mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-10-08 07:49:59 +08:00
添加with torch.no_grad()，速度快一大截
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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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@ -438,6 +435,8 @@ class TTS:
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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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@ -473,7 +472,6 @@ class TTS:
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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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@ -485,6 +483,8 @@ class TTS:
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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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@ -513,7 +513,6 @@ class TTS:
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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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@ -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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@ -596,6 +594,34 @@ 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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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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@ -643,7 +669,6 @@ class TTS:
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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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###### setting reference audio and prompt text preprocessing ########
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t0 = ttime()
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if (ref_audio_path is not None) and (ref_audio_path != self.prompt_cache["ref_audio_path"]):
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@ -664,7 +689,6 @@ class TTS:
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self.prompt_cache["bert_features"] = bert_features
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self.prompt_cache["norm_text"] = norm_text
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###### text preprocessing ########
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t1 = ttime()
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data:list = None
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@ -693,30 +717,7 @@ class TTS:
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data.append([])
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data[-1].append(texts[i])
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def make_batch(batch_texts):
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batch_data = []
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print(i18n("############ 提取文本Bert特征 ############"))
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for text in tqdm(batch_texts):
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phones, bert_features, norm_text = self.text_preprocessor.segment_and_extract_feature_for_text(text, text_lang)
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if phones is None:
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continue
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res={
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"phones": phones,
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"bert_features": bert_features,
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"norm_text": norm_text,
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}
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batch_data.append(res)
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if len(batch_data) == 0:
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return None
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batch, _ = self.to_batch(batch_data,
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prompt_data=self.prompt_cache if not no_prompt_text else None,
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batch_size=batch_size,
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threshold=batch_threshold,
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split_bucket=False,
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device=self.configs.device,
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precision=self.precision
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)
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return batch[0]
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t2 = ttime()
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try:
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@ -745,7 +746,7 @@ class TTS:
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else:
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prompt = self.prompt_cache["prompt_semantic"].expand(len(all_phoneme_ids), -1).to(self.configs.device)
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with torch.no_grad():
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pred_semantic_list, idx_list = self.t2s_model.model.infer_panel(
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all_phoneme_ids,
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all_phoneme_lens,
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@ -765,6 +766,9 @@ class TTS:
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batch_audio_fragment = []
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# 这里要记得加 torch.no_grad() 不然速度慢一大截
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# with torch.no_grad():
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# ## vits并行推理 method 1
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# pred_semantic_list = [item[-idx:] for item, idx in zip(pred_semantic_list, idx_list)]
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# pred_semantic_len = torch.LongTensor([item.shape[0] for item in pred_semantic_list]).to(self.configs.device)
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@ -791,7 +795,6 @@ class TTS:
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audio_frag_end_idx.insert(0, 0)
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batch_audio_fragment= [_batch_audio_fragment[audio_frag_end_idx[i-1]:audio_frag_end_idx[i]] for i in range(1, len(audio_frag_end_idx))]
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# ## vits串行推理
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# for i, idx in enumerate(idx_list):
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# phones = batch_phones[i].unsqueeze(0).to(self.configs.device)
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@ -878,14 +881,12 @@ 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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@ -898,8 +899,6 @@ 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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