_cache["bert_features"] = bert_features
|
||||
self.prompt_cache["norm_text"] = norm_text
|
||||
if not no_prompt_text:
|
||||
prompt_text = prompt_text.strip("\n")
|
||||
if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_lang != "en" else "."
|
||||
print(i18n("实际输入的参考文本:"), prompt_text)
|
||||
if self.prompt_cache["prompt_text"] != prompt_text:
|
||||
self.prompt_cache["prompt_text"] = prompt_text
|
||||
self.prompt_cache["prompt_lang"] = prompt_lang
|
||||
phones, bert_features, norm_text = \
|
||||
self.text_preprocessor.segment_and_extract_feature_for_text(
|
||||
prompt_text,
|
||||
prompt_lang)
|
||||
self.prompt_cache["phones"] = phones
|
||||
self.prompt_cache["bert_features"] = bert_features
|
||||
self.prompt_cache["norm_text"] = norm_text
|
||||
|
||||
###### text preprocessing ########
|
||||
t1 = ttime()
|
||||
data:list = None
|
||||
if not return_fragment:
|
||||
data = self.text_preprocessor.preprocess(text, text_lang, text_split_method)
|
||||
if len(data) == 0:
|
||||
yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate),
|
||||
dtype=np.int16)
|
||||
return
|
||||
|
||||
batch_index_list:list = None
|
||||
data, batch_index_list = self.to_batch(data,
|
||||
prompt_data=self.prompt_cache if not no_prompt_text else None,
|
||||
batch_size=batch_size,
|
||||
threshold=batch_threshold,
|
||||
split_bucket=split_bucket,
|
||||
device=self.configs.device,
|
||||
precision=self.precision
|
||||
)
|
||||
else:
|
||||
print(i18n("############ 切分文本 ############"))
|
||||
texts = self.text_preprocessor.pre_seg_text(text, text_lang, text_split_method)
|
||||
data = []
|
||||
for i in range(len(texts)):
|
||||
if i%batch_size == 0:
|
||||
data.append([])
|
||||
data[-1].append(texts[i])
|
||||
|
||||
|
||||
###### text preprocessing ########
|
||||
t1 = ttime()
|
||||
data:list = None
|
||||
if not return_fragment:
|
||||
data = self.text_preprocessor.preprocess(text, text_lang, text_split_method)
|
||||
if len(data) == 0:
|
||||
yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate),
|
||||
dtype=np.int16)
|
||||
return
|
||||
|
||||
batch_index_list:list = None
|
||||
data, batch_index_list = self.to_batch(data,
|
||||
prompt_data=self.prompt_cache if not no_prompt_text else None,
|
||||
batch_size=batch_size,
|
||||
threshold=batch_threshold,
|
||||
split_bucket=split_bucket,
|
||||
device=self.configs.device,
|
||||
precision=self.precision
|
||||
)
|
||||
else:
|
||||
print(i18n("############ 切分文本 ############"))
|
||||
texts = self.text_preprocessor.pre_seg_text(text, text_lang, text_split_method)
|
||||
data = []
|
||||
for i in range(len(texts)):
|
||||
if i%batch_size == 0:
|
||||
data.append([])
|
||||
data[-1].append(texts[i])
|
||||
t2 = ttime()
|
||||
try:
|
||||
print("############ 推理 ############")
|
||||
###### inference ######
|
||||
t_34 = 0.0
|
||||
t_45 = 0.0
|
||||
audio = []
|
||||
for item in data:
|
||||
t3 = ttime()
|
||||
if return_fragment:
|
||||
item = make_batch(item)
|
||||
if item is None:
|
||||
continue
|
||||
|
||||
def make_batch(batch_texts):
|
||||
batch_data = []
|
||||
print(i18n("############ 提取文本Bert特征 ############"))
|
||||
for text in tqdm(batch_texts):
|
||||
phones, bert_features, norm_text = self.text_preprocessor.segment_and_extract_feature_for_text(text, text_lang)
|
||||
if phones is None:
|
||||
continue
|
||||
res={
|
||||
"phones": phones,
|
||||
"bert_features": bert_features,
|
||||
"norm_text": norm_text,
|
||||
}
|
||||
