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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)

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* 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 2024-04-07 18:36:09 +08:00 committed by GitHub
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2 changed files with 43 additions and 38 deletions
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.gitignore vendored
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@ -10,6 +10,8 @@ reference
GPT_weights GPT_weights
SoVITS_weights SoVITS_weights
TEMP TEMP
PortableGit
ffmpeg.exe ffmpeg.exe
ffprobe.exe ffprobe.exe
tmp_audio
trained

77
GPT_SoVITS/TTS_infer_pack/TTS.py
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@ -249,8 +249,6 @@ class TTS:
if self.configs.is_half and str(self.configs.device)!="cpu": if self.configs.is_half and str(self.configs.device)!="cpu":
self.bert_model = self.bert_model.half() self.bert_model = self.bert_model.half()
def init_vits_weights(self, weights_path: str): def init_vits_weights(self, weights_path: str):
print(f"Loading VITS weights from {weights_path}") print(f"Loading VITS weights from {weights_path}")
self.configs.vits_weights_path = weights_path self.configs.vits_weights_path = weights_path
@ -437,7 +435,8 @@ class TTS:
device:torch.device=torch.device("cpu"), device:torch.device=torch.device("cpu"),
precision:torch.dtype=torch.float32, precision:torch.dtype=torch.float32,
): ):
# 但是这里不能套，反而会负优化
# with torch.no_grad():
_data:list = [] _data:list = []
index_and_len_list = [] index_and_len_list = []
for idx, item in enumerate(data): for idx, item in enumerate(data):
@ -485,6 +484,8 @@ class TTS:
norm_text_batch = [] norm_text_batch = []
bert_max_len = 0 bert_max_len = 0
phones_max_len = 0 phones_max_len = 0
# 但是这里也不能套，反而会负优化
# with torch.no_grad():
for item in item_list: for item in item_list:
if prompt_data is not None: if prompt_data is not None:
all_bert_features = torch.cat([prompt_data["bert_features"], item["bert_features"]], 1)\ all_bert_features = torch.cat([prompt_data["bert_features"], item["bert_features"]], 1)\
@ -567,8 +568,9 @@ class TTS:
Stop the inference process. Stop the inference process.
''' '''
self.stop_flag = True self.stop_flag = True
# 使用装饰器
@torch.no_grad()
def run(self, inputs:dict): def run(self, inputs:dict):
""" """
Text to speech inference. Text to speech inference.
@ -616,25 +618,25 @@ class TTS:
seed = inputs.get("seed", -1) seed = inputs.get("seed", -1)
seed = -1 if seed in ["", None] else seed seed = -1 if seed in ["", None] else seed
actual_seed = set_seed(seed) actual_seed = set_seed(seed)
if return_fragment: if return_fragment:
# split_bucket = False # split_bucket = False
print(i18n("分段返回模式已开启")) print(i18n("分段返回模式已开启"))
if split_bucket: if split_bucket:
split_bucket = False split_bucket = False
print(i18n("分段返回模式不支持分桶处理，已自动关闭分桶处理")) print(i18n("分段返回模式不支持分桶处理，已自动关闭分桶处理"))
if split_bucket: if split_bucket:
print(i18n("分桶处理模式已开启")) print(i18n("分桶处理模式已开启"))
if fragment_interval<0.01: if fragment_interval<0.01:
fragment_interval = 0.01 fragment_interval = 0.01
print(i18n("分段间隔过小，已自动设置为0.01")) print(i18n("分段间隔过小，已自动设置为0.01"))
no_prompt_text = False no_prompt_text = False
if prompt_text in [None, ""]: if prompt_text in [None, ""]:
no_prompt_text = True no_prompt_text = True
assert text_lang in self.configs.languages assert text_lang in self.configs.languages
if not no_prompt_text: if not no_prompt_text:
assert prompt_lang in self.configs.languages assert prompt_lang in self.configs.languages
@ -643,11 +645,10 @@ class TTS:
((self.prompt_cache["prompt_semantic"] is None) or (self.prompt_cache["refer_spec"] is None)): ((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()") 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 ######## ###### setting reference audio and prompt text preprocessing ########
t0 = ttime() t0 = ttime()
if (ref_audio_path is not None) and (ref_audio_path != self.prompt_cache["ref_audio_path"]): if (ref_audio_path is not None) and (ref_audio_path != self.prompt_cache["ref_audio_path"]):
self.set_ref_audio(ref_audio_path) self.set_ref_audio(ref_audio_path)
if not no_prompt_text: if not no_prompt_text:
prompt_text = prompt_text.strip("\n") prompt_text = prompt_text.strip("\n")
@ -663,8 +664,7 @@ class TTS:
self.prompt_cache["phones"] = phones self.prompt_cache["phones"] = phones
self.prompt_cache["bert_features"] = bert_features self.prompt_cache["bert_features"] = bert_features
self.prompt_cache["norm_text"] = norm_text self.prompt_cache["norm_text"] = norm_text
###### text preprocessing ######## ###### text preprocessing ########
t1 = ttime() t1 = ttime()
data:list = None data:list = None
@ -674,7 +674,7 @@ class TTS:
yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate), yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate),
dtype=np.int16) dtype=np.int16)
return return
batch_index_list:list = None batch_index_list:list = None
data, batch_index_list = self.to_batch(data, data, batch_index_list = self.to_batch(data,
prompt_data=self.prompt_cache if not no_prompt_text else None, prompt_data=self.prompt_cache if not no_prompt_text else None,
@ -692,7 +692,7 @@ class TTS:
if i%batch_size == 0: if i%batch_size == 0:
data.append([]) data.append([])
data[-1].append(texts[i]) data[-1].append(texts[i])
def make_batch(batch_texts): def make_batch(batch_texts):
batch_data = [] batch_data = []
print(i18n("############ 提取文本Bert特征 ############")) print(i18n("############ 提取文本Bert特征 ############"))
@ -717,7 +717,8 @@ class TTS:
precision=self.precision precision=self.precision
) )
return batch[0] return batch[0]
t2 = ttime() t2 = ttime()
try: try:
print("############ 推理 ############") print("############ 推理 ############")
@ -731,40 +732,43 @@ class TTS:
item = make_batch(item) item = make_batch(item)
if item is None: if item is None:
continue continue
batch_phones:List[torch.LongTensor] = item["phones"] batch_phones:List[torch.LongTensor] = item["phones"]
batch_phones_len:torch.LongTensor = item["phones_len"] batch_phones_len:torch.LongTensor = item["phones_len"]
all_phoneme_ids:List[torch.LongTensor] = item["all_phones"] all_phoneme_ids:List[torch.LongTensor] = item["all_phones"]
all_phoneme_lens:torch.LongTensor = item["all_phones_len"] all_phoneme_lens:torch.LongTensor = item["all_phones_len"]
all_bert_features:List[torch.LongTensor] = item["all_bert_features"] all_bert_features:List[torch.LongTensor] = item["all_bert_features"]
norm_text:str = item["norm_text"] norm_text:str = item["norm_text"]
print(i18n("前端处理后的文本(每句):"), norm_text) print(i18n("前端处理后的文本(每句):"), norm_text)
if no_prompt_text : if no_prompt_text :
prompt = None prompt = None
else: else:
prompt = self.prompt_cache["prompt_semantic"].expand(len(all_phoneme_ids), -1).to(self.configs.device) 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( pred_semantic_list, idx_list = self.t2s_model.model.infer_panel(
all_phoneme_ids, all_phoneme_ids,
all_phoneme_lens, all_phoneme_lens,
prompt, prompt,
all_bert_features, all_bert_features,
# prompt_phone_len=ph_offset, # prompt_phone_len=ph_offset,
top_k=top_k, top_k=top_k,
top_p=top_p, top_p=top_p,
temperature=temperature, temperature=temperature,
early_stop_num=self.configs.hz * self.configs.max_sec, early_stop_num=self.configs.hz * self.configs.max_sec,
) )
t4 = ttime() t4 = ttime()
t_34 += t4 - t3 t_34 += t4 - t3
refer_audio_spec:torch.Tensor = self.prompt_cache["refer_spec"]\ refer_audio_spec:torch.Tensor = self.prompt_cache["refer_spec"]\
.to(dtype=self.precision, device=self.configs.device) .to(dtype=self.precision, device=self.configs.device)
batch_audio_fragment = [] batch_audio_fragment = []
# 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 1 # ## vits并行推理 method 1
# pred_semantic_list = [item[-idx:] for item, idx in zip(pred_semantic_list, idx_list)] # 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_len = torch.LongTensor([item.shape[0] for item in pred_semantic_list]).to(self.configs.device)
@ -777,7 +781,7 @@ class TTS:
# batch_audio_fragment = (self.vits_model.batched_decode( # batch_audio_fragment = (self.vits_model.batched_decode(
# pred_semantic, pred_semantic_len, batch_phones, batch_phones_len,refer_audio_spec # pred_semantic, pred_semantic_len, batch_phones, batch_phones_len,refer_audio_spec
# )) # ))
# ## vits并行推理 method 2 # ## vits并行推理 method 2
pred_semantic_list = [item[-idx:] for item, idx in zip(pred_semantic_list, idx_list)] pred_semantic_list = [item[-idx:] for item, idx in zip(pred_semantic_list, idx_list)]
upsample_rate = math.prod(self.vits_model.upsample_rates) upsample_rate = math.prod(self.vits_model.upsample_rates)
@ -790,7 +794,6 @@ class TTS:
).detach()[0, 0, :]) ).detach()[0, 0, :])
audio_frag_end_idx.insert(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))] 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 # ## vits串行推理
# for i, idx in enumerate(idx_list): # for i, idx in enumerate(idx_list):
@ -802,7 +805,7 @@ class TTS:
# batch_audio_fragment.append( # batch_audio_fragment.append(
# audio_fragment # audio_fragment
# ) ###试试重建不带上prompt部分 # ) ###试试重建不带上prompt部分
t5 = ttime() t5 = ttime()
t_45 += t5 - t4 t_45 += t5 - t4
if return_fragment: if return_fragment:
@ -816,7 +819,7 @@ class TTS:
) )
else: else:
audio.append(batch_audio_fragment) audio.append(batch_audio_fragment)
if self.stop_flag: if self.stop_flag:
yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate), yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate),
dtype=np.int16) dtype=np.int16)