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【fast_inference_】通过简单添加torch.no_grad()修饰器，可能会使速度加快 (#930)
* 添加with torch.no_grad()，速度快一大截 * 恢复先前缩进 * 恢复make batch的位置 * 改用修饰器 * 去除没必要的增加的空行 --------- Co-authored-by: XTer <xxoy1234@outlook.com>
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箱庭XTer
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4
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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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@ -437,7 +435,8 @@ 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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@ -485,6 +484,8 @@ 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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@ -567,8 +568,9 @@ 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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# 使用装饰器
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@torch.no_grad()
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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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@ -616,25 +618,25 @@ class TTS:
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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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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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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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@ -643,11 +645,10 @@ 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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self.set_ref_audio(ref_audio_path)
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self.set_ref_audio(ref_audio_path)
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if not no_prompt_text:
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prompt_text = prompt_text.strip("\n")
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@ -663,8 +664,7 @@ class TTS:
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self.prompt_cache["phones"] = phones
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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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@ -674,7 +674,7 @@ class TTS:
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yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate),
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dtype=np.int16)
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return
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batch_index_list:list = None
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data, batch_index_list = self.to_batch(data,
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prompt_data=self.prompt_cache if not no_prompt_text else None,
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@ -692,7 +692,7 @@ class TTS:
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if i%batch_size == 0:
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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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@ -717,7 +717,8 @@ class TTS:
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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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print("############ 推理 ############")
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@ -731,40 +732,43 @@ class TTS:
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item = make_batch(item)
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if item is None:
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continue
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batch_phones:List[torch.LongTensor] = item["phones"]
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batch_phones_len:torch.LongTensor = item["phones_len"]
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all_phoneme_ids:List[torch.LongTensor] = item["all_phones"]
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all_phoneme_lens:torch.LongTensor = item["all_phones_len"]
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all_bert_features:List[torch.LongTensor] = item["all_bert_features"]
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norm_text:str = item["norm_text"]
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print(i18n("前端处理后的文本(每句):"), norm_text)
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if no_prompt_text :
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prompt = None
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else:
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prompt = self.prompt_cache["prompt_semantic"].expand(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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prompt,
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all_bert_features,
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# prompt_phone_len=ph_offset,
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top_k=top_k,
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top_p=top_p,
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temperature=temperature,
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early_stop_num=self.configs.hz * self.configs.max_sec,
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)
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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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prompt,
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all_bert_features,
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# prompt_phone_len=ph_offset,
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top_k=top_k,
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top_p=top_p,
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temperature=temperature,
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early_stop_num=self.configs.hz * self.configs.max_sec,
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)
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t4 = ttime()
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t_34 += t4 - t3
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refer_audio_spec:torch.Tensor = self.prompt_cache["refer_spec"]\
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.to(dtype=self.precision, device=self.configs.device)
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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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@ -777,7 +781,7 @@ class TTS:
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# batch_audio_fragment = (self.vits_model.batched_decode(
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# pred_semantic, pred_semantic_len, batch_phones, batch_phones_len,refer_audio_spec
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# ))
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# ## vits并行推理 method 2
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pred_semantic_list = [item[-idx:] for item, idx in zip(pred_semantic_list, idx_list)]
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upsample_rate = math.prod(self.vits_model.upsample_rates)
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@ -790,7 +794,6 @@ class TTS:
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).detach()[0, 0, :])
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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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@ -802,7 +805,7 @@ class TTS:
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# batch_audio_fragment.append(
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# audio_fragment
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# ) ###试试重建不带上prompt部分
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t5 = ttime()
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t_45 += t5 - t4
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if return_fragment:
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@ -816,7 +819,7 @@ class TTS:
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)
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else:
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audio.append(batch_audio_fragment)
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if self.stop_flag:
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yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate),
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dtype=np.int16)
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