batch_data.append(res)
|
||||
if len(batch_data) == 0:
|
||||
return None
|
||||
batch, _ = self.to_batch(batch_data,
|
||||
prompt_data=self.prompt_cache if not no_prompt_text else None,
|
||||
batch_size=batch_size,
|
||||
threshold=batch_threshold,
|
||||
split_bucket=False,
|
||||
device=self.configs.device,
|
||||
precision=self.precision
|
||||
)
|
||||
return batch[0]
|
||||
batch_phones:List[torch.LongTensor] = item["phones"]
|
||||
batch_phones_len:torch.LongTensor = item["phones_len"]
|
||||
all_phoneme_ids:List[torch.LongTensor] = item["all_phones"]
|
||||
all_phoneme_lens:torch.LongTensor = item["all_phones_len"]
|
||||
all_bert_features:List[torch.LongTensor] = item["all_bert_features"]
|
||||
norm_text:str = item["norm_text"]
|
||||
|
||||
t2 = ttime()
|
||||
try:
|
||||
print("############ 推理 ############")
|
||||
###### inference ######
|
||||
t_34 = 0.0
|
||||
t_45 = 0.0
|
||||
audio = []
|
||||
for item in data:
|
||||
t3 = ttime()
|
||||
if return_fragment:
|
||||
item = make_batch(item)
|
||||
if item is None:
|
||||
continue
|
||||
print(i18n("前端处理后的文本(每句):"), norm_text)
|
||||
if no_prompt_text :
|
||||
prompt = None
|
||||
else:
|
||||
prompt = self.prompt_cache["prompt_semantic"].expand(len(all_phoneme_ids), -1).to(self.configs.device)
|
||||
|
||||
batch_phones:List[torch.LongTensor] = item["phones"]
|
||||
batch_phones_len:torch.LongTensor = item["phones_len"]
|
||||
all_phoneme_ids:List[torch.LongTensor] = item["all_phones"]
|
||||
all_phoneme_lens:torch.LongTensor = item["all_phones_len"]
|
||||
all_bert_features:List[torch.LongTensor] = item["all_bert_features"]
|
||||
norm_text:str = item["norm_text"]
|
||||
|
||||
print(i18n("前端处理后的文本(每句):"), norm_text)
|
||||
if no_prompt_text :
|
||||
prompt = None
|
||||
else:
|
||||
prompt = self.prompt_cache["prompt_semantic"].expand(len(all_phoneme_ids), -1).to(self.configs.device)
|
||||
|
||||
with torch.no_grad():
|
||||
pred_semantic_list, idx_list = self.t2s_model.model.infer_panel(
|
||||
all_phoneme_ids,
|
||||
all_phoneme_lens,
|
||||
@ -757,96 +758,98 @@ class TTS:
|
||||
temperature=temperature,
|
||||
early_stop_num=self.configs.hz * self.configs.max_sec,
|
||||
)
|
||||
t4 = ttime()
|
||||
t_34 += t4 - t3
|
||||
t4 = ttime()
|
||||
t_34 += t4 - t3
|
||||
|
||||
refer_audio_spec:torch.Tensor = self.prompt_cache["refer_spec"]\
|
||||
.to(dtype=self.precision, device=self.configs.device)
|
||||
refer_audio_spec:torch.Tensor = self.prompt_cache["refer_spec"]\
|
||||
.to(dtype=self.precision, device=self.configs.device)
|
||||
|
||||
batch_audio_fragment = []
|
||||
batch_audio_fragment = []
|
||||
|
||||
# ## vits并行推理 method 1
|
||||
# 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 = self.batch_sequences(pred_semantic_list, axis=0, pad_value=0).unsqueeze(0)
|
||||
# max_len = 0
|
||||
# for i in range(0, len(batch_phones)):
|
||||
# max_len = max(max_len, batch_phones[i].shape[-1])
|
||||
# batch_phones = self.batch_sequences(batch_phones, axis=0, pad_value=0, max_length=max_len)
|
||||
# batch_phones = batch_phones.to(self.configs.device)
|
||||
# batch_audio_fragment = (self.vits_model.batched_decode(
|
||||
# pred_semantic, pred_semantic_len, batch_phones, batch_phones_len,refer_audio_spec
|
||||
# ))
|
||||
# 这里要记得加 torch.no_grad() 不然速度慢一大截
|
||||
# with torch.no_grad():
|
||||
|
||||
# ## vits并行推理 method 2
|
||||
pred_semantic_list = [item[-idx:] for item, idx in zip(pred_semantic_list, idx_list)]
|
||||
