mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-10-07 15:19:59 +08:00
恢复先前缩进
This commit is contained in:
XTer
parent
adb7f71b64
commit
3bfb20763d
@ -272,7 +272,7 @@ class TTS:
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# if ("pretrained" not in weights_path):
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if hasattr(vits_model, "enc_q"):
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del vits_model.enc_q
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vits_model = vits_model.to(self.configs.device)
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vits_model = vits_model.eval()
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vits_model.load_state_dict(dict_s2["weight"], strict=False)
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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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@ -296,7 +297,7 @@ class TTS:
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self.t2s_model = t2s_model
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if self.configs.is_half and str(self.configs.device)!="cpu":
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self.t2s_model = self.t2s_model.half()
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def enable_half_precision(self, enable: bool = True):
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'''
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To enable half precision for the TTS model.
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@ -307,7 +308,7 @@ class TTS:
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if str(self.configs.device) == "cpu" and enable:
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print("Half precision is not supported on CPU.")
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return
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self.configs.is_half = enable
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self.precision = torch.float16 if enable else torch.float32
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self.configs.save_configs()
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@ -329,7 +330,7 @@ class TTS:
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self.bert_model = self.bert_model.float()
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if self.cnhuhbert_model is not None:
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self.cnhuhbert_model = self.cnhuhbert_model.float()
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def set_device(self, device: torch.device):
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'''
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To set the device for all models.
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@ -346,7 +347,7 @@ class TTS:
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self.bert_model = self.bert_model.to(device)
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if self.cnhuhbert_model is not None:
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self.cnhuhbert_model = self.cnhuhbert_model.to(device)
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def set_ref_audio(self, ref_audio_path:str):
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'''
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To set the reference audio for the TTS model,
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@ -356,7 +357,7 @@ class TTS:
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'''
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self._set_prompt_semantic(ref_audio_path)
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self._set_ref_spec(ref_audio_path)
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def _set_ref_spec(self, ref_audio_path):
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audio = load_audio(ref_audio_path, int(self.configs.sampling_rate))
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audio = torch.FloatTensor(audio)
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@ -375,7 +376,8 @@ class TTS:
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spec = spec.half()
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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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@ -400,10 +402,10 @@ class TTS:
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1, 2
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) # .float()
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codes = self.vits_model.extract_latent(hubert_feature)
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prompt_semantic = codes[0, 0].to(self.configs.device)
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self.prompt_cache["prompt_semantic"] = prompt_semantic
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def batch_sequences(self, sequences: List[torch.Tensor], axis: int = 0, pad_value: int = 0, max_length:int=None):
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seq = sequences[0]
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ndim = seq.dim()
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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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@ -425,7 +426,7 @@ class TTS:
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padded_sequences.append(padded_seq)
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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,
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prompt_data:dict=None,
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batch_size:int=5,
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@ -434,115 +435,116 @@ 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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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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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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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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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_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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# 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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return _data, batch_index_list
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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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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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# 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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# 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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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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Recovery the order of the audio according to the batch_index_list.
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@ -566,7 +568,8 @@ class TTS:
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Stop the inference process.
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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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@ -850,7 +853,7 @@ class TTS:
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raise e
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finally:
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self.empty_cache()
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def empty_cache(self):
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try:
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if "cuda" in str(self.configs.device):
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@ -859,7 +862,7 @@ class TTS:
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torch.mps.empty_cache()
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except:
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pass
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def audio_postprocess(self,
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audio:List[torch.Tensor],
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sr:int,
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@ -873,32 +876,36 @@ class TTS:
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dtype=self.precision,
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device=self.configs.device
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)
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for i, batch in enumerate(audio):
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for j, audio_fragment in enumerate(batch):
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max_audio=torch.abs(audio_fragment).max()#简单防止16bit爆音
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if max_audio>1: audio_fragment/=max_audio
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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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try:
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if speed_factor != 1.0:
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audio = speed_change(audio, speed=speed_factor, sr=int(sr))
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except Exception as e:
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print(f"Failed to change speed of audio: \n{e}")
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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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@ -918,4 +925,4 @@ def speed_change(input_audio:np.ndarray, speed:float, sr:int):
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# 将管道输出解码为 NumPy 数组
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processed_audio = np.frombuffer(out, np.int16)
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return processed_audio
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return processed_audio
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Block a user