upsample_rate = math.prod(self.vits_model.upsample_rates)
|
||||
audio_frag_idx = [pred_semantic_list[i].shape[0]*2*upsample_rate for i in range(0, len(pred_semantic_list))]
|
||||
audio_frag_end_idx = [ sum(audio_frag_idx[:i+1]) for i in range(0, len(audio_frag_idx))]
|
||||
all_pred_semantic = torch.cat(pred_semantic_list).unsqueeze(0).unsqueeze(0).to(self.configs.device)
|
||||
_batch_phones = torch.cat(batch_phones).unsqueeze(0).to(self.configs.device)
|
||||
_batch_audio_fragment = (self.vits_model.decode(
|
||||
all_pred_semantic, _batch_phones, refer_audio_spec
|
||||
).detach()[0, 0, :])
|
||||
audio_frag_end_idx.insert(0, 0)
|
||||
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))]
|
||||
# ## vits并行推理 method 1
|
||||
# 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 = self.batch_sequences(pred_semantic_list, axis=0, pad_value=0).unsqueeze(0)
|
||||
# max_len = 0
|
||||
# for i in range(0, len(batch_phones)):
|
||||
# max_len = max(max_len, batch_phones[i].shape[-1])
|
||||
# batch_phones = self.batch_sequences(batch_phones, axis=0, pad_value=0, max_length=max_len)
|
||||
# batch_phones = batch_phones.to(self.configs.device)
|
||||
# batch_audio_fragment = (self.vits_model.batched_decode(
|
||||
# pred_semantic, pred_semantic_len, batch_phones, batch_phones_len,refer_audio_spec
|
||||
# ))
|
||||
|
||||
# ## vits并行推理 method 2
|
||||
pred_semantic_list = [item[-idx:] for item, idx in zip(pred_semantic_list, idx_list)]
|
||||
upsample_rate = math.prod(self.vits_model.upsample_rates)
|
||||
audio_frag_idx = [pred_semantic_list[i].shape[0]*2*upsample_rate for i in range(0, len(pred_semantic_list))]
|
||||
audio_frag_end_idx = [ sum(audio_frag_idx[:i+1]) for i in range(0, len(audio_frag_idx))]
|
||||
all_pred_semantic = torch.cat(pred_semantic_list).unsqueeze(0).unsqueeze(0).to(self.configs.device)
|
||||
_batch_phones = torch.cat(batch_phones).unsqueeze(0).to(self.configs.device)
|
||||
_batch_audio_fragment = (self.vits_model.decode(
|
||||
all_pred_semantic, _batch_phones, refer_audio_spec
|
||||
).detach()[0, 0, :])
|
||||
audio_frag_end_idx.insert(0, 0)
|
||||
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))]
|
||||
|
||||
# ## 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
|
||||
# ## 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],
|
||||
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,
|
||||
self.configs.sampling_rate,
|
||||
None,
|
||||
batch_index_list,
|
||||
speed_factor,
|
||||
False,
|
||||
split_bucket,
|
||||
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,
|
||||
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()
|
||||
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:
|
||||
@ -878,14 +881,12 @@ class TTS:
|
||||
audio_fragment:torch.Tensor = torch.cat([audio_fragment, zero_wav], dim=0)
|
||||
audio[i][j] = audio_fragment.cpu().numpy()
|
||||
|
||||
|
||||
if split_bucket:
|
||||
audio = self.recovery_order(audio, batch_index_list)
|
||||
else:
|
||||
# audio = [item for batch in audio for item in batch]
|
||||
audio = sum(audio, [])
|
||||
|
||||
|
||||
audio = np.concatenate(audio, 0)
|
||||
audio = (audio * 32768).astype(np.int16)
|
||||
|
||||
@ -898,8 +899,6 @@ class TTS:
|
||||
return sr, audio
|
||||
|
||||
|
||||
|
||||
|
||||
def speed_change(input_audio:np.ndarray, speed:float, sr:int):
|
||||
# 将 NumPy 数组转换为原始 PCM 流
|
||||
raw_audio = input_audio.astype(np.int16).tobytes()
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user